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Dohmatob E, Richard H, Pinho AL, Thirion B. Brain topography beyond parcellations: Local gradients of functional maps. Neuroimage 2021; 229:117706. [PMID: 33484851 DOI: 10.1016/j.neuroimage.2020.117706] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 11/09/2020] [Accepted: 12/19/2020] [Indexed: 01/21/2023] Open
Abstract
Functional neuroimaging provides the unique opportunity to characterize brain regions based on their response to tasks or ongoing activity. As such, it holds the premise to capture brain spatial organization. Yet, the conceptual framework to describe this organization has remained elusive: on the one hand, parcellations build implicitly on a piecewise constant organization, i.e. flat regions separated by sharp boundaries; on the other hand, the recently popularized concept of functional gradient hints instead at a smooth structure. Noting that both views converge to a topographic scheme that pieces together local variations of functional features, we perform a quantitative assessment of local gradient-based models. Using as a driving case the prediction of functional Magnetic Resonance Imaging (fMRI) data -concretely, the prediction of task-fMRI from rest-fMRI maps across subjects- we develop a parcel-wise linear regression model based on a dictionary of reference topographies. Our method uses multiple random parcellations -as opposed to a single fixed parcellation- and aggregates estimates across these parcellations to predict functional features in left-out subjects. Our experiments demonstrate the existence of an optimal cardinality of the parcellation to capture local gradients of functional maps.
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Affiliation(s)
- Elvis Dohmatob
- Inria, CEA, Université Paris-Saclay, Saclay, France; Criteo AI Lab, France
| | - Hugo Richard
- Inria, CEA, Université Paris-Saclay, Saclay, France
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Dohmatob E, Dumas G, Bzdok D. Dark control: The default mode network as a reinforcement learning agent. Hum Brain Mapp 2020; 41:3318-3341. [PMID: 32500968 PMCID: PMC7375062 DOI: 10.1002/hbm.25019] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Revised: 03/22/2020] [Accepted: 04/12/2020] [Indexed: 12/11/2022] Open
Abstract
The default mode network (DMN) is believed to subserve the baseline mental activity in humans. Its higher energy consumption compared to other brain networks and its intimate coupling with conscious awareness are both pointing to an unknown overarching function. Many research streams speak in favor of an evolutionarily adaptive role in envisioning experience to anticipate the future. In the present work, we propose a process model that tries to explain how the DMN may implement continuous evaluation and prediction of the environment to guide behavior. The main purpose of DMN activity, we argue, may be described by Markov decision processes that optimize action policies via value estimates through vicarious trial and error. Our formal perspective on DMN function naturally accommodates as special cases previous interpretations based on (a) predictive coding, (b) semantic associations, and (c) a sentinel role. Moreover, this process model for the neural optimization of complex behavior in the DMN offers parsimonious explanations for recent experimental findings in animals and humans.
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Affiliation(s)
- Elvis Dohmatob
- Criteo AI LabParisFrance
- INRIA, Parietal TeamSaclayFrance
- Neurospin, CEAGif‐sur‐YvetteFrance
| | - Guillaume Dumas
- Institut Pasteur, Human Genetics and Cognitive Functions UnitParisFrance
- CNRS UMR 3571 Genes, Synapses and Cognition, Institut PasteurParisFrance
- University Paris Diderot, Sorbonne Paris CitéParisFrance
- Centre de Bioinformatique, Biostatistique et Biologie IntégrativeParisFrance
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, Faculty of Medicine, School of Computer ScienceMcGill UniversityMontrealCanada
- Mila—Quebec Artificial Intelligence InstituteMontrealCanada
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Hadj-Selem F, Lofstedt T, Dohmatob E, Frouin V, Dubois M, Guillemot V, Duchesnay E. Continuation of Nesterov's Smoothing for Regression With Structured Sparsity in High-Dimensional Neuroimaging. IEEE Trans Med Imaging 2018; 37:2403-2413. [PMID: 29993684 DOI: 10.1109/tmi.2018.2829802] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Predictive models can be used on high-dimensional brain images to decode cognitive states or diagnosis/prognosis of a clinical condition/evolution. Spatial regularization through structured sparsity offers new perspectives in this context and reduces the risk of overfitting the model while providing interpretable neuroimaging signatures by forcing the solution to adhere to domain-specific constraints. Total variation (TV) is a promising candidate for structured penalization: it enforces spatial smoothness of the solution while segmenting predictive regions from the background. We consider the problem of minimizing the sum of a smooth convex loss, a non-smooth convex penalty (whose proximal operator is known) and a wide range of possible complex, non-smooth convex structured penalties such as TV or overlapping group Lasso. Existing solvers are either limited in the functions they can minimize or in their practical capacity to scale to high-dimensional imaging data. Nesterov's smoothing technique can be used to minimize a large number of non-smooth convex structured penalties. However, reasonable precision requires a small smoothing parameter, which slows down the convergence speed to unacceptable levels. To benefit from the versatility of Nesterov's smoothing technique, we propose a first order continuation algorithm, CONESTA, which automatically generates a sequence of decreasing smoothing parameters. The generated sequence maintains the optimal convergence speed toward any globally desired precision. Our main contributions are: gap to probe the current distance to the global optimum in order to adapt the smoothing parameter and the To propose an expression of the duality convergence speed. This expression is applicable to many penalties and can be used with other solvers than CONESTA. We also propose an expression for the particular smoothing parameter that minimizes the number of iterations required to reach a given precision. Furthermore, we provide a convergence proof and its rate, which is an improvement over classical proximal gradient smoothing methods. We demonstrate on both simulated and high-dimensional structural neuroimaging data that CONESTA significantly outperforms many state-of-the-art solvers in regard to convergence speed and precision.
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Pinho AL, Amadon A, Ruest T, Fabre M, Dohmatob E, Denghien I, 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. Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping. Sci Data 2018; 5:180105. [PMID: 29893753 PMCID: PMC5996851 DOI: 10.1038/sdata.2018.105] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Accepted: 02/23/2018] [Indexed: 01/11/2023] Open
Abstract
Functional Magnetic Resonance Imaging (fMRI) has furthered brain mapping on perceptual, motor, as well as higher-level cognitive functions. However, to date, no data collection has systematically addressed the functional mapping of cognitive mechanisms at a fine spatial scale. The Individual Brain Charting (IBC) project stands for a high-resolution multi-task fMRI dataset that intends to provide the objective basis toward a comprehensive functional atlas of the human brain. The data refer to a cohort of 12 participants performing many different tasks. The large amount of task-fMRI data on the same subjects yields a precise mapping of the underlying functions, free from both inter-subject and inter-site variability. The present article gives a detailed description of the first release of the IBC dataset. It comprises a dozen of tasks, addressing both low- and high- level cognitive functions. This openly available dataset is thus intended to become a reference for cognitive brain mapping.
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Affiliation(s)
- Ana Luísa Pinho
- Parietal Team, Inria, Saclay, France
- Neurospin, CEA, Saclay, France
- Paris-Saclay University, Paris, France
| | | | - Torsten Ruest
- Parietal Team, Inria, Saclay, France
- Neurospin, CEA, Saclay, France
- Paris-Saclay University, Paris, France
| | - Murielle Fabre
- Neurospin, CEA, Saclay, France
- Paris-Saclay University, Paris, France
- Cognitive Neuroimaging Unit, Saclay, France
- INSERM, Paris, France
- Paris-Sud University, Paris, France
| | - Elvis Dohmatob
- Parietal Team, Inria, Saclay, France
- Neurospin, CEA, Saclay, France
- Paris-Saclay University, Paris, France
| | - Isabelle Denghien
- Neurospin, CEA, Saclay, France
- Paris-Saclay University, Paris, France
- Cognitive Neuroimaging Unit, Saclay, France
- INSERM, Paris, France
- Paris-Sud University, Paris, France
| | | | | | - Séverine Roger
- Neurospin, CEA, Saclay, France
- UNIACT-U1129, Paris, France
| | | | | | | | | | | | | | - Evelyn Eger
- Neurospin, CEA, Saclay, France
- Paris-Saclay University, Paris, France
- Cognitive Neuroimaging Unit, Saclay, France
- INSERM, Paris, France
- Paris-Sud University, Paris, France
| | - Gaël Varoquaux
- Parietal Team, Inria, Saclay, France
- Neurospin, CEA, Saclay, France
- Paris-Saclay University, Paris, France
| | - Christophe Pallier
- Neurospin, CEA, Saclay, France
- Paris-Saclay University, Paris, France
- Cognitive Neuroimaging Unit, Saclay, France
- INSERM, Paris, France
- Paris-Sud University, Paris, France
| | - Stanislas Dehaene
- Neurospin, CEA, Saclay, France
- Paris-Saclay University, Paris, France
- Cognitive Neuroimaging Unit, Saclay, France
- INSERM, Paris, France
- Paris-Sud University, Paris, France
- Collège de France, Paris, France
| | - Lucie Hertz-Pannier
- Neurospin, CEA, Saclay, France
- INSERM, Paris, France
- UNIACT-U1129, Paris, France
- Paris Descartes University, Paris, France
| | - Bertrand Thirion
- Parietal Team, Inria, Saclay, France
- Neurospin, CEA, Saclay, France
- Paris-Saclay University, Paris, France
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Dohmatob E, Varoquaux G, Thirion B. Inter-subject Registration of Functional Images: Do We Need Anatomical Images? Front Neurosci 2018; 12:64. [PMID: 29497357 PMCID: PMC5819565 DOI: 10.3389/fnins.2018.00064] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 01/26/2018] [Indexed: 01/29/2023] Open
Abstract
In Echo-Planar Imaging (EPI)-based Magnetic Resonance Imaging (MRI), inter-subject registration typically uses the subject's T1-weighted (T1w) anatomical image to learn deformations of the subject's brain onto a template. The estimated deformation fields are then applied to the subject's EPI scans (functional or diffusion-weighted images) to warp the latter to a template space. Historically, such indirect T1w-based registration was motivated by the lack of clear anatomical details in low-resolution EPI images: a direct registration of the EPI scans to template space would be futile. A central prerequisite in such indirect methods is that the anatomical (aka the T1w) image of each subject is well aligned with their EPI images via rigid coregistration. We provide experimental evidence that things have changed: nowadays, there is a decent amount of anatomical contrast in high-resolution EPI data. That notwithstanding, EPI distortions due to B0 inhomogeneities cannot be fully corrected. Residual uncorrected distortions induce non-rigid deformations between the EPI scans and the same subject's anatomical scan. In this manuscript, we contribute a computationally cheap pipeline that leverages the high spatial resolution of modern EPI scans for direct inter-subject matching. Our pipeline is direct and does not rely on the T1w scan to estimate the inter-subject deformation. Results on a large dataset show that this new pipeline outperforms the classical indirect T1w-based registration scheme, across a variety of post-registration quality-assessment metrics including: Normalized Mutual Information, relative variance (variance-to-mean ratio), and to a lesser extent, improved peaks of group-level General Linear Model (GLM) activation maps.
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Affiliation(s)
- Elvis Dohmatob
- Parietal Team, INRIA, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Gael Varoquaux
- Parietal Team, INRIA, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Bertrand Thirion
- Parietal Team, INRIA, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
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Abstract
Analysis and interpretation of neuroimaging data often require one to divide the brain into a number of regions, or parcels, with homogeneous characteristics, be these regions defined in the brain volume or on the cortical surface. While predefined brain atlases do not adapt to the signal in the individual subject images, parcellation approaches use brain activity (e.g., found in some functional contrasts of interest) and clustering techniques to define regions with some degree of signal homogeneity. In this work, we address the question of which clustering technique is appropriate and how to optimize the corresponding model. We use two principled criteria: goodness of fit (accuracy), and reproducibility of the parcellation across bootstrap samples. We study these criteria on both simulated and two task-based functional Magnetic Resonance Imaging datasets for the Ward, spectral and k-means clustering algorithms. We show that in general Ward’s clustering performs better than alternative methods with regard to reproducibility and accuracy and that the two criteria diverge regarding the preferred models (reproducibility leading to more conservative solutions), thus deferring the practical decision to a higher level alternative, namely the choice of a trade-off between accuracy and stability.
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Affiliation(s)
- Bertrand Thirion
- Parietal Project-Team, Institut National de Recherche en Informatique et Automatique Palaiseau, France ; Commissariat à l'énergie Atomique et Aux Énergies Alternatives, DSV, Neurospin, I2 BM Gif-sur-Yvette, France
| | - Gaël Varoquaux
- Parietal Project-Team, Institut National de Recherche en Informatique et Automatique Palaiseau, France ; Commissariat à l'énergie Atomique et Aux Énergies Alternatives, DSV, Neurospin, I2 BM Gif-sur-Yvette, France
| | - Elvis Dohmatob
- Parietal Project-Team, Institut National de Recherche en Informatique et Automatique Palaiseau, France ; Commissariat à l'énergie Atomique et Aux Énergies Alternatives, DSV, Neurospin, I2 BM Gif-sur-Yvette, France
| | - Jean-Baptiste Poline
- Commissariat à l'énergie Atomique et Aux Énergies Alternatives, DSV, Neurospin, I2 BM Gif-sur-Yvette, France ; Henry H. Wheeler Jr. Brain Imaging Center, University of California at Berkeley Berkeley, CA, USA
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