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1. brainlife.io: a decentralized and open-source cloud platform to support neuroscience research

Author

Hayashi, Soichi, Caron, Bradley A., Heinsfeld, Anibal Sólon, Vinci-Booher, Sophia, McPherson, Brent, Bullock, Daniel N., Bertò, Giulia, Niso, Guiomar, Hanekamp, Sandra, Levitas, Daniel, Ray, Kimberly, MacKenzie, Anne, Avesani, Paolo, Kitchell, Lindsey, Leong, Josiah K., Nascimento-Silva, Filipi, Koudoro, Serge, Willis, Hanna, Jolly, Jasleen K., Pisner, Derek, Zuidema, Taylor R., Kurzawski, Jan W., Mikellidou, Kyriaki, Bussalb, Aurore, Chaumon, Maximilien, George, Nathalie, Rorden, Christopher, Victory, Conner, Bhatia, Dheeraj, Aydogan, Dogu Baran, Yeh, Fang-Cheng F., Delogu, Franco, Guaje, Javier, Veraart, Jelle, Fischer, Jeremy, Faskowitz, Joshua, Fabrega, Ricardo, Hunt, David, McKee, Shawn, Brown, Shawn T., Heyman, Stephanie, Iacovella, Vittorio, Mejia, Amanda F., Marinazzo, Daniele, Craddock, R. Cameron, Olivetti, Emanuale, Hanson, Jamie L., Garyfallidis, Eleftherios, Stanzione, Dan, Carson, James, Henschel, Robert, Hancock, David Y., Stewart, Craig A., Schnyer, David, Eke, Damian O., Poldrack, Russell A., Bollmann, Steffen, Stewart, Ashley, Bridge, Holly, Sani, Ilaria, Freiwald, Winrich A., Puce, Aina, Port, Nicholas L., and Pestilli, Franco

Published
2024
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2. brainlife.io: A decentralized and open source cloud platform to support neuroscience research

Author

Hayashi, Soichi, Caron, Bradley A., Heinsfeld, Anibal Sólon, Vinci-Booher, Sophia, McPherson, Brent, Bullock, Daniel N., Bertò, Giulia, Niso, Guiomar, Hanekamp, Sandra, Levitas, Daniel, Ray, Kimberly, MacKenzie, Anne, Kitchell, Lindsey, Leong, Josiah K., Nascimento-Silva, Filipi, Koudoro, Serge, Willis, Hanna, Jolly, Jasleen K., Pisner, Derek, Zuidema, Taylor R., Kurzawski, Jan W., Mikellidou, Kyriaki, Bussalb, Aurore, Rorden, Christopher, Victory, Conner, Bhatia, Dheeraj, Aydogan, Dogu Baran, Yeh, Fang-Cheng F., Delogu, Franco, Guaje, Javier, Veraart, Jelle, Bollman, Steffen, Stewart, Ashley, Fischer, Jeremy, Faskowitz, Joshua, Chaumon, Maximilien, Fabrega, Ricardo, Hunt, David, McKee, Shawn, Brown, Shawn T., Heyman, Stephanie, Iacovella, Vittorio, Mejia, Amanda F., Marinazzo, Daniele, Craddock, R. Cameron, Olivetti, Emanuele, Hanson, Jamie L., Avesani, Paolo, Garyfallidis, Eleftherios, Stanzione, Dan, Carson, James, Henschel, Robert, Hancock, David Y., Stewart, Craig A., Schnyer, David, Eke, Damian O., Poldrack, Russell A., George, Nathalie, Bridge, Holly, Sani, Ilaria, Freiwald, Winrich A., Puce, Aina, Port, Nicholas L., and Pestilli, Franco

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Quantitative Biology - Neurons and Cognition, Quantitative Biology - Quantitative Methods
Abstract

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR (Findable, Accessible, Interoperabile, and Reusable) data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.

Published
2023
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3. Author Correction: brainlife.io: a decentralized and open-source cloud platform to support neuroscience research

Author

Hayashi, Soichi, Caron, Bradley A., Heinsfeld, Anibal Sólon, Vinci-Booher, Sophia, McPherson, Brent, Bullock, Daniel N., Bertò, Giulia, Niso, Guiomar, Hanekamp, Sandra, Levitas, Daniel, Ray, Kimberly, MacKenzie, Anne, Avesani, Paolo, Kitchell, Lindsey, Leong, Josiah K., Nascimento-Silva, Filipi, Koudoro, Serge, Willis, Hanna, Jolly, Jasleen K., Pisner, Derek, Zuidema, Taylor R., Kurzawski, Jan W., Mikellidou, Kyriaki, Bussalb, Aurore, Chaumon, Maximilien, George, Nathalie, Rorden, Christopher, Victory, Conner, Bhatia, Dheeraj, Aydogan, Dogu Baran, Yeh, Fang-Cheng F., Delogu, Franco, Guaje, Javier, Veraart, Jelle, Fischer, Jeremy, Faskowitz, Joshua, Fabrega, Ricardo, Hunt, David, McKee, Shawn, Brown, Shawn T., Heyman, Stephanie, Iacovella, Vittorio, Mejia, Amanda F., Marinazzo, Daniele, Craddock, R. Cameron, Olivetti, Emanuale, Hanson, Jamie L., Garyfallidis, Eleftherios, Stanzione, Dan, Carson, James, Henschel, Robert, Hancock, David Y., Stewart, Craig A., Schnyer, David, Eke, Damian O., Poldrack, Russell A., Bollmann, Steffen, Stewart, Ashley, Bridge, Holly, Sani, Ilaria, Freiwald, Winrich A., Puce, Aina, Port, Nicholas L., and Pestilli, Franco

Published
2024
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6. BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks

Author

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

Subjects
Quantitative Biology - Neurons and Cognition
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

Published
2020
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10. Time course of EEG oscillations during creative problem solving

Author

Bieth, Théophile, Ovando Tellez, Marcela, Lopez-Persem, Alizée, Garcin, Béatrice, Hugueville, Laurent, Lehongre, Katia, George, Nathalie, and Volle, Emmanuelle

Subjects
cognitive science
Abstract

The Remote Associates Test is a creativity task that consists in finding a word that connects three unrelated words. We examined EEG power in healthy participants performing an adaptation of this task that allowed us to explore two mechanisms: remote semantic combination by varying the associative strength between the cue words and the solution, and the insight phenomenon reported by the subjects. Time-frequency analyses revealed that associative remoteness was associated with early synchronization in alpha and beta bands in laterotemporal and temporoparietal clusters and late frontal activity in the theta band just before the participants found the solution. Insight was associated with synchronization in alpha and gamma bands in inferotemporal clusters and a frontal synchronization in the theta band just preceded the response. Our findings provide new insights on the dynamic mechanisms involved in this verbal creativity task.

Published
2021

14. Basal Forebrain Volume, but Not Hippocampal Volume, Is a Predictor of Global Cognitive Decline in Patients With Alzheimer's Disease Treated With Cholinesterase Inhibitors

Author

Teipel, Stefan J, Cavedo, Enrica, Hampel, Harald, Grothe, Michel J, Initiative, Alzheimer's Disease Neuroimaging, Initiative, Alzheimer Precision Medicine, Aguilar, Lisi Flores, Babiloni, Claudio, Baldacci, Filippo, Benda, Norbert, Black, Keith L, Bokde, Arun LW, Bonuccelli, Ubaldo, Broich, Karl, Bun, René S, Cacciola, Francesco, Castrillo, Juan, Ceravolo, Roberto, chiesa, Patrizia A, Colliot, Olivier, Coman, Cristina-Maria, Corvol, Jean-Christophe, Cuello, Augusto Claudio, Cummings, Jeffrey L, Depypere, Herman, Dubois, Bruno, Duggento, Andrea, Durrleman, Stanley, Escott-price, Valentina, Federoff, Howard, Ferretti, Maria Teresa, Fiandaca, Massimo, Frank, Richard A, Garaci, Francesco, Genthon, Remy, George, Nathalie, Giorgi, Filippo S, Graziani, Manuela, Haberkamp, Marion, Habert, Marie-Odile, Herholz, Karl, Karran, Eric, Seung, H KIM, Koronyo, Yosef, Koronyo-Hamaoui, Maya, Lamari, Foudil, Langevin, Todd, Lehéricy, Stéphane, Lista, Simone, Lorenceau, Jean, Mapstone, Mark, Neri, Christian, Nisticò, Robert, Nyasse-Messene, Francis, O'Bryant, Sid E, Perry, George, Ritchie, Craig, Rojkova, Katrine, Rossi, Simone, Saidi, Amira, Santarnecchi, Emiliano, Schneider, Lon S, Sporns, Olaf, Toschi, Nicola, Verdooner, Steven R, Vergallo, Andrea, Villain, Nicolas, Welikovitch, Lindsay A, Woodcock, Janet, and Younesi, Erfan

Subjects
Biological Psychology, Psychology, Neurosciences, Brain Disorders, Dementia, Neurodegenerative, Clinical Research, Acquired Cognitive Impairment, Behavioral and Social Science, Alzheimer's Disease, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Aging, Neurological, Alzheimer's Disease Neuroimaging Initiative, Alzheimer Precision Medicine Initiative, MRI, basal forebrain, cholinergic treatment, executive function, hippocampus, memory, prediction, Clinical Sciences, Clinical sciences, Biological psychology
Abstract

Background: Predicting the progression of cognitive decline in Alzheimer's disease (AD) is important for treatment selection and patient counseling. Structural MRI markers such as hippocampus or basal forebrain volumes might represent useful instruments for the prediction of cognitive decline. The primary objective was to determine the predictive value of hippocampus and basal forebrain volumes for global and domain specific cognitive decline in AD dementia during cholinergic treatment. Methods: We used MRI and cognitive data from 124 patients with the clinical diagnosis of AD dementia, derived from the ADNI-1 cohort, who were on standard of care cholinesterase inhibitor treatment during a follow-up period between 0.4 and 3.1 years. We used linear mixed effects models with cognitive function as outcome to assess the main effects as well as two-way interactions between baseline volumes and time controlling for age, sex, and total intracranial volume. This model accounts for individual variation in follow-up times. Results: Basal forebrain volume, but not hippocampus volume, was a significant predictor of rates of global cognitive decline. Larger volumes were associated with smaller rates of cognitive decline. Left hippocampus volume had a modest association with rates of episodic memory decline. Baseline performance in global cognition and memory was significantly associated with hippocampus and basal forebrain volumes; in addition, basal forebrain volume was associated with baseline performance in executive function. Conclusions: Our findings indicate that in AD dementia patients, basal forebrain volume may be a useful marker to predict subsequent cognitive decline during cholinergic treatment.

Published
2018

19. The Memory Binding Test Detects Early Subtle Episodic Memory Decline in Preclinical Alzheimer's Disease: A Longitudinal Study.

Author

Rapos Pereira, Filipa, George, Nathalie, Dalla Barba, Gianfranco, Dubois, Bruno, and La Corte, Valentina

Subjects
*ALZHEIMER'S disease, *EPISODIC memory, *MEMORY testing, *RECOLLECTION (Psychology), *LAND title registration & transfer
Abstract

Background: The asymptomatic at-risk phase might be the optimal time-window to establish clinically meaningful endpoints in Alzheimer's disease (AD). Objective: We investigated whether, compared with the Free and Cued Selective Reminding Test (FCSRT), the Memory Binding Test (MBT) can anticipate the diagnosis of emergent subtle episodic memory (EM) deficits to an at-risk phase. Methods: Five-year longitudinal FCSRT and MBT scores from 45 individuals matched for age, education, and gender, were divided into 3 groups of 15 subjects: Aβ-/controls, Aβ+/stable, and Aβ+/progressors (preclinical-AD). The MBT adds an associative memory component (binding), particularly sensitive to subtle EM decline. Results: In the MBT, EM decline started in the Aβ+/progressors (preclinical-AD) up to 4 years prior to diagnosis in delayed free recall (FR), followed by decline in binding-associated scores 1 year later. Conversely, in the FCSRT, EM-decline began later, up to 3 years prior to diagnosis, in the same group on both immediate and delayed versions of FR, while on total recall (TR) and intrusions decline started only 1 year prior to diagnosis. Conclusions: The MBT seems more sensitive than the FCSRT for early EM-decline detection, regarding the year of diagnosis and the number of scores showing AD-linked EM deficits (associated with the AD-characteristic amnesic hippocampal syndrome). Considering the MBT as a detection tool of early subtle EM-decline in an asymptomatic at-risk phase, and the FCSRT as a classification tool of stages of EM-decline from a preclinical phase, these tests ought to potentially become complementary diagnostic tools that can foster therapies to delay cognitive decline. Clinical trial registration title: Electrophysiological markers of the progression to clinical Alzheimer disease in asymptomatic at-risk individuals: a longitudinal event-related potential study of episodic memory in the INSIGHT pre-AD cohort (acronym: ePARAD). [ABSTRACT FROM AUTHOR]

Published
2024
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21. Resting-state posterior alpha rhythms are abnormal in subjective memory complaint seniors with preclinical Alzheimer's neuropathology and high education level: the INSIGHT-preAD study

Author

Babiloni, Claudio, Lopez, Susanna, Del Percio, Claudio, Noce, Giuseppe, Pascarelli, Maria Teresa, Lizio, Roberta, Teipel, Stefan J., González-Escamilla, Gabriel, Bakardjian, Hovagim, George, Nathalie, Cavedo, Enrica, Lista, Simone, Chiesa, Patrizia Andrea, Vergallo, Andrea, Lemercier, Pablo, Spinelli, Giuseppe, Grothe, Michel J., Potier, Marie-Claude, Stocchi, Fabrizio, Ferri, Raffaele, Habert, Marie-Odile, Fraga, Francisco J., Dubois, Bruno, and Hampel, Harald

Published
2020
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27. Sex differences in functional and molecular neuroimaging biomarkers of Alzheimer's disease in cognitively normal older adults with subjective memory complaints

Author

Bakardjian, Hovagim, Benali, Habib, Bertin, Hugo, Bonheur, Joel, Boukadida, Laurie, Boukerrou, Nadia, Cavedo, Enrica, Chiesa, Patrizia, Colliot, Olivier, Dubois, Bruno, Dubois, Marion, Epelbaum, Stéphane, Gagliardi, Geoffroy, Genthon, Remy, Habert, Marie-Odile, Hampel, Harald, Houot, Marion, Kas, Aurélie, Lamari, Foudil, Levy, Marcel, Lista, Simone, Metzinger, Christiane, Mochel, Fanny, Nyasse, Francis, Poisson, Catherine, Potier, Marie-Claude, Revillon, Marie, Santos, Antonio, Andrade, Katia Santos, Sole, Marine, Surtee, Mohmed, de Schotten, Michel Thiebaud, Vergallo, Andrea, Younsi, Nadjia, Aguilar, Lisi Flores, Babiloni, Claudio, Baldacci, Filippo, Benda, Norbert, Black, Keith L., Bokde, Arun L.W., Bonuccelli, Ubaldo, Broich, Karl, Bun, René S., Cacciola, Francesco, Castrillo, Juan, Ceravolo, Roberto, Chiesa, Patrizia A., Coman, Cristina-Maria, Corvol, Jean-Christophe, Cuello, Augusto Claudio, Cummings, Jeffrey L., Depypere, Herman, Duggento, Andrea, Durrleman, Stanley, Escott-Price, Valentina, Federoff, Howard, Ferretti, Maria Teresa, Fiandaca, Massimo, Frank, Richard A., Garaci, Francesco, George, Nathalie, Giorgi, Filippo S., Graziani, Manuela, Haberkamp, Marion, Herholz, Karl, Karran, Eric, Kim, Seung H., Koronyo, Yosef, Koronyo-Hamaoui, Maya, Langevin, Todd, Lehéricy, Stéphane, Lorenceau, Jean, Mapstone, Mark, Neri, Christian, Nisticò, Robert, Nyasse-Messene, Francis, O'bryant, Sid E., Perry, George, Ritchie, Craig, Rojkova, Katrine, Rossi, Simone, Saidi, Amira, Santarnecchi, Emiliano, Schneider, Lon S., Sporns, Olaf, Toschi, Nicola, Verdooner, Steven R., Villain, Nicolas, Welikovitch, Lindsay A., Woodcock, Janet, Younesi, Erfan, Grothe, Michel J., and Teipel, Stefan J.

Published
2018
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28. Time course of EEG power during creative problem‐solving with insight or remote thinking.

Author

Bieth, Théophile, Ovando‐Tellez, Marcela, Lopez‐Persem, Alizée, Garcin, Béatrice, Hugueville, Laurent, Lehongre, Katia, Levy, Richard, George, Nathalie, and Volle, Emmanuelle

Subjects
PROBLEM solving, ELECTROENCEPHALOGRAPHY, LEXICAL access, WORD recognition
Abstract

Problem‐solving often requires creativity and is critical in everyday life. However, the neurocognitive mechanisms underlying creative problem‐solving remain poorly understood. Two mechanisms have been highlighted: the formation of new connections among problem elements and insight solving, characterized by sudden realization of a solution. In this study, we investigated EEG activity during a modified version of the remote associates test, a classical insight problem task that requires finding a word connecting three unrelated words. This allowed us to explore the brain correlates associated with the semantic remoteness of connections (by varying the remoteness of the solution word across trials) and with insight solving (identified as a Eurêka moment reported by the participants). Semantic remoteness was associated with power increase in the alpha band (8–12 Hz) in a left parieto‐temporal cluster, the beta band (13–30 Hz) in a right fronto‐temporal cluster in the early phase of the task, and the theta band (3–7 Hz) in a bilateral frontal cluster just prior to participants' responses. Insight solving was associated with power increase preceding participants' responses in the alpha and gamma (31–60 Hz) bands in a left temporal cluster and the theta band in a frontal cluster. Source reconstructions revealed the brain regions associated with these clusters. Overall, our findings shed new light on some of the mechanisms involved in creative problem‐solving. [ABSTRACT FROM AUTHOR]

Published
2024
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31. Using critical dynamics to capture processes underlying Brain-Computer Interface performance

Author

Corsi, Marie-Constance, Sorrentino, Pierpaolo, Schwartz, Denis P, George, Nathalie, Hugueville, Laurent, Kahn, Ari, Dupont, Sophie, Bassett, Danielle, Jirsa, Viktor, 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 de Neurosciences des Systèmes (INS), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), 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, and Société des Neurosciences

Subjects
[SCCO.NEUR]Cognitive science/Neuroscience, [NLIN]Nonlinear Sciences [physics]
Abstract

International audience; Despite being a promising tool for establishing direct communication and control from the brain over external effectors, non-invasive Brain-computer interface (BCI) systems yield suboptimal performance in ~30% of users. Measuring the dynamical features that are relevant to the execution of a task to improve BCI performance remains an open challenge. Functional imaging is dominated by aperiodic and scale-free perturbations called “Neuronal Avalanches”, spreading across the whole brain. The sequence of regions recruited by avalanches could convey the processes underpinning BCI performance. To test our hypothesis, we used source-reconstructed magnetoencephalography signals in a BCI framework, where 20 healthy subjects were compared in resting-state and while performing a motor imagery (MI) task, in order to track the dynamical features related to motor imagery. Each signal was z-scored over time. For each condition, we estimated the avalanche transition matrix (ATM), containing the probability that regions j would be active at time t+1, given region i was active at time t. We computed the difference between the ATMs obtained from the two conditions in each subject, and validated them via permutation analysis. Then, we correlated the difference of the probabilities to BCI performance. All the significantly different edges cluster upon the premotor areas, involved during the MI task, and the cunei, involved during visual processing. The differences in the probabilities associated with edges incident upon areas such as the right paracentral lobule and the caudal middle frontal bilaterally directly correlate with the BCI scores. Our results suggest that avalanches capture functionally-relevant processes which are of interest for alternative BCI designing. Future work will consist of investigating the use of avalanche transition matrices as potential alternative features for classification in BCI.

Published
2023

35. Informing motor imagery-based brain-computer interface via neuronal avalanches

Author

Sorrentino, Pierpaolo, Corsi, Marie-Constance, Schwartz, Denis P, George, Nathalie, Kahn, Ari, Dupont, Sophie, Bassett, Danielle, de Vico Fallani, Fabrizio, Jirsa, Viktor, Institut de Neurosciences des Systèmes (INS), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), 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, The authors acknowledge support from European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 864729), from the program 'Investissements d’avenir' ANR-10-IAIHU-06, from European Union’s Horizon 2020 research and innovation programme under grant agreement No. 945539 (SGA3) Human Brain Project, and from VirtualBrainCloud No.826421., ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), European Project: 945539,H2020,H2020-SGA-FETFLAG-HBP-2019,HBP SGA3(2020), European Project: 826421,TVB-Cloud (2018), and European Project: 864729,H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC),BCINET(2020)

Subjects
[SCCO.NEUR]Cognitive science/Neuroscience, [NLIN]Nonlinear Sciences [physics]
Abstract

International audience; Brain-Computer Interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive BCI systems remains a learned skill difficult to develop for a non-negligible proportion of users. Even though similarities have been shown between MI-based BCI learning and motor sequence learning our understanding of the dynamical processes, and their reflection on brain signals during BCI performance is still incomplete. In particular, whole-brain functional imaging is dominated by a 'bursty' dynamics, "neuronal avalanches", with fast, fat-tailed distributed, aperiodic perturbations spreading across the whole brain. Neuronal avalanches evolve over a manifold during resting-state, generating a rich functional connectivity dynamics. In this work, we evaluated to which extent neuronal avalanches can be used as a tool to differentiate mental states in the context of BCI experiments.

Published
2023

40. Exploiting brain critical dynamics to inform Brain-Computer Interfaces performance

Author

Corsi, Marie-Constance, Sorrentino, Pierpaolo, Schwartz, Denis P, George, Nathalie, Hugueville, Laurent, Kahn, Ari, Dupont, Sophie, Bassett, Danielle, Jirsa, Viktor, 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 de Neurosciences des Systèmes (INS), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Pennsylvania, European Project: 826421,TVB-Cloud (2018), European Project: 945539,H2020,H2020-SGA-FETFLAG-HBP-2019,HBP SGA3(2020), 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), and University of Pennsylvania [Philadelphia]

Subjects
[SCCO]Cognitive science, [INFO]Computer Science [cs], [MATH]Mathematics [math], ComputingMilieux_MISCELLANEOUS
Abstract

International audience; The reconfiguration of large-scale interactions among multiple brain regions is characterized by aperiodic perturbations, called “Neuronal Avalanches”, which can be tracked non-invasively as they expand across the brain. As such, learning a new task might affect the path of propagation of neuronal avalanches. Brain-Computer Interfaces (BCIs) constitute a promising tool for establishing direct communication and control from the brain over external effectors but mastering them remains a poorly-understood learned skill. Therefore, neuronal avalanche measures may constitute natural candidates to inform the underlying brain processes and their reflection on brain signals. To test this hypothesis, we used source-reconstructed magneto/electroencephalography, comparing resting-state to motor imagery conditions during a BCI protocol. For each experimental condition, we computed an individual avalanche transition matrix, to track the probability that an avalanche would spread across any two regions. We found a robust topography of the edges that were affected by the execution of the task, which mainly hinge upon the premotor regions. Finally, we related the individual differences to the task performance, showing that significant correlations are predominantly positive and involve edges connecting pre/motor regions to parietal ones. Our findings suggest that avalanches capture functionally-relevant processes crucial for alternative BCI designing.

Published
2022

41. A neuroscientific approach to increase gender equality

Author

Schreiweis, Christiane, Volle, Emmanuelle, Durr, Alexandra, Auffret, Alexandra, Delarasse, Cécile, George, Nathalie, Dumont, Magali, Hassan, Bassem A., Renier, Nicolas, Rosso, Charlotte, Thiebaut de Schotten, Michel, Burguière, Eric, and Zujovic, Violetta

Published
2019
Full Text
View/download PDF

42. Correction: Applicability of in vivo staging of regional amyloid burden in a cognitively normal cohort with subjective memory complaints: the INSIGHT-preAD study

Author

Sakr, Fatemah A, Grothe, Michel J, Teipel, Stefan, Perry, George, Ritchie, Craig, Rojkova, Katrine, Rossi, Simone, Saidi, Amira, Santarnecchi, Emiliano, Schneider, Lon S, Sporns, Olaf, Toschi, Nicola, Verdooner, Steven R, Dubois, Bruno, Vergallo, Andrea, Villain, Nicolas, Welikovitch, Lindsay A, Woodcock, Janet, Younesi, Erfan, Hampel, Harald, group, INSIGHT-preAD study, Initiative, Alzheimer Precision Medicine, Bakardjian, Hovagim, Benali, Habib, Bertin, Hugo, Bonheur, Joel, Boukadida, Laurie, Cavedo, Enrica, Boukerrou, Nadia, Chiesa, Patrizia, Colliot, Olivier, Dubois, Marion, Epelbaum, Stéphane, Gagliardi, Geoffroy, Genthon, Remy, Habert, Marie-Odile, Jelistratova, Irina, Houot, Marion, Kas, Aurélie, Lamari, Foudil, Levy, Marcel, Lista, Simone, Metzinger, Christiane, Mochel, Fanny, Nyasse, Francis, Poisson, Catherine, Potier, Marie-Claude, Revillon, Marie, Santos, Antonio, Andrade, Katia Santos, Sole, Marine, Surtee, Mohmed, de Schotten, Michel Thiebaud, Younsi, Nadjia, Aguilar, Lisi Flores, Dyrba, Martin, Babiloni, Claudio, Baldacci, Filippo, Benda, Norbert, Black, Keith L, Bokde, Arun L W, Bonuccelli, Ubaldo, Broich, Karl, Bun, René S, Cacciola, Francesco, Castrillo, Juan, Gonzalez-Escamilla, Gabriel, Ceravolo, Roberto, Chiesa, Patrizia A, Coman, Cristina-Maria, Corvol, Jean-Christophe, Cuello, Augusto Claudio, Cummings, Jeffrey L, Depypere, Herman, Duggento, Andrea, Durrleman, Stanley, Escott-Price, Valentina, Federoff, Howard, Ferretti, Maria Teresa, Fiandaca, Massimo, Frank, Richard A, Garaci, Francesco, George, Nathalie, Locatelli, Maxime, Giorgi, Filippo S, Graziani, Manuela, Haberkamp, Marion, Herholz, Karl, Karran, Eric, Kim, Seung H, Koronyo, Yosef, Koronyo-Hamaoui, Maya, Lehericy, Stephane, Langevin, Todd, Lehéricy, Stéphane, Lorenceau, Jean, Mapstone, Mark, Neri, Christian, Nisticò, Robert, Nyasse-Messene, Francis, and O'bryant, Sid E

Subjects
Neurology, Cognitive Neuroscience, ddc:610, Neurology (clinical)
Published
2022

43. Functional connectivity predicts MI-based BCI learning

Author

Corsi, Marie-Constance, Chavez, Mario, Schwartz, Denis, George, Nathalie, Hugueville, Laurent, Kahn, Ari E., Dupont, Sophie, Bassett, Danielle, 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), University of Pennsylvania, This work was partially supported by the 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), ANR-15-NEUC-0006,NETBCI,US-France Research Proposal- Modeling and Predicting Brain-Computer Interface Learning from Dynamic Networks(2015), Corsi, Marie-Constance, Institut de Neurosciences Translationnelles de Paris - - IHU-A-ICM2010 - ANR-10-IAHU-0006 - IAHU - VALID, and US-France Research Proposal- Modeling and Predicting Brain-Computer Interface Learning from Dynamic Networks - - NETBCI2015 - ANR-15-NEUC-0006 - CRCNS - VALID

Subjects
[SDV] Life Sciences [q-bio], [SPI]Engineering Sciences [physics], [SPI] Engineering Sciences [physics], [SDV]Life Sciences [q-bio], [MATH] Mathematics [math], [MATH]Mathematics [math]
Abstract

International audience; Non-invasive Brain-Computer Interfaces (BCIs) can exploit the ability of subjects to voluntary modulate their brain activity through mental imagery. Despite its clinical applications, controlling a BCI appears to be a learned skill that requires several weeks to reach relatively high-performance in control, without being sufficient for 15 to 30 % of the users. This gap has motivated a deeper understanding of mechanisms associated with motor imagery (MI) tasks. Here, we investigated dynamical changes in terms of cortical activations and network recruitment. We hypothesized that the associated characteristics would be able to predict the success of learning.

Published
2022

44. Exploiting brain critical dynamics to inform Brain-Computer Interfaces

Author

Sorrentino, Pierpaolo, Corsi, Marie-Constance, Schwartz, Denis P, George, Nathalie, Hugueville, Laurent, Kahn, Ari, Dupont, Sophie, Bassett, Danielle, de Vico Fallani, Fabrizio, Jirsa, Viktor, Institut de Neurosciences des Systèmes (INS), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), 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, European Project: 864729,H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC),BCINET(2020), European Project: 826421,TVB-Cloud (2018), European Project: 945539,H2020,H2020-SGA-FETFLAG-HBP-2019,HBP SGA3(2020), Corsi, Marie-Constance, Non-invasive decoding of brain communication patterns to ease motor restoration after stroke - BCINET - - H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)2020-10-01 - 2025-09-30 - 864729 - VALID, VirtualBrainCloud - TVB-Cloud - 2018-12-01 - 2022-11-30 - 826421 - VALID, and Human Brain Project Specific Grant Agreement 3 - HBP SGA3 - - H20202020-01-01 - 2023-09-30 - 945539 - VALID

Subjects
[SCCO]Cognitive science, [INFO]Computer Science [cs], [MATH] Mathematics [math], [SCCO] Cognitive science, [MATH]Mathematics [math], [INFO] Computer Science [cs]
Abstract

International audience; Brain-Computer Interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive BCI systems remains a learned skill difficult to develop for a non-negligible proportion of users. Even though similarities have been shown between MI-based BCI learning and motor sequence learning our understanding of the dynamical processes, and their reflection on brain signals during BCI performance is still incomplete. In particular, whole-brain functional imaging is dominated by a ‘bursty’ dynamics, “neuronal avalanches”, with fast, fat-tailed distributed, aperiodic perturbations spreading across the whole brain. Neuronal avalanches evolve over a manifold during resting-state, generating a rich functionalconnectivity dynamics. In this work, we evaluated to which extent neuronal avalanches can be used as a tool to differentiate mental states in the context of BCI experiments.

Published
2022

45. M/EEG networks integration to elicit patters of motor imagery-based Brain-Computer Interface (BCI) training

Author

Corsi, Marie-Constance, Chavez, Mario, Schwartz, Denis P, George, Nathalie, Hugueville, Laurent, Kahn, Ari, Dupont, Sophie, Bassett, Danielle, 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), University of Pennsylvania, This work was partially supported by the program 'Investissements d'avenir' ANR-10-IAIHU-06, 'ANR-NIH CRCNS' ANR-15-NEUC-0006-02 and by NICHD 1R01HD086888-01., ANR-15-NEUC-0006,NETBCI,US-France Research Proposal- Modeling and Predicting Brain-Computer Interface Learning from Dynamic Networks(2015), ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), Corsi, Marie-Constance, US-France Research Proposal- Modeling and Predicting Brain-Computer Interface Learning from Dynamic Networks - - NETBCI2015 - ANR-15-NEUC-0006 - CRCNS - VALID, and Institut de Neurosciences Translationnelles de Paris - - IHU-A-ICM2010 - ANR-10-IAHU-0006 - IAHU - VALID

Subjects
[SCCO]Cognitive science, [INFO]Computer Science [cs], [MATH] Mathematics [math], [SCCO] Cognitive science, [INFO] Computer Science [cs], [MATH]Mathematics [math]
Abstract

International audience; Non-invasive Brain-Computer Interfaces (BCIs) can exploit the ability of subjects to voluntary modulate their brain activity through mental imagery. Despite its clinical applications, controlling a BCI appears to be a learned skill that requires several weeks to reach relatively high-performance in control, without being sufficient for 15 to 30 % of the users [1]. This gap has motivated a deeper understanding of mechanisms associated with motor imagery (MI) tasks. Here, we investigated dynamical changes in multimodal network recruitment. We hypothesized that integrating information from EEG and MEG data, show a better description of the core-periphery changes occurring during a motor imagery-based BCI training. Such an enriched description could reveal fresh insights into learning processes that are difficult to observe at the signle layer level and eventually improve the prediction of future BCI performance.multimodal brain network properties could be considered as a potential marker of BCI learning.

Published
2022

47. M/EEG networks integration to elicit patterns of motor imagery-based BCI training

Author

Corsi, Marie-Constance, Chavez, Mario, Schwartz, Denis P, George, Nathalie, Hugueville, Laurent, Kahn, Ari, Dupont, Sophie, Bassett, Danielle, 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), University of Pennsylvania, This work was partially supported by the program 'Investissements d'avenir' ANR-10-IAIHU-06, 'ANR-NIH CRCNS' ANR-15-NEUC-0006-02 and by NICHD 1R01HD086888-01., ANR-15-NEUC-0006,NETBCI,US-France Research Proposal- Modeling and Predicting Brain-Computer Interface Learning from Dynamic Networks(2015), Corsi, Marie-Constance, and US-France Research Proposal- Modeling and Predicting Brain-Computer Interface Learning from Dynamic Networks - - NETBCI2015 - ANR-15-NEUC-0006 - CRCNS - VALID

Subjects
[SCCO]Cognitive science, [INFO]Computer Science [cs], [MATH] Mathematics [math], [SCCO] Cognitive science, [INFO] Computer Science [cs], [MATH]Mathematics [math]
Abstract

International audience; Non-invasive Brain-Computer Interfaces (BCIs) can exploit the ability of subjects to voluntary modulate their brain activity through mental imagery. Despite its clinical applications, controlling a BCI appears to be a learned skill that requires several weeks to reach relatively high-performance in control, without being sufficient for 15 to 30 % of the users [1]. This gap has motivated a deeper understanding of mechanisms associated with motor imagery (MI) tasks. Here, we investigated dynamical changes in multimodal network recruitment. We hypothesized that integrating information from EEG and MEG data, show a better description of the core-periphery changes occurring during a motor imagery-based BCI training. Such an enriched description could reveal fresh insights into learning processes that are difficult to observe at the signle layer level and eventually improve the prediction of future BCI performance.multimodal brain network properties could be considered as a potential marker of BCI learning.

Published
2022

48. Neuronal avalanches differentiate resting-state and task conditions in Brain-Computer Interfaces

Author

Corsi, Marie-Constance, Sorrentino, Pierpaolo, Schwartz, Denis P, George, Nathalie, Hugueville, Laurent, Kahn, Ari, Dupont, Sophie, Bassett, Danielle, Jirsa, Viktor, 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 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), Institut de Neurosciences des Systèmes (INS), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Princeton Neuroscience Institute [Princeton], 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), 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), and University of Pennsylvania

Subjects
[SCCO]Cognitive science, [INFO]Computer Science [cs], [MATH]Mathematics [math], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2022

49. Measuring Neuronal Avalanches to inform Brain-Computer Interfaces

Author

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

Published
2022
Full Text
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