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1. Node-layer duality in networked systems

Author

Charley Presigny, Marie-Constance Corsi, and Fabrizio De Vico Fallani

Subjects
Science
Abstract

Abstract Real-world networks typically exhibit several aspects, or layers, of interactions among their nodes. By permuting the role of the nodes and the layers, we establish a new criterion to construct the dual of a network. This approach allows to examine connectivity from either a node-centric or layer-centric viewpoint. Through rigorous analytical methods and extensive simulations, we demonstrate that nodewise and layerwise connectivity measure different but related aspects of the same system. Leveraging node-layer duality provides complementary insights, enabling a deeper comprehension of diverse networks across social science, technology and biology. Taken together, these findings reveal previously unappreciated features of complex systems and provide a fresh tool for delving into their structure and dynamics.

Published
2024
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2. Neuronal avalanches in temporal lobe epilepsy as a noninvasive diagnostic tool investigating large scale brain dynamics

Author

Marie-Constance Corsi, Emahnuel Troisi Lopez, Pierpaolo Sorrentino, Simone Cuozzo, Alberto Danieli, Paolo Bonanni, and Gian Marco Duma

Subjects
Medicine, Science
Abstract

Abstract The epilepsy diagnosis still represents a complex process, with misdiagnosis reaching 40%. We aimed at building an automatable workflow, helping the clinicians in the diagnosis of temporal lobe epilepsy (TLE). We hypothesized that neuronal avalanches (NA) represent a feature better encapsulating the rich brain dynamics compared to classically used functional connectivity measures (Imaginary Coherence; ImCoh). We analyzed large-scale activation bursts (NA) from source estimation of resting-state electroencephalography. Using a support vector machine, we reached a classification accuracy of TLE versus controls of 0.86 ± 0.08 (SD) and an area under the curve of 0.93 ± 0.07. The use of NA features increase by around 16% the accuracy of diagnosis prediction compared to ImCoh. Classification accuracy increased with larger signal duration, reaching a plateau at 5 min of recording. To summarize, NA represents an interpretable feature for an automated epilepsy identification, being related with intrinsic neuronal timescales of pathology-relevant regions.

Published
2024
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3. Measuring neuronal avalanches to inform brain-computer interfaces

Author

Marie-Constance Corsi, Pierpaolo Sorrentino, Denis Schwartz, Nathalie George, Leonardo L. Gollo, Sylvain Chevallier, Laurent Hugueville, Ari E. Kahn, Sophie Dupont, Danielle S. Bassett, Viktor Jirsa, and Fabrizio De Vico Fallani

Subjects
Neuroscience, Computer science, Science
Abstract

Summary: Large-scale interactions among multiple brain regions manifest as bursts of activations called neuronal avalanches, which reconfigure according to the task at hand and, hence, might constitute natural candidates to design brain-computer interfaces (BCIs). 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 significantly 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.

Published
2024
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4. Functional disconnection of associative cortical areas predicts performance during BCI training

Author

Marie-Constance Corsi, Mario Chavez, Denis Schwartz, Nathalie George, Laurent Hugueville, Ari E. Kahn, Sophie Dupont, Danielle S. Bassett, and Fabrizio De Vico Fallani

Subjects
Learning, Motor imagery, Brain-computer interface, EEG, MEG, Network, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Brain-computer interfaces (BCIs) have been largely developed to allow communication, control, and neurofeedback in human beings. Despite their great potential, BCIs perform inconsistently across individuals and the neural processes that enable humans to achieve good control remain poorly understood. To address this question, we performed simultaneous high-density electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings in a motor imagery-based BCI training involving a group of healthy subjects. After reconstructing the signals at the cortical level, we showed that the reinforcement of motor-related activity during the BCI skill acquisition is paralleled by a progressive disconnection of associative areas which were not directly targeted during the experiments. Notably, these network connectivity changes reflected growing automaticity associated with BCI performance and predicted future learning rate. Altogether, our findings provide new insights into the large-scale cortical organizational mechanisms underlying BCI learning, which have implications for the improvement of this technology in a broad range of real-life applications.

Published
2020
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12. Measuring brain critical dynamics to inform Brain-Computer Interfaces

Author

Marie-Constance Corsi, Pierpaolo Sorrentino, Denis Schwartz, Nathalie George, Laurent Hugueville, Ari E. Kahn, Sophie Dupont, Danielle S. Bassett, Viktor Jirsa, and Fabrizio De Vico Fallani

Abstract

Large-scale interactions among multiple brain regions manifest as bursts of activations, called neuronal avalanches, which reconfigure according to the task at hand and, hence, might constitute natural candidates to design brain-computer interfaces (BCI). To test this hypothesis, we compared source-reconstructed magneto/electroencephalography during resting-state and a motor imagery task performed within a BCI protocol. For each condition, we defined an individual avalanche transition matrix, tracking the probability that an avalanche would spread across any two regions. The edges whose transition probabilities significantly differed between conditions hinged selectively on premotor regions in all subjects, defining a topography related to the task. Furthermore, the individual differences in the transition probabilities for edges between pre/motor regions and parietal ones positively related to the individual task performance. Our results show that the patterns of propagation of large-scale perturbations are related to behavior and can be used to inform brain-computer interfaces.

Published
2022

13. Riemannian geometry for combining functional connectivity metrics and covariance in BCI

Author

Sylvain Chevallier, Marie-Constance Corsi, Florian Yger, Fabrizio De Vico Fallani, Laboratoire d'Ingénierie des Systèmes de Versailles (LISV), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Horizon 2020 Framework Programme, H2020, European Research Council, ERC, Agence Nationale de la Recherche, ANR, Horizon 2020: 864729, FDVF and MCC acknowledge support from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 864729 ). FY acknowledges the support of the ANR, France as part of the 'Investissements d’avenir' program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute)., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), HAL UVSQ, Équipe, and PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID

Subjects
Brain–computer interface, [SDV.IB] Life Sciences [q-bio]/Bioengineering, Functional connectivity, Ensemble learning, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.IB]Life Sciences [q-bio]/Bioengineering, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Riemannian geometry, [INFO.INFO-HC] Computer Science [cs]/Human-Computer Interaction [cs.HC], Software
Abstract

International audience; Brain–computer interfaces allow interactions based on brain activities detected in electroencephalography. Despite important improvements in the last decade, some subjects still achieve poor performances without any identified cause. On the one hand, State-of-the-art methods for online decoding are based on covariance matrices seen as elements of a Riemann manifold. On the other hand, functional connectivity is a powerful method to characterize the brain activity. The proposed software combines functional connectivity and covariance within a Riemannian framework to increase the robustness of brain–computer interfaces.

Published
2022

14. Ensemble learning based on functional connectivity and Riemannian geometry for robust workload estimation

Author

Marie-Constance Corsi, Sylvain Chevallier, Quentin Barthélemy, Isabelle Hoxha, Florian Yger, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Ingénierie des Systèmes de Versailles (LISV), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Foxstream, Complexité, Innovation, Activités Motrices et Sportives (CIAMS), Université d'Orléans (UO)-Université Paris-Saclay, Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Corsi, Marie-Constance, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Functional connectivity, [SCCO]Cognitive science, Ensemble learning, [INFO]Computer Science [cs], [MATH] Mathematics [math], [SCCO] Cognitive science, [INFO] Computer Science [cs], Riemannian geometry, [MATH]Mathematics [math]
Abstract

International audience; Context Passive Brain-Computer Interface (pBCI) has recently gained in popularity through its applications, e.g. workload and attention assessment. Nevertheless, one of the main limitations remains the important intra-and inter-subject variability. We propose a robust approach relying on ensemble learning, grounded in functional connectivity and Riemannian geometry to mitigate the high variability of the data with a large and diverse panel of classifiers.

Published
2021

15. A machine learning approach to screen for preclinical Alzheimer's disease Authors

Author

Jacobo D. Sitt, Stanley Durrleman, Manon Ansart, Marion Houot, Stéphane Epelbaum, Bruno Dubois, Marie-Odile Habert, Marie-Constance Corsi, Sinead Gaubert, Fabrizio De Vico Fallani, Lionel Naccache, Federico Raimondo, Université de Paris - UFR Médecine Paris Centre [Santé] (UP Médecine Paris Centre), Université de Paris (UP), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), GIGA [Université Liège], Université de Liège, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Imagerie Biomédicale (LIB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Service de Neurologie [CHU Pitié-Salpêtrière], IFR70-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), This work was supported by INSERM in collaboration with ICM, Instituts Hospitalo-Universitaires à ICM, and Pfizer and has received support within the 'Investissement d'Avenir' (ANR-10-AIHU-06 and ANR-19-P3IA-0001) programs. The study was promoted in collaboration with the 'CHU de Bordeaux' (coordination CIC EC7), the promoter of Memento cohort, funded by the Foundation Plan-Alzheimer. The study was further supported by AVID/Lilly. The funding sources had no role in the study design, data collection, data analysis, or data interpretation., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Université Paris Cité - UFR Médecine [Santé] (UPCité UFR Médecine), Université Paris Cité (UPCité), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Gestionnaire, HAL Sorbonne Université 5, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), UFR Médecine [Santé] - Université Paris Cité (UFR Médecine UPCité), Laboratoire d'Imagerie Biomédicale [Paris] (LIB), CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)

Subjects
0301 basic medicine, Male, Aging, Apolipoprotein E4, Disease, Electroencephalography, Neuropsychological Tests, computer.software_genre, Preclinical Alzheimer's disease, Hippocampus, Cohort Studies, 0302 clinical medicine, Mass Screening, EEG, Aged, 80 and over, medicine.diagnostic_test, General Neuroscience, Neurodegeneration, Neuropsychology, Predictive value, Magnetic Resonance Imaging, 3. Good health, Cohort, Female, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Amyloid, Genotype, Machine learning, 03 medical and health sciences, Alzheimer Disease, medicine, Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Aged, business.industry, medicine.disease, 030104 developmental biology, Positron-Emission Tomography, Nerve Degeneration, Multimodal, Neurology (clinical), Artificial intelligence, Geriatrics and Gerontology, business, computer, 030217 neurology & neurosurgery, Biomarkers, Developmental Biology, Follow-Up Studies
Abstract

International audience; Combining multimodal biomarkers could help in the early diagnosis of Alzheimer's disease (AD). We included 304 cognitively normal individuals from the INSIGHT-preAD cohort. Amyloid and neurodegeneration were assessed on 18F-florbetapir and 18F-fluorodeoxyglucose PET, respectively. We used a nested cross-validation approach with non-invasive features (electroencephalography [EEG], APOE4 genotype, demographic, neuropsychological and MRI data) to predict: 1/ amyloid status; 2/ neurodegeneration status; 3/ decline to prodromal AD at 5-year follow-up. Importantly, EEG was most strongly predictive of neurodegeneration, even when reducing the number of channels from 224 down to 4, as 4-channel EEG best predicted neurodegeneration (negative predictive value [NPV] = 82%, positive predictive value [PPV] = 38%, 77% specificity, 45% sensitivity). The combination of demographic, neuropsychological data, APOE4 and hippocampal volumetry most strongly predicted amyloid (80% NPV, 41% PPV, 70% specificity, 58% sensitivity) and most strongly predicted decline to prodromal AD at 5 years (97% NPV, 14% PPV, 83% specificity, 50% sensitivity). Thus, machine learning can help to screen patients at high risk of preclinical AD using non-invasive and affordable biomarkers.

Published
2021

16. Riemannian Geometry on Connectivity for Clinical BCI

Author

Camille Noûs, Sylvain Chevallier, Marie-Constance Corsi, Florian Yger, Laboratoire Cogitamus, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Ingénierie des Systèmes de Versailles (LISV), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Université Paris-Saclay, Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision (LAMSADE), Université Paris Dauphine-PSL, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Florian Yger acknowledges the support of the ANR as part of the 'Investissements d’avenir' program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute)., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), Chevallier, Sylvain, PaRis Artificial Intelligence Research InstitutE - - PRAIRIE2019 - ANR-19-P3IA-0001 - P3IA - VALID, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, Computer science, 0206 medical engineering, Computer Science::Human-Computer Interaction, 02 engineering and technology, Riemannian geometry, Machine learning, computer.software_genre, Task (project management), 03 medical and health sciences, symbols.namesake, Computer Science::Emerging Technologies, 0302 clinical medicine, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, BCI, Brain–computer interface, Quantitative Biology::Neurons and Cognition, business.industry, Functional connectivity, functional connectivity, Covariance, 020601 biomedical engineering, Ensemble learning, Computer Science::Sound, Task analysis, symbols, ensemble learning, Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract

International audience; Riemannian BCI based on EEG covariance have won many data competitions and achieved very high classification results on BCI datasets. To increase the accuracy of BCI systems, we propose an approach grounded on Riemannian geometry that extends this framework to functional connectivity measures. This paper describes the approach submitted to the Clinical BCI Challenge-WCCI2020 and that ranked 1 st on the task 1 of the competition.

Published
2021

17. Magnetoencephalography With Optically Pumped 4He Magnetometers at Ambient Temperature

Author

Sophie Morales, Francis Bertrand, William Fourcault, Gilles Cauffet, Matthieu Le Prado, Augustin Palacios Laloy, François Berger, Francois Lenouvel, Marie-Constance Corsi, Etienne Labyt, Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Laboratoire de Génie Electrique de Grenoble (G2ELab), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), G2Elab-Modèles, Méthodes et Méthodologies Appliqués au Génie Electrique (G2Elab-MAGE), Laboratoire de Génie Electrique de Grenoble (G2ELab ), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), and Centre Hospitalier Universitaire [Grenoble] (CHU)

Subjects
magnetoencephalography, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Auditory evoked field, Magnetometer, Optically pumped magnetometer, Temperature measurement, Imaging phantom, 030218 nuclear medicine & medical imaging, law.invention, 03 medical and health sciences, visual evoked field, 0302 clinical medicine, Optics, law, vector measurement, medicine, auditory evoked field, Electrical and Electronic Engineering, spontaneous activity, Physics, Signal processing, Radiological and Ultrasound Technology, medicine.diagnostic_test, business.industry, Attenuation, Magnetoencephalography, Helium 4, [SPI.TRON]Engineering Sciences [physics]/Electronics, Computer Science Applications, Magnetic field, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], room temperature, business, Software
Abstract

WOS:000455110500009; International audience; In this paper, we present the first proof of concept confirming the possibility to record magnetoencephalographic (MEG) signals with Optically Pumped Magnetometers (OPMs) based on the parametric resonance of 4 He atoms. The main advantage of this kind of OPM is the possibility to provide a tri-axis vector measurement of the magnetic field at room-temperature (the 4 He vapor is neither cooled nor heated). The sensor achieves a sensitivity of 210 fT/√Hz in the bandwidth [2 Hz-300 Hz]. MEG simulation studies with a brain phantom were cross-validated with real MEG measurements on a healthy subject. For both studies, MEG signal was recorded consecutively with OPMs and Superconducting Quantum Interference Devices (SQUIDs) used as reference sensors. For healthy subject MEG recordings, three MEG proofs of concept were carried out: auditory and visual evoked fields (AEF, VEF), and spontaneous activity. M100 peaks have been detected on evoked responses recorded by both OPMs and SQUIDs with no significant difference in latency. Concerning spontaneous activity, an attenuation of the signal power between 8-12 Hz (alpha band) related to eyes opening has been observed with OPM similarly to SQUID. All these results confirm that the room temperature vector 4 He OPMs can record MEG signals and provide reliable information on brain activity. Index Terms-Optically pumped magnetometer, Helium 4, room temperature, vector measurement, magnetoencephalography, auditory evoked field, visual evoked field, spontaneous activity.

Published
2019

18. EEG: A valuable tool to screen for neurodegeneration in preclinical Alzheimer’s disease

Author

Lionel Naccache, Bruno Dubois, Fabrizio De Vico Fallani, Geoffroy Gagliardi, Jacobo D. Sitt, Hovagim Bakardjian, Stéphane Epelbaum, Bertrand Hermann, Delphine Oudiette, Marion Houot, Marie-Odile Habert, Sinead Gaubert, Federico Raimondo, and Marie-Constance Corsi

Subjects
medicine.diagnostic_test, Epidemiology, business.industry, Health Policy, Neurodegeneration, Alzheimer's disease biomarkers, Electroencephalography, medicine.disease, Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Neuroimaging, Medicine, Neurology (clinical), Geriatrics and Gerontology, business, Neuroscience
Published
2020

19. Multimodal screening for neurodegeneration in preclinical Alzheimer’s disease using EEG, APOE4 genotype, neuropsychological and MRI data

Author

Stéphane Epelbaum, Delphine Oudiette, Marie-Odile Habert, Sinead Gaubert, Bruno Dubois, Jacobo D. Sitt, Bertrand Hermann, Fabrizio De Vico Fallani, Lionel Naccache, Hovagim Bakardjian, Marion Houot, Marie-Constance Corsi, Federico Raimondo, and Geoffroy Gagliardi

Subjects
medicine.diagnostic_test, Epidemiology, business.industry, Health Policy, Neurodegeneration, Neuropsychology, Disease, Electroencephalography, medicine.disease, Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Genotype, Medicine, Neurology (clinical), Geriatrics and Gerontology, business, Neuroscience
Published
2020

20. Network-based brain computer interfaces: principles and applications

Author

Fabrizio De Vico Fallani, Giulia Bassignana, Tiziana Cattai, Juliana Gonzalez-Astudillo, Marie-Constance Corsi, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU), Institut National de la Santé et de la Recherche Médicale (INSERM), The research leading to these results has received funding from the program 'Investissements d’avenir' ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6)., ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Computer science, [SDV]Life Sciences [q-bio], 0206 medical engineering, Biomedical Engineering, Network science, 02 engineering and technology, Network theory, Field (computer science), 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, Human–computer interaction, Humans, Brain connectivity, ComputingMilieux_MISCELLANEOUS, brain connectivity, brain-machine interfaces, network theory, Brain–computer interface, business.industry, Brain-machine interfaces, Neurosciences, Brain, Electroencephalography, Usability, Neurofeedback, 020601 biomedical engineering, Variety (cybernetics), Statistical classification, FOS: Biological sciences, Brain-Computer Interfaces, Quantitative Biology - Neurons and Cognition, Neurons and Cognition (q-bio.NC), business, Algorithms, 030217 neurology & neurosurgery
Abstract

Brain–computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback rehabilitation. In general, BCI usability depends on the ability to comprehensively characterize brain functioning and correctly identify the user’s mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modeling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from brain networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability.

Published
2020

21. Functional disconnection of associative cortical areas predicts performance during BCI training

Author

Ari E. Kahn, Danielle S. Bassett, Nathalie George, Mario Chavez, Laurent Hugueville, Denis Schwartz, Sophie Dupont, Fabrizio De Vico Fallani, Marie-Constance Corsi, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), University of Pennsylvania, Perelman School of Medicine, This work was partially supported by French program 'Investissements d’avenir' ANR-10-IAIHU-06, 'ANR-NIH CRCNS' ANR-15-NEUC-0006-02 and by NICHD 1R01HD086888-01. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work was performed on a platform of France Life Imaging network partly funded by the grant 'ANR-11-INBS-0006'., Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), University of Pennsylvania [Philadelphia], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU), Service de médecine physique et de réadaptation [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], and Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université

Subjects
Adult, Male, Computer science, Cognitive Neuroscience, Automaticity, Network, Electroencephalography, Motor Activity, 050105 experimental psychology, Article, lcsh:RC321-571, 03 medical and health sciences, Young Adult, 0302 clinical medicine, Motor imagery, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, medicine, Connectome, Humans, Learning, 0501 psychology and cognitive sciences, Functional disconnection, Longitudinal Studies, EEG, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Associative property, Brain–computer interface, Cerebral Cortex, MEG, medicine.diagnostic_test, [SDV.NEU.PC]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]/Psychology and behavior, [SCCO.NEUR]Cognitive science/Neuroscience, 05 social sciences, Magnetoencephalography, Neurology, Brain-computer interface, Brain-Computer Interfaces, [SCCO.PSYC]Cognitive science/Psychology, Imagination, Female, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Disconnection, Nerve Net, Neuroscience, Reinforcement, Psychology, 030217 neurology & neurosurgery
Abstract

International audience; Brain-computer interfaces (BCIs) have been largely developed to allow communication, control, and neurofeedback in human beings. Despite their great potential, BCIs perform inconsistently across individuals and the neural processes that enable humans to achieve good control remain poorly understood. To address this question, we performed simultaneous high-density electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings in a motor imagery-based BCI training involving a group of healthy subjects. After reconstructing the signals at the cortical level, we showed that the reinforcement of motor-related activity during the BCI skill acquisition is paralleled by a progressive disconnection of associative areas which were not directly targeted during the experiments. Notably, these network connectivity changes reflected growing automaticity associated with BCI performance and predicted future learning rate. Altogether, our findings provide new insights into the large-scale cortical organizational mechanisms underlying BCI learning, which have implications for the improvement of this technology in a broad range of real-life applications.

Published
2020

22. Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior

Author

Jennifer Stiso, Javier O. Garcia, Jean M. Vettel, Marie-Constance Corsi, Timothy H. Lucas, Fabio Pasqualetti, Danielle S. Bassett, Fabrizio De Vico Fallani, Gestionnaire, Hal Sorbonne Université, Institut de Neurosciences Translationnelles de Paris - - IHU-A-ICM2010 - ANR-10-IAHU-0006 - IAHU - VALID, Perelman School of Medicine, University of Pennsylvania, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Center for Control, Dynamical-Systems, and Computation [Santa Barbara] (CCDC), University of California [Santa Barbara] (UC Santa Barbara), University of California (UC)-University of California (UC), University of California (UC), University of California [Riverside] (UC Riverside), Santa Fe Institute, D.S.B. and J.S. acknowledge support from the John D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation,the ISI Foundation, the Paul Allen Foundation, the Army Research Laboratory (W911NF-10-2-0022), the Army Research Office (Bassett W911NF14-1-0679, Grafton-W911NF-16-1-0474, DCISTW911NF-17-2-0181), the Office of Naval Research, the National Institute of Mental Health (2-R01-DC-009209-11, R01 - MH112847, R01-MH107235,R21-M MH-106799), the National Institute of Child Health and Human Development (1R01HD086888-01), National Institute of Neurological Disorders and Stroke (R01 NS099348), the National Science Foundation (BCS-1441502, BCS-1430087, NSF PHY-1554488 and BCS-1631550), and French program 'Investissements d’avenir' ANR-10-IAIHU-06, 'ANRNIH CRCNS' ANR-15-NEUC-0006-02., ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), University of Pennsylvania [Philadelphia], Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), University of California [Santa Barbara] (UCSB), University of California-University of California, University of California, and University of California [Riverside] (UCR)

Subjects
Computer science, Brain activity and meditation, media_common.quotation_subject, [SDV]Life Sciences [q-bio], 0206 medical engineering, Biomedical Engineering, Network neuroscience, 02 engineering and technology, Machine learning, computer.software_genre, Article, Matrix decomposition, 03 medical and health sciences, Cellular and Molecular Neuroscience, 0302 clinical medicine, Motor imagery, Control theory, Task Performance and Analysis, medicine, Humans, Learning, media_common, Network model, Brain–computer interface, medicine.diagnostic_test, business.industry, Neurosciences, Brain, Magnetoencephalography, Electroencephalography, 020601 biomedical engineering, Expression (mathematics), [SDV] Life Sciences [q-bio], Brain-computer interface, Brain-Computer Interfaces, FOS: Biological sciences, Quantitative Biology - Neurons and Cognition, Conceptual model, Neurons and Cognition (q-bio.NC), Artificial intelligence, business, computer, 030217 neurology & neurosurgery
Abstract

International audience; Objective: Motor imagery-based brain-computer interfaces (BCIs) use an individual's ability to volitionally modulate localized brain activity, often as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific inquiry. Formal experiments designed to probe the nature of BCI learning have offered initial evidence that coherent activity across spatially distributed and functionally diverse cognitive systems is a hallmark of individuals who can successfully learn to control the BCI. However, little is known about how these distributed networks interact through time to support learning.Approach: Here, we address this gap in knowledge by constructing and applying a multimodal network approach to decipher brain-behavior relations in motor imagery-based brain-computer interface learning using magnetoencephalography. Specifically, we employ a minimally constrained matrix decomposition method - non-negative matrix factorization - to simultaneously identify regularized, covarying subgraphs of functional connectivity, to assess their similarity to task performance, and to detect their time-varying expression.Main results: We find that learning is marked by diffuse brain-behavior relations: good learners displayed many subgraphs whose temporal expression tracked performance. Individuals also displayed marked variation in the spatial properties of subgraphs such as the connectivity between the frontal lobe and the rest of the brain, and in the temporal properties of subgraphs such as the stage of learning at which they reached maximum expression. From these observations, we posit a conceptual model in which certain subgraphs support learning by modulating brain activity in sensors near regions important for sustaining attention. To test this model, we use tools that stipulate regional dynamics on a networked system (network control theory), and find that good learners display a single subgraph whose temporal expression tracked performance and whose architecture supports easy modulation of sensors located near brain regions important for attention.Significance: The nature of our contribution to the neuroscience of BCI learning is therefore both computational and theoretical; we first use a minimally-constrained, individual specific method of identifying mesoscale structure in dynamic brain activity to show how global connectivity and interactions between distributed networks supports BCI learning, and then we use a formal network model of control to lend theoretical support to the hypothesis that these identified subgraphs are well suited to modulate attention.

Published
2020

23. Improving J-divergence of brain connectivity states by graph Laplacian denoising

Author

Fabrizio De Vico Fallani, G. Scarano, Danielle S. Bassett, Stefania Colonnese, Tiziana Cattai, Marie-Constance Corsi, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Dipartimento di Matematica 'Guido Castelnuovo' [Roma I] (Sapienza University of Rome), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome], Hospital of the University of Pennsylvania (HUP), Perelman School of Medicine, University of Pennsylvania [Philadelphia]-University of Pennsylvania [Philadelphia], Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA), University of Pennsylvania-University of Pennsylvania, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Signal Processing (eess.SP), Computer Networks and Communications, Computer science, Noise reduction, Interval (mathematics), Synthetic data, Jensen Divergence, Motor imagery, Laplacian denoising, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, Maximum likelihood detection, Divergence (statistics), ComputingMilieux_MISCELLANEOUS, Brain–computer interface, business.industry, Pattern recognition, Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, Signal Processing, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Neurons and Cognition (q-bio.NC), Artificial intelligence, brain-computer interfaces, maximum likelihood detection, Jensen divergence, Brain-computer interfaces, Laplacian matrix, business, Laplace operator, Information Systems
Abstract

Functional connectivity (FC) can be represented as a network, and is frequently used to better understand the neural underpinnings of complex tasks such as motor imagery (MI) detection in brain-computer interfaces (BCIs). However, errors in the estimation of connectivity can affect the detection performances. In this work, we address the problem of denoising common connectivity estimates to improve the detectability of different connectivity states. Specifically, we propose a denoising algorithm that acts on the network graph Laplacian, which leverages recent graph signal processing results. Further, we derive a novel formulation of the Jensen divergence for the denoised Laplacian under different states. Numerical simulations on synthetic data show that the denoising method improves the Jensen divergence of connectivity patterns corresponding to different task conditions. Furthermore, we apply the Laplacian denoising technique to brain networks estimated from real EEG data recorded during MI-BCI experiments. Using our novel formulation of the J-divergence, we are able to quantify the distance between the FC networks in the motor imagery and resting states, as well as to understand the contribution of each Laplacian variable to the total J-divergence between two states. Experimental results on real MI-BCI EEG data demonstrate that the Laplacian denoising improves the separation of motor imagery and resting mental states, and shortens the time interval required for connectivity estimation. We conclude that the approach shows promise for the robust detection of connectivity states while being appealing for implementation in real-time BCI applications., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Published
2020
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24. P3-219: EEG EVIDENCE OF COMPENSATORY MECHANISMS IN PRECLINICAL ALZHEIMER'S DISEASE

Author

Geoffroy Gagliardi, Delphine Oudiette, Federico Raimondo, Marie-Odile Habert, Sinead Gaubert, Marie-Constance Corsi, Jacobo D. Sitt, Hovagim Bakardjian, Fabrizio De Vico Fallani, Marion Houot, Bruno Dubois, Alzheimer’s Disease Neuroimaging Initiative, Bertrand Hermann, Lionel Naccache, and Stéphane Epelbaum

Subjects
medicine.diagnostic_test, Epidemiology, business.industry, Health Policy, Disease, Electroencephalography, Psychiatry and Mental health, Cellular and Molecular Neuroscience, Developmental Neuroscience, Medicine, Neurology (clinical), Geriatrics and Gerontology, business, Neuroscience
Published
2019

25. Combination of connectivity and spectral features for motor-imagery BCI

Author

Cattai, Tiziana, Colonnese, Stefania, Marie-Constance, Corsi, Bassett, Danielle S., Scarano, Gaetano, DE VICO FALLANI, Fabrizio, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Sorbonne Université (SU), Dipartimento di Ingegneria Elettronica e delle Telecomunicazioni (DIET), University of Naples Federico II = Università degli studi di Napoli Federico II, Department of Bioengineering [Philadelphia], University of Pennsylvania, Department of Physics and Astronomy [Philadelphia], Department of Psychiatry [Philadelphia], Santa Fe Institute, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Università degli studi di Napoli Federico II, University of Pennsylvania [Philadelphia], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, connectivity, EEG, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], BCI
Abstract

International audience; In brain-computer interfaces (BCI), the detection of different mental states is a key element. In Motor Imagery (MI)-based BCIs, the considered features typically rely on the power spectral density (PSD) of brain signals, but alternative features can be explored looking for better performance. One possibility is the integration of functional connectivity (FC). These features quantify the interactions between different brain areas and they could represent a valuable tool to detect differences between two mental conditions. Here, we investigated the behavior of coherence-based FC features and PSD features, alone and in combination. For a better comparison, we characterized the network centrality of each brain area by computing the weighted node degrees from the estimated FC networks. Our findings show that in both alpha and beta frequency bands, and for almost all the subjects, the fusion of FC network indices and PSD features give better performance. This preliminary results open the way to the use for network-based approaches in BCIs.

Published
2019

26. Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior

Author

Jennifer Stiso, Marie-Constance Corsi, Javier Omar Garcia, Jean M Vettel, Fabrizio De Vico Fallani, Timothy H. Lucas, and Danielle S Bassett

Subjects
bepress|Life Sciences|Neuroscience and Neurobiology, PsyArXiv|Neuroscience, PsyArXiv|Neuroscience|Computational Neuroscience, bepress|Life Sciences|Neuroscience and Neurobiology|Computational Neuroscience
Abstract

Motor imagery-based brain-computer interfaces (BCIs) use an individual’s ability to volitionally modulate localized brain activity, often as a therapy for motor dysfunction or to probe causal relations between brain activity and behavior. However, many individuals cannot learn to successfully modulate their brain activity, greatly limiting the efficacy of BCI for therapy and for basic scientific inquiry. Formal experiments designed to probe the nature of BCI learning have offered initial evidence that coherent activity across diverse cognitive systems is a hallmark of individuals who can successfully learn to control the BCI. However, little is known about how these distributed networks interact through time to support learning. Here, we address this gap in knowledge by constructing and applying a multimodal network approach to decipher brain-behavior relations in motor imagery-based brain-computer interface learning using magnetoencephalography. Specifically, we employ a minimally constrained matrix decomposition method -- non-negative matrix factorization -- to simultaneously identify regularized, covarying subgraphs of functional connectivity and behavior, and to detect the time-varying expression of each subgraph. We find that learning is marked by distributed brain-behavior relations: swifter learners displayed many subgraphs whose temporal expression tracked performance. Learners also displayed marked variation in the spatial properties of subgraphs such as the connectivity between the frontal lobe and the rest of the brain, and in the temporal properties of subgraphs such as the stage of learning at which they reached maximum expression. From these observations, we posit a conceptual model in which certain subgraphs support learning by modulating brain activity in networks important for sustaining attention. After formalizing the model in the framework of network control theory, we test the model and find that good learners display a single subgraph whose temporal expression tracked performance and whose architecture supports easy modulation of brain regions important for attention. The nature of our contribution to the neuroscience of BCI learning is therefore both computational and theoretical; we first use a minimally-constrained, individual specific method of identifying mesoscale structure in dynamic brain activity to show how global connectivity and interactions between distributed networks supports BCI learning, and then we use a formal network model of control to lend theoretical support to the hypothesis that these identified subgraphs are well suited to modulate attention.

Published
2019

27. Looking for neurophysiological correlates of brain-computer interface learning

Author

Marie-Constance Corsi, Mario Chavez, Denis Schwartz, Nathalie GEORGE, Laurent Hugueville, Ari Kahn, Sophie Dupont, Bassett, Danielle S., Fabrizio de Vico Fallani, Corsi, Marie-Constance, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), University of Pennsylvania, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), University of Pennsylvania [Philadelphia], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

28. Spatiotemporal neural correlates of brain-computer interface learning

Author

Marie-Constance Corsi, Denis Schwartz, Ari E. Khan, Nathalie George, Sophie Dupont, Mario Chavez, Laurent Hugueville, Fabrizio De Vico Fallani, and Danielle S. Bassett

Subjects
0303 health sciences, 03 medical and health sciences, Neural correlates of consciousness, 0302 clinical medicine, Computer science, Automaticity, Neurofeedback, Reinforcement, Neuroscience, 030217 neurology & neurosurgery, 030304 developmental biology, Brain–computer interface, Dreyfus model of skill acquisition
Abstract

Brain-computer interfaces have been largely developed to allow communication, control, and neurofeedback in human beings. Despite their great potential, BCIs perform inconsistently across individuals. Moreover, the neural processes activated by training that enable humans to achieve good control remain poorly understood. In this study, we show that BCI skill acquisition is paralleled by a progressive reinforcement of task-related activity and by the reduction of connectivity between regions beyond those primarily targeted during the experiments. Notably, these patterns of activity and connectivity reflect growing automaticity and predict future BCI performance. Altogether, our findings provide new insights in the neural mechanisms underlying BCI learning, which have implications for the use of this technology in a broad range of real-life applications.

Published
2018
Full Text
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29. EEG evidence of compensatory mechanisms in preclinical Alzheimer's disease

Author

Geoffroy Gagliardi, Delphine Oudiette, Federico Raimondo, Marie-Constance Corsi, Stéphane Epelbaum, Marie-Odile Habert, Sinead Gaubert, Lionel Naccache, Hovagim Bakardjian, Fabrizio De Vico Fallani, Bruno Dubois, Bertrand Hermann, Jacobo D. Sitt, Marion Houot, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [APHP]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de la Mémoire et de la Maladie d'Alzheimer [Paris] (IM2A), Sorbonne Université (SU), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut de la Mémoire et de la Maladie d'Alzheimer [CHU Pitié-Salpétriêre] (IM2A), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), and Epelbaum, Stéphane

Subjects
0301 basic medicine, Male, Amyloid, Prodromal Symptoms, Disease, Electroencephalography, 03 medical and health sciences, Functional connectivity, 0302 clinical medicine, Alzheimer Disease, Fluorodeoxyglucose F18, medicine, Humans, EEG, Longitudinal Studies, Neurodegeneration, Preclinical Alzheimer’s disease, Aged, Aged, 80 and over, [SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, Amyloid beta-Peptides, Aniline Compounds, medicine.diagnostic_test, business.industry, Case-control study, Amyloid load, medicine.disease, Brain Waves, Magnetic Resonance Imaging, 3. Good health, 030104 developmental biology, Case-Control Studies, Positron-Emission Tomography, Cohort, Nerve Degeneration, Disease Progression, Observational study, Ethylene Glycols, Female, Neurology (clinical), business, Neuroscience, 030217 neurology & neurosurgery, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, Biomarkers, Alzheimer's Disease Neuroimaging Initiative
Abstract

Early biomarkers are needed to identify individuals at high risk of preclinical Alzheimer’s disease and to better understand the pathophysiological processes of disease progression. Preclinical Alzheimer’s disease EEG changes would be non-invasive and cheap screening tools and could also help to predict future progression to clinical Alzheimer’s disease. However, the impact of amyloid-β deposition and neurodegeneration on EEG biomarkers needs to be elucidated. We included participants from the INSIGHT-preAD cohort, which is an ongoing single-centre multimodal observational study that was designed to identify risk factors and markers of progression to clinical Alzheimer’s disease in 318 cognitively normal individuals aged 70–85 years with a subjective memory complaint. We divided the subjects into four groups, according to their amyloid status (based on 18F-florbetapir PET) and neurodegeneration status (evidenced by 18F-fluorodeoxyglucose PET brain metabolism in Alzheimer’s disease signature regions). The first group was amyloid-positive and neurodegeneration-positive, which corresponds to stage 2 of preclinical Alzheimer’s disease. The second group was amyloid-positive and neurodegeneration-negative, which corresponds to stage 1 of preclinical Alzheimer’s disease. The third group was amyloid-negative and neurodegeneration-positive, which corresponds to ‘suspected non-Alzheimer’s pathophysiology’. The last group was the control group, defined by amyloid-negative and neurodegeneration-negative subjects. We analysed 314 baseline 256-channel high-density eyes closed 1-min resting state EEG recordings. EEG biomarkers included spectral measures, algorithmic complexity and functional connectivity assessed with a novel information-theoretic measure, weighted symbolic mutual information. The most prominent effects of neurodegeneration on EEG metrics were localized in frontocentral regions with an increase in high frequency oscillations (higher beta and gamma power) and a decrease in low frequency oscillations (lower delta power), higher spectral entropy, higher complexity and increased functional connectivity measured by weighted symbolic mutual information in theta band. Neurodegeneration was associated with a widespread increase of median spectral frequency. We found a non-linear relationship between amyloid burden and EEG metrics in neurodegeneration-positive subjects, either following a U-shape curve for delta power or an inverted U-shape curve for the other metrics, meaning that EEG patterns are modulated differently depending on the degree of amyloid burden. This finding suggests initial compensatory mechanisms that are overwhelmed for the highest amyloid load. Together, these results indicate that EEG metrics are useful biomarkers for the preclinical stage of Alzheimer’s disease.

Published
2018

30. Magnetoencephalography With Optically Pumped

Author

Etienne, Labyt, Marie-Constance, Corsi, William, Fourcault, Augustin, Palacios Laloy, Francis, Bertrand, Francois, Lenouvel, Gilles, Cauffet, Matthieu, Le Prado, Francois, Berger, and Sophie, Morales

Subjects
Male, Phantoms, Imaging, Evoked Potentials, Auditory, Temperature, Brain, Evoked Potentials, Visual, Humans, Magnetoencephalography, Signal Processing, Computer-Assisted, Equipment Design, Middle Aged, Helium, Algorithms
Abstract

In this paper, we present the first proof of concept confirming the possibility to record magnetoencephalographic (MEG) signals with optically pumped magnetometers (OPMs) based on the parametric resonance of

Published
2018

31. Characterization of mental states through node connectivity between brain signals

Author

Marie-Constance Corsi, Tiziana Cattai, Gaetano Scarano, Fabrizio De Vico Fallani, Stefania Colonnese, Danielle S. Bassett, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), Sorbonne Université (SU), Dipartimento di Ingegneria Elettronica e delle Telecomunicazioni (DIET), Università degli studi di Napoli Federico II, Department of Bioengineering [Philadelphia], University of Pennsylvania [Philadelphia], Department of Physics and Astronomy [Philadelphia], Department of Psychiatry [Philadelphia], Santa Fe Institute, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), University of Naples Federico II = Università degli studi di Napoli Federico II, and University of Pennsylvania

Subjects
Computer science, Feature extraction, 02 engineering and technology, Electroencephalography, 03 medical and health sciences, 0302 clinical medicine, Motor imagery, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, 0202 electrical engineering, electronic engineering, information engineering, medicine, Coherence (signal processing), [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], ComputingMilieux_MISCELLANEOUS, EEG, spectral coherence, imaginary coherence, weighted node degree, motor imagery, Signal processing, Quantitative Biology::Neurons and Cognition, medicine.diagnostic_test, business.industry, Node (networking), 020206 networking & telecommunications, Pattern recognition, Cognition, Neurophysiology, Graph (abstract data type), Artificial intelligence, business, 030217 neurology & neurosurgery, Coherence (physics)
Abstract

Discriminating mental states from brain signals is crucial for many applications in cognitive and clinical neuroscience. Most of the studies relied on the feature extraction from the activity of single brain areas, thus neglecting the potential contribution of their functional coupling, or connectivity. Here, we consider spectral coherence and imaginary coherence to infer brain connectivity networks from electroencephalographic (EEG) signals recorded during motor imagery and resting states in a group of healthy subjects. By using a graph theoretic approach, we then extract the weighted node degree from each network and evaluate its ability to discriminate the two mental states as a function of the number of available observations. The obtained results show that the features extracted from spectral coherence networks outperform those obtained from imaginary coherence in terms of significant difference, neurophysiological interpretation and reliability with fewer observations. Taken together, these findings suggest that graph algebraic descriptors of brain connectivity networks can be further explored to classify mental states.

Published
2018

32. Magnetocardiography measurements with $^4$He vector optically pumped magnetometers at room temperature

Author

William Fourcault, C Gobbo, F Lenouvel, G Vanzetto, Gilles Cauffet, S. Morales, François Berger, Etienne Labyt, F Alcouffe, François Bertrand, M. Le Prado, Marie-Constance Corsi, Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Laboratoire de Génie Electrique de Grenoble (G2ELab), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Radiopharmaceutiques biocliniques (LRB), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Pôle Thorax et Vaisseaux [CHU Grenoble], Centre Hospitalier Universitaire [Grenoble] (CHU), French National Research Agency ANR Carnot Funding, CHU Grenoble, and LABYT, Etienne

Subjects
Magnetometer, [SPI] Engineering Sciences [physics], 02 engineering and technology, 030204 cardiovascular system & hematology, Imaging phantom, law.invention, 03 medical and health sciences, QRS complex, [SPI]Engineering Sciences [physics], 0302 clinical medicine, Optics, Nuclear magnetic resonance, law, Optically Pumped Magnetometers (OPM), Radiology, Nuclear Medicine and imaging, Magnetocardiography (MCG), Physics, Radiological and Ultrasound Technology, Cardiac cycle, business.industry, 4 helium, 021001 nanoscience & nanotechnology, Gradiometer, Magnetic field, Parametric oscillator, room temperature, 0210 nano-technology, business, Magnetocardiography
Abstract

Accepted Manuscript.; International audience; In this paper, we present a proof of concept study which demonstrates for the first time the possibility to record magnetocardiography (MCG) signals with $^4$He vector optically-pumped magnetometers (OPM) operated in a gradiometer mode. Resulting from a compromise between sensitivity, size and operability in a clinical environment, the developed magnetometers are based on the parametric resonance of helium in zero magnetic field. Sensors are operated at room-temperature and provide a tri-axis vector measurement of the magnetic field. Measured sensitivity is around 210 fT/√Hz in the bandwidth [2 Hz; 300 Hz]. MCG signals from a phantom and two healthy subjects are successfully recorded. Human MCG data obtained with the OPMs are compared to reference electrocardiogram (ECG) recordings: similar heart rates, shapes of the main patterns of the cardiac cycle (P/T waves, QRS complex) and QRS widths are obtained with both techniques.

Published
2017

33. Magnétomètres à pompage optique à Hélium 4 : développement et preuve de concept en magnétocardiographie et en magnétoencéphalographie

Author

Marie-Constance Corsi, Clinatec - Centre de recherche biomédicale Edmond J.Safra (SCLIN), Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Centre Hospitalier Universitaire [Grenoble] (CHU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Laboratoire de Génie Electrique de Grenoble (G2ELab), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut Polytechnique de Grenoble - Grenoble Institute of Technology-Centre National de la Recherche Scientifique (CNRS), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Université Grenoble Alpes, Gilles Cauffet, Sophie Morales, Étienne Labyt, STAR, ABES, and Sophie Le Vot-Morales

Subjects
[SPI.OTHER]Engineering Sciences [physics]/Other, MEG, [SPI.OTHER] Engineering Sciences [physics]/Other, OPM, Hélium 4, Optically pumped magnetometer, Helium 4, MCG
Abstract

Magnetocardiography (MCG) and magnetoencephalography (MEG) are non-invasive techniques consisting in measuring respectively cardiac and brain magnetic fields. Despite their performance, the sensors currently used need a cryogenic cooling system which engenders technical and financial constraints. New cryogenic-free sensors have recently emerged: the OPMs (Optically-Pumped Magnetometers). Among them, vector 4He magnetometers developed by CEA-LETI which work at room-temperature. This thesis is focused on the development of 4He magnetometers dedicated to MCG and MEG.After having optimized the key-parameters of a first non-miniaturized prototype, a sensitivity inferior to 100 fT/sqrt(Hz) has been obtained along two axes. In order to meet biomedical constraints, a miniaturization of the device has been processed. In parallel, preclinical tests were carried out which have enabled us to design a gradiometer mode, a new packaging, and a magnetically isolated system. A noise analysis revealed that laser and HF discharge were the main sources of disturbance, and lead us to consider improvements such as a new detection mode. Eventually, a sensor, with a 1cm-sided cell, and an intrinsic sensitivity of 350 fT/√Hz has been developed.Then, device has been successfully tested in the frame of the MCG application from a healthy subject, preceded by a simulation study with a phantom which enables us to demonstrate its operability within a clinical environment. Moreover, we have proved the reproducibility of the measurements and the possibility to detect the main features of the cardiac cycle within a 30 s recording time. A specific optimization of the optical part has enabled us to obtain a 210 fT/sqrt(Hz) sensitivity between 3 and 300 Hz, suitable for the MEG application. After having tested our device with a phantom, three MEG experiments were performed with a healthy subject: auditory evoked field, visual evoked field and spontaneous activities have been detected. The obtained results form the first clinical proof of concept of the device for MCG and MEG applications., La magnétocardiographie (MCG) et la magnétoencéphalographie (MEG) sont deux techniques d'imagerie non-invasives mesurant respectivement les champs magnétiques cardiaques et cérébraux. Les dispositifs actuels utilisent des capteurs supraconducteurs de haute performance mais nécessitant un dispositif de refroidissement cryogénique, engendrant de fortes contraintes tant techniques que financières. Les magnétomètres à pompage optique (OPM) tendent à constituer une réelle alternative. Parmi eux figurent ceux développés au CEA-LETI, basés sur l'utilisation de l'hélium 4. Cette thèse a pour objectif de développer des magnétomètres vectoriels à 4He (fonctionnant à température ambiante) dédiés aux applications MCG et MEG.Après une optimisation des paramètres-clés d'un prototype non-miniaturisé préexistant, une sensibilité inférieure à 100 fT/sqrt(Hz) a pu être obtenue suivant deux axes. Afin de respecter les besoins spécifiques de la MCG et de la MEG une étape de miniaturisation a dû être menée et une architecture gradient-métrique a été mise en place. Parallèlement, des tests précliniques menés à Clinatec nous ont permis de concevoir un nouveau conditionnement du prototype, ainsi qu'un système réduisant les perturbations magnétiques. Une analyse des principales sources de bruit a révélé que les deux principaux contributeurs au bruit sont le laser et le système de décharge HF. Nous avons ainsi envisagé plusieurs pistes d'amélioration du niveau de bruit dont une nouvelle technique de détection. Le prototype issu de ces travaux comporte une pièce élémentaire (la cellule) d'un centimètre de côté, et présente une sensibilité intrinsèque de 350 fT/sqrt(Hz).Le dispositif a ensuite été testé avec succès dans le cadre de mesures MCG sur un sujet sain, précédées de tests sur fantôme ayant permis de prouver l'opérabilité de nos capteurs dans un environnement clinique. Par ailleurs, la reproductibilité des résultats ainsi que la possibilité de réduire à 30 s le temps d'acquisition des données ont pu être démontrées. Une optimisation spécifique de la partie optique du prototype a permis d'obtenir une sensibilité de l'ordre de 210 fT/sqrt(Hz) entre 3 et 300 Hz, compatible avec l'application MEG. Après des tests menés avec succès sur fantôme, trois séries d'essais ont été réalisées sur un sujet sain. Nous avons pu ainsi détecter des potentiels évoqués auditifs, visuels ainsi qu'une modulation de l'activité cérébrale spontanée sous l'effet de l'ouverture des paupières. L'ensemble des résultats obtenus constitue les premières preuves de concept cliniques du dispositif en MCG et MEG.

Published
2015

34. BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks

Author

Fabrizio De Vico Fallani, Sophie Dupont, Laurent Hugueville, Nathalie George, Denis Schwartz, Ari E. Kahn, Mario Chavez, Danielle S. Bassett, Marie-Constance Corsi, Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Sorbonne Université (SU), University of Pennsylvania, Service de Médecine Physique et de Réadaptation [CHU Pitié-Salpêtrière] (MPR), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), This work was partially supported by French program 'Investissements d’avenir' ANR-10-IAIHU-06, 'ANR-NIH CRCNS' ANR-15-NEUC-0006-02 and by NICHD 1R01HD086888-01., ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), Corsi, Marie-Constance, Institut de Neurosciences Translationnelles de Paris - - IHU-A-ICM2010 - ANR-10-IAHU-0006 - IAHU - VALID, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM), University of Pennsylvania [Philadelphia], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Service de médecine physique et de réadaptation [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)

Subjects
[SPI] Engineering Sciences [physics], Process (engineering), Computer science, [SDV]Life Sciences [q-bio], 0206 medical engineering, Biomedical Engineering, [MATH] Mathematics [math], 02 engineering and technology, Electroencephalography, Somatosensory system, Visual processing, 03 medical and health sciences, Cellular and Molecular Neuroscience, [SPI]Engineering Sciences [physics], 0302 clinical medicine, medicine, Humans, Learning, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [MATH]Mathematics [math], Associative property, Brain–computer interface, Modality (human–computer interaction), medicine.diagnostic_test, Working memory, Brain, Magnetoencephalography, 020601 biomedical engineering, [SDV] Life Sciences [q-bio], Brain-Computer Interfaces, Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Neurons and Cognition (q-bio.NC), Neuroscience, 030217 neurology & neurosurgery
Abstract

Brain-computer interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive closed-loop systems remains a learned skill that is difficult to develop for a non-negligible proportion of users. The involved learning process induces neural changes associated with a brain network reorganization that remains poorly understood. To address this inter-subject variability, we adopted a multilayer approach to integrate brain network properties from electroencephalographic (EEG) and magnetoencephalographic (MEG) data resulting from a four-session BCI training program followed by a group of healthy subjects. Our method gives access to the contribution of each layer to multilayer network that tends to be equal with time. We show that regardless the chosen modality, a progressive increase in the integration of somatosensory areas in the alpha band was paralleled by a decrease of the integration of visual processing and working memory areas in the beta band. Notably, only brain network properties in multilayer network correlated with future BCI scores in the alpha2 band: positively in somatosensory and decision-making related areas and negatively in associative areas. Our findings cast new light on neural processes underlying BCI training. Integrating multimodal brain network properties provides new information that correlates with behavioral performance and could be considered as a potential marker of BCI learning., Comment: This is the version of the article before editing, as submitted by an author to the Journal of Neural Engineering. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online athttp://iopscience.iop.org/article/10.1088/1741-2552/abef39

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35. Brain fingerprint is based on the aperiodic, scale-free, neuronal activity

Author

Pierpaolo Sorrentino, Emahnuel Troisi Lopez, Antonella Romano, Carmine Granata, Marie Constance Corsi, Giuseppe Sorrentino, and Viktor Jirsa

Subjects
Neuronal Avalanches, Brain Dynamics, Brain Differentiability, Transition Matrices, Magnetoencephalography, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Subject differentiation bears the possibility to individualize brain analyses. However, the nature of the processes generating subject-specific features remains unknown. Most of the current literature uses techniques that assume stationarity (e.g., Pearson's correlation), which might fail to capture the non-linear nature of brain activity. We hypothesize that non-linear perturbations (defined as neuronal avalanches in the context of critical dynamics) spread across the brain and carry subject-specific information, contributing the most to differentiability. To test this hypothesis, we compute the avalanche transition matrix (ATM) from source-reconstructed magnetoencephalographic data, as to characterize subject-specific fast dynamics. We perform differentiability analysis based on the ATMs, and compare the performance to that obtained using Pearson's correlation (which assumes stationarity). We demonstrate that selecting the moments and places where neuronal avalanches spread improves differentiation (P < 0.0001, permutation testing), despite the fact that most of the data (i.e., the linear part) are discarded. Our results show that the non-linear part of the brain signals carries most of the subject-specific information, thereby clarifying the nature of the processes that underlie individual differentiation. Borrowing from statistical mechanics, we provide a principled way to link emergent large-scale personalized activations to non-observable, microscopic processes.

Published
2023
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36. A questionnaire to identify the links between athletes’ profiles and cognitive training practice: Heading for personalised neurofeedback procedures

Author

Izac, Margaux, Rossignol, Eléa, Pillette, Léa, Guillot, Aymeric, Guillaud, Etienne, Rienzo, Franck Di, Michelet, Thomas, N’kaoua, Bernard, Jeunet-Kelway, Camille, Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Neurosciences cognitives et intégratives d'Aquitaine (INCIA), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-SFR Bordeaux Neurosciences-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), 3D interaction with virtual environments using body and mind (Hybrid), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-RÉALITÉ VIRTUELLE, HUMAINS VIRTUELS, INTERACTIONS ET ROBOTIQUE (IRISA-D5), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Laboratoire Interuniversitaire de Biologie de la Motricité (LIBM ), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Université Jean Monnet - Saint-Étienne (UJM)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Marie-Constance Corsi, Sylvain Chevallier, Camille Jeunet-Kelway, and Raphaëlle Roy

Subjects
[SCCO]Cognitive science, Brain-Computer Interface, [SCCO.NEUR]Cognitive science/Neuroscience, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Motor Imagery, Neurofeedback, Sport
Abstract

International audience; Motor imagery (MI) is widely used in sports, in particular for motor learning and anxiety management [1]. Indeed, athletes consistently report using MI for rehearsing technique, maintaining arousal or enhancing self-confidence [2]. However, optimal gains are limited by the fact that they receive no feedback and cannot assess their performance. This can have detrimental effects on their motivation to diligently practice MI. Therefore, using Neurofeedback (NF) appears to be an adapted solution, allowing athletes to know if they are correctly performing the task. This addition also makes it possible to objectify performance and quantify progression throughout the sessions. In fact, three recent meta-analysis testify that NF improves the ability to self-regulate brain activity and sport performance [3, 4, 5]. The number of papers using MI-NF protocols has increased in the past few years. However, their efficiency has been shown to depend on various factors such as expertise in the imagined task [6], personality traits [7], MI ability [8] or NF acceptability [9]. Some of these factors can be assessed using validated questionnaires [10,11,12] but items are generally focused on questioning the current use of MI or thoughts on NF. An issue, as we know that practice can sometimes differ from real needs and expectations. To our knowledge, no previous work has investigated sports field actors’ preferences with the aim of designing specific NF tools, perfectly fitted to their profile, goals and expectations. Therefore, based on pre-existing questionnaires, we will build and broadcast an online questionnaire for athletes where personality, MI use, NF acceptability and NF preferences will be assessed. This method will enable us to personalise MI-NF trainings to fit each athlete’s needs. A longitudinal study will then require that athletes follow a personalised procedure to investigate how it impacts their MI and NF performances as well as their acceptability levels.[1] Guillot, A., & Collet, C. (2008). Construction of the Motor Imagery Integrative Model in Sport : A review and theoretical investigation of motor imagery use. International Review of Sport and Exercise Psychology, 1.[2] Munroe, K. J., Giacobbi Jr., P. R., Hall, C., & Weinberg, R. (2000). The Four Ws of Imagery Use : Where, When, Why, and What. Sport Psychologist, 14(2), 119.[3] Mirifar, A., Beckmann, J., & Ehrlenspiel, F. (2017). Neurofeedback as supplementary training for optimizing athletes’ performance: A systematic review with implications for future research. Neuroscience & Biobehavioral Reviews, 75, 419 432. [4] Xiang, M.-Q., Hou, X.-H., Liao, B.-G., Liao, J.-W., & Hu, M. (2018). The effect of neurofeedback training for sport performance in athletes: A meta-analysis. Psychology of Sport and Exercise, 36, 114 122.[5] Gong, A., Gu, F., Nan, W., Qu, Y., Jiang, C., & Fu, Y. (2021). A Review of Neurofeedback Training for Improving Sport Performance From the Perspective of User Experience. Frontiers in Neuroscience, 15.[6] Kraeutner, S. N., McWhinney, S. R., Solomon, J. P., Dithurbide, L., & Boe, S. G. (2018). Experience modulates motor imagery-based brain activity. European Journal of Neuroscience, 47(10), 1221 1229.[7] Jeunet, C., N’Kaoua, B., Subramanian, S., Hachet, M., & Lotte, F. (2015). Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns. PLOS ONE, 10(12), e0143962.[8] Guillot, A., Collet, C., Nguyen, V. A., Malouin, F., Richards, C., & Doyon, J. (2008). Functional neuroanatomical networks associated with expertise in motor imagery. NeuroImage. [9] Morone, G., Pisotta, I., Pichiorri, F., Kleih, S., Paolucci, S., Molinari, M., Cincotti, F., Kübler, A., & Mattia, D. (2015). Proof of principle of a brain-computer interface approach to support poststroke arm rehabilitation in hospitalized patients: Design, acceptability, and usability. Archives of Physical Medicine and Rehabilitation, 96(3 Suppl), S71-78. [10] Hall, C. R., Rodgers, W. M., & Barr, K. A. (1990). The Use of Imagery by Athletes in Selected Sports. The Sport Psychologist, 4(1), 1 10.[11] Plaisant, O., Courtois, R., Réveillère, C., Mendelsohn, G. A., & John, O. P. (2010). Validation par analyse factorielle du Big Five Inventory français (BFI-Fr). Analyse convergente avec le NEO-PI-R. Annales Médico-psychologiques, revue psychiatrique, 168(2), 97 106. [12] Grevet, E., Forge, K., Tadiello, S., Izac, M., Amadieu, F., Brunel, L., Pillette, L., Py, J., Gasq, D., & Jeunet-Kelway, C. (2023). Modeling the acceptability of BCIs for motor rehabilitation after stroke: A large scale study on the general public. Frontiers in Neuroergonomics, 3.

Published
2023

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