RESEARCH CENTRE

Inria Paris Centre
at Sorbonne University

2023
ACTIVITY REPORT

IN PARTNERSHIP WITH:

CNRS, INSERM, Sorbonne Université

Project-Team

ARAMIS

Algorithms, models and methods for
images and signals of the human brain

IN COLLABORATION WITH: Institut du Cerveau et de la Moelle Epinière

DOMAIN

Digital Health, Biology and Earth

THEME

Computational Neuroscience and
Medicine

Contents

Project-Team ARAMIS

1 Team members, visitors, external collaborators

2 Overall objectives

2.1 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 General aim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 Research program

3.1 Neuroimaging-based biomarkers and decision support systems . . . . . . . . . . . . . . . .
3.2 Models of brain networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Disease progression modeling with longitudinal data . . . . . . . . . . . . . . . . . . . . . . .
3.4 High-dimensional and multimodal data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5 Clinical research studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 Application domains

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Understanding brain disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.3 Supporting clinical decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 Brain computer interfaces for clinical applications . . . . . . . . . . . . . . . . . . . . . . . .

5 Highlights of the year

5.1 Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1

2

4
4
5

5
5
5
5
5
6

6
6
6
6
6

7
7

6 New software, platforms, open data

7
7
6.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
6.1.1 Clinica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
6.1.2 ClinicaDL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
6.1.3 Deformetrica . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
6.1.4 leaspy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1.5 Brain Networks Toolbox . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
6.1.6 OpenVIBE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
6.1.7 brainMapR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
6.2 New platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

7 New results

11
7.1 Book: Machine Learning for Brain Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
7.2 Evaluation of MRI-based machine learning approaches for computer-aided diagnosis of

dementia in a clinical data warehouse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

7.3 Automatic segmentation of the choroid plexuses: Method and validation in controls and

patients with multiple sclerosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
. . 12

7.4 How precise are performance estimates for typical medical image segmentation tasks?
7.5 Introducing Soft Topology Constraints in Deep Learning-based Segmentation using Projec-

ted Pooling Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

7.6 Interpretable automatic detection of incomplete hippocampal inversions using anatomical

criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
7.7 Interpretability of Machine Learning Methods Applied to Neuroimaging . . . . . . . . . . . 13
7.8 Evaluating machine learning models and their diagnostic value . . . . . . . . . . . . . . . . 14
7.9 Reproducibility in machine learning for medical imaging . . . . . . . . . . . . . . . . . . . . 14
7.10 Association Between Diseases and Symptoms Diagnosed in Primary Care and the Sub-

sequent Specific Risk of Multiple Sclerosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

7.11 Charting Disease Trajectories from Isolated REM Sleep Behavior Disorder to Parkinson’s

Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
7.12 A Multimodal Disease Progression Model for Genetic Associations with Disease Dynamics
15
7.13 Interaction of sex and onset site on the disease trajectory of amyotrophic lateral sclerosis . 16

16
7.14 Impact of sex and APOE-e4 genotype on regional brain metabolism in Alzheimer’s Disease
7.15 Forecasting individual progression trajectories in Alzheimer’s disease . . . . . . . . . . . . . 17
7.16 Measuring Neuronal Avalanches to inform Brain-Computer Interfaces . . . . . . . . . . . . 17
7.17 Automatic motion artefact detection in brain T1-weighted magnetic resonance images from

a clinical data warehouse using synthetic data . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
18

7.18 How can data augmentation improve attribution maps for disease subtype explainability?
7.19 Transfer learning from synthetic to routine clinical data for motion artefact detection in

brain T1-weighted MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

7.20 Simulation-based evaluation framework for deep learning unsupervised anomaly detection

on brain FDG PET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

7.21 Unsupervised anomaly detection in 3D brain FDG PET: A benchmark of 17 VAE-based

approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

7.22 Semi-supervised domain adaptation for automatic quality control of FLAIR MRIs in a

clinical data warehouse . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

7.23 A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via

Two-Phase Training Angiography-to-Venography Translation . . . . . . . . . . . . . . . . . . 20

7.24 Temporal dynamics of the Scale for the Assessment and Rating of Ataxia in Spinocerebellar

ataxias

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

7.25 Baseline Clinical and Blood Biomarker in Patients With Preataxic and Early-Stage Disease

Spinocerebellar Ataxia 1 and 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

7.26 A meta-graph approach for analyzing whole slide histopathological images of human brain

tissue with Alzheimer’s disease biomarkers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
7.27 Efficient 3D reconstruction of Whole Slide Images in Melanoma . . . . . . . . . . . . . . . . 22
7.28 Computational Pathology for Brain Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
7.29 Phagocytosis Unveiled: A Scalable and Interpretable Deep learning Framework for Neuro-

degenerative Disease Analysis

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
7.30 HappyFeat—An interactive and efficient BCI framework for clinical applications . . . . . . 23
7.31 Vizaj—A free online interactive software for visualizing spatial networks . . . . . . . . . . . 23

8 Bilateral contracts and grants with industry

24
8.1 Bilateral grants with industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
8.1.1 Sanofi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
8.1.2 Biogen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
8.1.3 VICO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

9 Partnerships and cooperations

9.2 European initiatives

25
9.1 International initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
9.1.1 Inria associate team not involved in an IIL or an international program . . . . . . . . 25
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
9.2.1 Horizon Europe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
9.3 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
9.3.1 ANR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
9.3.2 IHU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
9.3.3 3IA Institutes - PRAIRIE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
9.3.4 National Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
9.3.5 Other National Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

10 Dissemination

36
10.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
10.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
10.1.2 Scientific events: selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
10.1.3 Journal
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
10.1.4 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
10.1.5 Leadership within the scientific community . . . . . . . . . . . . . . . . . . . . . . . . 38
10.1.6 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

10.1.7 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
10.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
10.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
10.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
10.2.3 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
10.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
10.3.1 Articles and contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
10.3.2 Interventions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

11 Scientific production

43
11.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
11.2 Publications of the year

Project ARAMIS

1

Project-Team ARAMIS

Creation of the Project-Team: 2014 July 01

Keywords

Computer sciences and digital sciences

A3.4. – Machine learning and statistics

A3.4.1. – Supervised learning

A3.4.2. – Unsupervised learning

A3.4.4. – Optimization and learning

A3.4.6. – Neural networks

A3.4.8. – Deep learning

A5.3. – Image processing and analysis

A5.4. – Computer vision

A5.9. – Signal processing

A9. – Artificial intelligence

A9.2. – Machine learning

A9.3. – Signal analysis

A9.6. – Decision support

Other research topics and application domains

B2. – Health

B2.2.6. – Neurodegenerative diseases

B2.6. – Biological and medical imaging

B2.6.1. – Brain imaging

2

Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientists

• Olivier Colliot [Team leader, CNRS, Senior Researcher, HDR]

• Ninon Burgos [CNRS, Researcher, HDR]

• Marie-Constance Corsi [INRIA, Researcher]

• Baptiste Couvy-Duchesne [INRIA, ISFP, until Aug 2023]

• Stanley Durrleman [INRIA, Senior Researcher, HDR]

• Fabrizio de Vico Fallani [INRIA, Senior Researcher, HDR]

Faculty Members

• Didier Dormont [SORBONNE UNIVERSITE, Professor]

• Daniel Racoceanu [SORBONNE UNIVERSITE, Professor, HDR]

• Sophie Tezenas Du Montcel [SORBONNE UNIVERSITE, Associate Professor, HDR]

Post-Doctoral Fellows

• Janan Arslan [INSERM, Post-Doctoral Fellow, until Aug 2023]

• Diego Candia Rivera [ICM, Post-Doctoral Fellow]

• Lydia Chougar [AP/HP, until Oct 2023, "Poste accueil" AP-HP Inria]

• Rosana El Jurdi [CNRS, Post-Doctoral Fellow, until Aug 2023]

• Juliana Gonzalez Astudillo [INRIA, Post-Doctoral Fellow, until Oct 2023]

• Thomas Haschka [ICM, until Aug 2023]

• Anuradha Kar [ICM, Post-Doctoral Fellow, until Aug 2023]

• Thomas Nedelec [ICM, Post-Doctoral Fellow]

• Pierre-Emmanuel Poulet [INRIA, Post-Doctoral Fellow, from Oct 2023]

PhD Students

• Remy Ben Messaoud [INRIA]

• Camile Bousfiha [ICM]

• Elise Delzant [INRIA]

• Marc Dibling [SORBONNE UNIVERSITE, from Oct 2023]

• Vito Dichio [INRIA, until Oct 2023]

• Némo Fournier [ICM]

• Guanghui Fu [Other]

• Octave Guinebretiere [ICM]

• Ayse Gungor [Hôpital Fondation Adolphe de Rothschild, from Sep 2023, PhD co-supervision]

Project ARAMIS

3

• Ravi Hassanaly [INRIA, from Nov 2023]

• Ravi Hassanaly [CNRS, until Oct 2023]

• Lisa Hemforth [UNIV PARIS]

• Gabriel Jimenez [SORBONNE UNIVERSITE]

• Sofia Kaisaridi [INRIA]

• Esther Kozlowski [Institut du Cerveau, from Oct 2023, DIM-C BRAINS 2023 scholarship]

• Sophie Loizillon [SORBONNE UNIVERSITE]

• Camilla Mannino [INRIA, from Nov 2023]

• Juliette Ortholand [INRIA]

• Medhi Ounissi [INSERM]

• Pierre-Emmanuel Poulet [INRIA, until Sep 2023]

• Charley Presigny [INRIA, until Sep 2023]

• Ilias Sarbout [Hôpital Fondation Adolphe de Rothschild, from Oct 2023, PhD co-supervision]

• Benoit Sauty De Chalon [INRIA, until Aug 2023]

• Sophie Skriabine [Inria, until Jun 2023]

• Maelys Solal [SORBONNE UNIVERSITE, from Oct 2023]

• Tristan Venot [INRIA]

• Arya Yazdan Panah [ICM]

Technical Staff

• Antoine Belloir [ICM, Engineer, from Jul 2023]

• Camille Brianceau [CNRS, Engineer]

• Arthur Desbois [INRIA, Engineer]

• Cécile Di Folco [INSERM, Engineer, from Mar 2023]

• Cécile Di Folco [INRIA, Engineer, until Feb 2023]

• Charlotte Dubec [AGROPARISTECH, Engineer, until Jun 2023]

• Matthieu Joulot [INRIA, Engineer, from Apr 2023]

• Matthieu Joulot [ICM, Engineer, until Mar 2023]

• Etienne Maheux [INRIA, Engineer, until May 2023]

• Thibault Rolland [Inria, Engineer]

• Ghislain Vaillant [INRIA, Engineer, from May 2023]

• Ghislain Vaillant [CNRS, Engineer, until Apr 2023]

4

Inria Annual Report 2023

Interns and Apprentices

• Julietta Badalyan [ICM, Intern, from Sep 2023]

• Sofiene Boutaj [ICM, Intern, from Oct 2023]

• Marc Dibling [INSERM, Intern, from Feb 2023 until Jul 2023]

• Linda Ek Fliesberg [UNIV PSL, Intern, from Feb 2023 until Jun 2023]

• Rune Frateur [UNIV PARIS SACLAY, Intern, until Jun 2023]

• Leopold Hebert-Stevens [ICM, Intern, from May 2023 until Oct 2023]

• Stephane Mabille [INSERM, Intern, from Feb 2023 until Aug 2023]

• Charlotte Montaud [ICM, Intern, from Sep 2023]

• Julien Peyrache [ICM, Intern, until Aug 2023]

• Ranushan Ragavan [ICM, Intern, from May 2023 until Sep 2023]

• Maelys Solal [ICM, Intern, until Jun 2023]

• Karim Zaidi [INSERM, Intern, from Apr 2023 until Aug 2023]

Administrative Assistant

• Helene Milome [INRIA]

External Collaborator

• Marc Dibling [INSERM, from Jul 2023 until Sep 2023]

2 Overall objectives

2.1 Context

ARAMIS is an Inria project-team within the Paris Brain Institute (ICM) at the Pitié-Salpêtrière hospital
(AP-HP) in Paris. ARAMIS was created as a team of the Inria Paris Center in 2012 and became a project-
team in 2014. ARAMIS has a joint affiliation to Inria, CNRS, Inserm and Sorbonne University.

The Pitié-Salpêtrière hospital is the largest adult hospital in Europe. It is a leading center for neurolo-
gical diseases: in terms of size (around 20,000 neurological patients each year), level of clinical expertise
and quality of the technical facilities. Created in 2010, the Paris Brain Institute (ICM) gathers all research
activities in neuroscience and neurology of the Pitié-Salpêtrière hospital. The ICM is both a private found-
ation and a public research unit (affiliated to CNRS, Inserm and Sorbonne University). It hosts about 25
research teams as well as various high level technical facilities (neuroimaging, genotyping/sequencing,
cell culture, cellular imaging, bioinformatics . . . ), and gathers over 800 personnel. In addition, the ICM
hosts one of the six IHU (Instituts Hospitalo-Universitaires).

ARAMIS is thus located both within a leading neuroscience institute and within a large hospital. This
unique position has several advantages: direct contact with neuroscientists and clinicians allows us
to foresee the emergence of new problems and opportunities for new methodological developments,
provides access to unique datasets, and eases the transfer of our results to clinical research and clinical
practice.

Project ARAMIS

2.2 General aim

5

The ARAMIS team is devoted to the design of computational, mathematical and statistical approaches
for the analysis of multimodal patient data, with an emphasis on neuroimaging data. The core meth-
odological domains of our team are: machine learning, statistical modeling of complex geometric data,
connectivity and network analysis. These new approaches are applied to clinical research in neurolo-
gical diseases in collaboration with other teams of the ICM, clinical departments of the Pitié-Salpêtrière
hospital and external partners.

We develop various clinical applications of our research, in particular in neurodegenerative disorders
(Alzheimer’s disease and other dementias, Parkinson’s disease. . . ), multiple sclerosis, developmental
disorders, stroke and to design brain-computer interfaces for rehabilitation.

3 Research program

3.1 Neuroimaging-based biomarkers and decision support systems

Neuroimaging provides critical information on anatomical and functional alterations as well as on
specific molecular and cellular processes. Our work is focused on the development of computational
approaches to extract biomarkers and build computer-aided diagnosis (CAD) systems from MRI and
PET data. More specifically, we developed: i) image translation models that can generate biomarkers of
specific pathological processes from unspecific routine imaging data; ii) approaches for detecting local
abnormalities; iii) frameworks for reproducible and reliable evaluation of CAD systems; iv) methods for
training and validating from large-scale hospital data warehouses.

3.2 Models of brain networks

Functional imaging techniques (EEG, MEG and fMRI) allow characterizing the statistical interactions
between the activities of different brain areas, i.e. functional connectivity. Functional integration of
spatially distributed brain regions is a well-known mechanism underlying various cognitive tasks, and is
disrupted in brain disorders. Our team develops mathematical frameworks to analyze and model brain
networks, or graphs, estimated from experimental data via network-science approaches. More specifically,
we proposed analytical tools to infer brain networks, characterize their structure and dynamics, over
multiple spatial and temporal scales.

3.3 Disease progression modeling with longitudinal data

Longitudinal data sets contain observations of multiple subjects observed at multiple time-points. They
offer a unique opportunity to understand temporal processes such as ageing or disease progression.
We aim here to develop a new generation of statistical methods to infer the dynamics of changes of a
series of data such as biomarkers, images or clinical endpoints, together with the variability of such
multivariate trajectories within a population of reference. We apply these new models across an array of
neurodegenerative diseases to i) understand the heterogeneity in disease progression, in particular how
genetic factors may control variations in disease progression, ii) forecast the progression of a new patient
at entry of a clinical trial for stratification purposes and iii) the design of new clinical scales for use as
outcomes in trials.

3.4 High-dimensional and multimodal data

We then aim to develop tools to assist clinical decisions such as diagnosis, prognosis or inclusion in
therapeutic trials. To that purpose, we leverage the tools developed by the team, such as multimodal
representations, network indices and spatio-temporal models which are combined with advanced clas-
sification and regression approaches. We also dedicate strong efforts to rigorous, transparent and
reproducible validation of the decision support systems on large clinical datasets.

6

Inria Annual Report 2023

3.5 Clinical research studies

Finally, we aim to apply advanced computational and statistical tools to clinical research studies. These
studies are often performed in collaboration with other researchers of the ICM, clinicians of the Pitié-
Salpêtrière hospital or external partners. Our aim is to better understand brain disorders by characterizing
alterations and their progression, and to validate new tools to assist clinical decisions. While a large part
of these clinical studies were in the field of dementia (Alzheimer’s disease, fronto-temporal dementia), we
have developed successful collaborations in other fields including multiple sclerosis, Parkinson’s disease
and related disorders, Huntington’s disease or spino-cerebellar ataxia.

4 Application domains

4.1 Introduction

We develop different applications of our new methodologies to brain pathologies, mainly neurodegener-
ative diseases. These applications aim at:

• better understanding the pathophysiology of brain disorders;

• designing systems to support clinical decisions such as diagnosis, prognosis and design of clinical

trials;

• developing brain computer interfaces for clinical applications.

4.2 Understanding brain disorders

Computational and statistical approaches have the potential to help understand the pathophysiology of
brain disorders. We first aim to contribute to better understand the relationships between pathological
processes, anatomical and functional alterations, and symptoms. Moreover, within a single disease, there
is an important variability between patients. The models that we develop have the potential to identify
more homogeneous disease subtypes, that would constitute more adequate targets for new treatments.
Finally, we aim to establish the chronology of the different types of alterations. We focus these activities
on neurodegeneratives diseases: dementia (Alzheimer’s disease, fronto-temporal dementia), Parkinson’s
disease, multiple sclerosis.

4.3 Supporting clinical decisions

We aim to design computational tools to support clinical decisions, including diagnosis, prognosis and the
design of clinical trials. We design new approaches for extracting biomarkers from different types of data.
Our tools have the potential to help clinicians in their diagnosis by providing automated classification
that can integrate multiple types of data (clinical/cognitive tests, imaging, biomarkers). Predicting the
evolution of disease in individual patients is even more difficult. We aim to develop approaches that can
predict which alterations and symptoms will occur and when. Finally, new approaches are needed to
select participants in clinical trials. Indeed, it is widely recognized that, to have a chance to be successful,
treatments should be administered at a very early stage.

4.4 Brain computer interfaces for clinical applications

A brain computer interface (BCI) is a device aiming to decode brain activity, thus creating an alternate
communication channel between a person and the external environment. BCI systems can be categorized
on the basis of the classification of an induced or evoked brain activity. The central tenet of a BCI is
the capability to distinguish different patterns of brain activity, each being associated to a particular
intention or mental task. Hence adaptation, as well as learning, is a key component of a BCI because
users must learn to modulate their brainwaves to generate distinct brain patterns. Usually, a BCI is
considered a technology for people to substitute some lost functions. However, a BCI could also help in
clinical rehabilitation to recover motor functions. Indeed, in current neuroscience-based rehabilitation it

Project ARAMIS

7

is recognized that protocols based on mental rehearsal of movements (like motor imagery practicing) are
a way to access the motor system because they can induce an activation of sensorimotor networks that
were affected by lesions. Hence, a BCI based on movement imagery can objectively monitor patients’
progress and their compliance with the protocol, monitoring that they are actually imagining movements.
It also follows that feedback from such a BCI can provide patients with an early reinforcement in the
critical phase when there is not yet an overt sign of movement recovery.

5 Highlights of the year

• Olivier Colliot has edited the Open Access book Machine Learning for Brain Disorders (Springer,

2023, 1047 pages) which is the most comprehensive resource on this topic.

• Olivier Colliot is Conference Chair of the SPIE Medical Imaging: Image Processing conference for

the period 2022-2025.

• Baptiste Couvy-Duchesne is PI of an INRIA associate team Brainetics, which started in 2023 to

foster the collaboration between ARAMIS and the University of Queensland (Australia).

• Fabrizio De Vico Fallani created a new INRIA team-project called NERV devoted to complex systems

and neuroengineering.

5.1 Awards

• Ravi Hassanaly received the best poster runner-up award at SPIE Medical Imaging 2023: Image
processing for his paper ‘Simulation-based evaluation framework for deep learning unsupervised
anomaly detection on brain FDG PET’ [68].

6 New software, platforms, open data

6.1 New software

6.1.1 Clinica

Name: Clinica

Keywords: Neuroimaging, Brain MRI, MRI, Clinical analysis, Image analysis, Machine learning

Scientific Description: Clinica is a software platform for multimodal brain image analysis in clinical
research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies.
For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging
modalities: currently anatomical MRI, diffusion MRI, PET. For each modality, Clinica allows to easily
extract various types of features (regional measures, parametric maps, surfaces, curves, networks).
Such features are then subsequently used as input of machine learning, statistical modeling,
morphometry or network analysis methods. Processing pipelines are based on combinations of
freely available tools developed by the community. It provides an integrated data management
specification to store raw and processing data. Clinica is written in Python. It uses the Nipype
system for pipelining. It combines widely-used software for neuroimaging data analysis (SPM,
Freesurfer, FSL, MRtrix...), morphometry (Deformetrica), machine learning (Scikit-learn) and the
BIDS standard for data organization.

Functional Description: Clinica is a software platform for multimodal brain image analysis in clinical
research studies. It makes it easy to apply advanced analysis tools to large scale clinical studies.
For that purpose, it integrates a comprehensive set of processing tools for the main neuroimaging
modalities: currently anatomical MRI, diffusion MRI, PET. For each modality, Clinica allows to
easily extract various types of features (regional measures, parametric maps, surfaces, curves,
networks). Such features are then subsequently used as input of machine learning, statistical
modeling, morphometry or network analysis methods. Clinica also provides an integrated data

8

Inria Annual Report 2023

management specification to store raw and processing data. Overall, Clinica helps to: i) apply
advanced analysis tools to clinical research studies, ii) easily share data and results, iii) make
research more reproducible.

URL: http://www.clinica.run

Publications: hal-04278898, hal-03728243, hal-03513920, hal-02549242, hal-02132147, hal-02308126,

hal-02562504, hal-01518785, hal-01578479, hal-01858384, hal-01907482, hal-01654000, hal-02566361,
hal-01950933

Contact: Olivier Colliot

Participants: Olivier Colliot, Ninon Burgos, Stanley Durrleman, Nicolas Gensollen, Mauricio Diaz, Ghis-
lain Vaillant, Matthieu Joulot, Ravi Hassanaly, Michael Bacci, Simona Bottani, Sabrina Fontanella,
Pietro Gori, Jeremy Guillon, Thomas Jacquemont, Pascal Lu, Arnaud Marcoux, Tristan Moreau,
Alexandre Routier, Omar El Rifai, Jorge Samper Gonzalez, Elina Thibeau-sutre, Junhao Wen

Partners: Institut du Cerveau et de la Moelle épinière (ICM), CNRS, INSERM, Sorbonne Université

6.1.2 ClinicaDL

Keywords: Deep learning, Neuroimaging, Reproducibility

Scientific Description: As deep learning faces a reproducibility crisis and studies on deep learning ap-
plied to neuroimaging are contaminated by methodological flaws, there is an urgent need to provide
a safe environment for deep learning users to help them avoid common pitfalls that will bias and
discredit their results. Several tools have been proposed to help deep learning users design their
framework for neuroimaging data sets. ClinicaDL has been developed to bring answers to three
common issues encountered by deep learning users who are not always familiar with neuroima-
ging data: (1) the format and preprocessing of neuroimaging data sets, (2) the contamination
of the evaluation procedure by data leakage and (3) a lack of reproducibility. The combination
of ClinicaDL and its companion project Clinica allows performing an end-to-end neuroimaging
analysis, from the download of raw data sets to the interpretation of trained networks, including
neuroimaging preprocessing, quality check, label definition, architecture search, and network
training and evaluation.

Functional Description: ClinicaDL is a Python open-source software for neuroimaging data processing
with deep learning. This software includes many functionalities, such as neuroimaging prepro-
cessing, synthetic dataset generation, label definition, data split with similar demographics, archi-
tecture search, network training, performance evaluation and trained network interpretation. The
three main objectives of ClinicaDL are to (1) help manipulate neuroimaging data sets, (2) prevent
data leakage from biasing results and (3) reproduce deep learning experiments.

URL: https://clinicadl.readthedocs.io

Publications: hal-03351976, hal-04279014, hal-02562504

Contact: Ninon Burgos

Participants: Ninon Burgos, Olivier Colliot, Mauricio Diaz, Elina Thibeau-sutre, Ravi Hassanaly, Camille

Brianceau, Alexandre Routier, Maelys Solal

Partners: Institut du Cerveau et de la Moelle épinière (ICM), CNRS, INSERM, Sorbonne Université

6.1.3 Deformetrica

Keywords: Anatomy, Mesh, Automatic Learning, C++, 3D modeling, Image analysis

Project ARAMIS

9

Scientific Description: Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It
essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object
embedded in this space, whether this object is a curve, a surface, a structured or unstructured set
of points, or any combination of them.

Deformetrica comes with two applications:

registration, which computes the best possible deformation between two sets of objects, atlas
construction, which computes an average object configuration from a collection of object sets, and
the deformations from this average to each sample in the collection.

Deformetrica has very little requirements about the data it can deal with. In particular, it does not
require point correspondence between objects!

Functional Description: Deformetrica is a software for the statistical analysis of 2D and 3D shape data. It
essentially computes deformations of the 2D or 3D ambient space, which, in turn, warp any object
embedded in this space, whether this object is a curve, a surface, a structured or unstructured set
of points, or any combination of them.

Deformetrica comes with two applications:

- Registration, which computes the optimal deformation between two sets of objects,

- Atlas construction, which computes an average object configuration from a collection of object
sets, and the deformations from this average to each sample in the collection.

Deformetrica has very little requirements about the data it can deal with. In particular, it does not
require point correspondence between objects!

URL: http://www.deformetrica.org/

Contact: Stanley Durrleman

Participants: Alexandre Routier, Ana Fouquier, Barbara Gris, Benjamin Charlier, Cedric Doucet, Joan

Alexis Glaunès, Marcel Prastawa, Michael Bacci, Pietro Gori, Stanley Durrleman

Partners: University of Utah, Université de Montpellier 2, Université Paris-Descartes

6.1.4 leaspy

Name: Learning spatiotemporal patterns in python

Keywords: Clinical analysis, Medical applications, Personalized medicine

Functional Description: Leaspy, standing for LEArning Spatiotemporal Patterns in Python, has been
developed to analyze longitudinal (or sequential) data that correspond to the measurements of a
long-term progression. Said differently, each sequence of repeated observations derives from a
portion of the global process, with a certain variability between sequence.

Contact: Stanley Durrleman

6.1.5 Brain Networks Toolbox

Keywords: Neuroimaging, Medical imaging

Functional Description: Brain Networks Toolbox is an open-source package of documented routines im-
plementing new graph algorithms for brain network analysis. It mainly contains Matlab code of new
methods developed by the team and associated to publications (e.g., brain network thresholding,
extraction of the information redundancy, node accessibility, etc). It requires, as input, adjacency
matrices representing brain connectivity networks. Thus, it is independent on the specific ap-
proach used to construct brain networks and it can be used to extract network properties from any
neuroimaging modality in healthy and diseased subjects.

URL: https://github.com/brain-network/bnt

10

Inria Annual Report 2023

Contact: Fabrizio De Vico Fallani

Participants: Fabrizio De Vico Fallani, Jeremy Guillon, Mario Chavez

6.1.6 OpenVIBE

Keywords: Neurosciences, Interaction, Virtual reality, Health, Real time, Neurofeedback, Brain-Computer

Interface, EEG, 3D interaction

Functional Description: OpenViBE is a free and open-source software platform devoted to the design,
test and use of Brain-Computer Interfaces (BCI). The platform consists of a set of software modules
that can be integrated easily and efficiently to design BCI applications. The key features of Open-
ViBE software are its modularity, its high performance, its portability, its multiple-user facilities
and its connection with high-end/VR displays. The designer of the platform enables users to build
complete scenarios based on existing software modules using a dedicated graphical language and a
simple Graphical User Interface (GUI). This software is available on the Inria Forge under the terms
of the AGPL licence, and it was officially released in June 2009. Since then, the OpenViBE software
has already been downloaded more than 60000 times, and it is used by numerous laboratories, pro-
jects, or individuals worldwide. More information, downloads, tutorials, videos, documentations
are available on the OpenViBE website.

Release Contributions: Added: - Metabox to perform log of signal power - Artifacted files for algorithm

tests

Changed: - Refactoring of CMake build process - Update wildcards in gitignore - Update CSV File
Writer/Reader - Stimulations only

Removed: - Ogre games and dependencies - Mensia distribution

Fixed: - Intermittent compiler bug

News of the Year: Python2 support dropped in favour of Python3 New feature boxes: - Riemannian
geometry - Multimodal Graz visualisation - Artefact detection - Features selection - Stimulation
validator

Support for Ubuntu 18.04 Support for Fedora 31

URL: http://openvibe.inria.fr

Contact: Anatole Lecuyer

Participants: Cedric Riou, Thierry Gaugry, Anatole Lecuyer, Fabien Lotte, Jussi Lindgren, Laurent

Bougrain, Maureen Clerc Gallagher, Théodore Papadopoulo, Thomas Prampart

Partners: INSERM, GIPSA-Lab

6.1.7 brainMapR

Keywords: 3D rendering, Brain MRI, Clustering

Functional Description: brainMapR is an R package to analyse and plot brain association maps (results
of brain-wide association studies). It is tailored for brain MRI vertex-wise analyses, and requires
brain MRI to be processed with FreeSurfer (for cortical vertices) and/or ENIGMA-shape package (for
subcortical vertices). Functions include annotation of the association maps to describe and locate
associated brain regions, Manhattan plots for brain, high quality plots of cortical and subcortical
meshes, and GIFs generation.

Publication: hal-03118366

Author: Baptiste Couvy-Duchesne

Contact: Baptiste Couvy-Duchesne

Project ARAMIS

11

6.2 New platforms

Platform Brain-computer interface

Participants: Marie-Constance Corsi, Arthur Desbois, Fabrizio De Vico Fallani (Cor-

respondant).

Our team coordinates the developments of the Brain-Computer Interface (BCI) platform at the
Centre EEG/MEG of the neuroimaging core facility of the ICM. Several projects, including our NETBCI
NNIH/ANR and ATTACK Big-brain theory funded projects, as well as experiments by different researchers
of the Institute (ANR BETAPARK Project), and the BCINET ERC Consolidator grant are currently being run.
To reinforce the impact of the platform we have extended the contract of our Inria ADT engineer, who is
significantly contributing to the extension of the Inria software OpenVibe allowing for new functionalities
based on our methodological development on brain connectivity and develop new sofwtare for the
individual calibration (A. Desbois)

7 New results

7.1 Book: Machine Learning for Brain Disorders

Participants: Ninon Burgos, Baptiste Couvy-Duchesne, Marie-Constance Corsi,
Johann Faouzi, Federica Cacciamani, Gabriel Jimenez, Daniel Ra-
coceanu, Olivier Colliot (Correspondant).

This book provides readers with an up-to-date and comprehensive guide to both methodological
and applicative aspects of machine learning (ML) for brain disorders. The chapters in this book are
organized into five parts. Part One presents the fundamentals of ML. Part Two looks at the main types of
data used to characterize brain disorders, including clinical assessments, neuroimaging, electro- and
magnetoencephalography, genetics and omics data, electronic health records, mobile devices, connected
objects and sensors. Part Three covers the core methodologies of ML in brain disorders and the latest
techniques used to study them. Part Four is dedicated to validation and datasets, and Part Five discusses
applications of ML to various neurological and psychiatric disorders.

More details in [83].

7.2 Evaluation of MRI-based machine learning approaches for computer-aided

diagnosis of dementia in a clinical data warehouse

Participants:

Simona Bottani, Didier Dormont, Ninon Burgos, Olivier Colliot (Cor-
respondant).

A variety of algorithms have been proposed for computer-aided diagnosis of dementia from anatom-
ical brain MRI. These approaches achieve high accuracy when applied to research data sets but their
performance on real-life clinical routine data has not been evaluated yet. The aim of this work was to
study the performance of such approaches on clinical routine data, based on a hospital data warehouse,
and to compare the results to those obtained on a research data set. The clinical data set was extracted
from the hospital data warehouse of the Greater Paris area, which includes 39 different hospitals. The
research set was composed of data from the Alzheimer’s Disease Neuroimaging Initiative data set. In the
clinical set, the population of interest was identified by exploiting the diagnostic codes from the 10th
revision of the International Classification of Diseases that are assigned to each patient. We studied how
the imbalance of the training sets, in terms of contrast agent injection and image quality, may bias the
results. We demonstrated that computer-aided diagnosis performance was strongly biased upwards

12

Inria Annual Report 2023

(over 17 percent points of balanced accuracy) by the confounders of image quality and contrast agent
injection, a phenomenon known as the Clever Hans effect or shortcut learning. When these biases were
removed, the performance was very poor. In any case, the performance was considerably lower than
on the research data set. Our study highlights that there are still considerable challenges for translating
dementia computer-aided diagnosis systems to clinical routine.

More details in [39].

7.3 Automatic segmentation of the choroid plexuses: Method and validation in

controls and patients with multiple sclerosis

Participants:

Arya Yazdan-Panah, Olivier Colliot (Correspondant).

Choroid Plexuses (ChP) are structures located in the ventricles that produce the cerebrospinal fluid
(CSF) in the central nervous system. They are also a key component of the blood-CSF barrier. Recent stud-
ies have described clinically relevant ChP volumetric changes in several neurological diseases including
Alzheimer’s, Parkinson’s disease, and multiple sclerosis (MS). Therefore, a reliable and automated tool for
ChP segmentation on images derived from magnetic resonance imaging (MRI) is a crucial need for large
studies attempting to elucidate their role in neurological disorders. Here, we propose a novel automatic
method for ChP segmentation in large imaging datasets. The approach is based on a 2-step 3D U-Net to
keep preprocessing steps to a minimum for ease of use and to lower memory requirements. The models
are trained and validated on a first research cohort including people with MS and healthy subjects. A
second validation is also performed on a cohort of pre-symptomatic MS patients having acquired MRIs
in routine clinical practice. Our method reaches an average Dice coefficient of 0.72 with the ground truth
and a volume correlation of 0.86 on the first cohort while outperforming FreeSurfer and FastSurfer-based
ChP segmentations. On the dataset originating from clinical practice, the method reaches a Dice coeffi-
cient of 0.67 (being close to the inter-rater agreement of 0.64) and a volume correlation of 0.84. These
results demonstrate that this is a suitable and robust method for the segmentation of the ChP both on
research and clinical datasets.

More details in [61].

7.4 How precise are performance estimates for typical medical image segmentation

tasks?

Participants:

Rosana El Jurdi, Olivier Colliot (Correspondant).

An important issue in medical image processing is to be able to estimate not only the performances of
algorithms but also the precision of the estimation of these performances. Reporting precision typically
amounts to reporting standard-error of the mean (SEM) or equivalently confidence intervals. However,
this is rarely done in medical image segmentation studies. In this paper, we aim to estimate what is the
typical confidence that can be expected in such studies. To that end, we first perform experiments for Dice
metric estimation using a standard deep learning model (U-net) and a classical task from the Medical
Segmentation Decathlon. We extensively study precision estimation using both Gaussian assumption and
bootstrapping (which does not require any assumption on the distribution). We then perform simulations
for other test set sizes and performance spreads. Overall, our work shows that small test sets lead to wide
confidence intervals (e.g. 8 points of Dice for 20 samples with σ ≈ 10).

More details in [71].

7.5 Introducing Soft Topology Constraints in Deep Learning-based Segmentation

using Projected Pooling Loss

Project ARAMIS

13

Participants: Guanghui Fu, Rosana El Jurdi, Lydia Chougar, Didier Dormont,

Olivier Colliot (Correspondant).

Deep learning methods have achieved impressive results for 3D medical image segmentation. How-
ever, when the network is only guided by voxel-level information, it may provide anatomically aberrant
segmentations. When performing manual segmentations, experts heavily rely on prior anatomical know-
ledge. Topology is an important prior information due to its stability across patients. Recently, several
losses based on persistent homology were proposed to constrain topology. Persistent homology offers a
principled way to control topology. However, it is computationally expensive and complex to implement,
in particular in 3D. In this paper, we propose a novel loss function to introduce topological priors in
deep learning-based segmentation, which is fast to compute and easy to implement. The loss performs
a projected pooling within two steps. We first focus on errors from a global perspective by using 3D
MaxPooling to obtain projections of 3D data onto three planes: axial, coronal and sagittal. Then, 2D
MaxPooling layers with different kernel sizes are used to extract topological features from the multi-view
projections. These two steps are combined using only MaxPooling, thus ensuring the efficiency of the loss
function. Our approach was evaluated in several medical image datasets (spleen, heart, hippocampus,
red nucleus). It allowed reducing topological errors and, in some cases, improving voxel-level accuracy.

More details in [66].

7.6 Interpretable automatic detection of incomplete hippocampal inversions using

anatomical criteria

Participants:

Lisa Hemforth, Claire Cury, Baptiste Couvy-Duchesne, Olivier Col-
liot (Correspondant).

Incomplete Hippocampal Inversion (IHI) is an atypical anatomical pattern of the hippocampus that
has been associated with several brain disorders (epilepsy, schizophrenia). IHI can be visually detected on
coronal T1 weighted MRI images. IHI can be absent, partial or complete (no IHI, partial IHI, IHI). However,
visual evaluation can be long and tedious, justifying the need for an automatic method. In this paper,
we propose, to the best of our knowledge, the first automatic IHI detection method from T1-weighted
MRI. The originality of our approach is that, instead of directly detecting IHI, we propose to predict
several anatomical criteria, which each characterize a particular anatomical feature of IHI, and that
can ultimately be combined for IHI detection. Such individual criteria have the advantage of providing
interpretable anatomical information regarding the morphological aspect of a given hippocampus. We
relied on a large population of 2,008 participants from the IMAGEN study. The approach is general and
can be used with different machine learning models. In this paper, we explored two different backbone
models for the prediction: a linear method (ridge regression) and a deep convolutional neural network.
We demonstrated that the interpretable, anatomical based prediction was at least as good as when
predicting directly the presence of IHI, while providing interpretable information to the clinician or
neuroscientist. This approach may be applied to other diagnostic tasks which can be characterized
radiologically by several anatomical features.

More details in [69].

7.7 Interpretability of Machine Learning Methods Applied to Neuroimaging

Participants:

Elina Thibeau-Sutre, Ninon Burgos, Olivier Colliot (Correspondant).

Deep learning methods have become very popular for the processing of natural images, and were then
successfully adapted to the neuroimaging field. As these methods are non-transparent, interpretability
methods are needed to validate them and ensure their reliability. Indeed, it has been shown that deep

14

Inria Annual Report 2023

learning models may obtain high performance even when using irrelevant features, by exploiting biases
in the training set. Such undesirable situations can potentially be detected by using interpretability
methods. Recently, many methods have been proposed to interpret neural networks. However, this
domain is not mature yet. Machine learning users face two major issues when aiming to interpret their
models: which method to choose, and how to assess its reliability? Here, we aim at providing answers
to these questions by presenting the most common interpretability methods and metrics developed to
assess their reliability, as well as their applications and benchmarks in the neuroimaging context. Note
that this is not an exhaustive survey: we aimed to focus on the studies which we found to be the most
representative and relevant.
More details in [91].

7.8 Evaluating machine learning models and their diagnostic value

Participants: Gaël Varoquaux, Olivier Colliot (Correspondant).

This chapter describes model validation, a crucial part of machine learning whether it is to select
the best model or to assess risk of a given model. We start by detailing the main performance metrics
for different tasks (classification, regression), and how they may be interpreted, including in the face
of class imbalance, varying prevalence, or asymmetric cost-benefit trade-offs. We then explain how
to estimate these metrics in a unbiased manner using training, validation, and test sets. We describe
cross-validation procedures –to use a larger part of the data for both training and testing– and the dangers
of data leakage –optimism bias due to training data contaminating the test set. Finally, we discuss how to
obtain confidence intervals of performance metrics, distinguishing two situations: internal validation or
evaluation of learning algorithms, and external validation or evaluation of resulting prediction models.

More details in [93].

7.9 Reproducibility in machine learning for medical imaging

Participants: Olivier Colliot, Elina Thibeau-Sutre, Ninon Burgos (Correspondant).

Reproducibility is a cornerstone of science, as the replication of findings is the process through
which they become knowledge. It is widely considered that many fields of science are undergoing a
reproducibility crisis. This has led to the publications of various guidelines in order to improve research
reproducibility. This didactic chapter intends at being an introduction to reproducibility for researchers
in the field of machine learning for medical imaging. We first distinguish between different types of
reproducibility. For each of them, we aim at defining it, at describing the requirements to achieve it and
at discussing its utility. The chapter ends with a discussion on the benefits of reproducibility and with a
plea for a non-dogmatic approach to this concept and its implementation in research practice.

More details in [85].

7.10 Association Between Diseases and Symptoms Diagnosed in Primary Care and

the Subsequent Specific Risk of Multiple Sclerosis

Participants: Octave Guinebretiere, Thomas Nédelec,

Laurène Gantzer,
Béranger Lekens, Stanley Durrleman, Céline Louapre (Corres-
pondant).

Previous studies have reported a possible prodrome in multiple sclerosis (MS) defined by non-specific
symptoms including mood disorder or genito-urinary symptoms and increased health care use detected
several years before diagnosis. This study aimed to evaluate agnostically the associations between

Project ARAMIS

15

diseases and symptoms diagnosed in primary care and the risk of multiple sclerosis (MS) relative to
controls and two other autoimmune inflammatory diseases with similar population characteristics,
namely lupus and Crohn’s disease.

A case-control study was conducted using electronic health records from the Health Improvement
Network database in the UK and France. We agnostically assessed the associations between 113 diseases
and symptoms in the five years before and after diagnosis in patients with subsequent diagnosis of MS.
Individuals with a diagnosis of MS were compared to individuals without MS, and individuals with two
other auto-immune diseases, Crohn’s disease and lupus

The study population consisted of patients with MS (n= 20,174), patients without MS (n=54,790),
patients with Crohn’s disease (n=30,477) or patients with lupus (n=7,337). Twelve ICD-10 codes were
significantly positively associated with the risk of MS compared to controls without MS. After considering
ICD-10 codes suggestive of neurological symptoms as the first diagnosis of MS, five ICD-10 codes re-
mained significantly associated with MS: depression (UK OR 1.22 [95%CI 1.11-1.34]), sexual dysfunction
(1.47 [1.11-1.95]), constipation (1.5 [1.27-1.78]), cystitis (1.21 [1.05-1.39]), and urinary tract infections of
unspecified site (1.38 [1.18-1.61]). However, none of these conditions was selectively associated with MS
in comparisons with both lupus and Crohn’s disease. All five ICD-10 codes identified were still associated
with MS during the five years after diagnosis.

This study identified 5 health conditions associated with subsequent MS diagnosis, which may be
considered not only prodromal but also early-stage symptoms. However, these health conditions overlap
with prodrome of two other autoimmune diseases, hence lacking specificity to MS.

More details in [46].

7.11 Charting Disease Trajectories from Isolated REM Sleep Behavior Disorder to

Parkinson’s Disease

Participants: Cécile di Folco, Raphael Couronné, Isabelle Arnulf, Graziella Man-
gone, Smaranda Leu-Semenescu, Pauline Dodet, Marie Vidailhet,
Jean-Christophe Corvol, Stéphane Lehéricy, Stanley Durrleman (Cor-
respondant).

Clinical presentation and progression dynamics are variable in patients with Parkinson’s disease
(PD). Disease course mapping is an innovative disease modelling technique that summarizes the range
of possible disease trajectories and estimates dimensions related to onset, sequence, and speed of
progression of disease markers. Objective To propose a disease course map for PD and investigate
progression profiles in patients with or without rapid eye movement sleep behavioral disorders (RBD).
Data of 919 PD patients and 88 isolated RBD patients from three independent longitudinal cohorts
were analyzed (follow-up duration: 5.1; 95% confidence interval, 1.1-8.1] years). Disease course map was
estimated by using eight clinical markers (motor and non-motor symptoms) and four imaging markers
(dopaminergic denervation).

PD course map showed that the first changes occurred in the contralateral putamen 13 years before
diagnosis, followed by changes in motor symptoms, dysautonomia, sleep—all before diagnosis—and
finally cognitive decline at the time of diagnosis. The model showed earlier disease onset, earlier non-
motor and later motor symptoms, more rapid progression of cognitive decline in PD patients with RBD
than PD patients without RBD. This pattern was even more pronounced in patients with isolated RBD
with early changes in sleep, followed by cognition and non-motor symptoms and later changes in motor
symptoms.

These findings are consistent with the presence of distinct patterns of progression between patients
with and without RBD. Understanding heterogeneity of PD progression is key to decipher the underlying
pathophysiology and select homogeneous subgroups of patients for precision medicine.

More details in [43].

7.12 A Multimodal Disease Progression Model for Genetic Associations with Disease

Dynamics

16

Inria Annual Report 2023

Participants: Némo Fournier (Correspondant), Stanley Durrleman.

This article introduced a disease progression model suited for neurodegenerative pathologies that
allows to model associations between covariates and dynamic features of the disease course. It established
a statistical framework and implemented an algorithm for its estimation. It showed that the model is
reliable and can provide uncertainty estimates of the discovered associations thanks to its Bayesian
formulation. The model’s interest was showcased by shining a new light on genetic associations.

More details in [65].

7.13 Interaction of sex and onset site on the disease trajectory of amyotrophic lateral

sclerosis

Participants:

Juliette Ortholand (Correspondant),
Sophie Tezenas du Montcel, Stanley Durrleman.

Pierre-François Pradat,

Studies showed the impact of sex and onset site (spinal or bulbar) on disease onset and survival in
ALS. However, they mainly result from cross-sectional or survival analysis, and the interaction of sex and
onset site on the different proxies of disease trajectory has not been fully investigated.

We selected all patients with repeated observations in the PRO-ACT database. We divided them into
four groups depending on their sex and onset site. We estimated a multivariate disease progression model,
named ALS Course Map, to investigate the combined temporal changes of the four sub-scores of the
revised ALS Functional Rating Scale (ALSFRSr), the forced vital capacity (FVC), and the body mass index
(BMI). We then compared the progression rate, the estimated age at onset, and the relative progression of
the outcomes across each group.

We included 1,438 patients from the PRO-ACT database. They were 51% men with spinal onset, 12%
men with bulbar onset, 26% women with spinal onset, and 11% women with bulbar onset. We showed
a significant influence of both sex and onset site on the ALSFRSr progression. The BMI decreased 8.9
months earlier (95% CI = [3.9, 13.8]) in women than men, after correction for the onset site. Among pa-
tients with bulbar onset, FVC was impaired 2.6 months earlier (95% CI = [0.6, 4.6]) in women. Conclusion:
Using a multivariable disease modelling approach, we showed that sex and onset site are important
drivers of the progression of motor function, BMI, and FVC decline.

More details in [52].

7.14 Impact of sex and APOE-e4 genotype on regional brain metabolism in Alzheimer’s

Disease

Participants:

Benoît Sauty (Correspondant), Stanley Durrleman.

Age, sex and APOE-e4 genotype have been identified as the strongest predictors of the risk of de-
veloping Alzheimer’s Disease (AD). This work models the pathological progression of regional brain
hypometabolism, using mixed-effect models with latent time variable and longitudinal FDG-PET data.
Statistical comparisons then disentangle the effects of sex and APOE-e4 genotype on the onset age and
pace of progression of hympometabolism in each brain region, while correcting for education level. They
provide a brain map of the regions with earlier and/or faster alterations of the metabolism. We show
that females are associated with faster hypometabolism in the caudate nuclei, the thalamus and right
temporal and medial-occipital lobes, while APOE-e4 is associated with earlier hypometabolism in the
limbic system (hippocampus, parahippocampus and amygdala) and temporal lobe.

More details in [74].

Project ARAMIS

17

7.15 Forecasting individual progression trajectories in Alzheimer’s disease

The anticipation of progression of Alzheimer’s disease (AD) is crucial for evaluations of secondary pre-
vention measures thought to modify the disease trajectory. However, it is difficult to forecast the natural
progression of AD, notably because several functions decline at different ages and different rates in
different patients. We evaluate here AD Course Map, a statistical model predicting the progression of
neuropsychological assessments and imaging biomarkers for a patient from current medical and radi-
ological data at early disease stages. We tested the method on more than 96,000 cases, with a pool of
more than 4,600 patients from four continents. We measured the accuracy of the method for selecting
participants displaying a progression of clinical endpoints during a hypothetical trial. We show that
enriching the population with the predicted progressors decreases the required sample size by 38% to
50%, depending on trial duration, outcome, and targeted disease stage, from asymptomatic individuals
at risk of AD to subjects with early and mild AD. We show that the method introduces no biases regarding
sex or geographic locations and is robust to missing data. It performs best at the earliest stages of disease
and is therefore highly suitable for use in prevention trials.

Participants:

Etienne Maheux (Correspondant),
Juliette Ortho-
land, Colin Birkenbihl, Damanio Archetti, Vincent Bouteloup,
Stéphane Epelbaum, Carole Dufouil, Martin Hofmann-Apitius, Stan-
ley Durrleman.

Igor Koval,

More details in [48].

7.16 Measuring Neuronal Avalanches to inform Brain-Computer Interfaces

Participants: Marie-Constance Corsi (Correspondant), Fabrizio De Vico Fallani.

Large-scale interactions among multiple brain regions manifest as bursts of activations called neur-
onal avalanches, which reconfigure according to the task at hand and, hence, might constitute natural can-
didates to design brain-computer interfaces (BCI). To test this hypothesis, we used source-reconstructed
magneto/electroencephalography, during resting state and a motor imagery task performed within a BCI
protocol. To track the probability that an avalanche would spread across any two regions we built an
avalanche transition matrix (ATM) and demonstrated that the edges whose transition probabilities signi-
ficantly differed between conditions hinged selectively on premotor regions in all subjects. Furthermore,
we showed that the topology of the ATMs allows task-decoding above the current gold standard. Hence,
our results suggest that Neuronal Avalanches might capture interpretable differences between tasks that
can be used to inform brain-computer interfaces.

More details in [41].

7.17 Automatic motion artefact detection in brain T1-weighted magnetic resonance

images from a clinical data warehouse using synthetic data

Participants:

Sophie Loizillon, Simona Bottani, Didier Dormont, Olivier Colliot,
Ninon Burgos (Correspondant).

Containing the medical data of millions of patients, clinical data warehouses (CDWs) represent a
great opportunity to develop computational tools. Magnetic resonance images (MRIs) are particularly
sensitive to patient movements during image acquisition, which will result in artefacts (blurring, ghosting
and ringing) in the reconstructed image. As a result, a significant number of MRIs in CDWs are corrupted
by these artefacts and may be unusable. Since their manual detection is impossible due to the large
number of scans, it is necessary to develop tools to automatically exclude (or at least identify) images

18

Inria Annual Report 2023

with motion in order to fully exploit CDWs. In this paper, we propose a novel transfer learning method for
the automatic detection of motion in 3D T1-weighted brain MRI. The method consists of two steps: a
pre-training on research data using synthetic motion, followed by a fine-tuning step to generalise our
pre-trained model to clinical data, relying on the labelling of 4045 images. The objectives were both
(1) to be able to exclude images with severe motion, (2) to detect mild motion artefacts. Our approach
achieved excellent accuracy for the first objective with a balanced accuracy nearly similar to that of the
annotators (balanced accuracy>80%). However, for the second objective, the performance was weaker
and substantially lower than that of human raters. Overall, our framework will be useful to take advantage
of CDWs in medical imaging and highlight the importance of a clinical validation of models trained on
research data.

More details in [47].

7.18 How can data augmentation improve attribution maps for disease subtype

explainability?

Participants:

Elina Thibeau-Sutre, Olivier Colliot, Ninon Burgos (Correspondant).

As deep learning has been widely used for computer aided-diagnosis, we wished to know whether
attribution maps obtained using gradient back-propagation could correctly highlight the patterns of
disease subtypes discovered by a deep learning classifier. As the correctness of attribution maps is
difficult to evaluate directly on medical images, we used synthetic data mimicking the difference between
brain MRI of controls and demented patients to design more reliable evaluation criteria of attribution
maps. We demonstrated that attribution maps may mix the regions associated with different subtypes for
small data sets while they could accurately characterize both subtypes using a large data set. We then
proposed simple data augmentation techniques and showed that they could improve the coherence of
the explanations for a small data set.

More details in [77].

7.19 Transfer learning from synthetic to routine clinical data for motion artefact

detection in brain T1-weighted MRI

Participants:

Sophie Loizillon, Simona Bottani, Didier Dormont, Olivier Colliot,
Ninon Burgos (Correspondant).

Clinical data warehouses (CDWs) contain the medical data of millions of patients and represent a great
opportunity to develop computational tools. MRIs are particularly sensitive to patient movements during
image acquisition, which will result in artefacts (blurring, ghosting and ringing) in the reconstructed
image. As a result, a significant number of MRIs in CDWs are unusable because corrupted by these
artefacts. Since their manual detection is impossible due to the number of scans, it is necessary to
develop a tool to automatically exclude images with motion in order to fully exploit CDWs. In this
paper, we propose a CNN for the automatic detection of motion in 3D T1-weighted brain MRI. Our
transfer learning approach, based on synthetic motion generation, consists of two steps: a pre-training on
research data using synthetic motion, followed by a fine-tuning step to generalise our pre-trained model
to clinical data, relying on the manual labelling of 5500 images. The objectives were both (1) to be able to
exclude images with severe motion, (2) to detect mild motion artefacts. Our approach achieved excellent
accuracy for the first objective with a balanced accuracy nearly similar to that of the annotators (balanced
accuracy>80%). However, for the second objective, the performance was weaker and substantially lower
than that of human raters. Overall, our framework will be useful to take advantage of CDWs in medical
imaging and to highlight the importance of a clinical validation of models trained on research data.

More details in [72].

Project ARAMIS

19

7.20 Simulation-based evaluation framework for deep learning unsupervised an-

omaly detection on brain FDG PET

Participants: Ravi Hassanaly, Simona Bottani, Benoît Sauty, Olivier Colliot,

Ninon Burgos (Correspondant).

Unsupervised anomaly detection using deep learning models is a popular computer-aided diagnosis
approach because it does not need annotated data and is not restricted to the diagnosis of a disease
seen during training. Such approach consists in first learning the distribution of anomaly free images.
Images presenting anomalies are then detected as outliers of this distribution. These approaches have
been widely applied in neuroimaging to detect sharp and localized anomalies such as tumors or white
matter hyper-intensities from structural MRI. In this work, we aim to detect anomalies from FDG PET
images of patients with Alzheimer’s disease. In this context, the anomalies can be subtle and difficult
to delineate, making the task more difficult and meaning that no ground truth exists to evaluate the
approaches. We thus propose a framework to evaluate unsupervised anomaly detection approaches that
consists in simulating realistic anomalies from images of healthy subjects. We demonstrate the use of
this framework by evaluating an approach based on a 3D variational autoencoder.

More details in [68].

7.21 Unsupervised anomaly detection in 3D brain FDG PET: A benchmark of 17

VAE-based approaches

Participants:

Ravi Hassanaly, Camille Brianceau, Olivier Colliot, Ninon Burgos (Cor-
respondant).

The use of deep generative models for unsupervised anomaly detection is an area of research that
has gained interest in recent years in the field of medical imaging. Among all the existing models, the
variational autoencoder (VAE) has proven to be efficient while remaining simple to use. Much research to
improve the original method has been achieved in the computer vision literature, but rarely translated to
medical imaging applications. To fill this gap, we propose a benchmark of fifteen variants of VAE that we
compare with a vanilla autoencoder and VAE for a neuroimaging use case relying on a simulation-based
evaluation framework. The use case is the detection of anomalies related to Alzheimer’s disease and
other dementias in 3D FDG PET. We show that among the fifteen VAE variants tested, nine lead to a
good reconstruction accuracy and are able to generate healthy-looking images. This indicates that many
approaches developed for computer vision applications can generalize to the unsupervised detection
of anomalies of various shapes, intensities and locations in 3D FDG PET. However, these models do not
outperform the vanilla autoencoder and VAE. More details in [81].

7.22 Semi-supervised domain adaptation for automatic quality control of FLAIR

MRIs in a clinical data warehouse

Participants:

Sophie Loizillon, Olivier Colliot, Lydia Chougar, Didier Dormont,
Ninon Burgos (Correspondant).

Domain adaptation is a very useful approach to exploit the potential of clinical data warehouses, which
gather a vast amount of medical imaging encompassing various modalities, sequences, manufacturers
and machines. In this study, we propose a semi-supervised domain adaptation (SSDA) framework for
automatically detecting poor quality FLAIR MRIs within a clinical data warehouse. Leveraging a limited
number of labelled FLAIR and a large number of labelled T1-weighted MRIs, we introduce a novel
architecture based on the well known Domain Adversarial Neural Network (DANN) that incorporates a
specific classifier for the target domain. Our method effectively addresses the covariate shift and class

20

Inria Annual Report 2023

distribution shift between T1-weighted and FLAIR MRIs, surpassing existing SSDA approaches by more
than 10 percent points.
More details in [73].

7.23 A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Seg-

mentation via Two-Phase Training Angiography-to-Venography Translation

Participants:

Francesco Galati, Ninon Burgos, Maria A. Zualuaga (Correspondant).

We present a semi-supervised domain adaptation framework for brain vessel segmentation from
different image modalities. Existing state-of-the-art methods focus on a single modality, despite the wide
range of available cerebrovascular imaging techniques. This can lead to significant distribution shifts
that negatively impact the generalization across modalities. By relying on annotated angiographies and a
limited number of annotated venographies, our framework accomplishes image-to-image translation
and semantic segmentation, leveraging a disentangled and semantically rich latent space to represent
heterogeneous data and perform image-level adaptation from source to target domains. Moreover, we
reduce the typical complexity of cycle-based architectures and minimize the use of adversarial training,
which allows us to build an efficient and intuitive model with stable training. We evaluate our method on
magnetic resonance angiographies and venographies. While achieving state-of-the-art performance in
the source domain, our method attains a Dice score coefficient in the target domain that is only 8.9%
lower, highlighting its promising potential for robust cerebrovascular image segmentation across different
modalities.

More details in [67].

7.24 Temporal dynamics of the Scale for the Assessment and Rating of Ataxia in

Spinocerebellar ataxias

Participants:

Paul Moulaire, Pierre Emmanuel Poulet, Emilien Petit, Thomas Klock-
gether, Alexandra Durr, Tetsuo Ashisawa, Sophie Tezenas Du
Montcel (Correspondant).

Background The Scale for the Assessment and Rating of Ataxia (SARA) is the reference clinical scale to
assess the severity of cerebellar ataxia. In the context of upcoming therapeutic trials, a reliable clinical
outcome is needed to assess the efficiency of treatments. Objective The aim is to precisely assess and
compare temporal dynamics of SARA and a new f-SARA. Methods We analyzed data from four cohorts
(EUROSCA, RISCA, CRC-SCA, and SPATAX) comprising 1210 participants and 4092 visits. The linearity of
the progression and the variability were assessed using an ordinal Bayesian mixed-effect model (Leaspy).
We performed sample size calculations for therapeutic trials with different scenarios to improve the
responsiveness of the scale. Results Seven of the eight different items had a nonlinear progression. The
speed of progression was different between most of the items, with an average time for a one-point
increase from 3.5 years [3.4; 3.6] (median, 95% credible interval) for the fastest item to 11.4 [10.9; 12.0]
years. The total SARA score had a linear progression with an average time for a one-point increase of
0.95 [0.92; 0.98] years. After removing the four last items and rescaling all items from 0 to 4, variability
increased and progression was slower and thus would require a larger sample size in a future therapeutic
trial. Conclusion Despite a heterogeneous temporal dynamics at the item level, the global progression of
SARA was linear. Changing the initial scale deteriorates the responsiveness. This new information about
the temporal dynamics of the scale should help design the outcome of future clinical trials.

More details in [50].

7.25 Baseline Clinical and Blood Biomarker in Patients With Preataxic and Early-

Stage Disease Spinocerebellar Ataxia 1 and 3

Project ARAMIS

Participants:

Sophie Tezenas Du Montcel (Correspondant), Emilien Petit, Titilayo Ol-
ubajo, Jennifer Faber, Pauline Lallemant-Dudek, Khalaf Bushara,
Susan Perlman, Sub Subramony, David Morgan, Brianna Jackman,
Henry Lauris Paulson, Gülin Öz, Thomas Klockgether, Alexandra Durr,
Tetsuo Ashisawa.

21

Background and Objective: In spinocerebellar ataxia, ataxia onset can be preceded by mild clinical
manifestation, cerebellar and/or brainstem alterations or biomarkers modifications. READISCA is a
prospective, longitudinal observational study of patients with spinocerebellar ataxias type 1 and 3 to
provide essential markers for therapeutic interventions. We looked for clinical, imaging or biological
markers that are present at an early-stage of the disease. Methods: We enrolled carriers of a pathological
ATXN1 or ATXN3 expansion and controls from 18 US and two European ataxia referral centers. Clinical,
cognitive, quantitative motor, neuropsychological measures and plasma neurofilament light chain (NfL)
measurements were compared between mutation carriers with and without ataxia and controls. Results:
We enrolled 200 participants: 45 carriers of a pathological ATXN1 expansion (31 patients with ataxia
(median SARA: 9 [7;10]), 14 mutation carriers without ataxia (1 [0;2])) and 116 carriers of a pathological
ATXN3 expansion (80 patients with ataxia (7 [6;9]), 36 mutation carriers without ataxia (1 [0;2])). In
addition, we enrolled 39 controls who did not carry a pathological expansion in ATXN1 or ATXN3 . Plasma
NfL levels were significantly higher in mutation carriers without ataxia than controls, despite similar
mean age (controls: 5.7 pg/mL, SCA1: 18.0 pg/mL (P <0.0001), SCA3: 19.8 pg/mL (P<0.0001). Mutation
carriers without ataxia differed from controls by significantly more upper motor signs (SCA1 P=0.0003,
SCA3 P=0.003) and by the presence of sensor impairment and diplopia in SCA3 (P=0.0448, and 0.0445
respectively). Functional scales, fatigue and depression scores, swallowing difficulties, and cognitive
impairment were worse in mutation carriers with ataxia than those without ataxia. Ataxic SCA3 subjects
showed extrapyramidal signs, urinary dysfunction and lower motor neuron signs significantly more often
than mutation carriers without ataxia. Discussion: READISCA showed the feasibility of harmonized data
acquisition in a multi-national network. NfL alterations, early sensory ataxia and corticospinal signs
were quantifiable between preataxic participants and controls. Patients with ataxia differed in many
parameters from controls and mutation carriers without ataxia, with a graded increase of abnormal
measures from control to preataxic to ataxic cohorts.

More details in [59].

7.26 A meta-graph approach for analyzing whole slide histopathological images of

human brain tissue with Alzheimer’s disease biomarkers

Participants: Gabriel Jimenez, Pablo Mas, Anuradha Kar, Lea Ingrassia, Suz-
ana Boluda, Benoit Delatour, Lev Stimmer, Daniel Racoceanu (Corres-
pondant).

Recently, high performance deep learning models have allowed automatic and precise analysis of
high-content medical images. In digital histopathology, a typical challenge lies in analyzing whole slide
images (WSI) due to their large dimensions which most often requires splitting them into small patches
for feeding deep learning models. This leads to loss in global tissue level information and is particularly
limiting to classification or clustering of patients based on tissue characteristics. In this study, a meta-
graph approach is developed for a semantic spatial analysis of histopathological WSIs of human brain
tissue containing biomarkers of Alzheimer’s disease (AD) such as tau protein aggregates in brain gray
matter. We propose a pipeline that extracts morphological features of tau aggregates like neuritic plaques
or neurofibrillary tangles using a pre-trained U-Net model and uses these to build a graph based on
Delaunay triangulation at the WSI level in order to extract topological features from them. This pipeline is
generating morphological and topological tabular data from WSI for classification and clustering patients
using boosted trees, SVM or K-means algorithms. Further, combining locally extracted morphological
features - at the neuritic plaques or neurofibrillary tangle level - with the constructed Delaunay graph,
allows constructing a meta-graph that can be efficiently fed to graph neural network models, instead of

22

Inria Annual Report 2023

the voluminous WSIs. This pipeline is developed and tested on a dataset of 60 WSIs from various cohorts
of patients having normal and rapidly advancing AD. The purpose of this pipeline is to identify novel
insights into AD evolution, as well as to provide a generic framework for creating knowledge-rich graphs
for WSI characterization and analysis.

More details in [70].

7.27 Efficient 3D reconstruction of Whole Slide Images in Melanoma

Participants:

Janan Arslan, Mehdi Ounissi, Haocheng Luo, Matthieu Lacroix, Pier-
rick Dupré, Pawan Kumar, Arran Hodgkinson, Sarah Dandou, Ro-
main Larive, Christine Pignodel, Laurent Le Cam, Ovidiu Radulescu,
Daniel Racoceanu (Correspondant).

Cutaneous melanoma is an invasive cancer with a worldwide annual death toll of 57,000 (Arnold
et al., JAMA Dermatol 2022). In a metastatic state, surgical interventions are not curative and must be
coupled with targeted therapy, or immunotherapy. However, resistance appears almost systematically and
late-stage prognosis can remain poor. The complexity to eradicate melanoma stems from its plasticity;
these cancer cells continually adapt to the tumor microenvironment, which leads to treatment resistance.
Our primary assumption is that therapeutic resistance relies in part on a series of non-genetic transitions
including changes in the metabolic states of these cancer cells. The 3D spatial distribution of blood
vessels that are sources of nutrition and oxygen that drive this metabolic status is an important variable for
understanding zoning aspects of this adaptation process. Using Whole Slide Images (WSI) of melanoma
tumors from Patient-Derived Xenograft (PDX) mouse models, we build 3D vascular models to help
predict and understand the metabolic states of cancer cells within the tumor. Our 3D reconstruction
pipeline was based on PDX tumor samples sectioned over 2mm depth and stained with Hematoxylin
and Eosin (H&E). The pipeline involves three primary steps, including 2D vessel segmentation using
Deep Learning, intensity- and affine-based image registration, and 3D reconstruction using interpolation
and 3D rendering (allowing for better interaction with biologists, pathologists, and clinicians). The
originality of our computer-assisted pipeline is its capability to (a) deal with sparse data (i.e., not all tissue
sections were readily available), and (b) adapt to a multitude of WSI-related challenges (e.g., epistemic
uncertainty, extended processing times due to WSI scale, etc.). We posit both our 3D reconstruction
pipeline, quantitative results of the major stages of the process, and a detailed illustration of the challenges
faced, presenting resolutions to improve the pipeline’s efficiency.

More details in [63].

7.28 Computational Pathology for Brain Disorders

Participants: Gabriel Jimenez, Daniel Racoceanu (Correspondant).

Non-invasive brain imaging techniques allow understanding the behavior and macro changes in
the brain to determine the progress of a disease. However, computational pathology provides a deeper
understanding of brain disorders at cellular level, able to consolidate a diagnosis and make the bridge
between the medical image and the omics analysis. In traditional histopathology, histology slides are
visually inspected, under the microscope, by trained pathologists. This process is time-consuming
and labor-intensive; therefore, the emergence of Computational Pathology has triggered great hope to
ease this tedious task and make it more robust. This chapter focuses on understanding the state-of-
the-art machine learning techniques used to analyze whole slide images within the context of brain
disorders. We present a selective set of remarkable machine learning algorithms providing discriminative
approaches and quality results on brain disorders. These methodologies are applied to different tasks,
such as monitoring mechanisms contributing to disease progression and patient survival rates, analyzing
morphological phenotypes for classification and quantitative assessment of disease, improving clinical
care, diagnosing tumor specimens, and intraoperative interpretation. Thanks to the recent progress in

Project ARAMIS

23

machine learning algorithms for high-content image processing, computational pathology marks the rise
of a new generation of medical discoveries and clinical protocols, including in brain disorders.

More details in [90].

7.29 Phagocytosis Unveiled: A Scalable and Interpretable Deep learning Framework

for Neurodegenerative Disease Analysis

Participants: Mehdi Ounissi, Daniel Racoceanu (Correspondant).

Quantifying the phagocytosis of dynamic, unstained cells is essential for evaluating neurodegenerative
diseases. However, measuring rapid cell interactions and distinguishing cells from backgrounds make this
task challenging when processing time-lapse phase-contrast video microscopy. In this study, we introduce
a fully automated, scalable, and versatile realtime framework for quantifying and analyzing phagocytic
activity. Our proposed pipeline can process large data-sets and includes a data quality verification
module to counteract potential perturbations such as microscope movements and frame blurring. We
also propose an explainable cell segmentation module to improve the interpretability of deep learning
methods compared to black-box algorithms. This includes two interpretable deep learning capabilities:
visual explanation and model simplification. We demonstrate that interpretability in deep learning is not
the opposite of high performance, but rather provides essential deep learning algorithm optimization
insights and solutions. Incorporating interpretable modules results in an efficient architecture design and
optimized execution time. We apply this pipeline to quantify and analyze microglial cell phagocytosis
in frontotemporal dementia (FTD) and obtain statistically reliable results showing that FTD mutant
cells are larger and more aggressive than control cells. To stimulate translational approaches and future
research, we release an open-source pipeline and a unique microglial cells phagocytosis dataset for
immune system characterization in neurodegenerative diseases research. This pipeline and dataset will
consistently crystallize future advances in this field, promoting the development of efficient and effective
interpretable algorithms dedicated to this critical domain.

More details in [99].

7.30 HappyFeat—An interactive and efficient BCI framework for clinical applica-

tions

Participants:

Arthur Desbois (Correspondant), Fabrizio De Vico Fallani, Marie Con-
stance Corsi (Correspondant).

Brain–Computer Interface (BCI) systems allow to perform actions by translating brain activity into
commands. Such systems require training a classification algorithm to discriminate between mental
states, using specific features from the brain signals. This step is crucial and presents specific constraints
in clinical contexts. HappyFeat is an open-source software making BCI experiments easier in such
contexts: effortlessly extracting and selecting adequate features for training, in a single GUI. Novel
features based on Functional Connectivity can be used, allowing graph-oriented approaches. We describe
HappyFeat’s mechanisms, showing its performances in typical use cases, and showcasing how to compare
different types of features.
More details in [42].

7.31 Vizaj—A free online interactive software for visualizing spatial networks

Participants:

Thibault Rolland (Correspondant), Fabrizio De Vico Fallani (Corres-
pondant).

24

Inria Annual Report 2023

In many fields of science and technology we are confronted with complex networks. Making sense of
these networks often require the ability to visualize and explore their intermingled structure consisting of
nodes and links. To facilitate the identification of significant connectivity patterns, many methods have
been developed based on the rearrangement of the nodes so as to avoid link criss-cross. However, real
networks are often embedded in a geometrical space and the nodes code for an intrinsic physical feature
of the system that one might want to preserve. For these spatial networks, it is therefore crucial to find
alternative strategies operating on the links and not on the nodes. Here, we introduce Vizaj a javascript
web application to render spatial networks based on optimized geometrical criteria that reshape the
link profiles. While optimized for 3D networks, Vizaj can also be used for 2D networks and offers the
possibility to interactively customize the visualization via several controlling parameters, including
network filtering and the effect of internode distance on the link trajectories. Vizaj is further equipped
with additional options allowing to improve the final aesthetics, such as the color/size of both nodes
and links, zooming/rotating/translating, and superimposing external objects. Vizaj is an open-source
software which can be freely downloaded and updated via a github repository. Here, we provide a detailed
description of its main features and algorithms together with a guide on how to use it. Finally, we validate
its potential on several synthetic and real spatial networks from infrastructural to biological systems. We
hope that Vizaj will help scientists and practitioners to make sense of complex networks and provide
aesthetic while informative visualizations.

More details in [55].

8 Bilateral contracts and grants with industry

8.1 Bilateral grants with industry

8.1.1 Sanofi

Participants:

Stanley Durrleman (Correspondant).

• This project aims at modeling Parkinson disease progression for patients with mutations in the
GBA genes, selecting potential good responders in clinical trials based on their progression profile,
and evaluating new measures of drug efficacy.

• Coordinator: Stanley Durrleman

• Started in 2020

8.1.2 Biogen

Participants:

Stanley Durrleman (Correspondant).

• This project aims at analysing clinical trial data in neurodegenerative diseases.

• Coordinator: Stanley Durrleman

• Started in 2022

8.1.3 VICO

Participants:

Sophie Tezenas du Montcel (Correspondant).

• VO659 Strategic Advisory Board.

Project ARAMIS

25

• Coordinator: Sophie Tezenas du Montcel

• Started in 2023

9 Partnerships and cooperations

9.1 International initiatives

9.1.1 Inria associate team not involved in an IIL or an international program

Brainetics

Participants: Baptiste Couvy-Duchesne, Olivier Colliot, Arya Yazdan-Panah,

Elise Delzant, Camille Brianceau, Lisa Hemforth, Guanghui Fu.

• Project acronym: Brainetics

• Website

• Project title: Leveraging brain magnetic resonance images and genetics for neurodegenerative and

psychiatric disorders

• Duration: 3 years (started 2023)

• Coordinator: Baptiste Couvy-Duchesne

• Other partner: Progam in Complex Trait Genomics (PCTG), The University of Queensland, Brisbane,

Australia

• Abstract: The general objective of the associate team is to develop multi-modal methods and
analyses, that combine genetics and neuroimaging data. Each member of the associate team is
specialized in a data modality (genetics for PCTG, neuroimaging for ARAMIS) and both teams have
a strong track record in method and software development.

9.2 European initiatives

9.2.1 Horizon Europe

BCINET BCINET project on cordis.europa.eu

Title: Non-invasive decoding of brain communication patterns to ease motor restoration after stroke

Duration: From October 1, 2020 to September 30, 2026

Partners:

• INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET AUTOMATIQUE (INRIA),

France

• INSTITUT DU CERVEAU ET DE LA MOELLE EPINIERE (ICM), France

Inria contact: Fabrizio De Vico Fallani

Coordinator:

Summary: Human-computer interfaces are increasingly explored to facilitate interaction with the ex-
ternal world. Brain-computer interfaces (BCIs), bypassing the skeletomuscular system, are particu-
larly promising for assisting paralyzed people in control and communication, but also for boosting
neuromotor rehabilitation.

26

Inria Annual Report 2023

Despite their potential, the societal impact of BCIs is dramatically limited by the poor usability in
real-life applications. While many solutions have been proposed - from the identification of the
best classification algorithm to the type of sensory feedback - the accuracy is still highly variable
across subjects and BCIs cannot be used by everyone. Critically, these approaches have implicitly
assumed that the user’s intent could be decoded by examining the activity of single brain areas.
Today, we know that this is not true as the brain functioning essentially depends on a complex
network of interactions between differently specialized areas.

The grand challenge of this project is to develop a novel generation of BCIs that integrate the user’s
brain network information for enhancing accuracy and usability. Based on this approach, we will
experiment innovative BCI prototypes to restore the lost motor functions in patients suffering from
stroke.

This project relies on a unifying framework that analyses and models brain networks by means
of analytical tools derived from graph theory and complex systems science. By recruiting diverse
neuroimaging and experimental methods, within a modern computational framework, we aim to i)
identify new control features for enhancing BCI accuracy, ii) study the brain dynamics of human
learning for improving adaptive BCI architectures, and iii) optimize brain stimulation techniques
for boosting BCI skill acquisition.

This project can significantly improve BCI usability as well as determining how brain lesions
compromise brain functioning and which solutions are most effective to unlock motor restoration
after stroke.

VirtualBrainCloud VirtualBrainCloud project on cordis.europa.eu

Title: Personalized Recommendations for Neurodegenerative Disease

Duration: From December 1, 2018 to May 31, 2023

Partners:

• INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET AUTOMATIQUE (INRIA),

France

• HELSINGIN YLIOPISTO, Finland

• INSTITUT NATIONAL DE LA SANTE ET DE LA RECHERCHE MEDICALE, France

• FORSCHUNGSZENTRUM JULICH GMBH (FZJ), Germany

• UNIVERSITAT WIEN (UNIVIE), Austria

• FRAUNHOFER GESELLSCHAFT ZUR FORDERUNG DER ANGEWANDTEN FORSCHUNG EV

(Fraunhofer), Germany

• TP21 GMBH (TP21), Germany

• FUNDACIO INSTITUT DE BIOENGINYERIA DE CATALUNYA (IBEC-CERCA), Spain

• INSTITUT DU CERVEAU ET DE LA MOELLE EPINIERE (ICM), France

• EODYNE SYSTEMS SL, Spain

• UNIVERSIDAD COMPLUTENSE DE MADRID (UCM), Spain

• CHARITE - UNIVERSITAETSMEDIZIN BERLIN, Germany

• THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD (UOXF),

United Kingdom

• UNIVERSITE D’AIX MARSEILLE (AMU), France

• UNIVERSITA DEGLI STUDI DI GENOVA (UNIGE), Italy

• CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS (CNRS), France

• CODEMART SRL (CODEMART), Romania

• CODEBOX GMBH, Germany

Project ARAMIS

27

• ALZHEIMER EUROPE (AE), Luxembourg

Inria contact: Bertrand Thirion

Coordinator:

Summary: The annual worldwide cost of Alzheimer’s dementia was 777.81 billion Euro in 2015. This
number will rise to 7.41 trillion Euro in 2050. Early diagnosis would save up to $7.9 trillion in
medical and care costs by 2050 in the US alone. However, the emergent pathology is highly variable
across people, necehighly variable across people, necessitating individualized diagnostics and
interventions. The VirtualBrainCloud addresses this by bridging the gap between computational
neuroscience and subcellular systems biology, integrating both research streams into a unifying
computational model that supports personalized diagnostics and treatments in NDD. TheVirtu-
alBrainCloud not only integrates existing software tools, it also merges the efforts of two big EU
initiatives, namely The Virtual Brain large scale simulation platform of the EU Flagship Human
Brain Project and IMI-EPAD initiative (European prevention of Alzheimer’s dementia consortium).
VirtualBrainCloud will develop and validate a decision support system that provides access to
high quality multi-disciplinary data for clinical practice. The result will be a cloud-based brain
simulation platform to support personalized diagnostics and treatments in NDD. The EU PRACE
(Partnership for Advanced Computing in Europe) initiative, will provide the required computing
infrastructure. The VirtualBrainCloud will develop robust solutions for legal and ethical matters
by interacting with EU projects such as European Open Science Cloud (EOSC), ‘cloud4health’,
Alzheimer’s Europe patient organizations and ELIXIR, an organization that manages and safe-
guards EU research data. Our software developers have already produced highly successful brain
simulation and clinical decision support tools. The resulting software will be a cloud based compu-
tational modeling system that is tailored to the individual, and bridges multiple scales to identify
key mechanisms that predict NDD progression and serves as Precision Decision Support System.

9.3 National initiatives
Health Data Hub

Participants:

Stanley Durrleman.

• Project acronym: Precise-PD-HDH

• Project title: Modélisation et prédiction de la progression de la maladie de Parkinson

• Duration: 1 year (pilot project)

• Coordinator: Jean-Christophe Corvol

• Other partners: Inserm, réseau NS-PARK, ICM

AVIESAN - ITMO Cancer

Participants: Ovidiu Radulescu, Daniel Racoceanu, Laurent Le Cam, Janan Arslan,

Mehdi Ounissi, Ayse Gungor.

• Project acronym: MALMO

• Project title: Mathematical Approaches to Modelling Metabolic Plasticity and Heterogeneity in

Melanoma

• Duration: 3 years (2021-2023)

28

Inria Annual Report 2023

• Coordinator: Ovidiu Radulescu

• Other partners: University of Montpellier (LPHI - UMR CNRS 5235, LIRMM - UMR CNRS 5506)
and the Institut de Recherche en Cancérologie de Montpellier (IRCM – Inserm U1194), Paris Brain
Institute (CNRS UMR 7225 – Inserm U 1127).

• Abstract: Cutaneous melanoma is a highly invasive tumor and despite recent therapeutic advances,
most patients with advanced melanoma have a poor clinical outcome. At the molecular level,
the most frequent mutations in melanoma affect the BRAF oncogene, a protein kinase of the
MAPK pathway. Therapies targeting BRAF/MEK are effective for only 50% of the patients and
almost systematically generate resistance. Some non-genetic mechanisms of drug resistance are
associated with the strong heterogeneity and the plasticity and melanoma cells that still remain
poorly understood. In the proposed project, we will address the importance of metabolic plasticity
in melanoma cells in the context of drug resistance. In order to understand the mechanistic
origin of the resistance to targeted therapies, we will build a predictive multiscale mathematical
model. This model describes intracellular dynamics of the metabolic pathways and the dynamics
of the melanoma cell sub- populations in interaction with their micro-environment. The model
has spatial extension and takes into account cellular heterogeneity. Model initial conditions and
parameters describing the microenvironment are learned from image analysis of tumour sections
using deep learning as segmentation approach. In order to validate the model, we use a multiplexed
imaging technique applied to the detection of metabolic markers in samples prepared from murine
xenografted tumours submitted to treatment. Using the mathematical model and the in situ
imaging data, we expect to prove the role of the metabolic reprogramming in generating melanoma
heterogeneity and its contribution to resistance to targeted therapies. Our predictive mathematical
model will also allow us to investigate in silico the relationship between micro-environment,
metabolic/cellular plasticity and drug resistance, as well as the potential of combining several
therapies simultaneously or with optimized scheduling.

9.3.1 ANR
ANR-PRC BETPARK

Participants:

Fabrizio De Vico Fallani (Correspondant).

• Project acronym: BETPARK

• Project title: Neurofeedback for Parkinson’s disease

• Duration: Apr 2021 - Mar 2025

• Amount: 712k€

• Coordinator: Nathalie George

• Other partners: CNRS CCLE; ICM

• Abstract: Parkinson’s disease (PD) is a complex neurodegenerative disease caused by death of
midbrain dopaminergic neurons. This calls for better understanding the pathophysiology of PD in
order to pave the way to new non-pharmacological and non-invasive treatment options for PD.
We propose to use neurofeedback (NF) to test whether PD patients can learn to self-regulate their
brain activity to reduce pathological neural activity and thereby motor symptoms. We will leverage
NF to target regulation of pathological beta band (8-35 Hz) oscillations, and we will characterize
training-induced changes in cortical network activity and their relationship with symptom severity.
Our goal is to provide direct evidence of the functional role of beta rhythms in the pathophysiology
of PD while assessing NF as a new non-pharmacological and non-invasive tool for ameliorating PD
motor symptoms.

29

Project ARAMIS

ANR-PRC BRANDY

Participants:

Fabrizio De Vico Fallani (Correspondant).

• Project acronym: BRANDY

• Project title: Brain attention network’s dynamics

• Duration: Apr 2019 - Mar 2023

• Amount: 650k€

• Coordinator: Paolo Bartolomeo

• Other partners: ICM

• Abstract: Attention allows us to explore the environment and to effectively respond to external
events. Attention sets priorities on the basis of our goals and of the salience of external stimuli.
Human visual attention relies on distinct dorsal and ventral fronto-parietal networks, but little is
known about their dynamics, because hitherto our knowledge mostly depends on fMRI, which
has limited temporal resolution. BRANDY aims at building an anatomo-functional model of
human visual attention. Specifically, BRANDY has three main objectives: Work Package (WP) 1
will determine the precise dynamics of normal visual attention on a fine-scale; WP2 will provide
important evidence on neurotypical and impaired attention in neglect patients using a network-
perspective. WP3 will build comprehensive anatomical and functional models of neurotypical and
pathological human visual attention.

ANR-NIH-NSF CANDT

Participants:

Fabrizio De Vico Fallani (Correspondant).

• Project acronym: CANDT

• Project title: Advancing neuroscientific discovery and training by lowering the barrier of entry to

network neuroscience via open science

• Duration: Oct 2019 - Sep 2023

• Amount: 137k€

• Coordinator: Fabrizio De Vico Fallani

• Other partners: Indiana Univ., US; UPenn, US

• Abstract: This project will use open science methods and cloud-computing, effectively lowering
the barrier of entry to network neuroscience and increase the widespread availability of well-
maintained and reproducible network neuroscience tools. We will use the platform brainlife.io as a
digital marketplace for network neuroscience analysis methods; network neuroscience tools and
software will be packaged into self-contained, standardized, reproducible Apps, shared with and
modified by a burgeoning community of users, and seamlessly integrated into existing brainlife.io
processing and analysis pipelines. This approach will engage both experts in network science,
scientists from other domains, and users of the proposed methods. In addition, it will ensure correct
implementation, a high level of reproducibility, and maximal reusability of network neuroscience
methods. As a requirement, Apps will also be accompanied by links to primary sources, in-depth
tutorials, and documentation, and worked-through examples, highlighting their correct usage and
offering solutions for mitigating possible pitfalls. This proposed research lowers the barrier of entry
to network neuroscience, standardizes the software sharing process, and provides a cloud-based
repository of expertly-maintained network neuroscientific tools and software that is made available
to the broader neuroscientific community.

30

Inria Annual Report 2023

JPND project E-DADS

Participants:

Stanley Durrleman (Correspondant), Nemo Fournier.

• Project acronym: E-DADS

• Project title: Early Detection of Alzheimer’s Disease Subtypes

• Duration: 2019 - 2023

• Amount: 170k€

• Coordinator: Daniel Alexander (UCL)

• Other partners: University College London, Stichting VU University Medical Center, IRCCS Fatebene-

fratelli Brescia,Commonwealth Scientific and Industrial Research Organisation

• Abstract: Alzheimer’s disease (AD) is a global health and economic burden with currently about 47
million affected individuals worldwide. No provably disease-modifying treatments exist. Delaying
disease onset in dementia patients by five years can reduce care costs by 36% about €88B per year
across the EU. A key confound preventing successful outcomes in most treatment trials to date has
been AD’s high variation in onset, mechanism, and clinical expression. E-DADS aims to untangle
this heterogeneity by defining data-driven subtypes of the clinical manifestation of AD based on
brain imaging, cognitive markers, and fluid biomarkers that are robustly identifiable from predictive
risk factors (genetics, co-morbidities, physiological and lifestyle factors) years before disease onset.
To achieve this we develop a novel multi-view learning strategies that relates end-stage disease
manifestations observable in clinical cohorts to features of early-stage or at-risk individuals in
preclinical cohorts and the general pre-affected population from population or aging studies. This
approach is only possible now due to the availability of large population data, richly phenotyped
AD cohorts and advances in machine learning. E-DADS uniquely assembles the necessary data and
expertise. The ability to identify AD subtypes and predict them years before onset will significantly
advance AD research and clinical management via precision medicine. First, it identifies distinct
homogeneous groups, shedding new light on that nature and variability of disease mechanisms
ultimately pinpointing effective drug targets. Second, it enables enrichment of future clinical trials
for specific groups of patients likely to benefit from a particular intervention. Third, it highlights
potential lifestyle interventions that may affect or delay disease onset at very early stages. E-DADS
delivers the underpinning technology to achieve this through machine learning and big-data
analytics together with a prototype software tool enabling future translation and uptake.

JPND project Lemerend

Participants:

Stanley Durrleman (Correspondant), Octave Guinebretière,
Thomas Nedelec, Baptiste Couvy-Duchesne, Karim Zaidi.

• Project acronym: Lemerend

• Project title: Leveraging medical records to identify patients at risk of neurodegenerative disease

• Duration: 2022 - 2025

• Amount: 260k€

• Coordinator: Stanley Durrleman

• Other partners: Aix-Marseille université, Karolinska Institute, University of Queensland

Project ARAMIS

31

• Abstract: Neurodegenerative diseases represent one of the main public health issues in our western
societies and one of the greatest challenges in drug development. Prevention policies have become
essential to address these issues: primary prevention to prevent disease onset by acting on action-
able risk factors, or secondary prevention to slow disease progression with very early therapeutic
interventions, ideally at pre-symptomatic stages. Key to the implementation of such prevention
measures is the identification of at-risk patients, at the point of care, and preferably long before
disease onset. Our project, LeMeReND, proposes to use electronic health records (EHR) to identify
biomedical risk factors through studying previous diagnoses (pre-clinical comorbidities), drug
prescription, clinical care usage, and biological test results. This analysis will use longitudinal data
in EHR registries including millions of patients who have been followed for at least 10 years before
diagnosis in 4 different healthcare systems: Australia, France, the UK and Sweden and across 4
therapeutic areas: Alzheimer’s disease, Parkinson’s disease, dementia with Lewy bodies and motor
neuron diseases. We will identify the biomedical risk factors that are common to these diseases
and the ones differentiating them. We will stratify patients based on the progression profile of their
exposure to the set of risk factors, in order to design tailored primary prevention measures. We will
also design a screening tool which will give each patient a propensity score to develop one of these
neurodegenerative diseases. Such a tool could be deployed at the point of care to prioritise at-risk
individuals for further inclusion in secondary prevention trials. We will evaluate the economic and
social benefits of this new generation of precision prevention measures. We will study the public
acceptability of a secondary-prevention effort, among the French population, and the feasibility
of its implementation in primary care practices in France, Australia, and Sweden. Eventually, we
will progress our understanding of the genetic and imaging markers of the disorders by studying
the identified prodromal biomedical factors, using the UK BioBank and GWAS summary statistics.
This will progress our understanding of the pathological processes which result in an increased risk
to develop a specific neurodegenerative disease. LeMeReND gathers a multidisciplinary research
group with a leading expertise in epidemiology, statistics and machine learning, in particular
for the analysis of longitudinal EHR data. Partners have demonstrated a strong track record on
neurodegenerative diseases (Sweden, France, Australia), analyses of large-scale data including
neuroimaging (France), genetics (Australia), longitudinal modelling (Sweden, France), and machine
learning (Australia, France). An expert team in health economics and health policy complements
the consortium. LeMeReND will therefore provide invaluable insights to inform health policies and
highlight possible new therapeutic targets. It will provide unique screening tools to facilitate the
large-scale recruitment of patients in secondary prevention trials.

PEPR project REWIND

Participants:

Stanley Durrleman (Correspondant), Sophie Tezenas du Montcel.

• Project acronym: REWIND

• Project title: Médecine de précision avec données longitudinales

• Duration: 2023 - 2028

• Amount: k€

• Coordinator: Stéphanie Allassonnière

• Other partners: Universite de Paris Cité, Universite Grenoble-Alpes, Universite Claude Bernard
Lyon 1, Sorbonne Universite, CNRS, INRIA, INSERM,CHU Pitie-Salpêtrière, Hospices Civils de Lyon

• Abstract: Longitudinal data are essential for understanding the progression of chronic diseases.
They consist in the repeated observations of patients over time. Their analysis opens up new
perspectives, not only for the better understanding of the natural history of the disease but it also
allows earlier diagnosis, more precise prognosis, prediction of response to treatments or of the onset

32

Inria Annual Report 2023

of adverse events. Modeling longitudinal data means designing models to improve patients’ medical
care. These models have to take into account that the data have very different modalities (from
organ images to patient pathways), time dependencies, they exhibit different paces of acquisition.
This project aims to address these challenges. To this end, we will focus on the development of new
mathematical and statistical approaches for the analysis of multimodal multiscale longitudinal
data. These models will be designed, implemented as prototypes and then transferred to an
easy-used-well-documented platform where researchers from diverse communities, in particular
physicians, will be able to analyze their own data set. A first work-package (WP1) will be devoted
to models for time-to-event data. Existing methods often face with one or more of the following
limitations: numerical challenges, lack of scalability, requirement of strong assumptions of the
influence of the feature on the risk or intensity of events. This WP aims to propose new prediction
models for personalized medicine. These prediction models will integrate repeated (possibly
intensive) measurements of multiple exposures and/or (bio)markers, to predict complex health
events. Longitudinal models may also include spatial dependence or more generally multimodal
information with complex structures. The second work-package (WP2) will aim at developing
advanced spatio-temporal (ST) models and AI tools to extract, if it exists, a set of ST features which
characterize effects of different nature that may be associated either to post-treatment side-effects,
to treatment responses or to natural disease progression.This could also help in improving our
knowledge about the sensitivity of patients at an individual level. All the proposed models may
suffer from two issues: the high dimensionality of the data and their relevance with respect to a
clinical question. Work-package 3 (WP3) will propose new model selection criteria for longitudinal
models. A second aspect of this WP will be to work in the Bayesian framework to enable the integrate
expert knowledge. All the previous models belong to classical machine learning and statistical
models where one aims at proposing equations to mimic the generation of the observations. Work-
package 4 (WP4) will look at interpretable Deep-learning models to combine data-driven and
model-based approaches in order to learn mechanistic parameters allowing the interpretable
description of the disease progression. Particular focus will be given to the use of auto-encoder
architectures for learning compact representations of the dynamics governing spatio-temporal
relationship. The resulting models and their careful implementation will allow the development of
a new generation of decision support systems, which will help clinicians at the bedside to make
more informed decisions for the patient. They will contribute to the development of precision
medicine in several key areas.

9.3.2 IHU
General program

Participants: Olivier Colliot, Stanley Durrleman, Didier Dormont, Ninon Burgos,
Fabrizio De Vico Fallani, Marie-Constance Corsi, Sophie Tezenas du
Montcel, Baptiste Couvy-Duchesne, Daniel Racoceanu.

• Project acronym: IHU-A-ICM

• Project title: Institute of Translational Neuroscience

• Since 2011

• The IHU-A-ICM program was selected, in 2011, in a highly competitive national call for projects.
A 10-year, 55M€ program, has been implemented by a recently created foundation for scientific
cooperation. Based on the clinical and scientific strenghts of the ICM and the hospital Department
of Nervous System Diseases, it mainly supports neuroscience research, but is also invested in
improving care and teaching. ARAMIS is strongly involved in the IHU-A-ICM project, in particular
in WP6 (neuroimaging and electrophysiology), WP7 (biostatistics), WP2 (Alzheimer) and WP5
(epilepsy). We have started collaborations with the new bioinformatics/biostatistics platform (IHU
WP7, head: Ivan Moszer), in particular through a joint project on the integration of imaging and
genomics data.

Project ARAMIS

33

ICM BBT Program - project ImagingDealInMS

Participants: Olivier Colliot (Correspondant), Bruno Stankoff (Correspondant),

Arya Yazdan-Panah.

• Project title: Translating the biological mechanisms underlying neurodegeneration into multimodal

imaging signatures using deep learning in Multiple Sclerosis (ImagingDealInMS)

• Started in 2021

• Coordinators: Olivier Colliot and Bruno Stankoff (ICM)

• Following the impressive advancements in the treatment of the relapsing phase of multiple scler-
osis (MS), the major challenge remaining ahead is the development of treatments effective for
preventing or delaying the irreversible accumulation of disability in this disease. A deep under-
standing of the mechanisms underlying neuro-axonal degeneration, which is the substrate of
clinical progression, together with the development of reliable biomarkers, are pre-conditions for
the advent and the evaluation of breakthrough therapies. The Stankoff group has pioneered an
innovative imaging approach combining positron emission tomography and MRI, and succeeded
in generating individual maps or key biological processes such as endogenous remyelination,
neuroinflammation, or early damage preceding lesion formation. We further showed that these
mechanisms were influencing disability worsening over the disease course, and recently obtained
preliminary results suggesting that a multimodal combination of advanced MRI sequences may
have the potential to identify these mechanisms and reproduce the PET results. In this project
we propose a totally novel imaging approach that will capture remyelination of lesions, ongoing
inflammation invisible on T1 and T2 MRI sequences (subacute/chronic active lesions) and to
predict short-term future disease activity (identify prelesional areas), from a single multimodal MRI
acquisition in patients with MS. Using PET results as a reference, multimodal signatures of these
processes will be identified, and a deep learning approach integrating the whole MRI information
in the training procedure will be applied to generate masks for each of them. The accuracy of the
discovered algorithms will be validated on independent datasets acquired on a PET-MR system,
and their long-term clinical relevance will be tested in a clinical study evaluating patients around 10
years following their enrolment in pilot PET studies. As a result, novel tools assessing key biological
processes driving neurodegeneration and disability worsening in MS will become largely available
for the medical community, allowing an improved patients’ stratification and prognostication, and
opening the perspective of tailored care. These tools could also be use as novel endpoints in clinical
trials, and may serve to capture similar processes in other neurological diseases.

ICM BBT3 Program - project StratifIAD

Participants: Daniel Racoceanu (Correspondant), Benoit Delatour (Correspondant),
Stanley Durrleman, Lev Stimmer, Anuradha Kar, Gabriel Jimenez, Me-
hdi Ounissi, Leopold Herbert-Steven.

• Project title: STRATIFIAD - Refining Alzheimer Disease Patients’ stratification using effective,

traceable and explicable artificial intelligence approaches in computational histopathology.

• Duration: 2 years (2021-2023)

• Coordinators: Daniel Racoceanu and Benoit Delatour (ICM)

• Other partners: Histology Core plateform (HYSTOMICS) and the Data Analysis Core plateform

(DAC), IHU/ICM

34

Inria Annual Report 2023

• Alzheimer’s Disease (AD), the most frequent neurodegenerative disease, is defined by the misfolding
and accumulation of Aß peptides and of tau proteins in the brain. Sporadic AD is most commonly
present in later life as an amnestic syndrome. However, the clinical presentation of the patients
is heterogeneous and different subtypes of the disease have been described, including a rapidly
progressive subtype of AD (rAD). Until now, neuropathological assessment of rAD cases was not able
to identify specific neuropathological traits for this clinico-pathological entity, despite its unusual
fast progression and clinical presentation leading to frequent misdiagnosis as Creutzfeldt-Jakob
disease. Our hypothesis is that rAD brains, as well as other atypical variants of AD, display subtle
histological changes that would be undercovered by high-throughput automated microscopic
analysis. The topography and morphology of the tau and Aß aggregates, the two main brain lesions
characterizing the disease are heterogeneous. Aß accumulation takes the form of focal deposits
or diffuse plaques; tau lesions form the so-called neurofibrillary tangles but also present different
morphologies in dendrites or axons. We propose to study the topography and morphology of
these aggregates to better understand the morphological substratum of AD heterogeneity. To
address this question at a large scale, one needs to develop software systems for the automatic
segmentation, annotation and quantitation of brain lesions in histo-pathological whole slide
images (WSI). Therefore, the goal of the STRATIFIAD project is twofold:

1. to develop fully automated, traceable and explainable artificial intelligence (AI) approaches
for the histological location and characterization of the tau and Aß aggregates in whole slide
brain images, and to deploy it for routine use on the Histomics core facility of the Paris Brain
Institute,

2. to use the previous analytics tool to study to which extent the topography and morphology of
the different peptide aggregates present in the brain can be associated with the diversity of
symptoms observed in various AD variants.

We propose to design, test and implement a modern supervised (initial stratification) and semi-
supervised (advance refined stratification) deep reinforcement learning pipeline, combining meth-
ods able to generate high performances (quality and speed), high traceability / explicability and
facilitate its usability in biomedical research and discovery. Our pathologists have started to acquire
and to extensively annotate a unique set of histological images of postmortem brains from the rare
form of rpAD and from other identified AD variants. Preliminary results of the consortium suggests
that morphological features analysis is eligible for the first level of stratification. We believe that
combining these features with topology and semantic-driven image exploration approaches (see
MICO TecSan project’s references) would be able to guide our research toward a refined stratifica-
tion. Therefore, causal knowledge-based elements, together with semantic-driven WSI exploration
will be likely to create a reusable pipeline, able to structure our experience plan, as to justify the
numeric results. The tools within this project will contribute to open-source initiatives, and would
be therefore available to the scientific community for replicable massive data analysis. STRATIFIAD
will therefore contribute to advance the knowledge in AD and push forward the technological
development in this area.

ICM BBT3 Program - project HUMMAP

Participants:

Fabrizio De Vico Fallani.

• Project title : HUman Mutations to uncover human Macrophages Activation Pattern

• Started in 2020, Ended 2023

• Coordinator: Violetta Zujovic, Fanny Mochel

• Multiple sclerosis (MS) is an inflammatory demyelinating disease in which peripheral immune cells
infiltrate the central nervous system (CNS) and destroy myelin, a neuroprotective and conduction-
enhancing substance. Our previous work demonstrates that MAC from MS patients exhibit a

Project ARAMIS

35

preferential differentiation toward a pro-inflammatory state and present a mitochondrial energy
metabolism blockage resulting in a shift from oxidative phosphorylation to glycolysis. Recent
studies report that MAC are also implicated in two hereditary demyelinating diseases, X-linked
adrenoleukodystrophy (X-ALD) and Adult-onset Leukoencephalopathy with axonal Spheroids and
Pigmented glia (ALSP). In both X-ALD and ALSP, key components of MAC differentiation pattern are
affected that may be reversed by hematopoietic stem cell transplantation stressing that acting on
peripheral MAC has an important impact on CNS inflammation. Importantly, MAC differentiation
into the pro-regenerative M2 phenotype is dependent on both CSF1 receptor activation, a pathway
primarily affected in ALSP, and the oxidation of fatty acids, which accumulate in X-ALD. Therefore,
deep characterization of MAC activation pattern in X-ALD and ALSP can enlighten key aspects of
the innate immune response in health and disease. HUM-MAP will apply a cross-disease strategy
to decipher MAC activation pattern in response to extreme stimuli (neutral/ pro-inflammatory/
pro-regenerative) in normal and disease context using state of the art molecular profiling. HUM-
MAP proposes to apply mathematical algorithms to model MAC activation and determine which
networks of shared or disease-specific pathways contribute to pathophysiology. By exploring how
disease specific perturbations affect key components of MAC activation, we will build virtually
MAC molecular machinery and identify the master genes essential to gear macrophage towards
regenerative states.

9.3.3 3IA Institutes - PRAIRIE

Participants: Ninon Burgos, Olivier Colliot, Stanley Durrleman.

• Project acronym: PRAIRIE

• Project title: Paris Artificial Intelligence Research Institute

• Since 2019

• Director: Isabelle Ryl

• Website: PRAIRIE

• PRAIRIE is one of the four selected French Institutes of AI. It was selected within a call for creation
of interdisciplinary AI research institutes (or “3IAs” for “Instituts Interdisciplinaires d’Intelligence
Artificielle”), as part of the national French initiative on Artificial Intelligence (AI). PRAIRIE aspires
to become within five years a world leader in AI research and higher education, with an undeniable
impact on economy and technology at the French, European and global levels. ARAMIS team
members N. Burgos, O. Colliot and S. Durrleman hold a chair at PRAIRIE.

9.3.4 National Networks

• GdR Statistics and Medicine

• GdR (MaDICS) Masses de Données, Informations et Connaissances en Sciences Big Data - Data

Science, Statistics and Medicine

• F. De Vico Fallani participated to the GdR (HANDICAP) in the framework of the future strategy of

Inria

• F. De Vico Fallani was founding member of the CORTICO national network for brain-computer

interfaces

• M.-C. Corsi serves as Secretary General of the CORTICO national network for brain-computer

interfaces

• GdR ISIS (Signal and Image Processing)

36

Inria Annual Report 2023

9.3.5 Other National Programs

France Parkinson

Participants:

Jean-Christophe Corvol, Olivier Colliot, Stanley Durrleman
.

• Project title: PRECISE-PD - From pathophysiology to precision medicine for Parkinson’s disease

• 2019-2024

• Amount: 3M€

• Coordinator: Jean-Christophe Corvol

• Other partners: Inserm CIC-1436, Inserm CIC-P1421, Inserm U1171, Université de Bordeaux (IMN),

University of Glasgow, University of Calgary,

• Abstract: Parkinson’s disease (PD) is a complex neurodegenerative disease characterized by the
progression of motor and non-motor symptoms resulting from the spreading of the disease into
dopaminergic and non-dopaminergic areas. Clinical trials have failed to demonstrate efficacy to
slow PD progression because the relationships between progression profiles and their underlying
molecular mechanisms remain to be identified. The objective of PRECISE-PD is to propose a
mechanismsbased progression model of PD by combining genetic and longitudinal clinical data
from a large cohort of patients. We will implement a biobank to the NS-PARK/FCRIN cohort
collecting motor and non-motor symptoms from >22,000 PD patients followed in the 24 expert
centers in France. Genomic data will be generated by using a microarray platform developed for
neurodegenerative diseases studies, and brain imaging will be obtained from a subgroup of patients.
Computational and machine learning approaches will be developed to address the challenges of
analyzing the high dimensionality and the mixture of data necessary to move beyond empirical
stratification of patients. Replication will be performed in independent cohorts, and biological
validation will combine biomarkers and preclinical research. PRECISE-PD is an unpreceded
opportunity to open the path to the new era of precision and personalized medicine for PD.

10 Dissemination

10.1 Promoting scientific activities

10.1.1 Scientific events: organisation

• Olivier Colliot has co-organized the First French Congress on Artificial Intelligence for Biomedical
Imaging, Paris, France, 2023. This event was co-organized by the 3IA Institutes PRAIRIE, MIAI and
3IA Côte d’Azur.

• Marie-Constance Corsi has co-organized the annual meeting of the French BCI Society, CORTICO,

Paris, France; 2023.

• Fabrizio De Vico Fallani has co-organized the annual Network Neuroscience workshop at NETSCI,

Wien, Austria; 2023.

• Fabrizio De Vico Fallani has co-organized the workshop on Challenges in BCI-based Neurofeedback

Applications for Neurological Disorders workshop at BCI meeting, Bruxelles, Belgium; 2023.

• Stanley Durrleman and Ninon Burgos have co-organized the AI4Health 2023 summer school with

the Health Data Hub, held at Paris Santé Campus.

• Ninon Burgos has co-organized the Tutorial on Reproductibility during MICCAI 2023 (Vancouver,

Canada).

• Ninon Burgos has co-organized the Neuro OpenScience Workshop held at the Paris Brain Institute.

Project ARAMIS

37

General chair, scientific chair

• Olivier Colliot is Conference Chair of the SPIE Medical Imaging: Image Processing conference for

the period 2022-2025.

10.1.2 Scientific events: selection

Member of the conference program committees

• Olivier Colliot was Program Committee member SPIE Medical Imaging: Image Processing confer-

ence 2023

• Olivier Colliot was Meta-Reviewer, IEEE International Symposium on Biomedical ISBI 2023

• Ninon Burgos was Program Committee member of the SPIE Medical Imaging: Image Processing

conference 2023

• Ninon Burgos was Program Committee member of the Medical Imaging with Deep Learning

conference 2023

• Daniel Racoceanu was Meta-Reviewer / Area-Chair, 26th International Conference on Medical

Image Computing and Computer Assisted Intervention, MICCAI 2023

• Fabrizio De Vico Fallani was Program Committee member for the conferences NETSCI 2023,

Complenet 2023, Complex Networks 2023, CCS 2023

Reviewer

• Olivier Colliot acted as a reviewer for the international conferences IEEE International Symposium
on Biomedical Imaging (IEEE ISBI), Medical Image Computing and Computer-Assisted Intervention
(MICCAI), International Conference on Learning Representations (ICLR) and IEEE/CVF Computer
Vision and Pattern Recognition (IEEE/CVF CVPR).

• Baptiste Couvy-Duchesne acted as a reviewer for the international conferences IEEE International

Symposium on Biomedical Imaging (IEEE ISBI).

• Ninon Burgos acted as a reviewer for the international conferences IEEE International Symposium
on Biomedical Imaging (IEEE ISBI), Medical Image Computing and Computer-Assisted Interven-
tion (MICCAI), Organisation for Human Brain Mapping (OHBM), the international workshop on
Simulation and Synthesis in Medical Imaging (SASHIMI)

• Daniel Racoceanu acted as a reviewer for the Medical Image Computing and Computer-Assisted

Intervention (MICCAI).

• Fabrizio De Vico Fallani acted as a reviewer for the conferences NETSCI 2023, Complenet 2023,

Complex Networks 2023, CCS 2023

10.1.3 Journal

Member of the editorial boards

• Olivier Colliot is an Associate Editor and a member of the Editorial Board of the journal Medical

Image Analysis.

• Fabrizio De Vico Fallani is an Associate Editor and a member of the Editorial Board of PLoS One

and Brain Topography

38

Inria Annual Report 2023

Reviewer - reviewing activities

• Olivier Colliot acted as a reviewer for the IEEE Journal of Biomedical and Health Informations, for

Human Brain Mapping, for the SPIE Journal of Medical Imaging and for Neuroradiology.

• Baptiste Couvy-Duchesne acted as a reviewer for Medical Image Analysis, Developmental Cognitive

Neuroscience, Communications Biology, Genome Research, Nature Communications.

• Marie-Constance Corsi acted as a reviewer for eNeuro, NeuroImage: Clinical, Brain Topography,

Brain Connectivity, Journal of Neural Engineering, Frontiers

• Ninon Burgos acted as reviewer for Medical Image Analysis; MELBA; Computer Methods and Pro-
grams in Biomedicine; Nature Communications; Communications Biology; NeuroImage; Imaging
Neuroscience; Journal of Nuclear Medicine

• Sophie Tezenas du Montcel acted as a reviewer for Annals of Neurology, Movement Disorders,
Annals of Clinical and Translational Neurology, IEEE Journal of Biomedical and Health Informatics,
The Cerebellum

• Fabrizio De Vico Fallani acted as a reviewer for Nature Communications, IEEE reviews in Biomedical

Engineering, Plos One

10.1.4 Invited talks

• Ninon Burgos gave an invited talk at the SPIE Medical Imaging: Image Processing Conference (San

Diego, USA)

• Ninon Burgos gave an invited talk at the Neuro OpenScience Workshop (Paris Brain Institute,

France)

• Ninon Burgos gave an invited talk at the AI in Biology and Health Symposium (Institut Pasteur,

Paris, France)

• Ninon Burgos gave an invited talk for the Réunion régionale des ingénieurs biomédicaux (Grand

Est) (virtual)

• Ninon Burgos gave an invited talk at the ED3C Scientific Days (Paris, France)

• Ninon Burgos gave an invited talk at the Colloque Français d’Intelligence Artificielle en Imagerie

Biomédicale (IABM 2023) (Paris, France)

• Baptiste Couvy-Duchesne was invited to give a talk at the Australian Dementia Research Forum

2023

• Sophie Tezenas du Montcel was invited to give a talk at the AGI’s Tools & Methods studio series

2023 (virtual)

• Fabrizio De Vico Fallani gave an inaugural invited talk at the Euromov center in Montpellier

• Fabrizio De Vico Fallani gave an invited talk at Summer school of Groningen university, Neitherlands

10.1.5 Leadership within the scientific community

• Olivier Colliot is a member of the steering committee of the European infrastructure EBRAINS.

• Fabrizio De Vico Fallani is a member of the Executive Committee of the Complex Systems Society.

Project ARAMIS

39

10.1.6 Scientific expertise

• Olivier Colliot acts as an expert for GENCI (the national facility for high-performance computing).

• Stanley Durrleman is member of the Conseil scientifique consultatif du Health Data Hub.

• Baptiste Couvy-Duchesne reviewed grant applications for the INSERM (MESSIDORE [Méthodologie
des ESSais cliniques Innovants, Dispositifs, Outils et Recherches Exploitant les données de santé et
biobanques ]) and the UK Research and Innovation.

• Ninon Burgos reviewed grant applications for the Fonds de recherche du Québec (FRQ) NOVA

programme and the Netherlands Organisation for Scientific Research Vidi programme.

• Daniel Racoceanu has been elected as a member of the Inria Evaluation Committee (period

2024-2027).

• Sophie Tezenas Du Montcel is member of the Conseil scientifique de la Banque Nationale de

Données Maladies Rares (BNDMR).

10.1.7 Research administration

• Olivier Colliot is a member of the "Bureau du Comité des Projets" of the Inria Paris Center.

10.2 Teaching - Supervision - Juries

10.2.1 Teaching

• Master: Olivier Colliotcoordinates the course "Deep Learning for Medical Imaging" of the Master 2
MVA (Mathematics, Vision, Learning) of ENS Paris-Saclay, University of Paris, Centrale-Supelec
and teaches 15 hours (CM).

• Master: Olivier Colliotcoordinates the course "Artificial Intelligence" of the Master 2 Bioentrepren-

eur of Paris-Descartes University and teaches 20 hours (CM).

• Master: Daniel Racoceanu coordinates the teaching module (UE) "Introduction to Artificial In-
telligence" of the Master 1 : Control Sciences and Robotics (AR - Automatique, Robotique) and
Electronics, Electrical Energy, Control Sciences (E3A - Électronique, Énergie Électrique, Auto-
matique) at Sorbonne University, Faculty of Science and Engineering (110 students / 3 ECTS) and
teaches 30 hours (CM/courses and TP/labs).

• Master: Daniel Racoceanu coordinates the teaching module (UE) "Computer Vision for Biomedical"
of the Master 1 : Electronics, Electrical Energy, Control Sciences (E3A - Électronique, Énergie
Électrique, Automatique) at Sorbonne University, Faculty of Science and Engineering (50 students /
3 ECTS) and teaches 32 hours (CM/courses and TP/labs).

• Master: Daniel Racoceanu coordinates the teaching module (UE) "Image Processing" of the Master
1 : Control Sciences and Robotics (AR - Automatique, Robotique) at Sorbonne University, Faculty of
Science and Engineering (80 students / 3 ECTS) and teaches 36 hours (CM/courses and TP/labs) -
courses in English.

• Master: Daniel Racoceanu coordinates the teaching module (UE) "3D Computer Graphics" of
the Master 1 : Computer Sciences (Informatique) at Sorbonne University, Faculty of Science and
Engineering (20 students / 3 ECTS) and teaches 24 hours (CM/courses and TP/labs) - courses in
English (within the european programme EIT Health).

• Engineering school: Olivier Colliot, 5 hours (eqTD), Mines ParisTech

• Medical school: Didier Dormont is the Director of the University Diploma (DIU) “Diagnostic and

Therapeutic Neuroradiology", Sorbonne University

• Medical school: Didier Dormont, Courses for Medical Students, Sorbonne University

40

Inria Annual Report 2023

• Medical school: Didier Dormont organizes and participates in the practical teaching of Neuroradi-
ology for Medical Students in the Department of Diagnostic Neuroradiology of Pitié Salpêtrière
University Hospital

• Medical school: Didier Dormont organizes and participates in the practical teaching of Neuroradi-
ology for Radiology Specializing Residents in the Department of Diagnostic Neuroradiology of Pitié
Salpêtrière University Hospital

• Master: Fabrizio De Vico Fallani gave a lecture at UE Closed-loop Neuroscience, CentraleSupelec)

• Master: Daniel Racoceanu gives lectures / labs (14 hours) in "Visual Perception for Robotics" -
Master 2 : Control Science and Robotics (AR - Automatique, Robotique) at Sorbonne University,
Faculty of Science and Engineering.

• Master: Daniel Racoceanu gives lectures / labs (14 hours) in "Machine Learning" - Master 1 :
Control Science and Robotics (AR - Automatique, Robotique) at Sorbonne University, Faculty of
Science and Engineering.

• Master: Daniel Racoceanu gives lectures / labs (20 hours) in "Object-Oriented Programming" -
Master 1 : Control Science and Robotics (AR - Automatique, Robotique) at Sorbonne University,
Faculty of Science and Engineering.

• Licence: Daniel Racoceanu gives seminars (TD) and labs (TP) in "Programming" - Licence 1 : all
Licences - option Control Sciences at Sorbonne University, Faculty of Science and Engineering.

• Master : Stanley Durrleman gave 4hrs lecture for the Master bio-entrepreneur of the University of

Paris.

• Fabrizio De Vico Fallani was invited to give a teaching lesson at School on Complex Networks, Lake

Como School Advanced Studies, Italy.

• Daniel Racoceanu was invited to give a teaching lesson about explainable and interpretable artificial
intelligence in front of master students from Mines, Dauphine, ENS and ESPCI, at Paris Sciences et
Lettres (PSL), Paris, France.

• Baptiste Couvy-Duchesne gave a lecture at the Genetics and Genomics Winter School, held in

Brisbane Australia.

• Marie-Constance Corsi gave lectures on Brain-Computer Interfaces as part of the DU IA Santé, M2
Computational Neuroscience and Neuroengineering, the Master MVA and the CENIR courses.

• Master: Sophie Tezenas du Montcel coordinates the Master 1 of Public Health of Sorbonne Univer-

sity.

• Master: Sophie Tezenas du Montcel coordinates the course of Biostatistics of the Master 1 of Health

of Sorbonne University and teaches 18 hours (CM).

• Master: Sophie Tezenas du Montcel coordinates the course of "Bases de données médico-administratives:

aspects épidémiologiques" of the Master 2 of Public Health of Sorbonne University and teaches 9
hours (CM).

• Medical school: Sophie Tezenas du Montcel gives courses for Medical students (First year, 32 hours

TD).

• Ninon Burgos gave lectures on deep learning for medical imaging as part of the DU IA appliquée en
santé (Paris and Lille), the CENIR courses, the European Course on Advanced Imaging Techniques
in Neuroradiology and the École Saisonnière en Intelligence Artificielle.

Project ARAMIS

10.2.2 Supervision

41

• PhD in progress: Élise Delzant, “Methods for big-data neuroimaging analyses”, started in 2022,

advisors: Baptiste Couvy-Duchesne and Olivier Colliot

• PhD in progress: Ravi Hassanaly, “Deep generative models for the detection of anomalies in the

brain”, started in 2020, advisors: Olivier Colliot and Ninon Burgos

• PhD in progress: Guanghui Fu, “Segmentation, classification and generative models for computer-
aided diagnosis of neurological diseases from neuroimaging data”, started in 2021, advisors:
Olivier Colliot and Didier Dormont

• PhD in progress: Sophie Loizillon, “Deep learning for assisting diagnosis of neurological dis-
eases using a very large-scale clinical data warehouse”, started in 2021, advisors: Olivier Colliot,
Ninon Burgos and Didier Dormont

• PhD in progress: Lisa Hemforth, “Deep learning for rating of atypical anatomical patterns on MRI

data”, started in 2021, advisors: Olivier Colliot, Baptiste Couvy-Duchesne and C. Cury

• PhD in progress: Arya Yazdan-Panah, “Deep learning for multimodal image analysis in multiple

sclerosis”, started in 2021, advisors: Olivier Colliot and B. Stankoff

• PhD in progress: Maëlys Solal, “Robust anomaly detection in multimodal neuroimaging”, started in

2023, advisor: Ninon Burgos

• PhD in progress: Camilla Mannino, “Neuronal avalanches as a tool to improve Brain-Computer

Interfaces”, started in Nov 2023, advisors: Marie-Constance Corsi, and Mario Chavez

• PhD in progress: Marc Dibling, “Parcours de soin des patients atteints de maladies neurodégénérat-

ives rares”, started in 2023, advisor: Sophie Tezenas Du Montcel

• PhD in progress: Sofia Kaisaridi, “Modélisation multimarqueurs de l’évolution clinique et en
imagerie cérébrale de patients CADASIL et de son influence sur un évènement censure”, started in
2022, advisor: Sophie Tezenas Du Montcel

• PhD in progress: Juliette Ortholand, “Modeling changes of dynamics with longitudinal data sets”,

started in 2021, advisor: Stanley Durrleman and Sophie Tezenas Du Montcel

• PhD in progress: Mehdi Ounissi, “Decoding the Black Box: Unraveling explainability in deep learn-
ing for responsible biomedical and healthcare solutions”, started in 2021, supervisor: Daniel Ra-
coceanu

• PhD in progress: Gabriel Jimenez, “Interpretable deep learning in computational histopathology
for Alzheimer’s disease patients’ stratification refinement”, started in 2021, supervisor: Daniel Ra-
coceanu

• PhD in progress: Ayse Gungor, “Correlation between eye and brain pathologies”, started in 2023,

supervisors: D. Milea and Daniel Racoceanu

• PhD in progress: Ilias Sarbout, “Artificial Vision using XAI”, started in 2023, supervisors: D. Milea

and Daniel Racoceanu

• PhD in progress: Esther Kozlowski, “A responsible artificial intelligence framework for modeling the
progression of Parkinson’s disease”, started in 2023, supervisors: M. Vidailhet and Daniel Racoceanu

• PhD completed: Charley Presigny, “Characterization of multilayer networks: theory and applica-

tions to the brain”, started in 2020, supervisor: Fabrizio De Vico Fallani

• PhD completed: Vito Dichio, “The exploration-exploitation paradigm”, started in 2020, supervisor:

Fabrizio De Vico Fallani

42

Inria Annual Report 2023

• PhD completed: Tristan Venot, “Design and evaluation of a multimodal control of a robotic arm

with a brain-computer interface”, started in 2020, co-supervisor: Fabrizio De Vico Fallani

• PhD completed: Remy Ben Messaoud, “Low-dimensional controllability of complex networks and

applications to the human brain”, started in 2020, co-supervisor: Fabrizio De Vico Fallani

10.2.3 Juries

• Olivier Colliot participated, as reviewer, to the PhD committee of Huy-Dung Nguyen, University of

Bordeaux.

• Stanley Durrleman participated, as examiner, to the PhD committee of Mathieu Pont, Sorbonne

Université.

• Ninon Burgos participated, as reviewer, to the PhD committee of Hugo Schmutz, Université Côte

d’Azur.

• Ninon Burgos participated, as reviewer, to the PhD committee of Hilda Chourak, Université de

Rennes.

• Ninon Burgos participated, as examiner, to the PhD committee of Camille Ruppli, Institut Polytech-

nique de Paris.

• Ninon Burgos participated, as examiner, to the PhD committee of Clément Chadebec, Université

Paris Cité.

• Ninon Burgos participated, as examiner, to the PhD committee of Louise Guillon, Université Paris

Saclay.

• Daniel Racoceanu participated, as reviewer, to the PhD committee of Fahad Khalid, Université

Paris Saclay.

• Daniel Racoceanu participated, as reviewer, to the PhD committee of Tristan Lazard, Université

Paris Sciences et Lettres (PSL).

• Daniel Racoceanu participated, as reviewer, to the PhD committee of Antonin Deschemps, Uni-

versité de Rennes.

• Sophie Tezenas du Montcel participated, as reviewer, to the PhD committee of Yue Zhai, Université

de Lyon 1.

• Fabrizio De Vico Fallani participated, as president, to the PhD committee of Mahede Khalilian ,

Université Jules Verne Picardie

10.3 Popularization

10.3.1 Articles and contents

• Ninon Burgos was interviewed by the radio France Alzheimer (‘Le rôle de l’intelligence artificielle

dans la détection de la maladie d’Alzheimer’, link)

• Ninon Burgos was interviewed by Europe 1 (‘Santé : et si l’intelligence artificielle pouvait permettre

de détecter plus rapidement la maladie d’Alzheimer ?’, link)

• Ninon Burgos participated to an article and video by Le Figaro (‘L’intelligence artificielle pour

détecter la maladie d’Alzheimer’, link)

• Ninon Burgos participated to a TV report on LCP (‘État de santé - La santé numérique tiendra-t-elle

ses promesses ?’, link)

Project ARAMIS

10.3.2 Interventions

43

• Olivier Colliot gave a presentation for ICM founding members on artificial intelligence.

• Ninon Burgos gave a presentation for ICM founding members on artificial intelligence and neuroim-

age.

• Ninon Burgos gave a presentation for the Cérémonie des Olympiades de Mathématiques.

11 Scientific production

11.1 Major publications

[1] M. Ansart, S. Epelbaum, G. Bassignana, A. Bône, S. Bottani, T. Cattai, R. Couronné, J. Faouzi,
I. Koval, M. Louis, E. Thibeau-Sutre, J. Wen, A. Wild, N. Burgos, D. Dormont, O. Colliot and S.
Durrleman. ‘Predicting the Progression of Mild Cognitive Impairment Using Machine Learning: A
Systematic, Quantitative and Critical Review’. In: Medical Image Analysis 67 (Jan. 2021), p. 101848.
DOI: 10.1016/j.media.2020.101848. URL: https://hal.archives-ouvertes.fr/hal-02
337815.

[2] M. Ansart, S. Epelbaum, G. Gagliardi, O. Colliot, D. Dormont, B. Dubois, H. Hampel and S. Dur-
rleman. ‘Reduction of recruitment costs in preclinical AD trials. Validation of automatic pre-
screening algorithm for brain amyloidosis’. In: Statistical Methods in Medical Research (Jan. 2019),
p. 096228021882303. DOI: 10.1177/0962280218823036. URL: https://hal.archives-ouver
tes.fr/hal-01964942.

[3]

J. Arslan, H. Luo, M. Lacroix, P. Dupré, P. Kumar, A. Hodgkinson, S. Dandou, R. M. Larive, C.
Pignodel, L. Le cam, O. Radulescu and D. Racoceanu. ‘3D Reconstruction of H&E Whole Slide
Images in Melanoma’. In: SPIE Medical Imaging. SPIE Medical Imaging 2023. San Diego, California,
United States, 22nd Feb. 2023. URL: https://hal.science/hal-03834014.

[4] F. Battiston, J. Guillon, M. Chavez, V. Latora and F. De Vico Fallani. ‘Multiplex core–periphery
organization of the human connectome’. In: Journal of the Royal Society Interface 15.146 (Sept.
2018). DOI: 10.1098/rsif.2018.0514. URL: https://hal.archives-ouvertes.fr/hal-01
874871.

[5] A. Bône, O. Colliot and S. Durrleman. ‘Learning the spatiotemporal variability in longitudinal
shape data sets’. In: International Journal of Computer Vision (July 2020). DOI: 10.1007/s11263-
020-01343-w. URL: https://hal.inria.fr/hal-02091549.

[6] S. Bottani, N. Burgos, A. Maire, D. Saracino, S. Stroer, D. Dormont and O. Colliot. ‘Evaluation of
MRI-based machine learning approaches for computer-aided diagnosis of dementia in a clinical
data warehouse’. In: Medical Image Analysis 89 (Oct. 2023), p. 102903. URL: https://hal.scien
ce/hal-03656136.

[7] S. Bottani, N. Burgos, A. Maire, A. Wild, S. Ströer, D. Dormont and O. Colliot. ‘Automatic quality
control of brain T1-weighted magnetic resonance images for a clinical data warehouse’. In:
Medical Image Analysis Volume 75 (2021). DOI: 10.1016/j.media.2021.102219. URL: https:
//hal.inria.fr/hal-03154792.

[8] S. Brice, S. Reyes, A. Jabouley, C. Machado, C. Rogan, N. Gastellier, N. Alili, S. Guey, E. Jouvent,
D. Hervé, S. Tezenas Du Montcel and H. Chabriat. ‘Trajectory Pattern of Cognitive Decline in
Cerebral Autosomal Dominant Arteriopathy With Subcortical Infarcts and Leukoencephalopathy’.
In: Neurology 99.10 (5th Sept. 2022), e1019–e1031. DOI: 10.1212/WNL.0000000000200805. URL:
https://hal.inria.fr/hal-03774149.

[9] N. Burgos, S. Bottani, J. Faouzi, E. Thibeau-Sutre and O. Colliot. ‘Deep learning for brain disorders:
from data processing to disease treatment’. In: Briefings in Bioinformatics (Dec. 2020). DOI: 10.10
93/bib/bbaa310. URL: https://hal.archives-ouvertes.fr/hal-03070554.

44

Inria Annual Report 2023

[10] N. Burgos, J. M. Cardoso, J. Samper-González, M.-O. Habert, S. Durrleman, S. Ourselin and O.
Colliot. ‘Anomaly detection for the individual analysis of brain PET images’. In: Journal of Medical
Imaging 8.02 (5th Apr. 2021), p. 024003. DOI: 10.1117/1.JMI.8.2.024003. URL: https://hal
.inria.fr/hal-03193306.

[11] N. Burgos and O. Colliot. ‘Machine learning for classification and prediction of brain diseases:
recent advances and upcoming challenges’. In: Current Opinion in Neurology 33.4 (2020), pp. 439–
450. DOI: 10.1097/WCO.0000000000000838. URL: https://hal.inria.fr/hal-02902586.

[12] G. Coarelli, A. Heinzmann, C. Ewenczyk, C. Fischer, M. Chupin, M.-L. Monin, H. Hurmic, F. Calvas,
P. Calvas, C. Goizet, S. Thobois, M. Anheim, K. Nguyen, D. Devos, C. Verny, V. a. G. Ricigliano,
J.-F. Mangin, A. Brice, S. Tezenas Du Montcel and A. Durr. ‘Safety and efficacy of riluzole in
spinocerebellar ataxia type 2 in France (ATRIL): a multicentre, randomised, double-blind, placebo-
controlled trial’. In: The Lancet Neurology 21 (18th Jan. 2022), pp. 225–233. DOI: 10.1016/s1474-
4422(21)00457-9. URL: https://hal.science/hal-03852287.

[13] O. Colliot. Machine Learning for Brain Disorders. Vol. 197. Neuromethods. Springer, 2023. DOI:

10.1007/978-1-0716-3195-9. URL: https://hal.science/hal-04225627.

[14] O. Colliot, E. Thibeau-Sutre and N. Burgos. ‘Reproducibility in machine learning for medical
imaging’. In: Machine Learning for Brain Disorders. Springer, 2023. URL: https://hal.science
/hal-03957240.

[15] M.-C. Corsi, M. Chavez, D. Schwartz, N. George, L. Hugueville, A. E. Kahn, S. Dupont, D. Bassett and
F. De Vico Fallani. ‘Functional disconnection of associative cortical areas predicts performance
during BCI training’. In: NeuroImage (Jan. 2020), p. 116500. DOI: 10.1016/j.neuroimage.2019
.116500. URL: https://hal.inria.fr/hal-02438794.

[16] B. Couvy-Duchesne, F. Zhang, K. Kemper, J. Sidorenko, N. Wray, P. Visscher, O. Colliot and J. Yang.
‘Parsimonious model for mass-univariate vertexwise analysis’. In: Journal of Medical Imaging 9.05
(1st Sept. 2022). DOI: 10.1117/1.JMI.9.5.052404. URL: https://hal.inria.fr/hal-03675
041.

[17] B. Couvy-duchesne, L. T. Strike, F. Zhang, Y. Holtz, Z. Zheng, K. E. Kemper, L. Yengo, O. Colliot,
M. Wright, N. Wray, J. Yang and P. M. Visscher. ‘A unified framework for association and prediction
from vertex-wise grey-matter structure’. In: Human Brain Mapping (20th July 2020). DOI: 10.100
2/hbm.25109. URL: https://hal.archives-ouvertes.fr/hal-02904458.

[18] F. De Vico Fallani and D. Bassett. ‘Network neuroscience for optimizing brain–computer interfaces’.
In: Physics of Life Reviews 31 (Dec. 2019), pp. 304–309. DOI: 10.1016/j.plrev.2018.10.001.
URL: https://hal.archives-ouvertes.fr/hal-02428684.

[19] F. De Vico Fallani, V. Latora and M. Chavez. ‘A Topological Criterion for Filtering Information
in Complex Brain Networks’. In: PLoS Computational Biology 13.1 (Jan. 2017), pp. 1–18. DOI:
10.1371/journal.pcbi.1005305. URL: https://hal.inria.fr/hal-01443254.

[20] S. Diao, Y. Tian, W. Hu, J. Hou, R. Lambo, Z. Zhang, Y. Xie, X. Nie, F. Zhang, D. Racoceanu and
W. Qin. ‘Weakly Supervised Framework for Cancer Region Detection of Hepatocellular Carcinoma
in Whole-Slide Pathologic Images Based on Multiscale Attention Convolutional Neural Network’.
In: American Journal of Pathology 192.3 (Mar. 2022), pp. 553–563. DOI: 10.1016/j.ajpath.2021
.11.009. URL: https://hal.archives-ouvertes.fr/hal-03677649.

[21] A. Guyot, A. B. Graciano Fouquier, E. Gerardin, M. Chupin, J. Glaunès, L. Marrakchi-Kacem, J.
Germain, C. Boutet, C. Cury, L. Hertz-Pannier, A. Vignaud, S. Durrleman, T. Henry, P.-F. Van
De Moortele, A. Trouvé and O. Colliot. ‘A Diffeomorphic Vector Field Approach to Analyze the
Thickness of the Hippocampus from 7T MRI’. In: IEEE Transactions on Biomedical Engineering
68.2 (Feb. 2021), pp. 393–403. DOI: 10.1109/TBME.2020.2999941. URL: https://hal.inria.f
r/hal-02359660.

[22] G. Jimenez and D. Racoceanu. ‘Computational Pathology for Brain Disorders’. In: Machine Learn-
ing for Brain Disorders. Vol. 197. Part of the Neuromethods book series (NM,volume 197). Springer;
Humana, New York, NY, 23rd July 2023, pp. 533–572. DOI: 10.1007/978-1-0716-3195-9_18.
URL: https://hal.science/hal-03936550.

Project ARAMIS

45

[23] G. Jiménez, A. Kar, M. Ounissi, L. Ingrassia, S. Boluda, B. Delatour, L. Stimmer and D. Racoceanu.
‘Visual deep learning-based explanation for neuritic plaques segmentation in Alzheimer’s Disease
using weakly annotated whole slide histopathological images’. In: Lecture Notes in Computer Sci-
ence. MICCAI 2022 - 25th International Conference on Medical Image Computing and Computer
Assisted Intervention. Vol. LNCS-13432. Medical Image Computing and Computer Assisted Inter-
vention Part VIII. Singapore, Singapore: Springer Nature Switzerland, 16th Sept. 2022, pp. 336–344.
DOI: 10.1007/978-3-031-16434-7_33. URL: https://hal.science/hal-03810578.

[24]

[25]

I. Koval, A. Bône, M. Louis, T. Lartigue, S. Bottani, A. Marcoux, J. Samper-Gonzalez, N. Burgos,
B. Charlier, A. Bertrand, S. Epelbaum, O. Colliot, S. Allassonnière and S. Durrleman. ‘AD Course
Map charts Alzheimer’s disease progression’. In: Scientific Reports 11.1 (13th Apr. 2021). DOI:
10.1038/s41598-021-87434-1. URL: https://hal.inria.fr/hal-01964821.

I. Koval, T. Dighiero-Brecht, A. J. Tobin, S. J. Tabrizi, R. I. Scahill, S. Tezenas Du Montcel, S. Durrle-
man and A. Durr. ‘Forecasting individual progression trajectories in Huntington disease enables
more powered clinical trials’. In: Scientific Reports 12.1 (Dec. 2022), p. 18928. DOI: 10.1038/s415
98-022-18848-8. URL: https://hal.inria.fr/hal-03877293.

[26] K. de Matos, C. Cury, L. Chougar, L. T. Strike, T. Rolland, M. Riche, L. Hemforth, A. Martin, T.
Banaschewski, A. L. W. Bokde, S. Desrivières, H. Flor, A. Grigis, H. Garavan, P. Gowland, A. Heinz,
R. Brühl, J.-L. Martinot, M.-L. Paillère Martinot, E. Artiges, F. Nees, J. H. Fröhner, H. Lemaitre,
D. Papadopoulos Orfanos, T. Paus, L. Poustka, S. Hohmann, S. Millenet, J. H. Fröhner, M. N.
Smolka, N. Vaidya, H. Walter, R. Whelan, G. Schumann, V. Frouin, M. Bach Cuadra, O. Colliot and
B. Couvy-Duchesne. ‘Temporo-basal sulcal connections: a manual annotation protocol and an
investigation of sexual dimorphism and heritability’. In: Brain Structure and Function 228.6 (June
2023), pp. 1459–1478. DOI: 10.1007/s00429-023-02663-6. URL: https://hal.science/hal-
04132176.

[27] P. Moulaire, P.-E. Poulet, E. Petit, T. Klockgether, A. Durr, T. Ashizawa and S. Tezenas Du Montcel.
‘Temporal Dynamics of the Scale for the Assessment and Rating of Ataxia in Spinocerebellar
Ataxias’. In: Movement Disorders (23rd Oct. 2022). DOI: 10.1002/mds.29255. URL: https://hal
.science/hal-03831598.

[28] T. Nedelec, B. Couvy-Duchesne, F. Monnet, T. Daly, M. Ansart, L. Gantzer, B. Lekens, S. Epelbaum,
C. Dufouil and S. Durrleman. ‘Identifying health conditions associated with Alzheimer’s disease
up to 15 years before diagnosis: an agnostic study of French and British health records’. In: The
Lancet Digital Health (1st Mar. 2022). DOI: 10.1016/S2589-7500(21)00275-2. URL: https:
//hal.archives-ouvertes.fr/hal-03628268.

[29] M. Ounissi, M. Latouche and D. Racoceanu. Phagocytosis Unveiled: A Scalable and Interpretable
Deep learning Framework for Neurodegenerative Disease Analysis. 26th Apr. 2023. URL: https://h
al.science/hal-04067345.

[30] D. Racoceanu, M. Ounissi and Y. L. Kergosien. ‘Explainability in Artificial Intelligence; towards
Responsible AI: Instanciation dans le domaine de la santé’. In: Techniques de l’Ingenieur (10th Dec.
2022). DOI: 10.51257/a-v1-h5030. URL: https://hal.science/hal-03936135.

[31] A. Routier, N. Burgos, M. Diaz, M. Bacci, S. Bottani, O. El-Rifai, S. Fontanella, P. Gori, J. Guillon,
A. Guyot, R. Hassanaly, T. Jacquemont, P. Lu, A. Marcoux, T. Moreau, J. Samper-González, M.
Teichmann, E. Thibeau-Sutre, G. Vaillant, J. Wen, A. Wild, M.-O. Habert, S. Durrleman and O.
Colliot. ‘Clinica: an open source software platform for reproducible clinical neuroscience studies’.
In: Frontiers in Neuroinformatics 15 (13th Aug. 2021), p. 689675. DOI: 10.3389/fninf.2021.689
675. URL: https://hal.inria.fr/hal-02308126.

[32]

J. Samper-González, N. Burgos, S. Bottani, S. Fontanella, P. Lu, A. Marcoux, A. Routier, J. Guillon,
M. Bacci, J. Wen, A. Bertrand, H. Bertin, M.-O. Habert, S. Durrleman, T. Evgeniou and O. Colliot.
‘Reproducible evaluation of classification methods in Alzheimer’s disease: Framework and applic-
ation to MRI and PET data’. In: NeuroImage 183 (Dec. 2018), pp. 504–521. DOI: 10.1016/j.neuro
image.2018.08.042. URL: https://hal.inria.fr/hal-01858384.

46

[33]

[34]

Inria Annual Report 2023

J.-B. Schiratti, S. Allassonniere, O. Colliot and S. Durrleman. ‘A Bayesian mixed-effects model to
learn trajectories of changes from repeated manifold-valued observations’. In: Journal of Machine
Learning Research 18 (Dec. 2017), pp. 1–33. URL: https://hal.archives-ouvertes.fr/hal-
01540367.

S. Tezenas Du Montcel, E. Petit, T. Olubajo, J. Faber, P. Lallemant-Dudek, K. Bushara, S. Perlman,
S. Subramony, D. Morgan, B. Jackman, H. L. Paulson, G. Öz, T. Klockgether, A. Durr and T. Ashiz-
awa. ‘Baseline Clinical and Blood Biomarker in Patients With Preataxic and Early-Stage Disease
Spinocerebellar Ataxia 1 and 3’. In: Neurology (16th Feb. 2023), 10.1212/WNL.0000000000207088.
DOI: 10.1212/WNL.0000000000207088. URL: https://hal.science/hal-03996914.

[35] E. Thibeau-Sutre, M. Diaz, R. Hassanaly, A. M. Routier, D. Dormont, O. Colliot and N. Burgos.
‘ClinicaDL: an open-source deep learning software for reproducible neuroimaging processing’. In:
Computer Methods and Programs in Biomedicine 220 (June 2022), p. 106818. DOI: 10.1016/j.cm
pb.2022.106818. URL: https://hal.archives-ouvertes.fr/hal-03351976.

[36] Q. Vanderbecq, E. Xu, S. Stroër, B. Couvy-Duchesne, M. Diaz-Melo, D. Dormont and O. Colliot.
‘Comparison and validation of seven white matter hyperintensities segmentation software in
elderly patients’. In: Neuroimage-Clinical 27 (2020), p. 102357. DOI: 10.1016/j.nicl.2020.102
357. URL: https://hal.archives-ouvertes.fr/hal-02926474.

[37] W. Wei, E. Poirion, B. Bodini, S. Durrleman, N. Ayache, B. Stankoff and O. Colliot. ‘Predicting
PET-derived Demyelination from Multimodal MRI using Sketcher-Refiner Adversarial Training for
Multiple Sclerosis’. In: Medical Image Analysis 58.101546 (Dec. 2019). DOI: 10.1016/j.media.20
19.101546. URL: https://hal.archives-ouvertes.fr/hal-02276634.

[38]

J. Wen, E. Thibeau-Sutre, M. Diaz-Melo, J. Samper-González, A. Routier, S. Bottani, D. Dormont,
S. Durrleman, N. Burgos and O. Colliot. ‘Convolutional Neural Networks for Classification of
Alzheimer’s Disease: Overview and Reproducible Evaluation’. In: Medical Image Analysis 63 (July
2020), p. 101694. DOI: 10.1016/j.media.2020.101694. URL: https://hal.archives-ouver
tes.fr/hal-02562504.

11.2 Publications of the year

International journals

[39] S. Bottani, N. Burgos, A. Maire, D. Saracino, S. Stroer, D. Dormont and O. Colliot. ‘Evaluation of
MRI-based machine learning approaches for computer-aided diagnosis of dementia in a clinical
data warehouse’. In: Medical Image Analysis 89 (Oct. 2023), p. 102903. URL: https://hal.scien
ce/hal-03656136.

[40] W. Coath, M. Modat, M. J. Cardoso, P. Markiewicz, C. Lane, T. Parker, A. Keshavan, S. Buchanan,
S. Keuss, M. Harris, N. Burgos, J. Dickson, A. Barnes, D. Thomas, D. Beasley, I. Malone, A. Wong,
K. Erlandsson, B. Thomas, M. Schöll, S. Ourselin, M. Richards, N. Fox, J. Schott and D. Cash.
‘Operationalizing the centiloid scale for [18 F]florbetapir PET studies on PET/MRI’. In: Alzheimer’s
& Dementia: Diagnosis, Assessment & Disease Monitoring 15.2 (Apr. 2023). DOI: 10.1002/dad2.1
2434. URL: https://inria.hal.science/hal-04099627.

[41] M.-C. Corsi, P. Sorrentino, D. P. Schwartz, N. George, L. L. Gollo, S. Chevallier, L. Hugueville, A. E.
Kahn, S. Dupont, D. S. Bassett, V. Jirsa and F. de Vico Fallani. ‘Measuring Neuronal Avalanches to
inform Brain-Computer Interfaces’. In: iScience (13th Dec. 2023). DOI: 10.1016/j.isci.2023.1
08734. URL: https://inria.hal.science/hal-04345847.

[42] A. Desbois, T. Venot, F. de Vico Fallani and M.-C. Corsi. ‘HappyFeat—An interactive and efficient
BCI framework for clinical applications’. In: Software Impacts 19 (2024), p. 100610. DOI: 10.1016
/j.simpa.2023.100610. URL: https://hal.science/hal-04343247.

[43] C. Di Folco, R. Couronné, I. Arnulf, G. Mangone, S. Leu-Semenescu, P. Dodet, M. Vidailhet, J.-c.
Corvol, S. Lehéricy and S. Durrleman. ‘Charting Disease Trajectories from Isolated REM Sleep
Behavior Disorder to Parkinson’s Disease’. In: Movement Disorders (25th Nov. 2023). DOI: 10.1002
/mds.29662. URL: https://inria.hal.science/hal-04319442.

Project ARAMIS

47

[44] V. Dichio and F. De Vico Fallani. ‘Statistical models of complex brain networks: a maximum
entropy approach’. In: Reports on Progress in Physics 86.10 (22nd Aug. 2023), p. 102601. DOI:
10.1088/1361-6633/ace6bc. URL: https://inria.hal.science/hal-03781966.

[45] A. Fürtjes, J. Cole, B. Couvy-Duchesne and S. Ritchie. ‘A quantified comparison of cortical atlases
on the basis of trait morphometricity’. In: Cortex 158 (Jan. 2023), pp. 110–126. DOI: 10.1016/j.c
ortex.2022.11.001. URL: https://hal.science/hal-03918221.

[46] O. Guinebretiere, T. Nedelec, L. Gantzer, B. Lekens, S. Durrleman and C. Louapre. ‘Association
Between Diseases and Symptoms Diagnosed in Primary Care and the Subsequent Specific Risk of
Multiple Sclerosis’. In: Neurology 101.24 (12th Dec. 2023). DOI: 10.1212/WNL.000000000020798
1. URL: https://hal.science/hal-04330030.

[47] S. Loizillon, S. Bottani, A. Maire, S. Ströer, D. Dormont, O. Colliot and N. Burgos. ‘Automatic
motion artefact detection in brain T1-weighted magnetic resonance images from a clinical data
warehouse using synthetic data’. In: Medical Image Analysis 93 (Apr. 2024), p. 103073. DOI: 10.10
16/j.media.2023.103073. URL: https://inria.hal.science/hal-03910451.

[48] E. Maheux, I. Koval, J. Ortholand, C. Birkenbihl, D. Archetti, V. Bouteloup, S. Epelbaum, C. Dufouil,

M. Hofmann-Apitius and S. Durrleman. ‘Forecasting individual progression trajectories in Alzheimer’s
disease’. In: Nature Communications 14.1 (10th Feb. 2023), p. 761. DOI: 10.1038/s41467-022-3
5712-5. URL: https://inria.hal.science/hal-04023781.

[49] K. de Matos, C. Cury, L. Chougar, L. T. Strike, T. Rolland, M. Riche, L. Hemforth, A. Martin, T.
Banaschewski, A. L. W. Bokde, S. Desrivières, H. Flor, A. Grigis, H. Garavan, P. Gowland, A. Heinz,
R. Brühl, J.-L. Martinot, M.-L. Paillère Martinot, E. Artiges, F. Nees, J. H. Fröhner, H. Lemaitre,
D. Papadopoulos Orfanos, T. Paus, L. Poustka, S. Hohmann, S. Millenet, J. H. Fröhner, M. N.
Smolka, N. Vaidya, H. Walter, R. Whelan, G. Schumann, V. Frouin, M. Bach Cuadra, O. Colliot and
B. Couvy-Duchesne. ‘Temporo-basal sulcal connections: a manual annotation protocol and an
investigation of sexual dimorphism and heritability’. In: Brain Structure and Function 228.6 (June
2023), pp. 1459–1478. DOI: 10.1007/s00429-023-02663-6. URL: https://hal.science/hal-
04132176.

[50] P. Moulaire, P. E. Poulet, E. Petit, T. Klockgether, A. Durr, T. Ashisawa and S. Tezenas Du Montcel.
‘Temporal Dynamics of the Scale for the Assessment and Rating of Ataxia in Spinocerebellar
Ataxias’. In: Movement Disorders 38.1 (19th Jan. 2023), pp. 35–44. DOI: 10.1002/mds.29255. URL:
https://hal.science/hal-03831598.

[51] T. Nedelec, B. Couvy-Duchesne, A. Darves-bornoz, R. Couronné, F. Monnet, L. Gantzer, B. Lekens, Y.
Wu, N. Villain, A. Schrag, S. Durrleman and J.-C. Corvol. ‘A Comparison Between Early Presentation
of Dementia with Lewy Bodies, Alzheimer’s Disease, and Parkinson’s Disease: Evidence from
Routine Primary Care and UK Biobank Data’. In: Annals of Neurology 94.2 (Aug. 2023), pp. 259–270.
DOI: 10.1002/ana.26670. URL: https://hal.science/hal-04389605.

[52]

J. Ortholand, P.-F. Pradat, S. Tezenas Du Montcel and S. Durrleman. ‘Interaction of sex and onset
site on the disease trajectory of amyotrophic lateral sclerosis’. In: Journal of Neurology (23rd Aug.
2023). DOI: 10.1007/s00415-023-11932-7. URL: https://inria.hal.science/hal-04216
957.

[53] P.-e. Poulet and S. Durrleman. ‘Multivariate disease progression modelling with longitudinal
ordinal data’. In: Statistics in Medicine (25th May 2023). DOI: 10.1002/sim.9770. URL: https:
//inria.hal.science/hal-04095450.

[54] R. Real, A. Martinez-Carrasco, R. H. Reynolds, M. A. Lawton, M. M. Tan, M. Shoai, J. C. Corvol,
M. Ryten, C. Bresner, L. M. Hubbard, A. Brice, S. Lesage, J. Faouzi, A. Elbaz, F. Artaud, N. Williams,
M. T. Hu, Y. Ben-Shlomo, D. G. Grosset, J. A. Hardy and H. R. Morris. ‘Association between the
LRP1B and APOE loci and the development of Parkinson’s disease dementia’. In: Brain - A Journal
of Neurology 146.5 (2023), pp. 1873–1887. DOI: 10.1093/brain/awac414. URL: https://hal.s
cience/hal-04106341.

[55] T. Rolland and F. De Vico Fallani. ‘Vizaj—A free online interactive software for visualizing spatial
networks’. In: PLoS ONE 18.3 (23rd Mar. 2023), e0282181. DOI: 10.1371/journal.pone.028218
1. URL: https://inria.hal.science/hal-04402600.

48

Inria Annual Report 2023

[56] B. Sauty and S. Durrleman. ‘Impact of sex and APOE-ε4 genotype on patterns of regional brain
atrophy in Alzheimer’s Disease and healthy ageing’. In: Frontiers in Neurology. Frontiers in Neuro-
logy, section Dementia and Neurodegenerative Diseases. 14 (2nd June 2023). DOI: 10.3389/fneu
r.2023.1161527. URL: https://inria.hal.science/hal-03778801.

[57] T. Soulier, O. Colliot, N. Ayache and B. Rohaut. ‘How will tomorrow’s algorithms fuse multimodal
data? The example of the neuroprognosis in Intensive Care’. In: Anaesthesia Critical Care & Pain
Medicine (Sept. 2023), p. 101301. DOI: 10.1016/j.accpm.2023.101301. URL: https://hal.so
rbonne-universite.fr/hal-04227434.

[58] Y. Tamzali, A. Scemla, T. Bonduelle, C. Garandeau, M. Gilbert, S. Randhawa, T. de Nattes, H.
Hachad, V. Pourcher, P. Taupin, H. Kaminski, M. Hazzan, V. Moal, M. Matignon, V. Fihman, C. Levi,
M. Le Quintrec, J. M. Chemouny, E. Rondeau, D. Bertrand, E. Thervet, S. Tezenas Du Montcel, E.
Savoye, B. Barrou, N. Kamar and J. Tourret. ‘Specificities of Meningitis and Meningo-Encephalitis
After Kidney Transplantation: A French Retrospective Cohort Study’. In: Transplant International
36 (18th Jan. 2023), p. 10765. DOI: 10.3389/ti.2023.10765. URL: https://hal.science/hal-
03996962.

[59]

[60]

S. Tezenas Du Montcel, E. Petit, T. Olubajo, J. Faber, P. Lallemant-Dudek, K. Bushara, S. Perlman,
S. Subramony, D. Morgan, B. Jackman, H. L. Paulson, G. Öz, T. Klockgether, A. Durr and T. Ashiz-
awa. ‘Baseline Clinical and Blood Biomarker in Patients With Preataxic and Early-Stage Disease
Spinocerebellar Ataxia 1 and 3’. In: Neurology (16th Feb. 2023), 10.1212/WNL.0000000000207088.
DOI: 10.1212/WNL.0000000000207088. URL: https://hal.science/hal-03996914.

J. Wen, I. M. Nasrallah, A. Abdulkadir, T. D. Satterthwaite, Z. Yang, G. Erus, T. Robert-Fitzgerald, A.
Singh, A. Sotiras, A. Boquet-Pujadas et al. ‘Genomic loci influence patterns of structural covariance
in the human brain’. In: Proceedings of the National Academy of Sciences of the United States of
America 120.52 (21st Dec. 2023). DOI: 10.1073/pnas.2300842120. URL: https://hal.scienc
e/hal-04362321.

[61] A. Yazdan-Panah, M. Schmidt-Mengin, V. A. Ricigliano, T. Soulier, B. Stankoff and O. Colliot.
‘Automatic segmentation of the choroid plexuses: Method and validation in controls and patients
with multiple sclerosis’. In: Neuroimage-Clinical 38 (Mar. 2023), p. 103368. DOI: 10.1016/j.nicl
.2023.103368. URL: https://inria.hal.science/hal-04026889.

[62] R. Zhang, C.-H. Chen, S. Tezenas Du Montcel, J. Lebenberg, Y.-W. Cheng, M. Dichgans, S.-C. Tang
and H. Chabriat. ‘The CADA-MRIT: An MRI Inventory Tool for Evaluating Cerebral Lesions in
CADASIL Across Cohorts’. In: Neurology (2023). DOI: 10.1212/WNL.0000000000207713. URL:
https://hal.science/hal-04215696.

International peer-reviewed conferences

[63]

J. Arslan, M. Ounissi, H. Luo, M. Lacroix, P. Dupré, P. Kumar, A. Hodgkinson, S. Dandou, R. M.
Larive, C. Pignodel, L. Le cam, O. Radulescu and D. Racoceanu. ‘Efficient 3D Reconstruction of
H&E Whole Slide Images in Melanoma’. In: SPIE Medical Imaging. SPIE Medical Imaging 2023. San
Diego, California, United States, 22nd Feb. 2023. URL: https://hal.science/hal-03834014.

[64] M.-C. Corsi, S. Chevallier, F. de Vico Fallani and F. Yger. ‘Ensemble of Riemannian Classifiers
for Multimodal Data: FUCONE Approach for M/EEG Data’. In: ISBI 2023 - IEEE International
Symposium on Biomedical Imaging. Cartagena de Indias, Colombia, 18th Apr. 2023. URL: https:
//inria.hal.science/hal-04140126.

[65] N. Fournier and S. Durrleman. ‘A Multimodal Disease Progression Model for Genetic Associations
with Disease Dynamics’. In: Medical Image Computing and Computer Assisted Intervention –
MICCAI 2023. Lecture Notes in Computer Science. Medical Image Computing and Computer
Assisted Intervention – MICCAI 2023. Vol. 14224. Vancouver, Canada, 1st Oct. 2023, pp. 601–610.
DOI: 10.1007/978-3-031-43904-9_58. URL: https://hal.science/hal-04295080.

[66] G. Fu, R. E. Jurdi, L. Chougar, D. Dormont, R. Valabregue, S. Lehéricy and O. Colliot. ‘Introducing
Soft Topology Constraints in Deep Learning-based Segmentation using Projected Pooling Loss’.
In: SPIE Medical Imaging 2023. San Diego, United States, 19th Feb. 2023. URL: https://inria.h
al.science/hal-03832309.

Project ARAMIS

49

[67] F. Galati, D. Falcetta, R. Cortese, B. Casolla, F. Prados, N. Burgos and M. A. Zuluaga. ‘A2V: A Semi-
Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training
Angiography-to-Venography Translation’. In: BMVC 2023, 34th British Machine Vision Conference.
Aberdeen, United Kingdom, 12th Sept. 2023. URL: https://hal.science/hal-04195756.

[68] R. Hassanaly, S. Bottani, B. Sauty, O. Colliot and N. Burgos. ‘Simulation-based evaluation frame-
work for deep learning unsupervised anomaly detection on brain FDG PET’. In: SPIE Medical
Imaging. San Diego, United States, 19th Feb. 2023. URL: https://hal.science/hal-03835015.

[69] L. J. Hemforth, C. Cury, V. Frouin, S. Desrivières, A. Grigis, H. Garavan, R. Brühl, J.-L. Martinot,
M.-L. Paillère Martinot, E. Artiges, L. Poustka, S. Hohmann, S. Millenet, N. Vaidya, H. Walter, R.
Whelan, G. Schumann, B. Couvy-Duchesne and O. Colliot. ‘Interpretable automatic detection of
incomplete hippocampal inversions using anatomical criteria’. In: SPIE Medical Imaging 2023.
San Diego (California), United States, 19th Feb. 2023, pp. 1–7. URL: https://hal.science/hal-
03994348.

[70] G. Jiménez, P. Mas, A. Kar, L. Ingrassia, S. Boluda, B. Delatour, L. Stimmer and D. Racoceanu. ‘A
meta-graph approach for analyzing whole slide histopathological images of human brain tissue
with Alzheimer’s disease biomarkers’. In: SPIE Medical Imaging 2022. San Diego, United States,
19th Feb. 2023. URL: https://hal.science/hal-03936684.

[71] R. E. Jurdi and O. Colliot. ‘How precise are performance estimates for typical medical image
segmentation tasks?’ In: IEEE International Symposium on Biomedical Imaging (ISBI 2023).
Cartagena de Indias, Colombia, 18th Apr. 2023. URL: https://hal.science/hal-04104891.

[72]

S. Loizillon, S. Bottani, A. Maire, S. Stroer, D. Dormont, O. Colliot and N. Burgos. ‘Transfer learning
from synthetic to routine clinical data for motion artefact detection in brain T1-weighted MRI’.
In: SPIE Medical Imaging 2023: Image Processing. San Diego, United States, 19th Feb. 2023. URL:
https://inria.hal.science/hal-03831746.

[73] S. Loizillon, O. Colliot, L. Chougar, S. Stroer, Y. Jacob, A. Maire, D. Dormont and N. Burgos.
‘Semi-supervised Domain Adaptation for Automatic Quality Control of FLAIR MRIs in a Clinical
Data Warehouse’. In: LNCS - Lecture Notes in Computer Science Series. DART 2023 - 5th MICCAI
Workshop on Domain Adaptation and Representation Transfer. Vol. 14293. Vancouver (BC),
Canada: Springer Nature Switzerland, 14th Oct. 2023, pp. 84–93. DOI: 10.1007/978- 3- 031
-45857-6_9. URL: https://hal.science/hal-04273997.

[74] B. Sauty and S. Durrleman. ‘Impact of sex and APOE-ε4 genotype on regional brain metabolism in
Alzheimer’s Disease’. In: Proceedings of the 2023 International Symposium on Biomedical Imaging.
ISBI 2023 - IEEE 20th International Symposium on Biomedical Imaging. Cartagena (Colombia),
Colombia, 21st Apr. 2023. URL: https://inria.hal.science/hal-03951468.

[75] M. Solal, R. Hassanaly and N. Burgos. ‘Leveraging healthy population variability in deep learn-
ing unsupervised anomaly detection in brain FDG PET’. In: SPIE Medical Imaging. San Diego
(California), United States, 18th Feb. 2024. URL: https://hal.science/hal-04291561.

[76] T. Soulier, M. Hamzaoui, M. Sales Pitombeira, D. De Paula Faria, A. Yazdan-Panah, M. Tonietto,
C. Leroy, M. Bottlaender, B. Bodini, N. Burgos, N. Ayache, O. Colliot and B. Stankoff. ‘Generating
PET-derived maps of myelin content from clinical MRI using curricular discriminator training in
generative adversarial networks’. In: SPIE Medical Imaging. San Diego, United States, 20th Feb.
2024. URL: https://hal.sorbonne-universite.fr/hal-04362506.

[77] E. Thibeau-Sutre, J. M. Wolterink, O. Colliot and N. Burgos. ‘How can data augmentation improve
attribution maps for disease subtype explainability?’ In: SPIE Medical Imaging. San Diego, United
States: SPIE, 20th Feb. 2023. URL: https://inria.hal.science/hal-03966737.

Conferences without proceedings

[78]

J. Arslan, H. Luo, P. Kumar, M. Lacroix, P. Dupré, A. Hodgkinson, C. Pignodel, L. Le Cam, O. Rad-
ulescu and D. Racoceanu. ‘Reconstruction vasculaire 3D et analyse de lames virtuelles H&E dans
l’étude du mélanome’. In: IABM2023 : Colloque Français d’Intelligence Artificielle en Imagerie
Biomédicale. Paris, France, 30th Mar. 2023. URL: https://hal.science/hal-03928851.

50

Inria Annual Report 2023

[79] M.-C. Corsi, P. Sorrentino, D. P. Schwartz, N. George, L. Hugueville, A. E. Kahn, S. Dupont, D. S.
Bassett, V. Jirsa and F. de Vico Fallani. ‘Using critical dynamics to capture processes underlying
Brain-Computer Interface performance’. In: NeuroFrance 2023. Lyon, France, 24th May 2023. URL:
https://inria.hal.science/hal-04140131.

[80] N. Fournier and S. Durrleman. ‘Longitudinal Modelling for Single Nucleotide Polymorphism
Associations with Alzheimer’s Disease’. In: AAIC 2023 — Alzheimer’s Association International
Conference. Amsterdam, Netherlands, 16th July 2023. URL: https://hal.science/hal-04169
631.

[81] R. Hassanaly, C. Brianceau, O. Colliot and N. Burgos. ‘Unsupervised anomaly detection in 3D brain
FDG PET: A benchmark of 17 VAE-based approaches’. In: Deep Generative Models workshop at the
26th International Conference on Medical Image Computing and Computer Assisted Intervention
(MICCAI 2023). Vancouver, Canada, 8th Oct. 2023. URL: https://hal.science/hal-04185304.

[82] G. Vaillant, N. Gensollen, M. Joulot, O. El-Rifai, M. Diaz, O. Colliot and N. Burgos. ‘From Nipype to
Pydra: a Clinica story’. In: OHBM 2023 - Annual meeting of the Organization for Human Brain
Mapping. Montreal, Canada, 22nd July 2023. URL: https://inria.hal.science/hal-042788
98.

Scientific books

[83] O. Colliot. Machine Learning for Brain Disorders. Vol. 197. Neuromethods. Springer, 2023. DOI:

10.1007/978-1-0716-3195-9. URL: https://hal.science/hal-04225627.

Scientific book chapters

[84] O. Colliot. ‘A non-technical introduction to machine learning’. In: Machine Learning for Brain

Disorders. Springer, 24th July 2023. URL: https://hal.science/hal-03957125.

[85] O. Colliot, E. Thibeau-Sutre and N. Burgos. ‘Reproducibility in machine learning for medical
imaging’. In: Machine Learning for Brain Disorders. Springer, 2023. URL: https://hal.science
/hal-03957240.

[86] B. Couvy-Duchesne, S. Bottani, E. Camenen, F. Fang, M. Fikere, J. Gonzalez-Astudillo, J. Harvey,
R. Hassanaly, I. Kassam, P. Lind, Q. Liu, Y. Lu, M. Nabais, T. Rolland, J. Sidorenko, L. Strike and
M. Wright. ‘Main Existing Datasets for Open Brain Research on Humans’. In: Machine Learning
for Brain Disorders. Springer US, 23rd July 2023, pp. 753–804. DOI: 10.1007/978-1-0716-3195-
9_24. URL: https://hal.science/hal-04254339.

[87] S. Epelbaum and F. Cacciamani. ‘Clinical Assessment of Brain Disorders’. In: Machine Learning
for Brain Disorders. Vol. 197. Springer, 23rd July 2023, pp. 233–252. DOI: 10.1007/978-1-0716-3
195-9_7. URL: https://hal.science/hal-04239608.

[88]

[89]

J. Faouzi and O. Colliot. ‘Classic machine learning algorithms’. In: Machine Learning for Brain
Disorders. Springer, 24th July 2023. URL: https://hal.science/hal-03830094.

J. Faouzi, O. Colliot and J.-C. Corvol. ‘Machine learning for Parkinson’s disease and related dis-
orders’. In: Machine Learning for Brain Disorders. Springer, 23rd July 2023. DOI: 10.1007/978-1-
0716-3195-9_26. URL: https://hal.science/hal-03830073.

[90] G. Jimenez and D. Racoceanu. ‘Computational Pathology for Brain Disorders’. In: Machine Learn-
ing for Brain Disorders. Vol. 197. Part of the Neuromethods book series (NM,volume 197). Springer;
Humana, New York, NY, 23rd July 2023, pp. 533–572. DOI: 10.1007/978-1-0716-3195-9_18.
URL: https://hal.science/hal-03936550.

[91] E. Thibeau-Sutre, S. Collin, N. Burgos and O. Colliot. ‘Interpretability of Machine Learning Meth-
ods Applied to Neuroimaging’. In: Machine Learning for Brain Disorders. Springer, 2023. DOI:
10.1007/978-1-0716-3195-9_22. URL: https://hal.science/hal-03615163.

[92] M. Vakalopoulou, S. Christodoulidis, N. Burgos, O. Colliot and V. Lepetit. ‘Deep learning: basics
and convolutional neural networks (CNN)’. In: Machine Learning for Brain Disorders. Springer,
23rd July 2023. DOI: 10.1007/978-1-0716-3195-9_3. URL: https://hal.science/hal-039
57224.

Project ARAMIS

51

[93] G. Varoquaux and O. Colliot. ‘Evaluating machine learning models and their diagnostic value’. In:
Machine Learning for Brain Disorders. Springer, June 2023. URL: https://hal.science/hal-0
3682454.

Doctoral dissertations and habilitation theses

[94] P.-E. Poulet. ‘Modeling individual disease trajectory: application to the prediction of treatment

effect’. EDITE de Paris, 2nd Oct. 2023. URL: https://inria.hal.science/tel-04345196.

[95] B. Sauty. ‘Multimodal modelling of Alzheimer’s Disease progression’. Sorbonne Université, 13th Sept.

2023. URL: https://theses.hal.science/tel-04338932.

[96]

S. Skriabine. ‘Mapping of the mouse cerebral vascular network in adults and during development’.
Sorbonne Université, 8th June 2023. URL: https://theses.hal.science/tel-04318045.

Reports & preprints

[97] R. Ben Messaoud, V. L. Du, B. C. Kaufmann, B. Couvy-Duchesne, L. Migliaccio, P. Bartolomeo,
M. Chavez and F. de Vico Fallani. Low-dimensional controllability of brain networks. 2023. DOI:
10.48550/arXiv.2311.11132. URL: https://hal.science/hal-04302540.

[98] R. Hassanaly, C. Brianceau, M. Solal, O. Colliot and N. Burgos. Evaluation of pseudo-healthy image
reconstruction for anomaly detection with deep generative models: Application to brain FDG PET.
30th Nov. 2023. URL: https://hal.science/hal-04315738.

[99] M. Ounissi, M. Latouche and D. Racoceanu. Phagocytosis Unveiled: A Scalable and Interpretable
Deep learning Framework for Neurodegenerative Disease Analysis. 26th Apr. 2023. URL: https://h
al.science/hal-04067345.

[100] R. A. Poldrack, C. J. Markiewicz, S. Appelhoff, Y. K. Ashar, T. Auer, S. Baillet, S. Bansal, L. Beltrachini,
C. G. Benar, G. Bertazzoli et al. The Past, Present, and Future of the Brain Imaging Data Structure
(BIDS). 11th Sept. 2023. URL: https://hal.science/hal-04346097.

[101] C. Presigny, M.-C. Corsi and F. de Vico Fallani. Topological duality of multilayer networks. 21st June

2023. URL: https://inria.hal.science/hal-04163895.

[102] B. Sauty, E. Maheux and S. Durrleman. Feature Selection to Forecast Cognitive Decline Using
Multimodal Alzheimer’s Disease Models. 9th May 2023. URL: https://hal.science/hal-04195
850.

[103] T. Venot, A. Desbois, M.-C. Corsi, L. Hugueville, L. Saint-Bauzel and F. de Vico Fallani. Intentional
binding enhances hybrid BCI control. 2023. DOI: 10.48550/arXiv.2309.12195. URL: https:
//inria.hal.science/hal-04244638.

[104] A. Yazdan-Panah, R. El Jurdi, G. Fu, J. Arslan, R. Valabregue, D. Dormont, B. Stankoff and O. Colliot.
Scrolling 2D U-Net for the 3D segmentation of small vessels on 7T magnetic resonance angiography.
9th May 2023. URL: https://hal.science/hal-04090356.

[105] A. Yazdan-Panah, G. Fu, D. Dormont, B. Stankoff and O. Colliot. Scrolling 2D U-Net for the 3D
segmentation of the internal carotid artery. 9th May 2023. URL: https://hal.science/hal-040
90342.

Other scientific publications

[106] B. Aristimunha, R. Y. D. Camargo, W. H. L. Pinaya, F. Yger, M.-C. Corsi and S. Chevallier. ‘CON-
CERTO: Coherence & Functional Connectivity Graph Network’. In: Journée CORTICO 2023. Paris,
France, 9th May 2023. URL: https://hal.science/hal-04121445.

[107]

I. Carrara, B. Aristimunha, S. Chevallier, M.-C. Corsi, T. Papadopoulo and M.-C. Corsi. ‘Holographic
EEG: multi-view deep learning for BCI’. In: Journée CORTICO 2023 - COllectif pour la Recherche
Transdisciplinaire sur les Interfaces Cerveau-Ordinateur. Paris, France, 9th May 2023. URL: https
://hal.science/hal-04095900.

52

[108]

Inria Annual Report 2023

I. Carrara, B. Aristimunha, M.-C. Corsi, R. Y. D. Camargo, S. Chevallier and T. Papadopoulo.
‘Augmented SPDNet: Second-Order Neural Network for Motor Imagery-Based BCI’. In: Soph.IA
summit 2023. Sophia Antipolis, France, 22nd Nov. 2023. URL: https://inria.hal.science/ha
l-04308549.

[109] M.-C. Corsi, S. Chevallier, F. de Vico Fallani and F. Yger. ‘Ensemble of Riemannian classifiers for
multimodal data: FUCONE approach for M/EEG data’. In: BCI meeting 2023. Bruxelles, Belgium,
6th June 2023. URL: https://inria.hal.science/hal-04140113.

[110] L. Ek-Fliesberg, P. Sorrentino and M.-C. Corsi. ‘Neuronal avalanches as alternative features for
motor imagery-based brain-computer interface’. In: Journées CORTICO 2023. Paris, France,
9th May 2023. URL: https://inria.hal.science/hal-04140098.

[111] R. Frateur, S. Chevallier, F. Yger and M.-C. Corsi. ‘Dimensionality Reduction and Frequency
Bin Optimization To Improve a Riemannian-based Classification Pipeline’. In: CORTICO 2023 -
Journées COllectif pour la Recherche Transdisciplinaire sur les Interfaces Cerveau-Ordinateur.
Paris, France, 9th May 2023. URL: https://inria.hal.science/hal-04140107.

[112]

[113]

S. Kaisaridi, H. Chabriat and S. Tezenas Du Montcel. ‘A multivariate bayesian mixed-effect model
(Leaspy) to analyze the trajectory of cognitive decline in CADASIL’. In: 44th Annual Conference of
the International Society for Clinical Biostatistics. Milan, Italy, 27th Aug. 2023. URL: https://hal
.science/hal-04399800.

J. Ortholand, S. Durrleman and S. Tezenas Du Montcel. ‘Longitudinal and survival joint prediction:
time reparameterization in amyotrophic lateral sclerosis context’. In: ISCB 2023 - 44th Annual
Conference of the International Society for Clinical Biostatistics. Milan, Italy, 27th Aug. 2023. URL:
https://hal.science/hal-04216888.

[114] P.-E. Poulet, B. Jedynak, Z. Mari and S. Durrleman. ‘Non-parametric modeling for dopaminergic
treatment effect in Parkinson’s disease’. In: AD/PD 2023 - 17th International Conference on
Alzheimer‘s & Parkinson‘s Diseases. Goteborg, Sweden, 27th Mar. 2023. URL: https://inria.ha
l.science/hal-04158200.

[115] P. Sorrentino, M.-C. Corsi, D. P. Schwartz, N. George, A. E. Kahn, S. Dupont, D. S. Bassett, F. de
Vico Fallani and V. Jirsa. ‘Informing motor imagery-based brain-computer interface via neuronal
avalanches’. In: IEEE International Symposium on Biomedical Imaging (ISBI) 2023. Cartagena,
Colombia, 18th Apr. 2023. URL: https://inria.hal.science/hal-04140120.
<scratchpad>
- Main topic: Inria Research Centre's Annual Report 2023
- Key points:
  - ARAMIS project-team within the Paris Brain Institute (ICM) at the Pitié-Salpêtrière hospital
  - Focus on computational, mathematical, and statistical approaches for analyzing multimodal patient data
  - Emphasis on neuroimaging data, machine learning, statistical modeling, and network analysis
  - Applications in neurodegenerative diseases, such as Alzheimer’s disease and Parkinson’s disease
  - Development of new software, platforms, and open data for clinical research studies
- Structure:
  - The document is divided into sections, including Research Centre, Activity Report, Project-Team ARAMIS, and more
  - Each section provides an overview of the research conducted and the projects undertaken by the ARAMIS team
- Potential artifacts to ignore:
  - Web-related elements, such as links and HTML tags
  - Unnecessary or redundant information
</scratchpad>

<final_summary>
The Inria Research Centre's Annual Report 2023 highlights the ARAMIS project-team's focus on computational, mathematical, and statistical approaches for analyzing multimodal patient data. The team emphasizes neuroimaging data, machine learning, statistical modeling, and network analysis to tackle neurodegenerative diseases, including Alzheimer’s disease and Parkinson’s disease. The ARAMIS team has developed new software, platforms, and open data for clinical research studies, aiming to improve our understanding of brain disorders and develop more effective treatments. The report showcases the team's achievements in various fields, including brain-computer interfaces, machine learning, and statistical modeling.
The Inria Research Centre's Annual Report 2023 highlights the ARAMIS project-team's focus on computational, mathematical, and statistical approaches for analyzing multimodal patient data. The team emphasizes neuroimaging data, machine learning, statistical modeling, and network analysis to tackle neurodegenerative diseases, including Alzheimer’s disease and Parkinson’s disease. The ARAMIS team has developed new software, platforms, and open data for clinical research studies, aiming to improve our understanding of brain disorders and develop more effective treatments. The report showcases the team's achievements in various fields, including brain-computer interfaces, machine learning, and statistical modeling.