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Bhavna K, Akhter A, Banerjee R, Roy D. Explainable deep-learning framework: decoding brain states and prediction of individual performance in false-belief task at early childhood stage. Front Neuroinform 2024; 18:1392661. [PMID: 39006894 PMCID: PMC11239353 DOI: 10.3389/fninf.2024.1392661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Accepted: 06/14/2024] [Indexed: 07/16/2024] Open
Abstract
Decoding of cognitive states aims to identify individuals' brain states and brain fingerprints to predict behavior. Deep learning provides an important platform for analyzing brain signals at different developmental stages to understand brain dynamics. Due to their internal architecture and feature extraction techniques, existing machine-learning and deep-learning approaches are suffering from low classification performance and explainability issues that must be improved. In the current study, we hypothesized that even at the early childhood stage (as early as 3-years), connectivity between brain regions could decode brain states and predict behavioral performance in false-belief tasks. To this end, we proposed an explainable deep learning framework to decode brain states (Theory of Mind and Pain states) and predict individual performance on ToM-related false-belief tasks in a developmental dataset. We proposed an explainable spatiotemporal connectivity-based Graph Convolutional Neural Network (Ex-stGCNN) model for decoding brain states. Here, we consider a developmental dataset, N = 155 (122 children; 3-12 yrs and 33 adults; 18-39 yrs), in which participants watched a short, soundless animated movie, shown to activate Theory-of-Mind (ToM) and pain networs. After scanning, the participants underwent a ToM-related false-belief task, leading to categorization into the pass, fail, and inconsistent groups based on performance. We trained our proposed model using Functional Connectivity (FC) and Inter-Subject Functional Correlations (ISFC) matrices separately. We observed that the stimulus-driven feature set (ISFC) could capture ToM and Pain brain states more accurately with an average accuracy of 94%, whereas it achieved 85% accuracy using FC matrices. We also validated our results using five-fold cross-validation and achieved an average accuracy of 92%. Besides this study, we applied the SHapley Additive exPlanations (SHAP) approach to identify brain fingerprints that contributed the most to predictions. We hypothesized that ToM network brain connectivity could predict individual performance on false-belief tasks. We proposed an Explainable Convolutional Variational Auto-Encoder (Ex-Convolutional VAE) model to predict individual performance on false-belief tasks and trained the model using FC and ISFC matrices separately. ISFC matrices again outperformed the FC matrices in prediction of individual performance. We achieved 93.5% accuracy with an F1-score of 0.94 using ISFC matrices and achieved 90% accuracy with an F1-score of 0.91 using FC matrices.
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Affiliation(s)
- Km Bhavna
- Department of Computer Science and Engineering, IIT Jodhpur, Karwar, Rajasthan, India
| | - Azman Akhter
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, Gurugram, India
| | - Romi Banerjee
- Department of Computer Science and Engineering, IIT Jodhpur, Karwar, Rajasthan, India
| | - Dipanjan Roy
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, Gurugram, India
- School of AIDE, Center for Brain Science and Applications, Indian Institute of Technology (IIT), Jodhpur, India
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Pinho AL, Richard H, Ponce AF, Eickenberg M, Amadon A, Dohmatob E, Denghien I, Torre JJ, Shankar S, Aggarwal H, Thual A, Chapalain T, Ginisty C, Becuwe-Desmidt S, Roger S, Lecomte Y, Berland V, Laurier L, Joly-Testault V, Médiouni-Cloarec G, Doublé C, Martins B, Varoquaux G, Dehaene S, Hertz-Pannier L, Thirion B. Individual Brain Charting dataset extension, third release for movie watching and retinotopy data. Sci Data 2024; 11:590. [PMID: 38839770 PMCID: PMC11153490 DOI: 10.1038/s41597-024-03390-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 05/20/2024] [Indexed: 06/07/2024] Open
Abstract
The Individual Brain Charting (IBC) is a multi-task functional Magnetic Resonance Imaging dataset acquired at high spatial-resolution and dedicated to the cognitive mapping of the human brain. It consists in the deep phenotyping of twelve individuals, covering a broad range of psychological domains suitable for functional-atlasing applications. Here, we present the inclusion of task data from both naturalistic stimuli and trial-based designs, to uncover structures of brain activation. We rely on the Fast Shared Response Model (FastSRM) to provide a data-driven solution for modelling naturalistic stimuli, typically containing many features. We show that data from left-out runs can be reconstructed using FastSRM, enabling the extraction of networks from the visual, auditory and language systems. We also present the topographic organization of the visual system through retinotopy. In total, six new tasks were added to IBC, wherein four trial-based retinotopic tasks contributed with a mapping of the visual field to the cortex. IBC is open access: source plus derivatives imaging data and meta-data are available in public repositories.
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Affiliation(s)
- Ana Luísa Pinho
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France.
- Department of Computer Science, Western University, London, Ontario, Canada.
- Western Centre for Brain and Mind, Western University, London, Ontario, Canada.
| | - Hugo Richard
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
- Criteo AI Labs, Paris, France
- FAIRPLAY - IA coopérative: équité, vie privée, incitations, Paris, France
| | | | - Michael Eickenberg
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
- Flatiron Institute, New York, USA
| | - Alexis Amadon
- Université Paris-Saclay, CEA, CNRS, BAOBAB, NeuroSpin, 91191, Gif-sur-Yvette, France
| | - Elvis Dohmatob
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
- Meta FAIR, Paris, France
| | - Isabelle Denghien
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, 91191, Gif-sur-Yvette, France
| | | | - Swetha Shankar
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
| | | | - Alexis Thual
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, 91191, Gif-sur-Yvette, France
- Collège de France, Paris, France
| | | | | | | | | | - Yann Lecomte
- CEA Saclay/DRF/IFJ/NeuroSpin/UNIACT, Paris, France
| | | | | | | | | | | | | | - Gaël Varoquaux
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
| | - Stanislas Dehaene
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, 91191, Gif-sur-Yvette, France
- Collège de France, Paris, France
| | - Lucie Hertz-Pannier
- CEA Saclay/DRF/IFJ/NeuroSpin/UNIACT, Paris, France
- UMR 1141, NeuroDiderot, Université de Paris, Paris, France
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Kuai H, Chen J, Tao X, Cai L, Imamura K, Matsumoto H, Liang P, Zhong N. Never-Ending Learning for Explainable Brain Computing. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307647. [PMID: 38602432 PMCID: PMC11200082 DOI: 10.1002/advs.202307647] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 03/24/2024] [Indexed: 04/12/2024]
Abstract
Exploring the nature of human intelligence and behavior is a longstanding pursuit in cognitive neuroscience, driven by the accumulation of knowledge, information, and data across various studies. However, achieving a unified and transparent interpretation of findings presents formidable challenges. In response, an explainable brain computing framework is proposed that employs the never-ending learning paradigm, integrating evidence combination and fusion computing within a Knowledge-Information-Data (KID) architecture. The framework supports continuous brain cognition investigation, utilizing joint knowledge-driven forward inference and data-driven reverse inference, bolstered by the pre-trained language modeling techniques and the human-in-the-loop mechanisms. In particular, it incorporates internal evidence learning through multi-task functional neuroimaging analyses and external evidence learning via topic modeling of published neuroimaging studies, all of which involve human interactions at different stages. Based on two case studies, the intricate uncertainty surrounding brain localization in human reasoning is revealed. The present study also highlights the potential of systematization to advance explainable brain computing, offering a finer-grained understanding of brain activity patterns related to human intelligence.
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Affiliation(s)
- Hongzhi Kuai
- Faculty of EngineeringMaebashi Institute of TechnologyGunma371–0816Japan
- School of Psychology and Beijing Key Laboratory of Learning and CognitionCapital Normal UniversityBeijing100048China
| | - Jianhui Chen
- Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China
- Beijing International Collaboration Base on Brain Informatics and Wisdom ServicesBeijing100124China
| | - Xiaohui Tao
- School of Mathematics, Physics and ComputingUniversity of Southern QueenslandToowoomba4350Australia
| | - Lingyun Cai
- School of Psychology and Beijing Key Laboratory of Learning and CognitionCapital Normal UniversityBeijing100048China
| | - Kazuyuki Imamura
- Faculty of EngineeringMaebashi Institute of TechnologyGunma371–0816Japan
| | - Hiroki Matsumoto
- Faculty of EngineeringMaebashi Institute of TechnologyGunma371–0816Japan
| | - Peipeng Liang
- School of Psychology and Beijing Key Laboratory of Learning and CognitionCapital Normal UniversityBeijing100048China
| | - Ning Zhong
- Faculty of EngineeringMaebashi Institute of TechnologyGunma371–0816Japan
- School of Psychology and Beijing Key Laboratory of Learning and CognitionCapital Normal UniversityBeijing100048China
- Beijing International Collaboration Base on Brain Informatics and Wisdom ServicesBeijing100124China
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Peraza JA, Salo T, Riedel MC, Bottenhorn KL, Poline JB, Dockès J, Kent JD, Bartley JE, Flannery JS, Hill-Bowen LD, Lobo RP, Poudel R, Ray KL, Robinson JL, Laird RW, Sutherland MT, de la Vega A, Laird AR. Methods for decoding cortical gradients of functional connectivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.01.551505. [PMID: 37577598 PMCID: PMC10418206 DOI: 10.1101/2023.08.01.551505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Macroscale gradients have emerged as a central principle for understanding functional brain organization. Previous studies have demonstrated that a principal gradient of connectivity in the human brain exists, with unimodal primary sensorimotor regions situated at one end and transmodal regions associated with the default mode network and representative of abstract functioning at the other. The functional significance and interpretation of macroscale gradients remains a central topic of discussion in the neuroimaging community, with some studies demonstrating that gradients may be described using meta-analytic functional decoding techniques. However, additional methodological development is necessary to fully leverage available meta-analytic methods and resources and quantitatively evaluate their relative performance. Here, we conducted a comprehensive series of analyses to investigate and improve the framework of data-driven, meta-analytic methods, thereby establishing a principled approach for gradient segmentation and functional decoding. We found that a two-segment solution determined by a k-means segmentation approach and an LDA-based meta-analysis combined with the NeuroQuery database was the optimal combination of methods for decoding functional connectivity gradients. Finally, we proposed a method for decoding additional components of the gradient decomposition. The current work aims to provide recommendations on best practices and flexible methods for gradient-based functional decoding of fMRI data.
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Affiliation(s)
- Julio A. Peraza
- Department of Physics, Florida International University, Miami, FL, USA
| | - Taylor Salo
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Katherine L. Bottenhorn
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Jean-Baptiste Poline
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Jérôme Dockès
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - James D. Kent
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
| | | | - Jessica S. Flannery
- Department of Psychology and Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | | | | | - Ranjita Poudel
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, FL, USA
| | - Kimberly L. Ray
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
| | | | - Robert W. Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | | | | | - Angela R. Laird
- Department of Physics, Florida International University, Miami, FL, USA
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Abstract
It has become a truism that the brain is a complex structure. One idea associated with complex systems is that of emergence, which is often characterized as the occurrence of a novel collective property that results from the interactions of individual parts, each of which alone do not have that property. Pessoa in his book argues, plausibly, that given that cognition is the most complex thing the brain does then it will need a new emergence-inflected science to understand it. His subsequent argument, however, does not follow, namely that this will take the form of distributed networks with identity-switching nodes that morph pluripotently from one computation to another. This is not true for whole organisms, which became more complex through compartmentalization and specialization. The brain did the same with hierarchically organized specialized areas.
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Evaluating the impact of short educational videos on the cortical networks for mathematics. Proc Natl Acad Sci U S A 2023; 120:e2213430120. [PMID: 36730198 PMCID: PMC9963232 DOI: 10.1073/pnas.2213430120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
Many teaching websites, such as the Khan Academy, propose vivid videos illustrating a mathematical concept. Using functional magnetic resonance imaging, we asked whether watching such a video suffices to rapidly change the brain networks for mathematical knowledge. We capitalized on the finding that, when judging the truth of short spoken statements, distinct semantic regions activate depending on whether the statements bear on mathematical knowledge or on other domains of semantic knowledge. Here, participants answered such questions before and after watching a lively 5-min video, which taught them the rudiments of a new domain. During the video, a distinct math-responsive network, comprising anterior intraparietal and inferior temporal nodes, showed intersubject synchrony when viewing mathematics course rather than control courses in biology or law. However, this experience led to minimal subsequent changes in the activity of those domain-specific areas when answering questions on the same topics a few minutes later. All taught facts, whether mathematical or not, led to domain-general repetition enhancement, particularly prominent in the cuneus, posterior cingulate, and posterior parietal cortices. We conclude that short videos do not suffice to induce a meaningful lasting change in the brain's math-responsive network, but merely engage domain-general regions possibly involved in episodic short-term memory.
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Germani E, Fromont E, Maumet C. On the benefits of self-taught learning for brain decoding. Gigascience 2022; 12:giad029. [PMID: 37132522 PMCID: PMC10155221 DOI: 10.1093/gigascience/giad029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 01/24/2023] [Accepted: 04/14/2023] [Indexed: 05/04/2023] Open
Abstract
CONTEXT We study the benefits of using a large public neuroimaging database composed of functional magnetic resonance imaging (fMRI) statistic maps, in a self-taught learning framework, for improving brain decoding on new tasks. First, we leverage the NeuroVault database to train, on a selection of relevant statistic maps, a convolutional autoencoder to reconstruct these maps. Then, we use this trained encoder to initialize a supervised convolutional neural network to classify tasks or cognitive processes of unseen statistic maps from large collections of the NeuroVault database. RESULTS We show that such a self-taught learning process always improves the performance of the classifiers, but the magnitude of the benefits strongly depends on the number of samples available both for pretraining and fine-tuning the models and on the complexity of the targeted downstream task. CONCLUSION The pretrained model improves the classification performance and displays more generalizable features, less sensitive to individual differences.
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Affiliation(s)
- Elodie Germani
- Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U 1228, 35000 Rennes, France
| | - Elisa Fromont
- Univ Rennes, IUF, Inria, CNRS, IRISA UMR 6074, 35000 Rennes, France
| | - Camille Maumet
- Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U 1228, 35000 Rennes, France
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Bel-Bahar TS, Khan AA, Shaik RB, Parvaz MA. A scoping review of electroencephalographic (EEG) markers for tracking neurophysiological changes and predicting outcomes in substance use disorder treatment. Front Hum Neurosci 2022; 16:995534. [PMID: 36325430 PMCID: PMC9619053 DOI: 10.3389/fnhum.2022.995534] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 09/20/2022] [Indexed: 11/24/2022] Open
Abstract
Substance use disorders (SUDs) constitute a growing global health crisis, yet many limitations and challenges exist in SUD treatment research, including the lack of objective brain-based markers for tracking treatment outcomes. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity, and although much is known about EEG activity in acute and chronic substance use, knowledge regarding EEG in relation to abstinence and treatment outcomes is sparse. We performed a scoping review of longitudinal and pre-post treatment EEG studies that explored putative changes in brain function associated with abstinence and/or treatment in individuals with SUD. Following PRISMA guidelines, we identified studies published between January 2000 and March 2022 from online databases. Search keywords included EEG, addictive substances (e.g., alcohol, cocaine, methamphetamine), and treatment related terms (e.g., abstinence, relapse). Selected studies used EEG at least at one time point as a predictor of abstinence or other treatment-related outcomes; or examined pre- vs. post-SUD intervention (brain stimulation, pharmacological, behavioral) EEG effects. Studies were also rated on the risk of bias and quality using validated instruments. Forty-four studies met the inclusion criteria. More consistent findings included lower oddball P3 and higher resting beta at baseline predicting negative outcomes, and abstinence-mediated longitudinal decrease in cue-elicited P3 amplitude and resting beta power. Other findings included abstinence or treatment-related changes in late positive potential (LPP) and N2 amplitudes, as well as in delta and theta power. Existing studies were heterogeneous and limited in terms of specific substances of interest, brief times for follow-ups, and inconsistent or sparse results. Encouragingly, in this limited but maturing literature, many studies demonstrated partial associations of EEG markers with abstinence, treatment outcomes, or pre-post treatment-effects. Studies were generally of good quality in terms of risk of bias. More EEG studies are warranted to better understand abstinence- or treatment-mediated neural changes or to predict SUD treatment outcomes. Future research can benefit from prospective large-sample cohorts and the use of standardized methods such as task batteries. EEG markers elucidating the temporal dynamics of changes in brain function related to abstinence and/or treatment may enable evidence-based planning for more effective and targeted treatments, potentially pre-empting relapse or minimizing negative lifespan effects of SUD.
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Affiliation(s)
- Tarik S. Bel-Bahar
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Anam A. Khan
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Riaz B. Shaik
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Muhammad A. Parvaz
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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Walters J, King M, Bissett PG, Ivry RB, Diedrichsen J, Poldrack RA. Predicting brain activation maps for arbitrary tasks with cognitive encoding models. Neuroimage 2022; 263:119610. [PMID: 36064138 PMCID: PMC9981816 DOI: 10.1016/j.neuroimage.2022.119610] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Revised: 08/12/2022] [Accepted: 09/02/2022] [Indexed: 11/27/2022] Open
Abstract
A deep understanding of the neural architecture of mental function should enable the accurate prediction of a specific pattern of brain activity for any psychological task, based only on the cognitive functions known to be engaged by that task. Encoding models (EMs), which predict neural responses from known features (e.g., stimulus properties), have succeeded in circumscribed domains (e.g., visual neuroscience), but implementing domain-general EMs that predict brain-wide activity for arbitrary tasks has been limited mainly by availability of datasets that 1) sufficiently span a large space of psychological functions, and 2) are sufficiently annotated with such functions to allow robust EM specification. We examine the use of EMs based on a formal specification of psychological function, to predict cortical activation patterns across a broad range of tasks. We utilized the Multi-Domain Task Battery, a dataset in which 24 subjects completed 32 ten-minute fMRI scans, switching tasks every 35 s and engaging in 44 total conditions of diverse psychological manipulations. Conditions were annotated by a group of experts using the Cognitive Atlas ontology to identify putatively engaged functions, and region-wise cognitive EMs (CEMs) were fit, for individual subjects, on neocortical responses. We found that CEMs predicted cortical activation maps of held-out tasks with high accuracy, outperforming a permutation-based null model while approaching the noise ceiling of the data, without being driven solely by either cognitive or perceptual-motor features. Hierarchical clustering on the similarity structure of CEM generalization errors revealed relationships amongst psychological functions. Spatial distributions of feature importances systematically overlapped with large-scale resting-state functional networks (RSNs), supporting the hypothesis of functional specialization within RSNs while grounding their function in an interpretable data-driven manner. Our implementation and validation of CEMs provides a proof of principle for the utility of formal ontologies in cognitive neuroscience and motivates the use of CEMs in the further testing of cognitive theories.
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Affiliation(s)
- Jonathon Walters
- Department of Psychology, Stanford University, Stanford, CA, USA.
| | - Maedbh King
- Department of Psychology, University of California Berkeley, Berkeley, CA, USA
| | | | - Richard B. Ivry
- Department of Psychology, University of California Berkeley, Berkeley, CA, USA,Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - Jörn Diedrichsen
- Brain and Mind Institute, Western University, London, Ontario, Canada,Department of Computer Science, Western University, London, Ontario, Canada
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Blain A, Thirion B, Neuvial P. Notip: Non-parametric true discovery proportion control for brain imaging. Neuroimage 2022; 260:119492. [PMID: 35870698 DOI: 10.1016/j.neuroimage.2022.119492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 07/01/2022] [Accepted: 07/17/2022] [Indexed: 10/17/2022] Open
Abstract
Cluster-level inference procedures are widely used for brain mapping. These methods compare the size of clusters obtained by thresholding brain maps to an upper bound under the global null hypothesis, computed using Random Field Theory or permutations. However, the guarantees obtained by this type of inference - i.e. at least one voxel is truly activated in the cluster - are not informative with regards to the strength of the signal therein. There is thus a need for methods to assess the amount of signal within clusters; yet such methods have to take into account that clusters are defined based on the data, which creates circularity in the inference scheme. This has motivated the use of post hoc estimates that allow statistically valid estimation of the proportion of activated voxels in clusters. In the context of fMRI data, the All-Resolutions Inference framework introduced in Rosenblatt et al. (2018) provides post hoc estimates of the proportion of activated voxels. However, this method relies on parametric threshold families, which results in conservative inference. In this paper, we leverage randomization methods to adapt to data characteristics and obtain tighter false discovery control. We obtain Notip, for Non-parametric True Discovery Proportion control: a powerful, non-parametric method that yields statistically valid guarantees on the proportion of activated voxels in data-derived clusters. Numerical experiments demonstrate substantial gains in number of detections compared with state-of-the-art methods on 36 fMRI datasets. The conditions under which the proposed method brings benefits are also discussed.
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Affiliation(s)
- Alexandre Blain
- Inria, CEA, Université Paris-Saclay, Paris, France; Institut de Mathématiques de Toulouse, UMR 5219, Université de Toulouse, CNRS, UPS, Toulouse, France.
| | | | - Pierre Neuvial
- Institut de Mathématiques de Toulouse, UMR 5219, Université de Toulouse, CNRS, UPS, Toulouse, France
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11
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Zhang Y, Farrugia N, Bellec P. Deep learning models of cognitive processes constrained by human brain connectomes. Med Image Anal 2022; 80:102507. [DOI: 10.1016/j.media.2022.102507] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 03/13/2022] [Accepted: 05/31/2022] [Indexed: 01/02/2023]
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12
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Comprehensive decoding mental processes from Web repositories of functional brain images. Sci Rep 2022; 12:7050. [PMID: 35488032 PMCID: PMC9054752 DOI: 10.1038/s41598-022-10710-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 04/05/2022] [Indexed: 11/08/2022] Open
Abstract
Associating brain systems with mental processes requires statistical analysis of brain activity across many cognitive processes. These analyses typically face a difficult compromise between scope-from domain-specific to system-level analysis-and accuracy. Using all the functional Magnetic Resonance Imaging (fMRI) statistical maps of the largest data repository available, we trained machine-learning models that decode the cognitive concepts probed in unseen studies. For this, we leveraged two comprehensive resources: NeuroVault-an open repository of fMRI statistical maps with unconstrained annotations-and Cognitive Atlas-an ontology of cognition. We labeled NeuroVault images with Cognitive Atlas concepts occurring in their associated metadata. We trained neural networks to predict these cognitive labels on tens of thousands of brain images. Overcoming the heterogeneity, imbalance and noise in the training data, we successfully decoded more than 50 classes of mental processes on a large test set. This success demonstrates that image-based meta-analyses can be undertaken at scale and with minimal manual data curation. It enables broad reverse inferences, that is, concluding on mental processes given the observed brain activity.
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Tackling the Complexity of Lesion-Symptoms Mapping: How to Bridge the Gap Between Data Scientists and Clinicians? ACTA NEUROCHIRURGICA. SUPPLEMENT 2021; 134:195-203. [PMID: 34862543 DOI: 10.1007/978-3-030-85292-4_23] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Accurate and predictive lesion-symptoms mapping is a major goal in the field of clinical neurosciences. Recent studies have called for a reappraisal of the results given by the standard univariate voxel-based lesion-symptom mapping technique, emphasizing the need of developing multivariate methods. While the organization of large datasets and their analysis with machine learning (ML) approaches represents an opportunity to increase prediction accuracy, the complexity and dimensionality of the problem remain a major obstacle. Acknowledging the difficulty of inferring individual outcomes from the observation of spatial patterns of lesions, we propose here to base prediction on new individuals on models of brain connectivity, whereby the disruption of a given network predicts the occurrence of selective deficits. Well-suited ML tools are necessary to capture the relevant information from limited datasets and perform reliable inference.
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14
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Duong-Tran D, Abbas K, Amico E, Corominas-Murtra B, Dzemidzic M, Kareken D, Ventresca M, Goñi J. A morphospace of functional configuration to assess configural breadth based on brain functional networks. Netw Neurosci 2021; 5:666-688. [PMID: 34746622 PMCID: PMC8567831 DOI: 10.1162/netn_a_00193] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 03/17/2021] [Indexed: 11/07/2022] Open
Abstract
The quantification of human brain functional (re)configurations across varying cognitive demands remains an unresolved topic. We propose that such functional configurations may be categorized into three different types: (a) network configural breadth, (b) task-to task transitional reconfiguration, and (c) within-task reconfiguration. Such functional reconfigurations are rather subtle at the whole-brain level. Hence, we propose a mesoscopic framework focused on functional networks (FNs) or communities to quantify functional (re)configurations. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, trapping efficiency (TE) and exit entropy (EE), which capture topology and integration of information within and between a reference set of FNs. We use this framework to quantify the network configural breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks, and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence, and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN reconfigurations that result from the brain switching between mental states.
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Affiliation(s)
- Duy Duong-Tran
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
| | - Kausar Abbas
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
| | - Enrico Amico
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- Institute of Bioengineering/Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland
| | | | - Mario Dzemidzic
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - David Kareken
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Mario Ventresca
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute of Inflammation, Immunology, and Infectious Disease, Purdue University, West Lafayette, IN, USA
| | - Joaquín Goñi
- School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
- Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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15
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16
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Extracting representations of cognition across neuroimaging studies improves brain decoding. PLoS Comput Biol 2021; 17:e1008795. [PMID: 33939700 PMCID: PMC8118532 DOI: 10.1371/journal.pcbi.1008795] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 05/13/2021] [Accepted: 02/15/2021] [Indexed: 11/19/2022] Open
Abstract
Cognitive brain imaging is accumulating datasets about the neural substrate of many different mental processes. Yet, most studies are based on few subjects and have low statistical power. Analyzing data across studies could bring more statistical power; yet the current brain-imaging analytic framework cannot be used at scale as it requires casting all cognitive tasks in a unified theoretical framework. We introduce a new methodology to analyze brain responses across tasks without a joint model of the psychological processes. The method boosts statistical power in small studies with specific cognitive focus by analyzing them jointly with large studies that probe less focal mental processes. Our approach improves decoding performance for 80% of 35 widely-different functional-imaging studies. It finds commonalities across tasks in a data-driven way, via common brain representations that predict mental processes. These are brain networks tuned to psychological manipulations. They outline interpretable and plausible brain structures. The extracted networks have been made available; they can be readily reused in new neuro-imaging studies. We provide a multi-study decoding tool to adapt to new data.
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17
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Wendelberg L. An Ontological Framework to Facilitate Early Detection of 'Radicalization' (OFEDR)-A Three World Perspective. J Imaging 2021; 7:60. [PMID: 34460716 PMCID: PMC8321290 DOI: 10.3390/jimaging7030060] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 03/11/2021] [Accepted: 03/16/2021] [Indexed: 11/16/2022] Open
Abstract
This paper presents an ontology that involves using information from various sources from different disciplines and combining it in order to predict whether a given person is in a radicalization process. The purpose of the ontology is to improve the early detection of radicalization in persons, thereby contributing to increasing the extent to which the unwanted escalation of radicalization processes can be prevented. The ontology combines findings related to existential anxiety that are related to political radicalization with well-known criminal profiles or radicalization findings. The software Protégé, delivered by the technical field at Stanford University, including the SPARQL tab, is used to develop and test the ontology. The testing, which involved five models, showed that the ontology could detect individuals according to "risk profiles" for subjects based on existential anxiety. SPARQL queries showed an average detection probability of 5% including only a risk population and 2% on a whole test population. Testing by using machine learning algorithms proved that inclusion of less than four variables in each model produced unreliable results. This suggest that the Ontology Framework to Facilitate Early Detection of 'Radicalization' (OFEDR) ontology risk model should consist of at least four variables to reach a certain level of reliability. Analysis shows that use of a probability based on an estimated risk of terrorism may produce a gap between the number of subjects who actually have early signs of radicalization and those found by using probability estimates for extremely rare events. It is reasoned that an ontology exists as a world three object in the real world.
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Affiliation(s)
- Linda Wendelberg
- Department of Information Security and Communication Technology, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU), 2815 Gjøvik, Norway
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18
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Decoding with confidence: Statistical control on decoder maps. Neuroimage 2021; 234:117921. [PMID: 33722670 DOI: 10.1016/j.neuroimage.2021.117921] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 02/17/2021] [Accepted: 02/21/2021] [Indexed: 11/22/2022] Open
Abstract
In brain imaging, decoding is widely used to infer relationships between brain and cognition, or to craft brain-imaging biomarkers of pathologies. Yet, standard decoding procedures do not come with statistical guarantees, and thus do not give confidence bounds to interpret the pattern maps that they produce. Indeed, in whole-brain decoding settings, the number of explanatory variables is much greater than the number of samples, hence classical statistical inference methodology cannot be applied. Specifically, the standard practice that consists in thresholding decoding maps is not a correct inference procedure. We contribute a new statistical-testing framework for this type of inference. To overcome the statistical inefficiency of voxel-level control, we generalize the Family Wise Error Rate (FWER) to account for a spatial tolerance δ, introducing the δ-Family Wise Error Rate (δ-FWER). Then, we present a decoding procedure that can control the δ-FWER: the Ensemble of Clustered Desparsified Lasso (EnCluDL), a procedure for multivariate statistical inference on high-dimensional structured data. We evaluate the statistical properties of EnCluDL with a thorough empirical study, along with three alternative procedures including decoder map thresholding. We show that EnCluDL exhibits the best recovery properties while ensuring the expected statistical control.
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19
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Zhang Y, Tetrel L, Thirion B, Bellec P. Functional annotation of human cognitive states using deep graph convolution. Neuroimage 2021; 231:117847. [PMID: 33582272 DOI: 10.1016/j.neuroimage.2021.117847] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 02/04/2021] [Accepted: 02/05/2021] [Indexed: 01/17/2023] Open
Abstract
A key goal in neuroscience is to understand brain mechanisms of cognitive functions. An emerging approach is "brain decoding", which consists of inferring a set of experimental conditions performed by a participant, using pattern classification of brain activity. Few works so far have attempted to train a brain decoding model that would generalize across many different cognitive tasks drawn from multiple cognitive domains. To tackle this problem, we proposed a multidomain brain decoder that automatically learns the spatiotemporal dynamics of brain response within a short time window using a deep learning approach. We evaluated the decoding model on a large population of 1200 participants, under 21 different experimental conditions spanning six different cognitive domains, acquired from the Human Connectome Project task-fMRI database. Using a 10s window of fMRI response, the 21 cognitive states were identified with a test accuracy of 90% (chance level 4.8%). Performance remained good when using a 6s window (82%). It was even feasible to decode cognitive states from a single fMRI volume (720ms), with the performance following the shape of the hemodynamic response. Moreover, a saliency map analysis demonstrated that the high decoding performance was driven by the response of biologically meaningful brain regions. Together, we provide an automated tool to annotate human brain activity with fine temporal resolution and fine cognitive granularity. Our model shows potential applications as a reference model for domain adaptation, possibly making contributions in a variety of domains, including neurological and psychiatric disorders.
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Affiliation(s)
- Yu Zhang
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montreal, QC H3W 1W6, Canada; Department of Psychology, Université de Montréal, Montreal QC H3C 3J7, Canada
| | - Loïc Tetrel
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montreal, QC H3W 1W6, Canada
| | - Bertrand Thirion
- Parietal team, INRIA, Neurospin, CEA Saclay, Gif-sur-Yvette, France
| | - Pierre Bellec
- Centre de recherche de l'Institut universitaire de gériatrie de Montréal, Montreal, QC H3W 1W6, Canada; Department of Psychology, Université de Montréal, Montreal QC H3C 3J7, Canada.
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20
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The Functional Relevance of Task-State Functional Connectivity. J Neurosci 2021; 41:2684-2702. [PMID: 33542083 DOI: 10.1523/jneurosci.1713-20.2021] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 11/24/2020] [Accepted: 01/04/2021] [Indexed: 02/08/2023] Open
Abstract
Resting-state functional connectivity has provided substantial insight into intrinsic brain network organization, yet the functional importance of task-related change from that intrinsic network organization remains unclear. Indeed, such task-related changes are known to be small, suggesting they may have only minimal functional relevance. Alternatively, despite their small amplitude, these task-related changes may be essential for the ability of the human brain to adaptively alter its functionality via rapid changes in inter-regional relationships. We used activity flow mapping-an approach for building empirically derived network models-to quantify the functional importance of task-state functional connectivity (above and beyond resting-state functional connectivity) in shaping cognitive task activations in the (female and male) human brain. We found that task-state functional connectivity could be used to better predict independent fMRI activations across all 24 task conditions and all 360 cortical regions tested. Further, we found that prediction accuracy was strongly driven by individual-specific functional connectivity patterns, while functional connectivity patterns from other tasks (task-general functional connectivity) still improved predictions beyond resting-state functional connectivity. Additionally, since activity flow models simulate how task-evoked activations (which underlie behavior) are generated, these results may provide mechanistic insight into why prior studies found correlations between task-state functional connectivity and individual differences in behavior. These findings suggest that task-related changes to functional connections play an important role in dynamically reshaping brain network organization, shifting the flow of neural activity during task performance.SIGNIFICANCE STATEMENT Human cognition is highly dynamic, yet the functional network organization of the human brain is highly similar across rest and task states. We hypothesized that, despite this overall network stability, task-related changes from the intrinsic (resting-state) network organization of the brain strongly contribute to brain activations during cognitive task performance. Given that cognitive task activations emerge through network interactions, we leveraged connectivity-based models to predict independent cognitive task activations using resting-state versus task-state functional connectivity. This revealed that task-related changes in functional network organization increased prediction accuracy of cognitive task activations substantially, demonstrating their likely functional relevance for dynamic cognitive processes despite the small size of these task-related network changes.
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21
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Abstract
Human brain atlases have been evolving tremendously, propelled recently by brain big projects, and driven by sophisticated imaging techniques, advanced brain mapping methods, vast data, analytical strategies, and powerful computing. We overview here this evolution in four categories: content, applications, functionality, and availability, in contrast to other works limited mostly to content. Four atlas generations are distinguished: early cortical maps, print stereotactic atlases, early digital atlases, and advanced brain atlas platforms, and 5 avenues in electronic atlases spanning the last two generations. Content-wise, new electronic atlases are categorized into eight groups considering their scope, parcellation, modality, plurality, scale, ethnicity, abnormality, and a mixture of them. Atlas content developments in these groups are heading in 23 various directions. Application-wise, we overview atlases in neuroeducation, research, and clinics, including stereotactic and functional neurosurgery, neuroradiology, neurology, and stroke. Functionality-wise, tools and functionalities are addressed for atlas creation, navigation, individualization, enabling operations, and application-specific. Availability is discussed in media and platforms, ranging from mobile solutions to leading-edge supercomputers, with three accessibility levels. The major application-wise shift has been from research to clinical practice, particularly in stereotactic and functional neurosurgery, although clinical applications are still lagging behind the atlas content progress. Atlas functionality also has been relatively neglected until recently, as the management of brain data explosion requires powerful tools. We suggest that the future human brain atlas-related research and development activities shall be founded on and benefit from a standard framework containing the core virtual brain model cum the brain atlas platform general architecture.
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Affiliation(s)
- Wieslaw L Nowinski
- John Paul II Center for Virtual Anatomy and Surgical Simulation, University of Cardinal Stefan Wyszynski, Woycickiego 1/3, Block 12, room 1220, 01-938, Warsaw, Poland.
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22
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Pinho AL, Amadon A, Fabre M, Dohmatob E, Denghien I, Torre JJ, Ginisty C, Becuwe-Desmidt S, Roger S, Laurier L, Joly-Testault V, Médiouni-Cloarec G, Doublé C, Martins B, Pinel P, Eger E, Varoquaux G, Pallier C, Dehaene S, Hertz-Pannier L, Thirion B. Subject-specific segregation of functional territories based on deep phenotyping. Hum Brain Mapp 2020; 42:841-870. [PMID: 33368868 PMCID: PMC7856658 DOI: 10.1002/hbm.25189] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 07/11/2020] [Accepted: 08/04/2020] [Indexed: 11/08/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) has opened the possibility to investigate how brain activity is modulated by behavior. Most studies so far are bound to one single task, in which functional responses to a handful of contrasts are analyzed and reported as a group average brain map. Contrariwise, recent data-collection efforts have started to target a systematic spatial representation of multiple mental functions. In this paper, we leverage the Individual Brain Charting (IBC) dataset-a high-resolution task-fMRI dataset acquired in a fixed environment-in order to study the feasibility of individual mapping. First, we verify that the IBC brain maps reproduce those obtained from previous, large-scale datasets using the same tasks. Second, we confirm that the elementary spatial components, inferred across all tasks, are consistently mapped within and, to a lesser extent, across participants. Third, we demonstrate the relevance of the topographic information of the individual contrast maps, showing that contrasts from one task can be predicted by contrasts from other tasks. At last, we showcase the benefit of contrast accumulation for the fine functional characterization of brain regions within a prespecified network. To this end, we analyze the cognitive profile of functional territories pertaining to the language network and prove that these profiles generalize across participants.
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Affiliation(s)
| | - Alexis Amadon
- Université Paris-Saclay, CEA, CNRS, BAOBAB, NeuroSpin, Gif-sur-Yvette, France
| | - Murielle Fabre
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, 91191, France
| | - Elvis Dohmatob
- Université Paris-Saclay, Inria, CEA, Palaiseau, France.,Criteo AI Lab, Paris, France
| | - Isabelle Denghien
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, 91191, France
| | | | | | | | | | | | | | | | | | | | - Philippe Pinel
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, 91191, France
| | - Evelyn Eger
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, 91191, France
| | | | - Christophe Pallier
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, 91191, France
| | - Stanislas Dehaene
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, 91191, France.,Collège de France, Paris, France
| | - Lucie Hertz-Pannier
- CEA Saclay/DRF/IFJ/NeuroSpin/UNIACT, Paris, France.,UMR 1141, NeuroDiderot, Université de Paris, Paris, France
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23
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Abstract
Human and animal behaviour exhibits complex but regular patterns over time, often referred to as expressions of personality. Yet it remains unclear what personality really is: is it just the behavioural patterns themselves, something in the brain, in the genes or perhaps all of these? Here we offer a set of causal hypotheses about the role of personality, integrating psychological and neuroscientific approaches to personality in a testable framework. These hypotheses clarify the causal and constitutive relations that personality has with genes, environment, brain, mind and behaviour, and we suggest specific experiments that can adjudicate amongst the different hypotheses. We focus on a set of models that propose that personality is instantiated in the brain, distally caused by genes and environment and, in turn, causing the overt behaviours from which it is often inferred. We argue that articulating and testing such models will be essential in a mature science of personality.
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24
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Developing a neurally informed ontology of creativity measurement. Neuroimage 2020; 221:117166. [DOI: 10.1016/j.neuroimage.2020.117166] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Revised: 07/03/2020] [Accepted: 07/10/2020] [Indexed: 12/14/2022] Open
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25
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Pinho AL, Amadon A, Gauthier B, Clairis N, Knops A, Genon S, Dohmatob E, Torre JJ, Ginisty C, Becuwe-Desmidt S, Roger S, Lecomte Y, Berland V, Laurier L, Joly-Testault V, Médiouni-Cloarec G, Doublé C, Martins B, Salmon E, Piazza M, Melcher D, Pessiglione M, van Wassenhove V, Eger E, Varoquaux G, Dehaene S, Hertz-Pannier L, Thirion B. Individual Brain Charting dataset extension, second release of high-resolution fMRI data for cognitive mapping. Sci Data 2020; 7:353. [PMID: 33067452 PMCID: PMC7567863 DOI: 10.1038/s41597-020-00670-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 09/01/2020] [Indexed: 11/09/2022] Open
Abstract
We present an extension of the Individual Brain Charting dataset -a high spatial-resolution, multi-task, functional Magnetic Resonance Imaging dataset, intended to support the investigation on the functional principles governing cognition in the human brain. The concomitant data acquisition from the same 12 participants, in the same environment, allows to obtain in the long run finer cognitive topographies, free from inter-subject and inter-site variability. This second release provides more data from psychological domains present in the first release, and also yields data featuring new ones. It includes tasks on e.g. mental time travel, reward, theory-of-mind, pain, numerosity, self-reference effect and speech recognition. In total, 13 tasks with 86 contrasts were added to the dataset and 63 new components were included in the cognitive description of the ensuing contrasts. As the dataset becomes larger, the collection of the corresponding topographies becomes more comprehensive, leading to better brain-atlasing frameworks. This dataset is an open-access facility; raw data and derivatives are publicly available in neuroimaging repositories.
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Affiliation(s)
- Ana Luísa Pinho
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France.
| | - Alexis Amadon
- Université Paris-Saclay, CEA, CNRS, BAOBAB, NeuroSpin, 91191, Gif-sur-Yvette, France
| | - Baptiste Gauthier
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, 91191, Gif/Yvette, France
- Laboratory of Cognitive Neuroscience, Brain Mind Institute, School of Life Sciences and Center for Neuroprosthetics, Swiss Federal Institute of Technology (EPFL), Campus Biotech, Geneva, Switzerland
| | - Nicolas Clairis
- Motivation, Brain and Behavior (MBB) team, Institut du Cerveau (ICM), Inserm UMRS 1127, CNS UMR 7225, Sorbonne Université, Paris, France
| | - André Knops
- Center for Mind/Brain Sciences, University of Trento, I-38068, Rovereto, Italy
- LaPsyDÉ, UMR CNRS 8240, Université de Paris, Paris, France
| | - Sarah Genon
- GIGA-CRC In vivo Imaging, University of Liège, Liège, Belgium
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7) Research Centre Jülich, Jülich, Germany
| | - Elvis Dohmatob
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
- Criteo AI Lab, Paris, France
| | | | - Chantal Ginisty
- Université Paris-Saclay, CEA, UNIACT, NeuroSpin, 91191 Gif-sur-Yvette, France
| | | | - Séverine Roger
- Université Paris-Saclay, CEA, UNIACT, NeuroSpin, 91191 Gif-sur-Yvette, France
| | - Yann Lecomte
- Université Paris-Saclay, CEA, UNIACT, NeuroSpin, 91191 Gif-sur-Yvette, France
| | - Valérie Berland
- Université Paris-Saclay, CEA, UNIACT, NeuroSpin, 91191 Gif-sur-Yvette, France
| | - Laurence Laurier
- Université Paris-Saclay, CEA, UNIACT, NeuroSpin, 91191 Gif-sur-Yvette, France
| | | | | | - Christine Doublé
- Université Paris-Saclay, CEA, UNIACT, NeuroSpin, 91191 Gif-sur-Yvette, France
| | - Bernadette Martins
- Université Paris-Saclay, CEA, UNIACT, NeuroSpin, 91191 Gif-sur-Yvette, France
| | - Eric Salmon
- GIGA-CRC In vivo Imaging, University of Liège, Liège, Belgium
| | - Manuela Piazza
- Center for Mind/Brain Sciences, University of Trento, I-38068, Rovereto, Italy
| | - David Melcher
- Center for Mind/Brain Sciences, University of Trento, I-38068, Rovereto, Italy
| | - Mathias Pessiglione
- Motivation, Brain and Behavior (MBB) team, Institut du Cerveau (ICM), Inserm UMRS 1127, CNS UMR 7225, Sorbonne Université, Paris, France
| | - Virginie van Wassenhove
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, 91191, Gif/Yvette, France
| | - Evelyn Eger
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, 91191, Gif/Yvette, France
| | - Gaël Varoquaux
- Université Paris-Saclay, Inria, CEA, Palaiseau, 91120, France
| | - Stanislas Dehaene
- Cognitive Neuroimaging Unit, INSERM, CEA, Université Paris-Saclay, NeuroSpin center, 91191, Gif/Yvette, France
- Collège de France, Université Paris-Sciences-Lettres, Paris, France
| | - Lucie Hertz-Pannier
- Université Paris-Saclay, CEA, UNIACT, NeuroSpin, 91191 Gif-sur-Yvette, France
- UMR 1141, NeuroDiderot, Université de Paris, Paris, France
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26
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Weichwald S, Peters J. Causality in Cognitive Neuroscience: Concepts, Challenges, and Distributional Robustness. J Cogn Neurosci 2020; 33:226-247. [PMID: 32812827 DOI: 10.1162/jocn_a_01623] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Whereas probabilistic models describe the dependence structure between observed variables, causal models go one step further: They predict, for example, how cognitive functions are affected by external interventions that perturb neuronal activity. In this review and perspective article, we introduce the concept of causality in the context of cognitive neuroscience and review existing methods for inferring causal relationships from data. Causal inference is an ambitious task that is particularly challenging in cognitive neuroscience. We discuss two difficulties in more detail: the scarcity of interventional data and the challenge of finding the right variables. We argue for distributional robustness as a guiding principle to tackle these problems. Robustness (or invariance) is a fundamental principle underlying causal methodology. A (correctly specified) causal model of a target variable generalizes across environments or subjects as long as these environments leave the causal mechanisms of the target intact. Consequently, if a candidate model does not generalize, then either it does not consist of the target variable's causes or the underlying variables do not represent the correct granularity of the problem. In this sense, assessing generalizability may be useful when defining relevant variables and can be used to partially compensate for the lack of interventional data.
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27
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Abbas K, Amico E, Svaldi DO, Tipnis U, Duong-Tran DA, Liu M, Rajapandian M, Harezlak J, Ances BM, Goñi J. GEFF: Graph embedding for functional fingerprinting. Neuroimage 2020; 221:117181. [PMID: 32702487 DOI: 10.1016/j.neuroimage.2020.117181] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 06/23/2020] [Accepted: 07/16/2020] [Indexed: 11/16/2022] Open
Abstract
It has been well established that Functional Connectomes (FCs), as estimated from functional MRI (fMRI) data, have an individual fingerprint that can be used to identify an individual from a population (subject-identification). Although identification rate is high when using resting-state FCs, other tasks show moderate to low values. Furthermore, identification rate is task-dependent, and is low when distinct cognitive states, as captured by different fMRI tasks, are compared. Here we propose an embedding framework, GEFF (Graph Embedding for Functional Fingerprinting), based on group-level decomposition of FCs into eigenvectors. GEFF creates an eigenspace representation of a group of subjects using one or more task FCs (Learning Stage). In the Identification Stage, we compare new instances of FCs from the Learning subjects within this eigenspace (validation dataset). The validation dataset contains FCs either from the same tasks as the Learning dataset or from the remaining tasks that were not included in Learning. Assessment of validation FCs within the eigenspace results in significantly increased subject-identification rates for all fMRI tasks tested and potentially task-independent fingerprinting process. It is noteworthy that combining resting-state with one fMRI task for GEFF Learning Stage covers most of the cognitive space for subject identification. Thus, while designing an experiment, one could choose a task fMRI to ask a specific question and combine it with resting-state fMRI to extract maximum subject differentiability using GEFF. In addition to subject-identification, GEFF was also used for identification of cognitive states, i.e. to identify the task associated to a given FC, regardless of the subject being already in the Learning dataset or not (subject-independent task-identification). In addition, we also show that eigenvectors from the Learning Stage can be characterized as task- and subject-dominant, subject-dominant or neither, using two-way ANOVA of their corresponding loadings, providing a deeper insight into the extent of variance in functional connectivity across individuals and cognitive states.
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Affiliation(s)
- Kausar Abbas
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Diana Otero Svaldi
- Indiana University School of Medicine, Indiana University, Indianapolis, IN, USA; Indiana Alzheimer Disease Center, Indiana University, Indianapolis, IN, USA
| | - Uttara Tipnis
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Duy Anh Duong-Tran
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Mintao Liu
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Meenusree Rajapandian
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, Indiana University, IN, USA
| | - Beau M Ances
- Washington University School of Medicine, Washington University, St Louis, MO, USA
| | - Joaquín Goñi
- Purdue Institute for Integrative Neuroscience, Purdue University, West-Lafayette, IN, USA; School of Industrial Engineering, Purdue University, West Lafayette, IN, USA; Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
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28
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Wang X, Liang X, Jiang Z, Nguchu BA, Zhou Y, Wang Y, Wang H, Li Y, Zhu Y, Wu F, Gao J, Qiu B. Decoding and mapping task states of the human brain via deep learning. Hum Brain Mapp 2020; 41:1505-1519. [PMID: 31816152 PMCID: PMC7267978 DOI: 10.1002/hbm.24891] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 11/20/2019] [Accepted: 11/27/2019] [Indexed: 02/06/2023] Open
Abstract
Support vector machine (SVM)-based multivariate pattern analysis (MVPA) has delivered promising performance in decoding specific task states based on functional magnetic resonance imaging (fMRI) of the human brain. Conventionally, the SVM-MVPA requires careful feature selection/extraction according to expert knowledge. In this study, we propose a deep neural network (DNN) for directly decoding multiple brain task states from fMRI signals of the brain without any burden for feature handcrafts. We trained and tested the DNN classifier using task fMRI data from the Human Connectome Project's S1200 dataset (N = 1,034). In tests to verify its performance, the proposed classification method identified seven tasks with an average accuracy of 93.7%. We also showed the general applicability of the DNN for transfer learning to small datasets (N = 43), a situation encountered in typical neuroscience research. The proposed method achieved an average accuracy of 89.0 and 94.7% on a working memory task and a motor classification task, respectively, higher than the accuracy of 69.2 and 68.6% obtained by the SVM-MVPA. A network visualization analysis showed that the DNN automatically detected features from areas of the brain related to each task. Without incurring the burden of handcrafting the features, the proposed deep decoding method can classify brain task states highly accurately, and is a powerful tool for fMRI researchers.
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Affiliation(s)
- Xiaoxiao Wang
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
| | - Xiao Liang
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
| | - Zhoufan Jiang
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
| | - Benedictor A. Nguchu
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
| | - Yawen Zhou
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
| | - Yanming Wang
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
| | - Huijuan Wang
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
| | - Yu Li
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
| | - Yuying Zhu
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
| | - Feng Wu
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
| | - Jia‐Hong Gao
- MRI Research Center and Beijing City Key Lab for Medical Physics and EngineeringPeking UniversityBeijingChina
- McGovern Institute for Brain Research, Peking UniversityBeijingChina
| | - Bensheng Qiu
- Centers for Biomedical EngineeringUniversity of Science and Technology of ChinaHefeiChina
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29
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Cignetti F, Nemmi F, Vaugoyeau M, Girard N, Albaret JM, Chaix Y, Péran P, Assaiante C. Intrinsic Cortico-Subcortical Functional Connectivity in Developmental Dyslexia and Developmental Coordination Disorder. Cereb Cortex Commun 2020; 1:tgaa011. [PMID: 34296090 PMCID: PMC8152893 DOI: 10.1093/texcom/tgaa011] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 03/31/2020] [Accepted: 04/02/2020] [Indexed: 12/11/2022] Open
Abstract
Developmental dyslexia (DD) and developmental coordination disorder (DCD) are distinct diagnostic disorders. However, they also frequently co-occur and may share a common etiology. It was proposed conceptually a neural network framework that explains differences and commonalities between DD and DCD through impairments of distinct or intertwined cortico-subcortical connectivity pathways. The present study addressed this issue by exploring intrinsic cortico-striatal and cortico-cerebellar functional connectivity in a large (n = 136) resting-state fMRI cohort study of 8–12-year-old children with typical development and with DD and/or DCD. We delineated a set of cortico-subcortical functional circuits believed to be associated with the brain’s main functions (visual, somatomotor, dorsal attention, ventral attention, limbic, frontoparietal control, and default-mode). Next, we assessed, using general linear and multiple kernel models, whether and which circuits distinguished between the groups. Findings revealed that somatomotor cortico-cerebellar and frontoparietal cortico-striatal circuits are affected in the presence of DCD, including abnormalities in cortico-cerebellar connections targeting motor-related regions and cortico-striatal connections mapping onto posterior parietal cortex. Thus, DCD but not DD may be considered as an impairment of cortico-subcortical functional circuits.
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Affiliation(s)
- Fabien Cignetti
- University of Grenoble Alpes, CNRS, TIMC-IMAG, F-38000 Grenoble, France
| | - Federico Nemmi
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, 31024 Toulouse, France
| | - Marianne Vaugoyeau
- Aix Marseille University, CNRS, LNC, 13331 Marseille, France.,Aix Marseille University, CNRS, Fédération 3C, 13331 Marseille, France
| | - Nadine Girard
- Aix Marseille University, CNRS, CRMBM, 13385 Marseille, France
| | - Jean-Michel Albaret
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, 31024 Toulouse, France
| | - Yves Chaix
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, 31024 Toulouse, France
| | - Patrice Péran
- ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, 31024 Toulouse, France
| | - Christine Assaiante
- Aix Marseille University, CNRS, LNC, 13331 Marseille, France.,Aix Marseille University, CNRS, Fédération 3C, 13331 Marseille, France
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30
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Gilson M, Zamora-López G, Pallarés V, Adhikari MH, Senden M, Campo AT, Mantini D, Corbetta M, Deco G, Insabato A. Model-based whole-brain effective connectivity to study distributed cognition in health and disease. Netw Neurosci 2020; 4:338-373. [PMID: 32537531 PMCID: PMC7286310 DOI: 10.1162/netn_a_00117] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 12/02/2019] [Indexed: 12/12/2022] Open
Abstract
Neuroimaging techniques are now widely used to study human cognition. The functional associations between brain areas have become a standard proxy to describe how cognitive processes are distributed across the brain network. Among the many analysis tools available, dynamic models of brain activity have been developed to overcome the limitations of original connectivity measures such as functional connectivity. This goes in line with the many efforts devoted to the assessment of directional interactions between brain areas from the observed neuroimaging activity. This opinion article provides an overview of our model-based whole-brain effective connectivity to analyze fMRI data, while discussing the pros and cons of our approach with respect to other established approaches. Our framework relies on the multivariate Ornstein-Uhlenbeck (MOU) process and is thus referred to as MOU-EC. Once tuned, the model provides a directed connectivity estimate that reflects the dynamical state of BOLD activity, which can be used to explore cognition. We illustrate this approach using two applications on task-evoked fMRI data. First, as a connectivity measure, MOU-EC can be used to extract biomarkers for task-specific brain coordination, understood as the patterns of areas exchanging information. The multivariate nature of connectivity measures raises several challenges for whole-brain analysis, for which machine-learning tools present some advantages over statistical testing. Second, we show how to interpret changes in MOU-EC connections in a collective and model-based manner, bridging with network analysis. Our framework provides a comprehensive set of tools that open exciting perspectives to study distributed cognition, as well as neuropathologies.
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Affiliation(s)
- Matthieu Gilson
- Center for Brain and Cognition and Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Gorka Zamora-López
- Center for Brain and Cognition and Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Vicente Pallarés
- Center for Brain and Cognition and Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Mohit H. Adhikari
- Center for Brain and Cognition and Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Mario Senden
- Department of Cognitive Neuroscience, University of Maastricht, Maastricht, The Netherlands
| | | | - Dante Mantini
- Neuroplasticity and Motor Control Research Group, KU Leuven, Leuven, Belgium
- Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - Maurizio Corbetta
- Department of Neuroscience, Venetian Institute of Molecular Medicine (VIMM) and Padova Neuroscience Center (PNC), University of Padua, Italy
- Department of Neurology, Radiology, and Neuroscience, Washington University School of Medicine, St. Louis, MO, USA
| | - Gustavo Deco
- Center for Brain and Cognition and Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Andrea Insabato
- Institut de Neurosciences de la Timone, CNRS, Marseille, France
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31
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Dockès J, Poldrack RA, Primet R, Gözükan H, Yarkoni T, Suchanek F, Thirion B, Varoquaux G. NeuroQuery, comprehensive meta-analysis of human brain mapping. eLife 2020; 9:53385. [PMID: 32129761 PMCID: PMC7164961 DOI: 10.7554/elife.53385] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 03/03/2020] [Indexed: 11/13/2022] Open
Abstract
Reaching a global view of brain organization requires assembling evidence on widely different mental processes and mechanisms. The variety of human neuroscience concepts and terminology poses a fundamental challenge to relating brain imaging results across the scientific literature. Existing meta-analysis methods perform statistical tests on sets of publications associated with a particular concept. Thus, large-scale meta-analyses only tackle single terms that occur frequently. We propose a new paradigm, focusing on prediction rather than inference. Our multivariate model predicts the spatial distribution of neurological observations, given text describing an experiment, cognitive process, or disease. This approach handles text of arbitrary length and terms that are too rare for standard meta-analysis. We capture the relationships and neural correlates of 7547 neuroscience terms across 13 459 neuroimaging publications. The resulting meta-analytic tool, neuroquery.org, can ground hypothesis generation and data-analysis priors on a comprehensive view of published findings on the brain.
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Affiliation(s)
- Jérôme Dockès
- Inria, CEA, Université Paris-Saclay, Essonne, France
| | | | | | | | - Tal Yarkoni
- University of Texas at Austin, Austin, United States
| | | | | | - Gaël Varoquaux
- Inria, CEA, Université Paris-Saclay, Essonne, France.,Montréal Neurological Institute, McGill University, Montreal, Canada
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32
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Quantitative models reveal the organization of diverse cognitive functions in the brain. Nat Commun 2020; 11:1142. [PMID: 32123178 PMCID: PMC7052192 DOI: 10.1038/s41467-020-14913-w] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Accepted: 02/06/2020] [Indexed: 11/08/2022] Open
Abstract
Our daily life is realized by the complex orchestrations of diverse brain functions, including perception, decision-making, and action. The essential goal of cognitive neuroscience is to reveal the complete representations underlying these functions. Recent studies have characterised perceptual experiences using encoding models. However, few attempts have been made to build a quantitative model describing the cortical organization of multiple active, cognitive processes. Here, we measure brain activity using fMRI, while subjects perform 103 cognitive tasks, and examine cortical representations with two voxel-wise encoding models. A sparse task-type model reveals a hierarchical organization of cognitive tasks, together with their representation in cognitive space and cortical mapping. A cognitive factor model utilizing continuous, metadata-based intermediate features predicts brain activity and decodes tasks, even under novel conditions. Collectively, our results show the usability of quantitative models of cognitive processes, thus providing a framework for the comprehensive cortical organization of human cognition.
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33
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Kurashige H, Kaneko J, Yamashita Y, Osu R, Otaka Y, Hanakawa T, Honda M, Kawabata H. Revealing Relationships Among Cognitive Functions Using Functional Connectivity and a Large-Scale Meta-Analysis Database. Front Hum Neurosci 2020; 13:457. [PMID: 31998102 PMCID: PMC6965330 DOI: 10.3389/fnhum.2019.00457] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 12/12/2019] [Indexed: 11/13/2022] Open
Abstract
To characterize each cognitive function per se and to understand the brain as an aggregate of those functions, it is vital to relate dozens of these functions to each other. Knowledge about the relationships among cognitive functions is informative not only for basic neuroscientific research but also for clinical applications and developments of brain-inspired artificial intelligence. In the present study, we propose an exhaustive data mining approach to reveal relationships among cognitive functions based on functional brain mapping and network analysis. We began our analysis with 109 pseudo-activation maps (cognitive function maps; CFM) that were reconstructed from a functional magnetic resonance imaging meta-analysis database, each of which corresponds to one of 109 cognitive functions such as ‘emotion,’ ‘attention,’ ‘episodic memory,’ etc. Based on the resting-state functional connectivity between the CFMs, we mapped the cognitive functions onto a two-dimensional space where the relevant functions were located close to each other, which provided a rough picture of the brain as an aggregate of cognitive functions. Then, we conducted so-called conceptual analysis of cognitive functions using clustering of voxels in each CFM connected to the other 108 CFMs with various strengths. As a result, a CFM for each cognitive function was subdivided into several parts, each of which is strongly associated with some CFMs for a subset of the other cognitive functions, which brought in sub-concepts (i.e., sub-functions) of the cognitive function. Moreover, we conducted network analysis for the network whose nodes were parcels derived from whole-brain parcellation based on the whole-brain voxel-to-CFM resting-state functional connectivities. Since each parcel is characterized by associations with the 109 cognitive functions, network analyses using them are expected to inform about relationships between cognitive and network characteristics. Indeed, we found that informational diversities of interaction between parcels and densities of local connectivity were dependent on the kinds of associated functions. In addition, we identified the homogeneous and inhomogeneous network communities about the associated functions. Altogether, we suggested the effectiveness of our approach in which we fused the large-scale meta-analysis of functional brain mapping with the methods of network neuroscience to investigate the relationships among cognitive functions.
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Affiliation(s)
- Hiroki Kurashige
- Institute of Innovative Science and Technology, Tokai University, Tokyo, Japan.,National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Jun Kaneko
- Institute of Innovative Science and Technology, Tokai University, Tokyo, Japan
| | - Yuichi Yamashita
- National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Rieko Osu
- Faculty of Human Sciences, Waseda University, Tokyo, Japan
| | - Yohei Otaka
- Department of Rehabilitation Medicine I, School of Medicine, Fujita Health University, Aichi, Japan.,Department of Rehabilitation Medicine, Tokyo Bay Rehabilitation Hospital, Chiba, Japan
| | - Takashi Hanakawa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Manabu Honda
- National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
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34
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Tsapanou A, Habeck C, Gazes Y, Razlighi Q, Sakhardande J, Stern Y, Salthouse TA. Brain biomarkers and cognition across adulthood. Hum Brain Mapp 2019; 40:3832-3842. [PMID: 31111980 DOI: 10.1002/hbm.24634] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Revised: 03/29/2019] [Accepted: 05/06/2019] [Indexed: 12/25/2022] Open
Abstract
Understanding the associations between brain biomarkers (BMs) and cognition across age is of paramount importance. Five hundred and sixty-two participants (19-80 years old, 16 mean years of education) were studied. Data from structural T1, diffusion tensor imaging, fluid-attenuated inversion recovery, and resting-state functional magnetic resonance imaging scans combined with a neuropsychological evaluation were used. More specifically, the measures of cortical, entorhinal, and parahippocampal thickness, hippocampal and striatal volume, default-mode network and fronto-parietal control network, fractional anisotropy (FA), and white matter hyperintensity (WMH) were assessed. z-Scores for three cognitive domains measuring episodic memory, executive function, and speed of processing were computed. Multiple linear regressions and interaction effects between each of the BMs and age on cognition were examined. Adjustments were made for age, sex, education, intracranial volume, and then, further, for general cognition and motion. BMs were significantly associated with cognition. Across the adult lifespan, slow speed was associated with low striatal volume, low FA, and high WMH burden. Poor executive function was associated with low FA, while poor memory was associated with high WMH burden. After adjustments, results were significant for the associations: speed-FA and WMH, memory-entorhinal thickness. There was also a significant interaction between hippocampal volume and age in memory. In age-stratified analyses, the most significant associations for the young group occurred between FA and executive function, WMH, and memory, while for the old group, between entorhinal thickness and speed, and WMH and speed, executive function. Unique sets of BMs can explain variation in specific cognitive domains across adulthood. Such results provide essential information about the neurobiology of aging.
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Affiliation(s)
- Angeliki Tsapanou
- Cognitive Neuroscience Division, Department of Neurology and the Taub Institute, Columbia University, New York, New York
| | - Christian Habeck
- Cognitive Neuroscience Division, Department of Neurology and the Taub Institute, Columbia University, New York, New York
| | - Yunglin Gazes
- Cognitive Neuroscience Division, Department of Neurology and the Taub Institute, Columbia University, New York, New York
| | - Qolamreza Razlighi
- Cognitive Neuroscience Division, Department of Neurology and the Taub Institute, Columbia University, New York, New York
| | - Jayant Sakhardande
- Cognitive Neuroscience Division, Department of Neurology and the Taub Institute, Columbia University, New York, New York
| | - Yaakov Stern
- Cognitive Neuroscience Division, Department of Neurology and the Taub Institute, Columbia University, New York, New York
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35
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Abstract
Cerebellar neuroscience has undergone a paradigm shift. The theories of the universal cerebellar transform and dysmetria of thought and the principles of organization of cerebral cortical connections, together with neuroanatomical, brain imaging, and clinical observations, have recontextualized the cerebellum as a critical node in the distributed neural circuits subserving behavior. The framework for cerebellar cognition stems from the identification of three cognitive representations in the posterior lobe, which are interconnected with cerebral association areas and distinct from the primary and secondary cerebellar sensorimotor representations linked with the spinal cord and cerebral motor areas. Lesions of the anterior lobe primary sensorimotor representations produce dysmetria of movement, the cerebellar motor syndrome. Lesions of the posterior lobe cognitive-emotional cerebellum produce dysmetria of thought and emotion, the cerebellar cognitive affective/Schmahmann syndrome. The notion that the cerebellum modulates thought and emotion in the same way that it modulates motor control advances the understanding of the mechanisms of cognition and opens new therapeutic opportunities in behavioral neurology and neuropsychiatry.
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Affiliation(s)
- Jeremy D Schmahmann
- Ataxia Unit, Cognitive Behavioral Neurology Unit, Laboratory for Neuroanatomy and Cerebellar Neurobiology, and Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114, USA;
| | - Xavier Guell
- Ataxia Unit, Cognitive Behavioral Neurology Unit, Laboratory for Neuroanatomy and Cerebellar Neurobiology, and Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114, USA; .,Department of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - Catherine J Stoodley
- Department of Psychology and Center for Behavioral Neuroscience, American University, Washington, DC 20016, USA
| | - Mark A Halko
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts 02215, USA
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36
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Varoquaux G, Poldrack RA. Predictive models avoid excessive reductionism in cognitive neuroimaging. Curr Opin Neurobiol 2018; 55:1-6. [PMID: 30513462 DOI: 10.1016/j.conb.2018.11.002] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Revised: 09/15/2018] [Accepted: 11/19/2018] [Indexed: 11/28/2022]
Abstract
Understanding the organization of complex behavior as it relates to the brain requires modeling the behavior, the relevant mental processes, and the corresponding neural activity. Experiments in cognitive neuroscience typically study a psychological process via controlled manipulations, reducing behavior to one of its components. Such reductionism can easily lead to paradigm-bound theories. Predictive models can generalize brain-mind associations to arbitrary new tasks and stimuli. We argue that they are needed to broaden theories beyond specific paradigms. Predicting behavior from neural activity can support robust reverse inference, isolating brain structures that support particular mental processes. The converse prediction enables modeling brain responses as a function of a complete description of the task, rather than building on oppositions.
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