Search

Your search keyword '"Fabrizio De Vico Fallani"' showing total 151 results
Start Over Author "Fabrizio De Vico Fallani"
151 results on '"Fabrizio De Vico Fallani"'

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
Full Text
View/download PDF

2. 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
Full Text
View/download PDF

3. Decreased brain network global efficiency after attachment memories retrieval in individuals with unresolved/disorganized attachment-related state of mind

Author

Chiara Massullo, Claudio Imperatori, Fabrizio De Vico Fallani, Rita B. Ardito, Mauro Adenzato, Luigia Palmiero, Giuseppe Alessio Carbone, and Benedetto Farina

Subjects
Medicine, Science
Abstract

Abstract The main aim of the study was to examine how brain network metrics change after retrieval of attachment memories in individuals with unresolved/disorganized (U/D) attachment-related state of mind and those with organized/resolved (O/R) state of mind. We focused on three main network metrics associated with integration and segregation: global (Eglob) efficiency for the first function, local (Eloc) efficiency and modularity for the second. We also examined assortativity and centrality metrics. Electroencephalography (EEG) recordings were performed before and after the Adult Attachment Interview (AAI) in a sample of 50 individuals previously assessed for parenting quality. Functional connectivity matrices were constructed by means of the exact Low-Resolution Electromagnetic Tomography (eLORETA) software and then imported into MATLAB to compute brain network metrics. Compared to individuals with O/R attachment-related state of mind, those with U/D show a significant decrease in beta Eglob after AAI. No statistically significant difference among groups emerged in Eloc and modularity metrics after AAI, neither in assortativity nor in betweenness centrality. These results may help to better understand the neurophysiological patterns underlying the disintegrative effects of retrieving traumatic attachment memories in individuals with disorganized state of mind in relation to attachment.

Published
2022
Full Text
View/download PDF

4. Vizaj-A free online interactive software for visualizing spatial networks.

Author

Thibault Rolland and Fabrizio De Vico Fallani

Subjects
Medicine, Science
Abstract

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

Published
2023
Full Text
View/download PDF

5. Stepwise target controllability identifies dysregulations of macrophage networks in multiple sclerosis

Author

Giulia Bassignana, Jennifer Fransson, Vincent Henry, Olivier Colliot, Violetta Zujovic, and Fabrizio De Vico Fallani

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

AbstractIdentifying the nodes able to drive the state of a network is crucial to understand, and eventually control, biological systems. Despite recent advances, such identification remains difficult because of the huge number of equivalent controllable configurations, even in relatively simple networks. Based on the evidence that in many applications it is essential to test the ability of individual nodes to control a specific target subset, we develop a fast and principled method to identify controllable driver-target configurations in sparse and directed networks. We demonstrate our approach on simulated networks and experimental gene networks to characterize macrophage dysregulation in human subjects with multiple sclerosis.

Published
2021
Full Text
View/download PDF

6. Disrupted core-periphery structure of multimodal brain networks in Alzheimer’s disease

Author

Jeremy Guillon, Mario Chavez, Federico Battiston, Yohan Attal, Valentina La Corte, Michel Thiebaut de Schotten, Bruno Dubois, Denis Schwartz, Olivier Colliot, and Fabrizio De Vico Fallani

Subjects
Neurodegenerative diseases, Brain connectivity, Multilayer network theory, MEG, DWI, fMRI, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

In Alzheimer’s disease (AD), the progressive atrophy leads to aberrant network reconfigurations both at structural and functional levels. In such network reorganization, the core and peripheral nodes appear to be crucial for the prediction of clinical outcome because of their ability to influence large-scale functional integration. However, the role of the different types of brain connectivity in such prediction still remains unclear. Using a multiplex network approach we integrated information from DWI, fMRI, and MEG brain connectivity to extract an enriched description of the core-periphery structure in a group of AD patients and age-matched controls. Globally, the regional coreness—that is, the probability of a region to be in the multiplex core—significantly decreased in AD patients as result of a random disconnection process initiated by the neurodegeneration. Locally, the most impacted areas were in the core of the network—including temporal, parietal, and occipital areas—while we reported compensatory increments for the peripheral regions in the sensorimotor system. Furthermore, these network changes significantly predicted the cognitive and memory impairment of patients. Taken together these results indicate that a more accurate description of neurodegenerative diseases can be obtained from the multimodal integration of neuroimaging-derived network data. Alzheimer’s disease includes a progressive destruction of axonal pathways leading to global network changes. While these changes affect both the anatomy and the function of the brain, a joint characterization of the impact on the nodes of the network is still lacking. By integrating information from multiple neuroimaging data, within a modern complex systems framework, we show that the nodes constituting the core of the brain network are the most impacted by the disconnection process. Furthermore, these network alterations significantly predict the cognitive and memory impairment of patients and represent potential biomarkers of disease progression. We posit that a more accurate description of neurodegenerative diseases can be obtained by analyzing and modeling brain networks derived from multimodal neuroimaging data.

Published
2019
Full Text
View/download PDF

7. 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
Full Text
View/download PDF

8. Editorial: Bridging Scales and Levels

Author

Emma K. Towlson and Fabrizio De Vico Fallani

Subjects
Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Network neuroscience strives to understand the networks of the brain on all spatiotemporal scales and levels of observation. Current experimental and theoretical capabilities are beginning to facilitate a more holistic perspective, uniting these networks. This focus feature, “Bridging Scales and Levels,” aims to document current research and looks to future progress towards this vision.

Published
2018
Full Text
View/download PDF

9. A Topological Criterion for Filtering Information in Complex Brain Networks.

Author

Fabrizio De Vico Fallani, Vito Latora, and Mario Chavez

Subjects
Biology (General), QH301-705.5
Abstract

In many biological systems, the network of interactions between the elements can only be inferred from experimental measurements. In neuroscience, non-invasive imaging tools are extensively used to derive either structural or functional brain networks in-vivo. As a result of the inference process, we obtain a matrix of values corresponding to a fully connected and weighted network. To turn this into a useful sparse network, thresholding is typically adopted to cancel a percentage of the weakest connections. The structural properties of the resulting network depend on how much of the inferred connectivity is eventually retained. However, how to objectively fix this threshold is still an open issue. We introduce a criterion, the efficiency cost optimization (ECO), to select a threshold based on the optimization of the trade-off between the efficiency of a network and its wiring cost. We prove analytically and we confirm through numerical simulations that the connection density maximizing this trade-off emphasizes the intrinsic properties of a given network, while preserving its sparsity. Moreover, this density threshold can be determined a-priori, since the number of connections to filter only depends on the network size according to a power-law. We validate this result on several brain networks, from micro- to macro-scales, obtained with different imaging modalities. Finally, we test the potential of ECO in discriminating brain states with respect to alternative filtering methods. ECO advances our ability to analyze and compare biological networks, inferred from experimental data, in a fast and principled way.

Published
2017
Full Text
View/download PDF

10. Quality Assessment of Single-Channel EEG for Wearable Devices

Author

Fanny Grosselin, Xavier Navarro-Sune, Alessia Vozzi, Katerina Pandremmenou, Fabrizio De Vico Fallani, Yohan Attal, and Mario Chavez

Subjects
electroencephalography (EEG), single-channel EEG, muscular artefacts, quality assessment, artefact detection, wearable systems, Chemical technology, TP1-1185
Abstract

The recent embedding of electroencephalographic (EEG) electrodes in wearable devices raises the problem of the quality of the data recorded in such uncontrolled environments. These recordings are often obtained with dry single-channel EEG devices, and may be contaminated by many sources of noise which can compromise the detection and characterization of the brain state studied. In this paper, we propose a classification-based approach to effectively quantify artefact contamination in EEG segments, and discriminate muscular artefacts. The performance of our method were assessed on different databases containing either artificially contaminated or real artefacts recorded with different type of sensors, including wet and dry EEG electrodes. Furthermore, the quality of unlabelled databases was evaluated. For all the studied databases, the proposed method is able to rapidly assess the quality of the EEG signals with an accuracy higher than 90%. The obtained performance suggests that our approach provide an efficient, fast and automated quality assessment of EEG signals from low-cost wearable devices typically composed of a dry single EEG channel.

Published
2019
Full Text
View/download PDF

11. Green Compressive Sampling Reconstruction in IoT Networks

Author

Stefania Colonnese, Mauro Biagi, Tiziana Cattai, Roberto Cusani, Fabrizio De Vico Fallani, and Gaetano Scarano

Subjects
IoT network, energy efficiency, compressed sensing (CS), CS recovery, sensor networks, Chemical technology, TP1-1185
Abstract

In this paper, we address the problem of green Compressed Sensing (CS) reconstruction within Internet of Things (IoT) networks, both in terms of computing architecture and reconstruction algorithms. The approach is novel since, unlike most of the literature dealing with energy efficient gathering of the CS measurements, we focus on the energy efficiency of the signal reconstruction stage given the CS measurements. As a first novel contribution, we present an analysis of the energy consumption within the IoT network under two computing architectures. In the first one, reconstruction takes place within the IoT network and the reconstructed data are encoded and transmitted out of the IoT network; in the second one, all the CS measurements are forwarded to off-network devices for reconstruction and storage, i.e., reconstruction is off-loaded. Our analysis shows that the two architectures significantly differ in terms of consumed energy, and it outlines a theoretically motivated criterion to select a green CS reconstruction computing architecture. Specifically, we present a suitable decision function to determine which architecture outperforms the other in terms of energy efficiency. The presented decision function depends on a few IoT network features, such as the network size, the sink connectivity, and other systems’ parameters. As a second novel contribution, we show how to overcome classical performance comparison of different CS reconstruction algorithms usually carried out w.r.t. the achieved accuracy. Specifically, we consider the consumed energy and analyze the energy vs. accuracy trade-off. The herein presented approach, jointly considering signal processing and IoT network issues, is a relevant contribution for designing green compressive sampling architectures in IoT networks.

Published
2018
Full Text
View/download PDF

12. Defecting or not defecting: how to 'read' human behavior during cooperative games by EEG measurements.

Author

Fabrizio De Vico Fallani, Vincenzo Nicosia, Roberta Sinatra, Laura Astolfi, Febo Cincotti, Donatella Mattia, Christopher Wilke, Alex Doud, Vito Latora, Bin He, and Fabio Babiloni

Subjects
Medicine, Science
Abstract

Understanding the neural mechanisms responsible for human social interactions is difficult, since the brain activities of two or more individuals have to be examined simultaneously and correlated with the observed social patterns. We introduce the concept of hyper-brain network, a connectivity pattern representing at once the information flow among the cortical regions of a single brain as well as the relations among the areas of two distinct brains. Graph analysis of hyper-brain networks constructed from the EEG scanning of 26 couples of individuals playing the Iterated Prisoner's Dilemma reveals the possibility to predict non-cooperative interactions during the decision-making phase. The hyper-brain networks of two-defector couples have significantly less inter-brain links and overall higher modularity--i.e., the tendency to form two separate subgraphs--than couples playing cooperative or tit-for-tat strategies. The decision to defect can be "read" in advance by evaluating the changes of connectivity pattern in the hyper-brain network.

Published
2010
Full Text
View/download PDF

40. 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

42. Network neuroscience for optimizing brain–computer interfaces

Author

Danielle S. Bassett, Fabrizio De Vico Fallani, 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), University of Pennsylvania [Philadelphia], Fabrizio De Vico Fallani acknowledges support by the ANR French program through the contractsANR-10-IAIHU-06 and ANR-15-NEUC-0006-02. Danielle S. Bassett acknowledges support from theJohn D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, theISI Foundation, and the NIH 1R01HD086888-01., 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), University of Pennsylvania, Fabrizio De Vico Fallani acknowledges support by the ANR French program through the contracts ANR-10-IAIHU-06 and ANR-15-NEUC-0006-02. Danielle S. Bassett acknowledges support from theJohn D. and Catherine T. MacArthur Foundation, the Alfred P. Sloan Foundation, theISI Foundation, and the NIH 1R01HD086888-01., and CCSD, Accord Elsevier

Subjects
Computer science, media_common.quotation_subject, Models, Neurological, General Physics and Astronomy, Network science, Interconnectivity, Field (computer science), 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence, Humans, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Brain connectivity, Neural plasticity, Function (engineering), 030304 developmental biology, media_common, Brain–computer interface, 0303 health sciences, Perspective (graphical), Neurosciences, Societal impact of nanotechnology, Neurofeedback, Graph theory, FOS: Biological sciences, Quantitative Biology - Neurons and Cognition, Brain-Computer Interfaces, Human-machine interaction, Neurons and Cognition (q-bio.NC), [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], General Agricultural and Biological Sciences, Neuroscience, 030217 neurology & neurosurgery, Natural language
Abstract

International audience; Human-machine interactions are being increasingly explored to create alternative waysof communication and to improve our daily life. Based on a classification of the user’sintention from the user’s underlying neural activity, brain-computer interfaces (BCIs)allow direct interactions with the external environment while bypassing the traditionaleffector of the musculoskeletal system. Despite the enormous potential of BCIs, thereare still a number of challenges that limit their societal impact, ranging from the correctdecoding of a human’s thoughts, to the application of effective learning strategies. Despiteseveral important engineering advances, the basic neuroscience behind these challengesremains poorly explored. Indeed, BCIs involve complex dynamic changes related toneural plasticity at a diverse range of spatiotemporal scales. One promising antidote tothis complexity lies in network science, which provides a natural language in which tomodel the organizational principles of brain architecture and function as manifest in itsinterconnectivity. Here, we briefly review the main limitations currently affecting BCIs,and we offer our perspective on how they can be addressed by means of network theoreticapproaches. We posit that the emerging field of network neuroscience will prove to bean effective tool to unlock human-machine interactions.

Published
2019

43. 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

44. Multi-atlas Multilayer Brain Networks, a new multimodal approach to neurodegenerative disease

Author

Vincent Le Du, Charley Presigny, Arabella Bouzigues, Valerie Godefroy, Benedicte Batrancourt, Richard Levy, Fabrizio De Vico Fallani, Raffaella Migliaccio, FRONTLAB: Fonctions et dysfonctions de systèmes frontaux [ICM Paris] (FRONTlab), 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)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), 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)-Sorbonne Université (SU)-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 [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), and 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)

Subjects
bvFTD, Coreness, Multilayer, Brain, Network, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Atlas
Abstract

International audience; Multilayer networks (MNs) constitute an elegant and insightful multidimensional or multimodal framework. Bimodal MNs made from brain functional and structural networks extracted from neuroimaging modalities commonly lay the ground for truly emergent multimodal analysis. Thus far, they are computed using the same atlas for both layers. However, different atlases are required for specific imaging modalities. Depending on which atlas is chosen for a specific modality, this can lead to information from the other modalities being compromised. In this paper, we propose a new way to build such networks using specific atlases suited to each modality. The new technique is based on the computation of spatial overlaps between regions from different parcellations used for each available modality. We generalized the multiplex core-periphery method used to distinguish core and peripheral brain regions to apply it to such MNs, and to evaluate the approach and compare it to previous versions. We applied this new method in behavioral variant frontotemporal dementia (bvFTD) patients and healthy controls. First, we chose two specific atlases, the AAL2 and Schaefer100-Yeo17, for our DWI and fMRI data respectively. Subsequently, we computed richness and coreness for each subject. Finally, we benchmarked our results to evaluate the technique. We obtained higher peaks of significance and Fishers Criterion than with the previous method in the conditions that replicates previous findings. This highlights the potential of our multi-atlas MNs as well as their usefulness in MN analysis.

Published
2021

45. 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

47. The impact of aging on human brain network target controllability

Author

Giulia Bassignana, Giordano Lacidogna, Paolo Bartolomeo, Olivier Colliot, Fabrizio De Vico Fallani, Institut National de la Santé et de la Recherche Médicale (INSERM), Università degli Studi di Roma Tor Vergata [Roma], 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), 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)-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), CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), The research leading to these results has received funding from the French government under management of Agence Nationale de la Recherche as part of the 'Investissements d’avenir' program, reference ANR-19-P3IA-0001 (PRAIRIE 3IA Institute) and reference ANR-10-IAIHU-06 (Agence Nationale de la Recherche-10-IA Institut Hospitalo-Universitaire-6), and from the Inria Project Lab Program (project Neuromarkers), the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (Grant Agreement No. 864729)., ANR-19-P3IA-0001,PRAIRIE,PaRis Artificial Intelligence Research InstitutE(2019), and European Project: 864729,H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC),BCINET(2020)

Subjects
Aging, Histology, General Neuroscience, [SCCO.NEUR]Cognitive science/Neuroscience, Brain, Magnetic Resonance Imaging, Stroke, Parietal Lobe, Quantitative Biology - Neurons and Cognition, FOS: Biological sciences, Connectome, Humans, Neurons and Cognition (q-bio.NC), Anatomy, [MATH]Mathematics [math], Aged
Abstract

International audience; Understanding how few distributed areas can steer large-scale brain activity is a fundamental question that has practical implications, which range from inducing specific patterns of behavior to counteracting disease. Recent endeavors based on network controllability provided fresh insights into the potential ability of single regions to influence whole brain dynamics through the underlying structural connectome. However, controlling the entire brain activity is often unfeasible and might not always be necessary. The question whether single areas can control specific target subsystems remains crucial, albeit still poorly explored. Furthermore, the structure of the brain network exhibits progressive changes across the lifespan, but little is known about the possible consequences in the controllability properties. To address these questions, we adopted a novel target controllability approach that quantifies the centrality of brain nodes in controlling specific target anatomo-functional systems. We then studied such target control centrality in human connectomes obtained from healthy individuals aged from 5 to 85. Main results showed that the sensorimotor system has a high influencing capacity, but it is difficult for other areas to influence it. Furthermore, we reported that target control centrality varies with age and that temporal-parietal regions, whose cortical thinning is crucial in dementia-related diseases, exhibit lower values in older people. By simulating targeted attacks, such as those occurring in focal stroke, we showed that the ipsilesional hemisphere is the most affected one regardless of the damaged area. Notably, such degradation in target control centrality was more evident in younger people, thus supporting early-vulnerability hypotheses after stroke.

Published
2021
Full Text
View/download PDF

48. 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

49. 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

50. Brainhack: Developing a culture of open, inclusive, community-driven neuroscience

Author

Selim Melvin Atay, Iska Moxon-Emre, Anders Eklund, Paula P. Brooks, Nandita Vijayakumar, Anibal Sólon Heinsfeld, Jean-Baptiste Poline, Yaroslav O. Halchenko, David Meunier, Daniela M. Hohmann, Martin Szinte, Arshitha Basavaraj, Andrija Štajduhar, Link Tejavibulya, Michael Dayan, Anisha Keshavan, Chao Jiang, Felix Hoffstaedter, Michael P. Milham, Davide Momi, Hua Xie, Ai Wern Chung, Georg Langs, Hao-Ting Wang, Xenia Kobeleva, Robert Oostenveld, Thomas G. Close, Lena Dorfschmidt, Jesse A. Brown, Serge Koudoro, J.P. Manzano-Patron, Isil P. Bilgin, Shreyas Fadnavis, Sylvain Takerkart, R. Cameron Craddock, Stephan Heunis, Scott Peltier, Kathryn Berluti, Hayli Spence, Pablo F. Damasceno, David H. O’Connor, Eleftherios Garyfallidis, Eugene P. Duff, Rudolph Pienaar, Nicolas Traut, AmanPreet Badhwar, Ali R. Khan, Marissa Laws, Abigail S. Greene, Richard A. I. Bethlehem, Angela R. Laird, Krista DeStasio, Ina Thome, Camille Maumet, Alexandru D. Iordan, P. Christiaan Klink, Damion V. Demeter, John A. Onofrey, Cassandra D. Gould van Praag, Jakub Vohryzek, Suzanne T. Witt, Aysha Motala, Valerie Hayot-Sasson, Geetika Gupta, Alexandre Rosa Franco, John W. VanMeter, Michel Thiebaut de Schotten, James M. Shine, Lucina Q. Uddin, Thomas S. Hartmann, Guillaume Flandin, Clara Moreau, Tomislav Lipic, Claire Bradley, Fernando A. Barrios, Daniel S. Margulies, R. Austin Benn, Sofie Van Den Bossche, Lydia Riedl, Xihe Xie, Christopher R. Madan, Roberto Toro, Viviana Siless, Fabrizio De Vico Fallani, Nikoloz Sirmpilatze, Emily Olafson, Anqi Qiu, Theresa W. Cheng, Valentina Borghesani, Sidhant Chopra, Claire Cury, Giorgio Marinato, Horea-Ioan Ioanas, Giorgia Cona, Michael Joseph, Angela Tam, Mathias Scharinger, Daniel A. Handwerker, Katherine L. Bottenhorn, Sina Mansour L, Kathryn L. Mills, Christopher J. Markiewicz, Elizabeth Levitis, Cyril Pernet, Stephanie J. Forkel, Agah Karakuzu, Edwina R Orchard, Sarah L. Dziura, Saige Rutherford, Kamalaker Dadi, Enrico Glerean, Desiree Lussier, Davide Poggiali, Molly Simmonite, Jason Kai, Jessica Flannery, Ting Xu, Jon Haitz Legarreta, Nasim Anousheh, Marco Bedini, Tristan Glatard, Thomas E. Nichols, Joscelin Rocha-Hidalgo, Erin W. Dickie, Dipankar Bachar, Malin Sandström, Xi-Nian Zuo, Pedro Pinheiro-Chagas, Laura C. Rice, Jakša Vukojević, Javier Gonzalez-Castillo, José C. García Alanis, Lorenzo Pasquini, Yu-Fang Yang, Matteo Mancini, Deena Shariq, Chao-Gan Yan, Laura Tomaz da Silva, César Caballero-Gaudes, Yasmine Bassil, Aurina Arnatkeviciute, Dustin Scheinost, Satrajit S. Ghosh, Gaël Varoquaux, Etienne Combrisson, Bramsh Qamar Chandio, Kelly Garner, Tiago Quendera, Patrick Friedrich, Shawn A. Rhoads, Roxane Licandro, Elizabeth DuPre, Aki Nikolaidis, Simon Schwab, Stephanie Noble, Guillaume Auzias, Daniel J. Lurie, Mahboobeh Parsapoor, Eneko Uruñuela, Andrew Doyle, Peer Herholz, Saampras Ganesan, Vincent Koppelmans, Corey Horien, Samir Das, Junaid S. Merchant, Siyuan Gao, Matheus Marcon, Nathalia Bianchini Esper, B.T. Thomas Yeo, Katja Heuer, Caroline O’Brien, Micaela Y. Chan, Sook-Lei Liew, Lindsay D. Oliver, Kirstie Whitaker, Christoph Vogelbacher, Dylan M. Nielson, Krisanne Litinas, Dorien C. Huijser, Pierre Bellec, R. Todd Constable, David N. Kennedy, Julia Sprenger, Lea-Theresa Mais, Oscar Esteban, Patrick J. Park, Patrick Callahan, Christopher R. Nolan, Johanna Bayer, Guillaume Dumas, Elise Bannier, Elizabeth A. McDevitt, Ariel Rokem, Samuel A. Nastase, Olivia W. Stanley, Ruggero Basanisi, Daniele Marinazzo, Gregory Kiar, Lisa Novello, Samuel Guay, John C. Flournoy, Stefano Moia, Kendra Oudyk, Fang-Cheng Yeh, Gustav Nilsonne, Thomas B. Shaw, Steve Wideman, Saskia Bollmann, Steffen Bollmann, Julia M. Huntenburg, Augusto Buchweitz, Matteo Visconti di Oleggio Castello, Dimitra Maoutsa, Lucy B. Whitmore, Catherine Alice Hahn, Antonino Vallesi, Remi Gau, Felipe Meneguzzi, Université Catholique de Louvain = Catholic University of Louvain (UCL), Yale University [New Haven], Max Planck Institute for Human Cognitive and Brain Sciences [Leipzig] (IMPNSC), Max-Planck-Gesellschaft, Florida International University [Miami] (FIU), University of Reading (UOR), University of Würzburg, Champalimaud Centre for the Unknown [Lisbon], University of Melbourne, University of Cambridge [UK] (CAM), Georgetown University [Washington] (GU), Philipps Universität Marburg = Philipps University of Marburg, Université de Montréal (UdeM), University College of London [London] (UCL), University of Sussex, Universiteit Gent = Ghent University (UGENT), German Research Center for Neurodegenerative Diseases - Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Université de Sherbrooke (UdeS), Middle East Technical University [Ankara] (METU), McGill University = Université McGill [Montréal, Canada], Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Universiteit Leiden, Karolinska Institutet [Stockholm], Princeton University, Universität Zürich [Zürich] = University of Zurich (UZH), University of the Basque Country/Euskal Herriko Unibertsitatea (UPV/EHU), University medical center and campus biotech Geneva, Emory University [Atlanta, GA], Cardiff University's Brain Research Imaging Centre [Cardiff] (CUBRIC), School of Psychology [Cardiff University], Cardiff University-Cardiff University, The University of Sydney, Weill Cornell Medicine [Cornell University], Cornell University [New York], Università degli Studi di Padova = University of Padua (Unipd), Jülich Research Centre, University of Texas at Austin [Austin], Institut Pasteur [Paris] (IP), Centre de Recherche Interdisciplinaire / Center for Research and Interdisciplinarity [Paris, France] (CRI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Basque Center on Cognition Brain and Language [Gipuzkoa, Espagne] (BCBL), Linköping University (LIU), University of Queensland [Brisbane], University of New South Wales [Sydney] (UNSW), Universidad Nacional Autónoma de México = National Autonomous University of Mexico (UNAM), University of Maryland [College Park], University of Maryland System, University of Washington [Seattle], Harvard Medical School [Boston] (HMS), Child Mind Institute, University of Western Ontario (UWO), Indiana University [Bloomington], Indiana University System, Monash university, Institut de Neurosciences de la Timone (INT), Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Neuroimagerie: méthodes et applications (Empenn), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), 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)-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), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), National Institute of Mental Health (NIMH), Università degli Studi di Trento (UNITN), Centro Nacional de Investigaciones Cardiovasculares Carlos III [Madrid, Spain] (CNIC), Instituto de Salud Carlos III [Madrid] (ISC), Queensland University of Technology [Brisbane] (QUT), University of California [San Francisco] (UC San Francisco), University of California (UC), Universidade Federal do Rio Grande do Sul [Porto Alegre] (UFRGS), University of Texas at Dallas [Richardson] (UT Dallas), University of Oregon [Eugene], Monash University [Melbourne], Boston Children's Hospital, 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 des Maladies Neurodégénératives [Bordeaux] (IMN), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Neuroimagerie: méthodes et applications (EMPENN), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAL, IMAGE ET LANGAGE (IRISA-D6), Chinese Academy of Sciences [Beijing] (CAS), Beijing Normal University (BNU), This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 867458 awarded to Julia M. Huntenburg, from ANR-19-DATA-0025 NeuroWebLab for Katja Heuer, Roberto Toro, and Nicholas Traut, and from NIH K00MH122372 for Stephanie Noble., Brainhack Community: Nasim Anousheh, Aurina Arnatkeviciute, Guillaume Auzias, Dipankar Bachar, Elise Bannier, Ruggero Basanisi, Arshitha Basavaraj, Marco Bedini, Pierre Bellec, R Austin Benn, Kathryn Berluti, Steffen Bollmann, Saskia Bollmann, Claire Bradley, Jesse Brown, Augusto Buchweitz, Patrick Callahan, Micaela Y Chan, Bramsh Q Chandio, Theresa Cheng, Sidhant Chopra, Ai Wern Chung, Thomas G Close, Etienne Combrisson, Giorgia Cona, R Todd Constable, Claire Cury, Kamalaker Dadi, Pablo F Damasceno, Samir Das, Fabrizio De Vico Fallani, Krista DeStasio, Erin W Dickie, Lena Dorfschmidt, Eugene P Duff, Elizabeth DuPre, Sarah Dziura, Nathalia B Esper, Oscar Esteban, Shreyas Fadnavis, Guillaume Flandin, Jessica E Flannery, John Flournoy, Stephanie J Forkel, Alexandre R Franco, Saampras Ganesan, Siyuan Gao, José C García Alanis, Eleftherios Garyfallidis, Tristan Glatard, Enrico Glerean, Javier Gonzalez-Castillo, Cassandra D Gould van Praag, Abigail S Greene, Geetika Gupta, Catherine Alice Hahn, Yaroslav O Halchenko, Daniel Handwerker, Thomas S Hartmann, Valérie Hayot-Sasson, Stephan Heunis, Felix Hoffstaedter, Daniela M Hohmann, Corey Horien, Horea-Ioan Ioanas, Alexandru Iordan, Chao Jiang, Michael Joseph, Jason Kai, Agah Karakuzu, David N Kennedy, Anisha Keshavan, Ali R Khan, Gregory Kiar, P Christiaan Klink, Vincent Koppelmans, Serge Koudoro, Angela R Laird, Georg Langs, Marissa Laws, Roxane Licandro, Sook-Lei Liew, Tomislav Lipic, Krisanne Litinas, Daniel J Lurie, Désirée Lussier, Christopher R Madan, Lea-Theresa Mais, Sina Mansour L, J P Manzano-Patron, Dimitra Maoutsa, Matheus Marcon, Daniel S Margulies, Giorgio Marinato, Daniele Marinazzo, Christopher J Markiewicz, Camille Maumet, Felipe Meneguzzi, David Meunier, Michael P Milham, Kathryn L Mills, Davide Momi, Clara A Moreau, Aysha Motala, Iska Moxon-Emre, Thomas E Nichols, Dylan M Nielson, Gustav Nilsonne, Lisa Novello, Caroline O'Brien, Emily Olafson, Lindsay D Oliver, John A Onofrey, Edwina R Orchard, Kendra Oudyk, Patrick J Park, Mahboobeh Parsapoor, Lorenzo Pasquini, Scott Peltier, Cyril R Pernet, Rudolph Pienaar, Pedro Pinheiro-Chagas, Jean-Baptiste Poline, Anqi Qiu, Tiago Quendera, Laura C Rice, Joscelin Rocha-Hidalgo, Saige Rutherford, Mathias Scharinger, Dustin Scheinost, Deena Shariq, Thomas B Shaw, Viviana Siless, Molly Simmonite, Nikoloz Sirmpilatze, Hayli Spence, Julia Sprenger, Andrija Stajduhar, Martin Szinte, Sylvain Takerkart, Angela Tam, Link Tejavibulya, Michel Thiebaut de Schotten, Ina Thome, Laura Tomaz da Silva, Nicolas Traut, Lucina Q Uddin, Antonino Vallesi, John W VanMeter, Nandita Vijayakumar, Matteo Visconti di Oleggio Castello, Jakub Vohryzek, Jakša Vukojević, Kirstie Jane Whitaker, Lucy Whitmore, Steve Wideman, Suzanne T Witt, Hua Xie, Ting Xu, Chao-Gan Yan, Fang-Cheng Yeh, B T Thomas Yeo, Xi-Nian Zuo, ANR-19-DATA-0025,NeuroWebLab,Un laboratoire de neuroscience collectif: Au delà de FAIR(2019), European Project: 867458,LC-FMR, Maumet, Camille, Un laboratoire de neuroscience collectif: Au delà de FAIR - - NeuroWebLab2019 - ANR-19-DATA-0025 - DONNEES - VALID, Brainhack: Developing a culture of open, inclusive, community-driven neuroscience - LC-FMR - 867458 - INCOMING, Leiden University, Weill Cornell Medicine [New York], Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPC), Universiteit Gent = Ghent University [Belgium] (UGENT), University Hospital Bonn, Sherbrooke Connectivity Imaging Lab [Sherbrooke] (SCIL), Département d'informatique [Sherbrooke] (UdeS), Faculté des sciences [Sherbrooke] (UdeS), Université de Sherbrooke (UdeS)-Université de Sherbrooke (UdeS)-Faculté des sciences [Sherbrooke] (UdeS), Université de Sherbrooke (UdeS)-Université de Sherbrooke (UdeS), Videos and Images Theory and Analytics Laboratory (VITAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Erasmus University Rotterdam, Origami (Origami), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Princeton Neuroscience Institute [Princeton], Human Neuroscience Platform, Brighton and Sussex Medical School (BSMS), Azienda Ospedale Università di Padova = Hospital-University of Padua (AOUP), Institute of Neuroscience and Medicine [Jülich] (INM-1), Département de Neuroscience - Department of Neuroscience, Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPC), Queensland Brain Institute, Universidad Nacional Autónoma de México (UNAM), Radboud university [Nijmegen], McGovern Institute for Brain Research [Cambridge], Massachusetts Institute of Technology (MIT), The Brainhack Community - https://pubmed.ncbi.nlm.nih.gov/33932337, Philipps University of Marburg, University of the Basque Country [Bizkaia] (UPV/EHU), Universita degli Studi di Padova, Philipps Universität Marburg, Institut Pasteur [Paris], Empenn, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), University of California [San Francisco] (UCSF), University of California, Geochemistry, and University of Padova [Padova, Italy]

Subjects
0301 basic medicine, Open science, Community building, Best practice, Neuroscience(all), Brainhack, Article, neuroscience, 03 medical and health sciences, 0302 clinical medicine, best practices, collaboration, community building, hackathon, inclusivity, open science, reproducibility, training, Congresses as Topic, Neurosciences, Practice Guidelines as Topic, Communication, Internet, Research community, Medical Bioscience, Psychology, ddc:610, Sociology, Brainhack Community, Training, Neurology & Neurosurgery, Scientific progress, organization & administration [Neurosciences], General Neuroscience, [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.NEUR] Cognitive science/Neuroscience, Medicinsk biovetenskap, 030104 developmental biology, [SDV.IB]Life Sciences [q-bio]/Bioengineering, Cognitive Sciences, Engineering ethics, 030217 neurology & neurosurgery
Abstract

Available online 30 April 2021. Brainhack is an innovative meeting format that promotes scientific collaboration and education in an open, inclusive environment. This NeuroView describes the myriad benefits for participants and the research community and how Brainhacks complement conventional formats to augment scientific progress. The present manuscript is part of a growing community effort to collate Brainhack-related insights and expertise into a Jupyter Book (http://brainhack.org/brainhack_jupyter_book/) that will serve as a centralized set of resources for the community; we acknowledge all the individuals who contributed and will make ongoing contributions to these resources. A pre-print version of the present manuscript is available as part of the Jupyter Book. Moreover, we would like to acknowledge all Brainhack organizers, supporters, presenters, and participants for their contribution to growing and maintaining this community. The benefits described in this manuscript would not be possible without them. We also thank all institutions, labs, and organizations who have helped this community grow, meet in stimulating environments, and add an excellent educational resource pool and agenda. With an expanding community, Brainhack’s support network keeps growing, and we thank all labs and individual researchers for their dedication and expertise offered to this community (see http://brainhack.org/brainhack_jupyter_book/acknowledgments.html for a full list of individual acknowledgments; an updated list will be maintained in the Jupyter Book). Grants and funding bodies: This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 867458 awarded to Julia M. Huntenburg; from ANR-19-DATA-0025 NeuroWebLab for Katja Heuer, Roberto Toro, and Nicholas Traut; and from NIH K00MH122372 for Stephanie Noble. The Brainhack Community member list and contributions of the different authors are detailed at http://brainhack.org/brainhack_jupyter_book/contributors.html. Our crediting system is described here: http://brainhack.org/brainhack_jupyter_book/neuroview_authorship-agreement.html.

Published
2021

Catalog

Books, media, physical & digital resources
See catalog results