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Seidel Malkinson T, Bayle DJ, Kaufmann BC, Liu J, Bourgeois A, Lehongre K, Fernandez-Vidal S, Navarro V, Lambrecq V, Adam C, Margulies DS, Sitt JD, Bartolomeo P. Intracortical recordings reveal vision-to-action cortical gradients driving human exogenous attention. Nat Commun 2024; 15:2586. [PMID: 38531880 DOI: 10.1038/s41467-024-46013-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 02/09/2024] [Indexed: 03/28/2024] Open
Abstract
Exogenous attention, the process that makes external salient stimuli pop-out of a visual scene, is essential for survival. How attention-capturing events modulate human brain processing remains unclear. Here we show how the psychological construct of exogenous attention gradually emerges over large-scale gradients in the human cortex, by analyzing activity from 1,403 intracortical contacts implanted in 28 individuals, while they performed an exogenous attention task. The timing, location and task-relevance of attentional events defined a spatiotemporal gradient of three neural clusters, which mapped onto cortical gradients and presented a hierarchy of timescales. Visual attributes modulated neural activity at one end of the gradient, while at the other end it reflected the upcoming response timing, with attentional effects occurring at the intersection of visual and response signals. These findings challenge multi-step models of attention, and suggest that frontoparietal networks, which process sequential stimuli as separate events sharing the same location, drive exogenous attention phenomena such as inhibition of return.
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Affiliation(s)
- Tal Seidel Malkinson
- Sorbonne Université, Inserm UMRS 1127, CNRS UMR 7225, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France.
- Université de Lorraine, CNRS, IMoPA, F-54000, Nancy, France.
| | - Dimitri J Bayle
- Licae Lab, Université Paris Ouest-La Défense, 92000, Nanterre, France
| | - Brigitte C Kaufmann
- Sorbonne Université, Inserm UMRS 1127, CNRS UMR 7225, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
| | - Jianghao Liu
- Sorbonne Université, Inserm UMRS 1127, CNRS UMR 7225, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
- Dassault Systèmes, Vélizy-Villacoublay, France
| | - Alexia Bourgeois
- Laboratory of Cognitive Neurorehabilitation, Faculty of Medicine, University of Geneva, 1206, Geneva, Switzerland
| | - Katia Lehongre
- CENIR - Centre de Neuro-Imagerie de Recherche, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
| | - Sara Fernandez-Vidal
- CENIR - Centre de Neuro-Imagerie de Recherche, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
| | - Vincent Navarro
- Sorbonne Université, Inserm UMRS 1127, CNRS UMR 7225, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
- AP-HP, Epilepsy and EEG Units, Pitié-Salpêtrière Hospital, 75013, Paris, France
- Reference center of rare epilepsies, EpiCare, Pitié-Salpêtrière Hospital, 75013, Paris, France
| | - Virginie Lambrecq
- Sorbonne Université, Inserm UMRS 1127, CNRS UMR 7225, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
- AP-HP, Epilepsy and EEG Units, Pitié-Salpêtrière Hospital, 75013, Paris, France
- Reference center of rare epilepsies, EpiCare, Pitié-Salpêtrière Hospital, 75013, Paris, France
| | - Claude Adam
- Sorbonne Université, Inserm UMRS 1127, CNRS UMR 7225, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
- AP-HP, Epilepsy and EEG Units, Pitié-Salpêtrière Hospital, 75013, Paris, France
- Reference center of rare epilepsies, EpiCare, Pitié-Salpêtrière Hospital, 75013, Paris, France
| | - Daniel S Margulies
- Laboratoire INCC, équipe Perception, Action, Cognition, Université de Paris, 75005, Paris, France
| | - Jacobo D Sitt
- Sorbonne Université, Inserm UMRS 1127, CNRS UMR 7225, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
| | - Paolo Bartolomeo
- Sorbonne Université, Inserm UMRS 1127, CNRS UMR 7225, Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière, 75013, Paris, France
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2
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Tan JB, Müller EJ, Orlando IF, Taylor NL, Margulies DS, Szeto J, Lewis SJG, Shine JM, O'Callaghan C. Abnormal higher-order network interactions in Parkinson's disease visual hallucinations. Brain 2024; 147:458-471. [PMID: 37677056 DOI: 10.1093/brain/awad305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 04/11/2023] [Revised: 07/14/2023] [Accepted: 08/11/2023] [Indexed: 09/09/2023] Open
Abstract
Visual hallucinations in Parkinson's disease can be viewed from a systems-level perspective, whereby dysfunctional communication between brain networks responsible for perception predisposes a person to hallucinate. To this end, abnormal functional interactions between higher-order and primary sensory networks have been implicated in the pathophysiology of visual hallucinations in Parkinson's disease, however the precise signatures remain to be determined. Dimensionality reduction techniques offer a novel means for simplifying the interpretation of multidimensional brain imaging data, identifying hierarchical patterns in the data that are driven by both within- and between-functional network changes. Here, we applied two complementary non-linear dimensionality reduction techniques-diffusion-map embedding and t-distributed stochastic neighbour embedding (t-SNE)-to resting state functional MRI data, in order to characterize the altered functional hierarchy associated with susceptibility to visual hallucinations. Our study involved 77 people with Parkinson's disease (31 with hallucinations; 46 without hallucinations) and 19 age-matched healthy control subjects. In patients with visual hallucinations, we found compression of the unimodal-heteromodal gradient consistent with increased functional integration between sensory and higher order networks. This was mirrored in a traditional functional connectivity analysis, which showed increased connectivity between the visual and default mode networks in the hallucinating group. Together, these results suggest a route by which higher-order regions may have excessive influence over earlier sensory processes, as proposed by theoretical models of hallucinations across disorders. By contrast, the t-SNE analysis identified distinct alterations in prefrontal regions, suggesting an additional layer of complexity in the functional brain network abnormalities implicated in hallucinations, which was not apparent in traditional functional connectivity analyses. Together, the results confirm abnormal brain organization associated with the hallucinating phenotype in Parkinson's disease and highlight the utility of applying convergent dimensionality reduction techniques to investigate complex clinical symptoms. In addition, the patterns we describe in Parkinson's disease converge with those seen in other conditions, suggesting that reduced hierarchical differentiation across sensory-perceptual systems may be a common transdiagnostic vulnerability in neuropsychiatric disorders with perceptual disturbances.
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Affiliation(s)
- Joshua B Tan
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
| | - Eli J Müller
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
- Centre for Complex Systems, School of Physics, University of Sydney, Sydney 2050, Australia
| | - Isabella F Orlando
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
| | - Natasha L Taylor
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
| | - Daniel S Margulies
- Integrative Neuroscience and Cognition Center, Center National de la Recherche Scientifique (CNRS) and Université de Paris, 75006 Paris, France
| | - Jennifer Szeto
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
| | - Simon J G Lewis
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
| | - James M Shine
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
- Centre for Complex Systems, School of Physics, University of Sydney, Sydney 2050, Australia
| | - Claire O'Callaghan
- Brain and Mind Centre, School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney 2050, Australia
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3
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Leech R, Vos De Wael R, Váša F, Xu T, Austin Benn R, Scholz R, Braga RM, Milham MP, Royer J, Bernhardt BC, Jones EJH, Jefferies E, Margulies DS, Smallwood J. Variation in spatial dependencies across the cortical mantle discriminates the functional behaviour of primary and association cortex. Nat Commun 2023; 14:5656. [PMID: 37704600 PMCID: PMC10499916 DOI: 10.1038/s41467-023-41334-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [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: 01/12/2023] [Accepted: 08/29/2023] [Indexed: 09/15/2023] Open
Abstract
Recent theories of cortical organisation suggest features of function emerge from the spatial arrangement of brain regions. For example, association cortex is located furthest from systems involved in action and perception. Association cortex is also 'interdigitated' with adjacent regions having different patterns of functional connectivity. It is assumed that topographic properties, such as distance between regions, constrains their functions, however, we lack a formal description of how this occurs. Here we use variograms, a quantification of spatial autocorrelation, to profile how function changes with the distance between cortical regions. We find function changes with distance more gradually within sensory-motor cortex than association cortex. Importantly, systems within the same type of cortex (e.g., fronto-parietal and default mode networks) have similar profiles. Primary and association cortex, therefore, are differentiated by how function changes over space, emphasising the value of topographical features of a region when estimating its contribution to cognition and behaviour.
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Affiliation(s)
- Robert Leech
- Centre for Neuroimaging Science, King's College London, London, UK.
| | | | - František Váša
- Centre for Neuroimaging Science, King's College London, London, UK
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, USA
| | - R Austin Benn
- Integrative Neuroscience and Cognition Center (UMR 8002), Centre National de la Recherche Scientifique (CNRS) and Université de Paris, Paris, France
| | | | - Rodrigo M Braga
- Neurology, Interdepartmental Neuroscience Program, Northwestern University, Evanston, IL, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, USA
| | - Jessica Royer
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
| | - Emily J H Jones
- Centre for Brain and Cognitive Development, Birkbeck College, University of London, London, UK
| | | | - Daniel S Margulies
- Integrative Neuroscience and Cognition Center (UMR 8002), Centre National de la Recherche Scientifique (CNRS) and Université de Paris, Paris, France
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4
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Froudist-Walsh S, Xu T, Niu M, Rapan L, Zhao L, Margulies DS, Zilles K, Wang XJ, Palomero-Gallagher N. Gradients of neurotransmitter receptor expression in the macaque cortex. Nat Neurosci 2023; 26:1281-1294. [PMID: 37336976 PMCID: PMC10322721 DOI: 10.1038/s41593-023-01351-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [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: 02/28/2021] [Accepted: 05/01/2023] [Indexed: 06/21/2023]
Abstract
Dynamics and functions of neural circuits depend on interactions mediated by receptors. Therefore, a comprehensive map of receptor organization across cortical regions is needed. In this study, we used in vitro receptor autoradiography to measure the density of 14 neurotransmitter receptor types in 109 areas of macaque cortex. We integrated the receptor data with anatomical, genetic and functional connectivity data into a common cortical space. We uncovered a principal gradient of receptor expression per neuron. This aligns with the cortical hierarchy from sensory cortex to higher cognitive areas. A second gradient, driven by serotonin 5-HT1A receptors, peaks in the anterior cingulate, default mode and salience networks. We found a similar pattern of 5-HT1A expression in the human brain. Thus, the macaque may be a promising translational model of serotonergic processing and disorders. The receptor gradients may enable rapid, reliable information processing in sensory cortical areas and slow, flexible integration in higher cognitive areas.
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MESH Headings
- Aged
- Animals
- Female
- Humans
- Male
- Rats
- Autoradiography
- Brain Mapping
- Cerebral Cortex/cytology
- Cerebral Cortex/metabolism
- Cognition
- Dendritic Spines
- Gyrus Cinguli/cytology
- Gyrus Cinguli/metabolism
- Macaca fascicularis
- Rats, Inbred Lew
- Receptor, Serotonin, 5-HT1A/analysis
- Receptor, Serotonin, 5-HT1A/metabolism
- Receptors, Cholinergic/analysis
- Receptors, Cholinergic/metabolism
- Receptors, Dopamine/analysis
- Receptors, Dopamine/metabolism
- Receptors, Neurotransmitter/analysis
- Receptors, Neurotransmitter/metabolism
- Serotonin/metabolism
- Species Specificity
- Myelin Sheath/metabolism
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Affiliation(s)
- Sean Froudist-Walsh
- Computational Neuroscience Unit, Faculty of Engineering, University of Bristol, Bristol, UK
- Center for Neural Science, New York University, New York, NY, USA
| | - Ting Xu
- Child Mind Institute, New York, NY, USA
| | - Meiqi Niu
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Lucija Rapan
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Ling Zhao
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Daniel S Margulies
- Integrative Neuroscience and Cognition Center, University of Paris Cité, Paris, France
| | | | - Xiao-Jing Wang
- Center for Neural Science, New York University, New York, NY, USA
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.
- Cécile and Oskar Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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Samara A, Eilbott J, Margulies DS, Xu T, Vanderwal T. Cortical gradients during naturalistic processing are hierarchical and modality-specific. Neuroimage 2023; 271:120023. [PMID: 36921679 DOI: 10.1016/j.neuroimage.2023.120023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 02/21/2023] [Accepted: 03/10/2023] [Indexed: 03/14/2023] Open
Abstract
Understanding cortical topographic organization and how it supports complex perceptual and cognitive processes is a fundamental question in neuroscience. Previous work has characterized functional gradients that demonstrate large-scale principles of cortical organization. How these gradients are modulated by rich ecological stimuli remains unknown. Here, we utilize naturalistic stimuli via movie-fMRI to assess macroscale functional organization. We identify principal movie gradients that delineate separate hierarchies anchored in sensorimotor, visual, and auditory/language areas. At the opposite/heteromodal end of these perception-to-cognition axes, we find a more central role for the frontoparietal network along with the default network. Even across different movie stimuli, movie gradients demonstrated good reliability, suggesting that these hierarchies reflect a brain state common across different naturalistic conditions. The relative position of brain areas within movie gradients showed stronger and more numerous correlations with cognitive behavioral scores compared to resting state gradients. Together, these findings provide an ecologically valid representation of the principles underlying cortical organization while the brain is active and engaged in multimodal, dynamic perceptual and cognitive processing.
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Affiliation(s)
- Ahmad Samara
- Department of Psychiatry, University of British Columbia, 2255 Wesbrook Mall, Vancouver, BC V6T 2A1, Canada
| | - Jeffrey Eilbott
- BC Children's Hospital Research Institute, Vancouver, BC V5Z 4H4, Canada
| | - Daniel S Margulies
- CNRS, Integrative Neuroscience and Cognition Center (UMR 8002), Université de Paris, Paris 75006, France
| | - Ting Xu
- Center for the Developing Brain, The Child Mind Institute, New York, NY 10022, USA
| | - Tamara Vanderwal
- Department of Psychiatry, University of British Columbia, 2255 Wesbrook Mall, Vancouver, BC V6T 2A1, Canada; BC Children's Hospital Research Institute, Vancouver, BC V5Z 4H4, Canada; Yale Child Study Center, Yale University, New Haven, CT 06519, USA.
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6
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Harry BB, Margulies DS, Falkiewicz M, Keller PE. Brain networks for temporal adaptation, anticipation, and sensory-motor integration in rhythmic human behavior. Neuropsychologia 2023; 183:108524. [PMID: 36868500 DOI: 10.1016/j.neuropsychologia.2023.108524] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 01/21/2023] [Accepted: 02/22/2023] [Indexed: 03/05/2023]
Abstract
Human interaction often requires the precise yet flexible interpersonal coordination of rhythmic behavior, as in group music making. The present fMRI study investigates the functional brain networks that may facilitate such behavior by enabling temporal adaptation (error correction), prediction, and the monitoring and integration of information about 'self' and the external environment. Participants were required to synchronize finger taps with computer-controlled auditory sequences that were presented either at a globally steady tempo with local adaptations to the participants' tap timing (Virtual Partner task) or with gradual tempo accelerations and decelerations but without adaptation (Tempo Change task). Connectome-based predictive modelling was used to examine patterns of brain functional connectivity related to individual differences in behavioral performance and parameter estimates from the adaptation and anticipation model (ADAM) of sensorimotor synchronization for these two tasks under conditions of varying cognitive load. Results revealed distinct but overlapping brain networks associated with ADAM-derived estimates of temporal adaptation, anticipation, and the integration of self-controlled and externally controlled processes across task conditions. The partial overlap between ADAM networks suggests common hub regions that modulate functional connectivity within and between the brain's resting-state networks and additional sensory-motor regions and subcortical structures in a manner reflecting coordination skill. Such network reconfiguration might facilitate sensorimotor synchronization by enabling shifts in focus on internal and external information, and, in social contexts requiring interpersonal coordination, variations in the degree of simultaneous integration and segregation of these information sources in internal models that support self, other, and joint action planning and prediction.
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Affiliation(s)
- Bronson B Harry
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, Australia.
| | - Daniel S Margulies
- Integrative Neuroscience and Cognition Center, Centre National de la Recherche Scientifique (CNRS) and Université de Paris, Paris, France; Max Planck Research Group for Neuroanatomy and Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Marcel Falkiewicz
- Max Planck Research Group for Neuroanatomy and Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Peter E Keller
- The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Sydney, Australia; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark.
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Nenning KH, Xu T, Franco AR, Swallow K, Tambini A, Margulies DS, Smallwood J, Colcombe SJ, Milham MP. Omnipresence of the sensorimotor-association axis topography in the human connectome. Neuroimage 2023; 272:120059. [PMID: 37001835 DOI: 10.1016/j.neuroimage.2023.120059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 03/04/2023] [Accepted: 03/27/2023] [Indexed: 04/03/2023] Open
Abstract
Low-dimensional representations are increasingly used to study meaningful organizational principles within the human brain. Most notably, the sensorimotor-association axis consistently explains the most variance in the human connectome as its so-called principal gradient, suggesting that it represents a fundamental organizational principle. While recent work indicates these low dimensional representations are relatively robust, they are limited by modeling only certain aspects of the functional connectivity structure. To date, the majority of studies have restricted these approaches to the strongest connections in the brain, treating weaker or negative connections as noise despite evidence of meaningful structure among them. The present work examines connectivity gradients of the human connectome across a full range of connectivity strengths and explores the implications for outcomes of individual differences, identifying potential dependencies on thresholds and opportunities to improve prediction tasks. Interestingly, the sensorimotor-association axis emerged as the principal gradient of the human connectome across the entire range of connectivity levels. Moreover, the principal gradient of connections at intermediate strengths encoded individual differences, better followed individual-specific anatomical features, and was also more predictive of intelligence. Taken together, our results add to evidence of the sensorimotor-association axis as a fundamental principle of the brain's functional organization, since it is evident even in the connectivity structure of more lenient connectivity thresholds. These more loosely coupled connections further appear to contain valuable and potentially important information that could be used to improve our understanding of individual differences, diagnosis, and the prediction of treatment outcomes.
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8
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Wang Y, Royer J, Park BY, Vos de Wael R, Larivière S, Tavakol S, Rodriguez-Cruces R, Paquola C, Hong SJ, Margulies DS, Smallwood J, Valk SL, Evans AC, Bernhardt BC. Long-range functional connections mirror and link microarchitectural and cognitive hierarchies in the human brain. Cereb Cortex 2023; 33:1782-1798. [PMID: 35596951 PMCID: PMC9977370 DOI: 10.1093/cercor/bhac172] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 03/30/2022] [Accepted: 04/01/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Higher-order cognition is hypothesized to be implemented via distributed cortical networks that are linked via long-range connections. However, it is unknown how computational advantages of long-range connections reflect cortical microstructure and microcircuitry. METHODS We investigated this question by (i) profiling long-range cortical connectivity using resting-state functional magnetic resonance imaging (MRI) and cortico-cortical geodesic distance mapping, (ii) assessing how long-range connections reflect local brain microarchitecture, and (iii) examining the microarchitectural similarity of regions connected through long-range connections. RESULTS Analysis of 2 independent datasets indicated that sensory/motor areas had more clustered short-range connections, while transmodal association systems hosted distributed, long-range connections. Meta-analytical decoding suggested that this topographical difference mirrored shifts in cognitive function, from perception/action towards emotional/social processing. Analysis of myelin-sensitive in vivo MRI as well as postmortem histology and transcriptomics datasets established that gradients in functional connectivity distance are paralleled by those present in cortical microarchitecture. Notably, long-range connections were found to link spatially remote regions of association cortex with an unexpectedly similar microarchitecture. CONCLUSIONS By mapping covarying topographies of long-range functional connections and cortical microcircuits, the current work provides insights into structure-function relations in human neocortex.
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Affiliation(s)
- Yezhou Wang
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| | - Bo-Yong Park
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada.,Department of Data Science, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, South Korea.,Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Seobu-ro 2066, Jangan-gu, Suwon 16419, South Korea
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| | - Raul Rodriguez-Cruces
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Seok-Jun Hong
- Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Seobu-ro 2066, Jangan-gu, Suwon 16419, South Korea.,Department of Biomedical Engineering, Sungkyunkwan University, Seobu-ro 2066, Jangan-gu, Suwon 16419, South Korea
| | - Daniel S Margulies
- Cognitive Neuroanatomy Lab, Integrative Neuroscience and Cognition Centre, University of Paris and CRNS, INCC - UMR 8002, Rue des Saint-Pères 75006, Paris
| | - Jonathan Smallwood
- Department of Psychology, Queen's University, 62 Arch Street, Humphrey Hall, Room 232 Kingston, Ontario K7L 3N6, Canada
| | - Sofie L Valk
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.,Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1A. Leipzig D-04103, Germany.,Institute of Systems Neuroscience, Heinrich Heine University, Moorenstr. 5, Düsseldorf 40225, Germany
| | - Alan C Evans
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Department of Neurology and Neurosurgery and Montreal Neurological Institute and Hospital, McGill University, 3801 University Street, Montreal, Quebec H3A2B4, Canada
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9
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Dong D, Yao D, Wang Y, Hong SJ, Genon S, Xin F, Jung K, He H, Chang X, Duan M, Bernhardt BC, Margulies DS, Sepulcre J, Eickhoff SB, Luo C. Compressed sensorimotor-to-transmodal hierarchical organization in schizophrenia. Psychol Med 2023; 53:771-784. [PMID: 34100349 DOI: 10.1017/s0033291721002129] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
BACKGROUND Schizophrenia has been primarily conceptualized as a disorder of high-order cognitive functions with deficits in executive brain regions. Yet due to the increasing reports of early sensory processing deficit, recent models focus more on the developmental effects of impaired sensory process on high-order functions. The present study examined whether this pathological interaction relates to an overarching system-level imbalance, specifically a disruption in macroscale hierarchy affecting integration and segregation of unimodal and transmodal networks. METHODS We applied a novel combination of connectome gradient and stepwise connectivity analysis to resting-state fMRI to characterize the sensorimotor-to-transmodal cortical hierarchy organization (96 patients v. 122 controls). RESULTS We demonstrated compression of the cortical hierarchy organization in schizophrenia, with a prominent compression from the sensorimotor region and a less prominent compression from the frontal-parietal region, resulting in a diminished separation between sensory and fronto-parietal cognitive systems. Further analyses suggested reduced differentiation related to atypical functional connectome transition from unimodal to transmodal brain areas. Specifically, we found hypo-connectivity within unimodal regions and hyper-connectivity between unimodal regions and fronto-parietal and ventral attention regions along the classical sensation-to-cognition continuum (voxel-level corrected, p < 0.05). CONCLUSIONS The compression of cortical hierarchy organization represents a novel and integrative system-level substrate underlying the pathological interaction of early sensory and cognitive function in schizophrenia. This abnormal cortical hierarchy organization suggests cascading impairments from the disruption of the somatosensory-motor system and inefficient integration of bottom-up sensory information with attentional demands and executive control processes partially account for high-level cognitive deficits characteristic of schizophrenia.
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Affiliation(s)
- Debo Dong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, China
- Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, 2019RU035, Chengdu, China
| | - Yulin Wang
- Faculty of Psychological and Educational Sciences, Department of Experimental and Applied Psychology, Vrije Universiteit Brussel, Belgium
- Faculty of Psychology and Educational Sciences, Department of Data Analysis, Ghent University, Belgium
| | - Seok-Jun Hong
- Center for the Developing Brain, Child Mind Institute, NY, USA
- Department of Biomedical Engineering, Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, South Korea
| | - Sarah Genon
- Institute for Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Fei Xin
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, China
| | - Kyesam Jung
- Institute for Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, China
- Department of Psychiatry, The Fourth People's Hospital of Chengdu, Chengdu, China
| | - Xuebin Chang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, China
| | - Mingjun Duan
- Department of Psychiatry, The Fourth People's Hospital of Chengdu, Chengdu, China
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique (CNRS) UMR 7225, Institut du Cerveau et de la Moelle épinière, Paris, France
| | - Jorge Sepulcre
- Department of Radiology, Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, School of Life Science and Technology, University of Electronic Science and Technology of China, China
- Department of Neurology, Brain Disorders and Brain Function Key Laboratory, First Affiliated Hospital of Hainan Medical University, Haikou, China
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10
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Setton R, Mwilambwe-Tshilobo L, Girn M, Lockrow AW, Baracchini G, Hughes C, Lowe AJ, Cassidy BN, Li J, Luh WM, Bzdok D, Leahy RM, Ge T, Margulies DS, Misic B, Bernhardt BC, Stevens WD, De Brigard F, Kundu P, Turner GR, Spreng RN. Age differences in the functional architecture of the human brain. Cereb Cortex 2022; 33:114-134. [PMID: 35231927 PMCID: PMC9758585 DOI: 10.1093/cercor/bhac056] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [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: 11/07/2021] [Revised: 01/04/2022] [Accepted: 01/26/2022] [Indexed: 11/12/2022] Open
Abstract
The intrinsic functional organization of the brain changes into older adulthood. Age differences are observed at multiple spatial scales, from global reductions in modularity and segregation of distributed brain systems, to network-specific patterns of dedifferentiation. Whether dedifferentiation reflects an inevitable, global shift in brain function with age, circumscribed, experience-dependent changes, or both, is uncertain. We employed a multimethod strategy to interrogate dedifferentiation at multiple spatial scales. Multi-echo (ME) resting-state fMRI was collected in younger (n = 181) and older (n = 120) healthy adults. Cortical parcellation sensitive to individual variation was implemented for precision functional mapping of each participant while preserving group-level parcel and network labels. ME-fMRI processing and gradient mapping identified global and macroscale network differences. Multivariate functional connectivity methods tested for microscale, edge-level differences. Older adults had lower BOLD signal dimensionality, consistent with global network dedifferentiation. Gradients were largely age-invariant. Edge-level analyses revealed discrete, network-specific dedifferentiation patterns in older adults. Visual and somatosensory regions were more integrated within the functional connectome; default and frontoparietal control network regions showed greater connectivity; and the dorsal attention network was more integrated with heteromodal regions. These findings highlight the importance of multiscale, multimethod approaches to characterize the architecture of functional brain aging.
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Affiliation(s)
- Roni Setton
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Laetitia Mwilambwe-Tshilobo
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Manesh Girn
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Amber W Lockrow
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Giulia Baracchini
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Colleen Hughes
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | | | | | - Jian Li
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Wen-Ming Luh
- National Institutes of Health, National Institute on Aging, Baltimore, MD, USA
| | - Danilo Bzdok
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
- School of Computer Science, McGill University, Montreal, QC, Canada
- Mila – Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Richard M Leahy
- Department of Electrical Engineering-Systems, University of Southern California, Los Angeles, CA, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Daniel S Margulies
- Integrative Neuroscience and Cognition Center (UMR 8002), Centre National de la Recherche Scientifique (CNRS) and Université de Paris, Paris, France
| | - Bratislav Misic
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
| | - Boris C Bernhardt
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
| | - W Dale Stevens
- Department of Psychology, York University, Toronto, ON, Canada
| | - Felipe De Brigard
- Department of Philosophy, Duke University, Durham, NC, USA
- Department of Psychology and Neuroscience, Durham, NC, USA
| | - Prantik Kundu
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gary R Turner
- Department of Psychology, York University, Toronto, ON, Canada
| | - R Nathan Spreng
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, McGill University, Montreal, QC, Canada
- Departments of Psychiatry and Psychology, McGill University, Montreal, QC, Canada
- Douglas Mental Health University Institute, Verdun, QC, Canada
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11
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Vieira BH, Liem F, Dadi K, Engemann DA, Gramfort A, Bellec P, Craddock RC, Damoiseaux JS, Steele CJ, Yarkoni T, Langer N, Margulies DS, Varoquaux G. Predicting future cognitive decline from non-brain and multimodal brain imaging data in healthy and pathological aging. Neurobiol Aging 2022; 118:55-65. [PMID: 35878565 PMCID: PMC9853405 DOI: 10.1016/j.neurobiolaging.2022.06.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 06/21/2022] [Accepted: 06/23/2022] [Indexed: 01/24/2023]
Abstract
Previous literature has focused on predicting a diagnostic label from structural brain imaging. Since subtle changes in the brain precede a cognitive decline in healthy and pathological aging, our study predicts future decline as a continuous trajectory instead. Here, we tested whether baseline multimodal neuroimaging data improve the prediction of future cognitive decline in healthy and pathological aging. Nonbrain data (demographics, clinical, and neuropsychological scores), structural MRI, and functional connectivity data from OASIS-3 (N = 662; age = 46-96 years) were entered into cross-validated multitarget random forest models to predict future cognitive decline (measured by CDR and MMSE), on average 5.8 years into the future. The analysis was preregistered, and all analysis code is publicly available. Combining non-brain with structural data improved the continuous prediction of future cognitive decline (best test-set performance: R2 = 0.42). Cognitive performance, daily functioning, and subcortical volume drove the performance of our model. Including functional connectivity did not improve predictive accuracy. In the future, the prognosis of age-related cognitive decline may enable earlier and more effective individualized cognitive, pharmacological, and behavioral interventions.
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Affiliation(s)
- Bruno Hebling Vieira
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland,Neuroscience Center Zurich (ZNZ), University of Zurich & ETH Zurich, Zurich, Switzerland,Corresponding author. (B. Hebling Vieira)
| | - Franziskus Liem
- University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Zurich, Switzerland
| | | | - Denis A. Engemann
- UniversitéParis-Saclay, Inria, CEA, Palaiseau, France,Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | | | - Pierre Bellec
- Functional Neuroimaging Unit, Geriatric Institute, University of Montreal, Montreal, Quebec, Canada
| | | | - Jessica S. Damoiseaux
- Institute of Gerontology and the Department of Psychology, Wayne State University, Detroit, MI, USA
| | | | - Tal Yarkoni
- Department of Psychology, The University of Texas, Austin, TX, USA
| | - Nicolas Langer
- Methods of Plasticity Research, Department of Psychology, University of Zurich, Zurich, Switzerland,Neuroscience Center Zurich (ZNZ), University of Zurich & ETH Zurich, Zurich, Switzerland,University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Zurich, Switzerland
| | - Daniel S. Margulies
- Cognitive Neuroanatomy Lab, Institut du Cerveau et de la Moelle épinière, Paris, France
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12
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Gao Z, Zheng L, Krieger-Redwood K, Halai A, Margulies DS, Smallwood J, Jefferies E. Flexing the principal gradient of the cerebral cortex to suit changing semantic task demands. eLife 2022; 11:80368. [PMID: 36169281 PMCID: PMC9555860 DOI: 10.7554/elife.80368] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [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: 05/18/2022] [Accepted: 09/27/2022] [Indexed: 11/13/2022] Open
Abstract
Understanding how thought emerges from the topographical structure of the cerebral cortex is a primary goal of cognitive neuroscience. Recent work has revealed a principal gradient of intrinsic connectivity capturing the separation of sensory-motor cortex from transmodal regions of the default mode network (DMN); this is thought to facilitate memory-guided cognition. However, studies have not explored how this dimension of connectivity changes when conceptual retrieval is controlled to suit the context. We used gradient decomposition of informational connectivity in a semantic association task to establish how the similarity in connectivity across brain regions changes during familiar and more original patterns of retrieval. Multivoxel activation patterns at opposite ends of the principal gradient were more divergent when participants retrieved stronger associations; therefore, when long-term semantic information is sufficient for ongoing cognition, regions supporting heteromodal memory are functionally separated from sensory-motor experience. In contrast, when less related concepts were linked, this dimension of connectivity was reduced in strength as semantic control regions separated from the DMN to generate more flexible and original responses. We also observed fewer dimensions within the neural response towards the apex of the principal gradient when strong associations were retrieved, reflecting less complex or varied neural coding across trials and participants. In this way, the principal gradient explains how semantic cognition is organised in the human cerebral cortex: the separation of DMN from sensory-motor systems is a hallmark of the retrieval of strong conceptual links that are culturally shared.
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Affiliation(s)
- Zhiyao Gao
- Department of Psychology, University of York, York, United Kingdom
| | - Li Zheng
- Department of Psychology, University of Arizona, Tucson, United States
| | | | - Ajay Halai
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Daniel S Margulies
- Integrative Neuroscience and Cognition Center (UMR 8002), Centre National de la Recherche Scientifique, Paris, France
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13
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Zhang H, Zhao R, Hu X, Guan S, Margulies DS, Meng C, Biswal BB. Cortical connectivity gradients and local timescales during cognitive states are modulated by cognitive loads. Brain Struct Funct 2022; 227:2701-2712. [PMID: 36098843 DOI: 10.1007/s00429-022-02564-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 08/29/2022] [Indexed: 11/02/2022]
Abstract
Although resting-state fMRI studies support that human brain is topographically organized regarding localized and distributed processes, it is still unclear about the task-modulated cortical hierarchy in terms of distributed functional connectivity and localized timescales. To address, current study investigated the effect of cognitive load on cortical connectivity gradients and local timescales in the healthy brain using resting state fMRI as well as 1- and 2-back working memory task fMRI. The results demonstrated that (1) increased cognitive load was associated with lower principal gradient in transmodal cortices, higher principal gradient in primary cortices, decreased decay rate and reduced timescale variability; (2) global properties including gradient variability, timescale decay rate, timescale variability and network topology were all modulated by cognitive load, with timescale variability related to behavioral performance; and (3) at 2-back state, the timescale variability was indirectly and negatively linked with global network integration, which was mediated by gradient variability. In conclusion, current study provides novel evidence for load-modulated cortical connectivity gradients and local timescales during cognitive states, which could contribute to better understanding about cognitive load theory and brain disorders with cognitive dysfunction.
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Affiliation(s)
- Heming Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China
| | - Rong Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China
| | - Xin Hu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China
| | - Sihai Guan
- Key Laboratory of Electronic and Information Engineering (Southwest Minzu University), State Ethnic Affairs Commission. College of Electronic and Information, Southwest Minzu University, Chengdu, 610225, China
| | - Daniel S Margulies
- Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103, Leipzig, Germany
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China.
| | - Bharat B Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Avenue, Chengdu, 611731, China. .,Department of Biomedical Engineering, New Jersey Institute of Technology, University Height, 607 Fenster Hall, Newark, NJ, 07102, USA.
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14
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Wang X, Krieger-Redwood K, Zhang M, Cui Z, Wang X, Karapanagiotidis T, Du Y, Leech R, Bernhardt BC, Margulies DS, Smallwood J, Jefferies E. Physical distance to sensory-motor landmarks predicts language function. Cereb Cortex 2022; 33:4305-4318. [PMID: 36066439 PMCID: PMC10110440 DOI: 10.1093/cercor/bhac344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/01/2022] [Accepted: 08/02/2022] [Indexed: 11/14/2022] Open
Abstract
Auditory language comprehension recruits cortical regions that are both close to sensory-motor landmarks (supporting auditory and motor features) and far from these landmarks (supporting word meaning). We investigated whether the responsiveness of these regions in task-based functional MRI is related to individual differences in their physical distance to primary sensorimotor landmarks. Parcels in the auditory network, that were equally responsive across story and math tasks, showed stronger activation in individuals who had less distance between these parcels and transverse temporal sulcus, in line with the predictions of the "tethering hypothesis," which suggests that greater proximity to input regions might increase the fidelity of sensory processing. Conversely, language and default mode parcels, which were more active for the story task, showed positive correlations between individual differences in activation and sensory-motor distance from primary sensory-motor landmarks, consistent with the view that physical separation from sensory-motor inputs supports aspects of cognition that draw on semantic memory. These results demonstrate that distance from sensorimotor regions provides an organizing principle of functional differentiation within the cortex. The relationship between activation and geodesic distance to sensory-motor landmarks is in opposite directions for cortical regions that are proximal to the heteromodal (DMN and language network) and unimodal ends of the principal gradient of intrinsic connectivity.
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Affiliation(s)
- Xiuyi Wang
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.,Department of Psychology, University of York, Heslington, York YO10 5DD, UK
| | | | - Meichao Zhang
- Department of Psychology, University of York, Heslington, York YO10 5DD, UK
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Xiaokang Wang
- Department of Biomedical Engineering, University of California, Davis, CA 95616, USA
| | | | - Yi Du
- CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China.,Chinese Institute for Brain Research, Beijing 102206, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Robert Leech
- Centre for Neuroimaging Science, Kings College London, London, UK
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, McGill University, Montreal, Quebec, Canada
| | - Daniel S Margulies
- Integrative Neuroscience and Cognition Center (UMR 8002), Centre National de la Recherche Scientifique (CNRS) and Université de Paris, Paris, France.,Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
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15
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Zhang M, Bernhardt BC, Wang X, Varga D, Krieger-Redwood K, Royer J, Rodríguez-Cruces R, Vos de Wael R, Margulies DS, Smallwood J, Jefferies E. Perceptual coupling and decoupling of the default mode network during mind-wandering and reading. eLife 2022; 11:74011. [PMID: 35311643 PMCID: PMC8937216 DOI: 10.7554/elife.74011] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [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: 09/17/2021] [Accepted: 02/12/2022] [Indexed: 11/24/2022] Open
Abstract
While reading, our mind can wander to unrelated autobiographical information, creating a perceptually decoupled state detrimental to narrative comprehension. To understand how this mind-wandering state emerges, we asked whether retrieving autobiographical content necessitates functional disengagement from visual input. In Experiment 1, brain activity was recorded using functional magnetic resonance imaging (fMRI) in an experimental situation mimicking naturally occurring mind-wandering, allowing us to precisely delineate neural regions involved in memory and reading. Individuals read expository texts and ignored personally relevant autobiographical memories, as well as the opposite situation. Medial regions of the default mode network (DMN) were recruited during memory retrieval. In contrast, left temporal and lateral prefrontal regions of the DMN, as well as ventral visual cortex, were recruited when reading for comprehension. Experiment two used functional connectivity both at rest and during tasks to establish that (i) DMN regions linked to memory are more functionally decoupled from regions of ventral visual cortex than regions in the same network engaged when reading; and (ii) individuals with more self-generated mental contents and poorer comprehension, while reading in the lab, showed more decoupling between visually connected DMN sites important for reading and primary visual cortex. A similar pattern of connectivity was found in Experiment 1, with greater coupling between this DMN site and visual cortex when participants reported greater focus on reading in the face of conflict from autobiographical memory cues; moreover, the retrieval of personally relevant memories increased the decoupling of these sites. These converging data suggest we lose track of the narrative when our minds wander because generating autobiographical mental content relies on cortical regions within the DMN which are functionally decoupled from ventral visual regions engaged during reading. As your eyes scan these words, you may be thinking about what to make for dinner, how to address an unexpected hurdle at work, or how many emails are sitting, unread, in your inbox. This type of mind-wandering disrupts our focus and limits how much information we comprehend, whilst also being conducive to creative thinking and problem-solving. Despite being an everyday occurrence, exactly how our mind wanders remains elusive. One possible explanation is that the brain disengages from visual information from the external world and turns its attention inwards. A greater understanding of which neural circuits are involved in this process could reveal insights about focus, attention, and reading comprehension. Here, Zhang et al. investigated whether the brain becomes disengaged from visual input when our mind wanders while reading. Recalling personal events was used as a proxy for mind-wandering. Brain activity was recorded as participants were shown written statements; sometimes these were preceded by cues to personal memories. People were asked to focus on reading the statements or they were instructed to concentrate on their memories while ignoring the text. The analyses showed that recalling memories and reading stimulated distinct parts of the brain, which were in direct competition during mind-wandering. Further work examined how these regions were functionally connected. In individuals who remained focused on reading despite memory cues, the areas activated by reading showed strong links to the visual cortex. Conversely, these reading-related areas became ‘decoupled’ from visual processing centres in people who were focusing more on their internal thoughts. These results shed light on why we lose track of what we are reading when our mind wanders: recalling personal memories activates certain brain areas which are functionally decoupled from the regions involved in processing external information – such as the words on a page. In summary, the work by Zhang et al. builds a mechanistic understanding of mind-wandering, a natural feature of our daily brain activity. These insights may help to inform future interventions in education to improve reading, comprehension and focus.
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Affiliation(s)
- Meichao Zhang
- Department of Psychology, University of York, York, United Kingdom
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Xiuyi Wang
- Department of Psychology, University of York, York, United Kingdom
| | - Dominika Varga
- Department of Psychology, University of York, York, United Kingdom
| | | | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Raúl Rodríguez-Cruces
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Daniel S Margulies
- Integrative Neuroscience and Cognition Centre (UMR 8002), Centre National de la Recherche Scientifique (CNRS) and Université de Paris, Paris, France
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16
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Affiliation(s)
- Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
| | | | - Shella Keilholz
- Biomedical Engineering, Emory University / Georgia Institute of Technology, Atlanta, Georgia
| | - Daniel S Margulies
- Integrative Neuroscience and Cognition Center, Centre National de la Recherche Scientifique (CNRS) and Université de Paris, Paris, France
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17
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Shao X, Mckeown B, Karapanagiotidis T, Vos de Wael R, Margulies DS, Bernhardt B, Smallwood J, Krieger-Redwood K, Jefferies E. Individual differences in gradients of intrinsic connectivity within the semantic network relate to distinct aspects of semantic cognition. Cortex 2022; 150:48-60. [DOI: 10.1016/j.cortex.2022.01.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 12/20/2021] [Accepted: 01/21/2022] [Indexed: 11/03/2022]
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18
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Burger B, Nenning KH, Schwartz E, Margulies DS, Goulas A, Liu H, Neubauer S, Dauwels J, Prayer D, Langs G. Disentangling cortical functional connectivity strength and topography reveals divergent roles of genes and environment. Neuroimage 2021; 247:118770. [PMID: 34861392 DOI: 10.1016/j.neuroimage.2021.118770] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 11/10/2021] [Accepted: 11/29/2021] [Indexed: 10/19/2022] Open
Abstract
The human brain varies across individuals in its morphology, function, and cognitive capacities. Variability is particularly high in phylogenetically modern regions associated with higher order cognitive abilities, but its relationship to the layout and strength of functional networks is poorly understood. In this study we disentangled the variability of two key aspects of functional connectivity: strength and topography. We then compared the genetic and environmental influences on these two features. Genetic contribution is heterogeneously distributed across the cortex and differs for strength and topography. In heteromodal areas genes predominantly affect the topography of networks, while their connectivity strength is shaped primarily by random environmental influence such as learning. We identified peak areas of genetic control of topography overlapping with parts of the processing stream from primary areas to network hubs in the default mode network, suggesting the coordination of spatial configurations across those processing pathways. These findings provide a detailed map of the diverse contribution of heritability and individual experience to the strength and topography of functional brain architecture.
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Affiliation(s)
- Bianca Burger
- Department of Biomedical Imaging and Image-Guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna 1090, Austria
| | - Karl-Heinz Nenning
- Department of Biomedical Imaging and Image-Guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna 1090, Austria; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, United States
| | - Ernst Schwartz
- Department of Biomedical Imaging and Image-Guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna 1090, Austria
| | - Daniel S Margulies
- Université de Paris, CNRS, Integrative Neuroscience and Cognition Center, 75006 Paris, France; Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, United Kingdom
| | - Alexandros Goulas
- Institute for Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinstr. 52, 20246 Hamburg, Germany
| | - Hesheng Liu
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, 29466, USAs
| | - Simon Neubauer
- Department of Human Evolution, Max Planck Institute for Evolutionary Anthropology, Leipzig 04103, Germany
| | - Justin Dauwels
- TU Delft Fac. EEMCS Mekelweg 4 2628 CD Delft; Nayang Technological University, 639798, Singapore
| | - Daniela Prayer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of Neuroradiology and Musculo-skeletal Radiology, Medical University of Vienna, Vienna 1090, Austria
| | - Georg Langs
- Department of Biomedical Imaging and Image-Guided Therapy, Computational Imaging Research Lab, Medical University of Vienna, Vienna 1090, Austria; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
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19
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Gonzalez Alam TRDJ, Mckeown BLA, Gao Z, Bernhardt B, Vos de Wael R, Margulies DS, Smallwood J, Jefferies E. A tale of two gradients: differences between the left and right hemispheres predict semantic cognition. Brain Struct Funct 2021; 227:631-654. [PMID: 34510282 PMCID: PMC8844158 DOI: 10.1007/s00429-021-02374-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [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: 02/23/2021] [Accepted: 08/27/2021] [Indexed: 01/21/2023]
Abstract
Decomposition of whole-brain functional connectivity patterns reveals a principal gradient that captures the separation of sensorimotor cortex from heteromodal regions in the default mode network (DMN). Functional homotopy is strongest in sensorimotor areas, and weakest in heteromodal cortices, suggesting there may be differences between the left and right hemispheres (LH/RH) in the principal gradient, especially towards its apex. This study characterised hemispheric differences in the position of large-scale cortical networks along the principal gradient, and their functional significance. We collected resting-state fMRI and semantic, working memory and non-verbal reasoning performance in 175 + healthy volunteers. We then extracted the principal gradient of connectivity for each participant, tested which networks showed significant hemispheric differences on the gradient, and regressed participants’ behavioural efficiency in tasks outside the scanner against interhemispheric gradient differences for each network. LH showed a higher overall principal gradient value, consistent with its role in heteromodal semantic cognition. One frontotemporal control subnetwork was linked to individual differences in semantic cognition: when it was nearer heteromodal DMN on the principal gradient in LH, participants showed more efficient semantic retrieval—and this network also showed a strong hemispheric difference in response to semantic demands but not working memory load in a separate study. In contrast, when a dorsal attention subnetwork was closer to the heteromodal end of the principal gradient in RH, participants showed better visual reasoning. Lateralization of function may reflect differences in connectivity between control and heteromodal regions in LH, and attention and visual regions in RH.
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Affiliation(s)
| | | | - Zhiyao Gao
- Department of Psychology, University of York, York, UK
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique (CNRS) and Université de Paris, INCC UMR 8002, Paris, France
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20
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Abstract
The transition from childhood to adolescence is marked by pronounced shifts in brain structure and function that coincide with the development of physical, cognitive, and social abilities. Prior work in adult populations has characterized the topographical organization of the cortex, revealing macroscale functional gradients that extend from unimodal (somatosensory/motor and visual) regions through the cortical association areas that underpin complex cognition in humans. However, the presence of these core functional gradients across development as well as their maturational course have yet to be established. Here, leveraging 378 resting-state functional MRI scans from 190 healthy individuals aged 6 to 17 y old, we demonstrate that the transition from childhood to adolescence is reflected in the gradual maturation of gradient patterns across the cortical sheet. In children, the overarching organizational gradient is anchored within the unimodal cortex, between somatosensory/motor and visual territories. Conversely, in adolescence, the principal gradient of connectivity transitions into an adult-like spatial framework, with the default network at the opposite end of a spectrum from primary sensory and motor regions. The observed gradient transitions are gradually refined with age, reaching a sharp inflection point in 13 and 14 y olds. Functional maturation was nonuniformly distributed across cortical networks. Unimodal networks reached their mature positions early in development, while association regions, in particular the medial prefrontal cortex, reached a later peak during adolescence. These data reveal age-dependent changes in the macroscale organization of the cortex and suggest the scheduled maturation of functional gradient patterns may be critically important for understanding how cognitive and behavioral capabilities are refined across development.
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Affiliation(s)
- Hao-Ming Dong
- State Key Laboratory of Cognitive Neuroscience and Learning, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Department of Psychology, Yale University, New Haven, CT 06511
| | - Daniel S Margulies
- CNRS, Integrative Neuroscience and Cognition Center (UMR 8002), Université de Paris, 75006 Paris, France
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, International Data Group/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China;
- National Basic Science Data Center, Beijing 100190, China
- Key Laboratory of Brain and Education, School of Education Science, Nanning Normal University, Nanning 530001, China
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT 06511;
- Department of Psychiatry, Yale University, New Haven, CT 06511
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21
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van Vugt FT, Hartmann K, Altenmüller E, Mohammadi B, Margulies DS. The impact of early musical training on striatal functional connectivity. Neuroimage 2021; 238:118251. [PMID: 34116147 DOI: 10.1016/j.neuroimage.2021.118251] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 05/05/2021] [Accepted: 06/07/2021] [Indexed: 12/26/2022] Open
Abstract
Evidence from language, visual and sensorimotor learning suggests that training early in life is more effective. The present work explores the hypothesis that learning during sensitive periods involves distinct brain networks in addition to those involved when learning later in life. Expert pianists were tested who started their musical training early (<7 years of age; n = 21) or late (n = 15), but were matched for total lifetime practice. Motor timing expertise was assessed using a musical scale playing task. Brain activity at rest was measured using fMRI and compared with a control group of nonmusicians (n = 17). Functional connectivity from seeds in the striatum revealed a striatal-cortical-sensorimotor network that was observed only in the early-onset group. In this network, higher connectivity correlated with greater motor timing expertise, which resulted from early/late group differences in motor timing expertise. By contrast, networks that differentiated musicians and nonmusicians, namely a striatal-occipital-frontal-cerebellar network in which connectivity was higher in musicians, tended to not show differences between early and late musicians and not be correlated with motor timing expertise. These results parcel musical sensorimotor neuroplasticity into a set of musicianship-related networks and a distinct set of predominantly early-onset networks. The findings lend support to the possibility that we can learn skills more easily early in development because during sensitive periods we recruit distinct brain networks that are no longer implicated in learning later in life.
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Affiliation(s)
- F T van Vugt
- Institute of Music Physiology and Musicians' Medicine, Emmichplatz 1, 30175 Hannover, Germany; Psychology Department, International Laboratory for Brain, Music, and Sound Research, University of Montreal, Canada; Psychology Department, McGill University, Montreal, Canada.
| | - K Hartmann
- Institute of Music Physiology and Musicians' Medicine, Emmichplatz 1, 30175 Hannover, Germany; Universitätsklinik für Neurochirurgie, Otto-von-Guericke-Universität Magdeburg, Leipziger Str. 44, 39120 Magdeburg, Germany
| | - E Altenmüller
- Institute of Music Physiology and Musicians' Medicine, Emmichplatz 1, 30175 Hannover, Germany
| | - B Mohammadi
- CNS-LAB, International Neuroscience Institute (INI), Rudolf-Pichlmayr-Str., 4, 30625 Hannover, Germany
| | - D S Margulies
- CNRS UMR 8002, Integrative Neuroscience and Cognition Center, University of Paris, Paris, France
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22
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Smallwood J, Turnbull A, Wang HT, Ho NS, Poerio GL, Karapanagiotidis T, Konu D, Mckeown B, Zhang M, Murphy C, Vatansever D, Bzdok D, Konishi M, Leech R, Seli P, Schooler JW, Bernhardt B, Margulies DS, Jefferies E. The neural correlates of ongoing conscious thought. iScience 2021; 24:102132. [PMID: 33665553 PMCID: PMC7907463 DOI: 10.1016/j.isci.2021.102132] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [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] [Indexed: 01/03/2023] Open
Abstract
A core goal in cognitive neuroscience is identifying the physical substrates of the patterns of thought that occupy our daily lives. Contemporary views suggest that the landscape of ongoing experience is heterogeneous and can be influenced by features of both the person and the context. This perspective piece considers recent work that explicitly accounts for both the heterogeneity of the experience and context dependence of patterns of ongoing thought. These studies reveal that systems linked to attention and control are important for organizing experience in response to changing environmental demands. These studies also establish a role of the default mode network beyond task-negative or purely episodic content, for example, implicating it in the level of vivid detail in experience in both task contexts and in spontaneous self-generated experiential states. Together, this work demonstrates that the landscape of ongoing thought is reflected in the activity of multiple neural systems, and it is important to distinguish between processes contributing to how the experience unfolds from those linked to how these experiences are regulated.
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Affiliation(s)
- Jonathan Smallwood
- Department of Psychology / York Imaging Centre, University of York, York, England
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA
| | - Adam Turnbull
- Department of Psychology / York Imaging Centre, University of York, York, England
- University of Rochester School of Nursing, Rochester, NY, USA
| | | | - Nerissa S.P. Ho
- Department of Psychology / York Imaging Centre, University of York, York, England
| | - Giulia L. Poerio
- Department of Psychology, University of Essex, Colchester, England
| | | | - Delali Konu
- Department of Psychology / York Imaging Centre, University of York, York, England
| | - Brontë Mckeown
- Department of Psychology / York Imaging Centre, University of York, York, England
| | - Meichao Zhang
- Department of Psychology / York Imaging Centre, University of York, York, England
| | | | | | - Danilo Bzdok
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Mahiko Konishi
- Laboratoire de Sciences Cognitives et de Psycholinguistique, Department d'Etudes Cognitives, ENS, PSL University, EHESS, CNRS, Paris, France
| | | | | | - Jonathan W. Schooler
- Department of Psychology, duke University, Durham, NC, USA
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA, USA
| | - Boris Bernhardt
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Daniel S. Margulies
- Centre Nationale de la Researche Scientifique, Institute du Cerveau et de la Moelle epiniere, Paris, France
| | - Elizabeth Jefferies
- Department of Psychology / York Imaging Centre, University of York, York, England
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23
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Schurz M, Radua J, Tholen MG, Maliske L, Margulies DS, Mars RB, Sallet J, Kanske P. Toward a hierarchical model of social cognition: A neuroimaging meta-analysis and integrative review of empathy and theory of mind. Psychol Bull 2021; 147:293-327. [PMID: 33151703 DOI: 10.1037/bul0000303] [Citation(s) in RCA: 173] [Impact Index Per Article: 57.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Along with the increased interest in and volume of social cognition research, there has been higher awareness of a lack of agreement on the concepts and taxonomy used to study social processes. Two central concepts in the field, empathy and Theory of Mind (ToM), have been identified as overlapping umbrella terms for different processes of limited convergence. Here, we review and integrate evidence of brain activation, brain organization, and behavior into a coherent model of social-cognitive processes. We start with a meta-analytic clustering of neuroimaging data across different social-cognitive tasks. Results show that understanding others' mental states can be described by a multilevel model of hierarchical structure, similar to models in intelligence and personality research. A higher level describes more broad and abstract classes of functioning, whereas a lower one explains how functions are applied to concrete contexts given by particular stimulus and task formats. Specifically, the higher level of our model suggests 3 groups of neurocognitive processes: (a) predominantly cognitive processes, which are engaged when mentalizing requires self-generated cognition decoupled from the physical world; (b) more affective processes, which are engaged when we witness emotions in others based on shared emotional, motor, and somatosensory representations; (c) combined processes, which engage cognitive and affective functions in parallel. We discuss how these processes are explained by an underlying principal gradient of structural brain organization. Finally, we validate the model by a review of empathy and ToM task interrelations found in behavioral studies. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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Affiliation(s)
- Matthias Schurz
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford
| | - Joaquim Radua
- Imaging of Mood- and Anxiety-Related Disorders (IMARD) Group, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)
| | | | - Lara Maliske
- Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden
| | - Daniel S Margulies
- Frontlab, Institut du Cerveau et de la Moelle Epinière, Centre National de la Recherche Scientifique (CNRS) UMR 7225
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford
| | - Jerome Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford
| | - Philipp Kanske
- Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden
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24
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Friedrich P, Forkel SJ, Amiez C, Balsters JH, Coulon O, Fan L, Goulas A, Hadj-Bouziane F, Hecht EE, Heuer K, Jiang T, Latzman RD, Liu X, Loh KK, Patil KR, Lopez-Persem A, Procyk E, Sallet J, Toro R, Vickery S, Weis S, Wilson CRE, Xu T, Zerbi V, Eickoff SB, Margulies DS, Mars RB, Thiebaut de Schotten M. Imaging evolution of the primate brain: the next frontier? Neuroimage 2021; 228:117685. [PMID: 33359344 PMCID: PMC7116589 DOI: 10.1016/j.neuroimage.2020.117685] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Revised: 12/14/2020] [Accepted: 12/16/2020] [Indexed: 11/22/2022] Open
Abstract
Evolution, as we currently understand it, strikes a delicate balance between animals' ancestral history and adaptations to their current niche. Similarities between species are generally considered inherited from a common ancestor whereas observed differences are considered as more recent evolution. Hence comparing species can provide insights into the evolutionary history. Comparative neuroimaging has recently emerged as a novel subdiscipline, which uses magnetic resonance imaging (MRI) to identify similarities and differences in brain structure and function across species. Whereas invasive histological and molecular techniques are superior in spatial resolution, they are laborious, post-mortem, and oftentimes limited to specific species. Neuroimaging, by comparison, has the advantages of being applicable across species and allows for fast, whole-brain, repeatable, and multi-modal measurements of the structure and function in living brains and post-mortem tissue. In this review, we summarise the current state of the art in comparative anatomy and function of the brain and gather together the main scientific questions to be explored in the future of the fascinating new field of brain evolution derived from comparative neuroimaging.
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Affiliation(s)
- Patrick Friedrich
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France; Institute of Neuroscience and Medicine (Brain & Behaviour, INM-7), Research Center Jülich, Germany.
| | - Stephanie J Forkel
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France; Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Céline Amiez
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, U1208 Bron, France
| | - Joshua H Balsters
- Department of Psychology, Royal Holloway University of London, United Kingdom
| | - Olivier Coulon
- Institut de Neurosciences de la Timone, Aix Marseille Univ, CNRS, UMR 7289, Marseille, France; Institute for Language, Communication, and the Brain, Aix-Marseille University, Marseille, France
| | - Lingzhong Fan
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Alexandros Goulas
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Hamburg, Germany
| | - Fadila Hadj-Bouziane
- Lyon Neuroscience Research Center, ImpAct Team, INSERM U1028, CNRS UMR5292, Université Lyon 1, Bron, France
| | - Erin E Hecht
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA, United States
| | - Katja Heuer
- Center for Research and Interdisciplinarity (CRI), Université de Paris, Inserm, Paris 75004, France; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; The Queensland Brain Institute, University of Queensland, Brisbane QLD 4072, Australia
| | - Robert D Latzman
- Department of Psychology, Georgia State University, Atlanta, United States
| | - Xiaojin Liu
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Neuroscience and Medicine (Brain & Behaviour, INM-7), Research Center Jülich, Germany
| | - Kep Kee Loh
- Institut de Neurosciences de la Timone, Aix Marseille Univ, CNRS, UMR 7289, Marseille, France; Institute for Language, Communication, and the Brain, Aix-Marseille University, Marseille, France
| | - Kaustubh R Patil
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Neuroscience and Medicine (Brain & Behaviour, INM-7), Research Center Jülich, Germany
| | - Alizée Lopez-Persem
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225, Paris, France; Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Emmanuel Procyk
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, U1208 Bron, France
| | - Jerome Sallet
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, U1208 Bron, France; Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Roberto Toro
- Center for Research and Interdisciplinarity (CRI), Université de Paris, Inserm, Paris 75004, France; Neuroscience department, Institut Pasteur, UMR 3571, CNRS, Université de Paris, Paris 75015, France
| | - Sam Vickery
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Neuroscience and Medicine (Brain & Behaviour, INM-7), Research Center Jülich, Germany
| | - Susanne Weis
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Neuroscience and Medicine (Brain & Behaviour, INM-7), Research Center Jülich, Germany
| | - Charles R E Wilson
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, U1208 Bron, France
| | - Ting Xu
- Child Mind Institute, New York, United States
| | - Valerio Zerbi
- Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Simon B Eickoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Neuroscience and Medicine (Brain & Behaviour, INM-7), Research Center Jülich, Germany
| | - Daniel S Margulies
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Integrative Neuroscience and Cognition Center (UMR 8002), Centre National de la Recherche Scientifique (CNRS) and Université de Paris, 75006, Paris, France
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA, University of Bordeaux, Bordeaux, France.
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25
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Karapanagiotidis T, Vidaurre D, Quinn AJ, Vatansever D, Poerio GL, Turnbull A, Ho NSP, Leech R, Bernhardt BC, Jefferies E, Margulies DS, Nichols TE, Woolrich MW, Smallwood J. The psychological correlates of distinct neural states occurring during wakeful rest. Sci Rep 2020; 10:21121. [PMID: 33273566 PMCID: PMC7712889 DOI: 10.1038/s41598-020-77336-z] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [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: 03/12/2020] [Accepted: 07/27/2020] [Indexed: 12/22/2022] Open
Abstract
When unoccupied by an explicit external task, humans engage in a wide range of different types of self-generated thinking. These are often unrelated to the immediate environment and have unique psychological features. Although contemporary perspectives on ongoing thought recognise the heterogeneity of these self-generated states, we lack both a clear understanding of how to classify the specific states, and how they can be mapped empirically. In the current study, we capitalise on advances in machine learning that allow continuous neural data to be divided into a set of distinct temporally re-occurring patterns, or states. We applied this technique to a large set of resting state data in which we also acquired retrospective descriptions of the participants' experiences during the scan. We found that two of the identified states were predictive of patterns of thinking at rest. One state highlighted a pattern of neural activity commonly seen during demanding tasks, and the time individuals spent in this state was associated with descriptions of experience focused on problem solving in the future. A second state was associated with patterns of activity that are commonly seen under less demanding conditions, and the time spent in it was linked to reports of intrusive thoughts about the past. Finally, we found that these two neural states tended to fall at either end of a neural hierarchy that is thought to reflect the brain's response to cognitive demands. Together, these results demonstrate that approaches which take advantage of time-varying changes in neural function can play an important role in understanding the repertoire of self-generated states. Moreover, they establish that important features of self-generated ongoing experience are related to variation along a similar vein to those seen when the brain responds to cognitive task demands.
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Affiliation(s)
| | - Diego Vidaurre
- Department of Psychiatry, Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 7JX, UK
- Department of Clinical Medicine, Center of Functionally Integrative Neuroscience, Aarhus University, 8000, Aarhus, Denmark
| | - Andrew J Quinn
- Department of Psychiatry, Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 7JX, UK
| | - Deniz Vatansever
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, 200433, People's Republic of China
| | - Giulia L Poerio
- Department of Psychology, University of Essex, Colchester, Essex, CO4 3SQ, UK
| | - Adam Turnbull
- Department of Psychology, York Neuroimaging Centre, University of York, York, YO10 5DD, UK
| | - Nerissa Siu Ping Ho
- Department of Psychology, York Neuroimaging Centre, University of York, York, YO10 5DD, UK
| | - Robert Leech
- Centre for Neuroimaging Science, Kings College, London, SE5 8AF, UK
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, QC, H3A 2B4, Canada
| | - Elizabeth Jefferies
- Department of Psychology, York Neuroimaging Centre, University of York, York, YO10 5DD, UK
| | - Daniel S Margulies
- Brain and Spine Institute (ICM), National Center for Scientific Research, Paris, 75013, France
| | - Thomas E Nichols
- Oxford Centre for Functional MRI of the Brain, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Mark W Woolrich
- Department of Psychiatry, Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 7JX, UK
| | - Jonathan Smallwood
- Department of Psychology, York Neuroimaging Centre, University of York, York, YO10 5DD, UK
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26
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Hong SJ, Xu T, Nikolaidis A, Smallwood J, Margulies DS, Bernhardt B, Vogelstein J, Milham MP. Toward a connectivity gradient-based framework for reproducible biomarker discovery. Neuroimage 2020; 223:117322. [PMID: 32882388 DOI: 10.1016/j.neuroimage.2020.117322] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 07/13/2020] [Accepted: 08/23/2020] [Indexed: 12/21/2022] Open
Abstract
Despite myriad demonstrations of feasibility, the high dimensionality of fMRI data remains a critical barrier to its utility for reproducible biomarker discovery. Recent efforts to address this challenge have capitalized on dimensionality reduction techniques applied to resting-state fMRI, identifying principal components of intrinsic connectivity which describe smooth transitions across different cortical systems, so called "connectivity gradients". These gradients recapitulate neurocognitively meaningful organizational principles that are present in both human and primate brains, and also appear to differ among individuals and clinical populations. Here, we provide a critical assessment of the suitability of connectivity gradients for biomarker discovery. Using the Human Connectome Project (discovery subsample=209; two replication subsamples= 209 × 2) and the Midnight scan club (n = 9), we tested the following key biomarker traits - reliability, reproducibility and predictive validity - of functional gradients. In doing so, we systematically assessed the effects of three analytical settings, including i) dimensionality reduction algorithms (i.e., linear vs. non-linear methods), ii) input data types (i.e., raw time series, [un-]thresholded functional connectivity), and iii) amount of the data (resting-state fMRI time-series lengths). We found that the reproducibility of functional gradients across algorithms and subsamples is generally higher for those explaining more variances of whole-brain connectivity data, as well as those having higher reliability. Notably, among different analytical settings, a linear dimensionality reduction (principal component analysis in our study), more conservatively thresholded functional connectivity (e.g., 95-97%) and longer time-series data (at least ≥20mins) was found to be preferential conditions to obtain higher reliability. Those gradients with higher reliability were able to predict unseen phenotypic scores with a higher accuracy, highlighting reliability as a critical prerequisite for validity. Importantly, prediction accuracy with connectivity gradients exceeded that observed with more traditional edge-based connectivity measures, suggesting the added value of a low-dimensional and multivariate gradient approach. Finally, the present work highlights the importance and benefits of systematically exploring the parameter space for new imaging methods before widespread deployment.
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Affiliation(s)
- Seok-Jun Hong
- Center for the Developing Brain, Child Mind Institute, NY, USA; Center for Neuroscience Imaging Research, Institute for Basic Science, South Korea; Department of Biomedical Engineering, SungKyunKwan University, Suwon, South Korea.
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, NY, USA
| | - Aki Nikolaidis
- Center for the Developing Brain, Child Mind Institute, NY, USA
| | | | - Daniel S Margulies
- Frontlab, Institut du Cerveau et de la Moelle épinière, UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225 Paris, France
| | - Boris Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Joshua Vogelstein
- Department of Biomedical Engineering Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University, MD, USA
| | - Michael P Milham
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, NY, USA.
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27
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Messinger A, Sirmpilatze N, Heuer K, Loh KK, Mars RB, Sein J, Xu T, Glen D, Jung B, Seidlitz J, Taylor P, Toro R, Garza-Villarreal EA, Sponheim C, Wang X, Benn RA, Cagna B, Dadarwal R, Evrard HC, Garcia-Saldivar P, Giavasis S, Hartig R, Lepage C, Liu C, Majka P, Merchant H, Milham MP, Rosa MGP, Tasserie J, Uhrig L, Margulies DS, Klink PC. A collaborative resource platform for non-human primate neuroimaging. Neuroimage 2020; 226:117519. [PMID: 33227425 PMCID: PMC9272762 DOI: 10.1016/j.neuroimage.2020.117519] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [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: 07/31/2020] [Revised: 10/15/2020] [Accepted: 10/24/2020] [Indexed: 01/12/2023] Open
Abstract
Neuroimaging non-human primates (NHPs) is a growing, yet highly specialized field of neuroscience. Resources that were primarily developed for human neuroimaging often need to be significantly adapted for use with NHPs or other animals, which has led to an abundance of custom, in-house solutions. In recent years, the global NHP neuroimaging community has made significant efforts to transform the field towards more open and collaborative practices. Here we present the PRIMatE Resource Exchange (PRIME-RE), a new collaborative online platform for NHP neuroimaging. PRIME-RE is a dynamic community-driven hub for the exchange of practical knowledge, specialized analytical tools, and open data repositories, specifically related to NHP neuroimaging. PRIME-RE caters to both researchers and developers who are either new to the field, looking to stay abreast of the latest developments, or seeking to collaboratively advance the field.
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Affiliation(s)
- Adam Messinger
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, USA
| | - Nikoloz Sirmpilatze
- German Primate Center - Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany; Georg-August-University Göttingen, 37073 Göttingen, Germany
| | - Katja Heuer
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Center for Research and Interdisciplinarity (CRI), INSERM U1284, Université de Paris, Paris, France
| | - Kep Kee Loh
- Institut de Neurosciences de la Timone (INT), Aix-Marseille Université, CNRS, UMR 7289, 13005 Marseille, France; Institute for Language, Communication, and the Brain, Aix-Marseille University, Marseille, France
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK; Donders Institute for Brain, Cognition, and Behaviour, Radboud University Nijmegen, Montessorilaan 3, 6525 HR Nijmegen, The Netherlands
| | - Julien Sein
- Institut de Neurosciences de la Timone (INT), Aix-Marseille Université, CNRS, UMR 7289, 13005 Marseille, France
| | - Ting Xu
- Child Mind Institute, 101 E 56th St, New York, NY 10022, USA
| | - Daniel Glen
- Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, USA
| | - Benjamin Jung
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, USA; Department of Neuroscience, Brown University, Providence RI USA
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia PA USA; Department of Psychiatry, University of Pennsylvania, Philadelphia PA USA
| | - Paul Taylor
- Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, USA
| | - Roberto Toro
- Center for Research and Interdisciplinarity (CRI), INSERM U1284, Université de Paris, Paris, France; Department of Neuroscience, Institut Pasteur, UMR 3571 CNRS, Université de Paris, Paris, France
| | - Eduardo A Garza-Villarreal
- Instituto de Neurobiologia, Universidad Nacional Autónoma de México campus Juriquilla, Queretaro, Mexico
| | - Caleb Sponheim
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago IL USA
| | - Xindi Wang
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute (MNI), Quebec, Canada
| | - R Austin Benn
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Madrid, Spain
| | - Bastien Cagna
- Institut de Neurosciences de la Timone (INT), Aix-Marseille Université, CNRS, UMR 7289, 13005 Marseille, France
| | - Rakshit Dadarwal
- German Primate Center - Leibniz Institute for Primate Research, Kellnerweg 4, 37077 Göttingen, Germany; Georg-August-University Göttingen, 37073 Göttingen, Germany
| | - Henry C Evrard
- Centre for Integrative Neurosciences, University of Tübingen, Tübingen, Germany; Max Planck Institute for Biological Cybernetics, Tübingen, Germany; Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, USA; International Center for Primate Brain Research, Chinese Academy of Science, Shanghai, PRC
| | - Pamela Garcia-Saldivar
- Instituto de Neurobiologia, Universidad Nacional Autónoma de México campus Juriquilla, Queretaro, Mexico
| | - Steven Giavasis
- Child Mind Institute, 101 E 56th St, New York, NY 10022, USA
| | - Renée Hartig
- Centre for Integrative Neurosciences, University of Tübingen, Tübingen, Germany; Max Planck Institute for Biological Cybernetics, Tübingen, Germany; Focus Program Translational Neurosciences, University Medical Center, Mainz, Germany
| | - Claude Lepage
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute (MNI), Quebec, Canada
| | - Cirong Liu
- Department of Neurobiology, University of Pittsburgh Brain Institute, Pittsburgh PA, USA
| | - Piotr Majka
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, 02-093 Warsaw, Poland; Australian Research Council, Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, VIC 3800, Australia; Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC 3800, Australia
| | - Hugo Merchant
- Instituto de Neurobiologia, Universidad Nacional Autónoma de México campus Juriquilla, Queretaro, Mexico
| | - Michael P Milham
- Child Mind Institute, 101 E 56th St, New York, NY 10022, USA; Nathan S. Kline Institute for Psychiatric Research, Orangeburg, New York, USA
| | - Marcello G P Rosa
- Australian Research Council, Centre of Excellence for Integrative Brain Function, Monash University Node, Clayton, VIC 3800, Australia; Biomedicine Discovery Institute and Department of Physiology, Monash University, Clayton, VIC 3800, Australia
| | - Jordy Tasserie
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Direction de la Recherche Fondamentale, NeuroSpin Center, Gif-sur-Yvette, France; Cognitive Neuroimaging Unit, Institut National de la Santé et de la Recherche Médicale U992, Gif-sur-Yvette, France; Université Paris-Saclay, France
| | - Lynn Uhrig
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Direction de la Recherche Fondamentale, NeuroSpin Center, Gif-sur-Yvette, France; Cognitive Neuroimaging Unit, Institut National de la Santé et de la Recherche Médicale U992, Gif-sur-Yvette, France
| | - Daniel S Margulies
- Integrative Neuroscience and Cognition Center, Centre National de la Recherche Scientifique (CNRS) UMR 8002, Paris, France
| | - P Christiaan Klink
- Department of Vision & Cognition, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands.
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28
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Nenning KH, Xu T, Schwartz E, Arroyo J, Woehrer A, Franco AR, Vogelstein JT, Margulies DS, Liu H, Smallwood J, Milham MP, Langs G. Joint embedding: A scalable alignment to compare individuals in a connectivity space. Neuroimage 2020; 222:117232. [PMID: 32771618 PMCID: PMC7779372 DOI: 10.1016/j.neuroimage.2020.117232] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 07/31/2020] [Accepted: 08/02/2020] [Indexed: 11/15/2022] Open
Abstract
A common coordinate space enabling comparison across individuals is vital to understanding human brain organization and individual differences. By leveraging dimensionality reduction algorithms, high-dimensional fMRI data can be represented in a low-dimensional space to characterize individual features. Such a representative space encodes the functional architecture of individuals and enables the observation of functional changes across time. However, determining comparable functional features across individuals in resting-state fMRI in a way that simultaneously preserves individual-specific connectivity structure can be challenging. In this work we propose scalable joint embedding to simultaneously embed multiple individual brain connectomes within a common space that allows individual representations across datasets to be aligned. Using Human Connectome Project data, we evaluated the joint embedding approach by comparing it to the previously established orthonormal alignment model. Alignment using joint embedding substantially increased the similarity of functional representations across individuals while simultaneously capturing their distinct profiles, allowing individuals to be more discriminable from each other. Additionally, we demonstrated that the common space established using resting-state fMRI provides a better overlap of task-activation across participants. Finally, in a more challenging scenario - alignment across a lifespan cohort aged from 6 to 85 - joint embedding provided a better prediction of age (r2 = 0.65) than the prior alignment model. It facilitated the characterization of functional trajectories across lifespan. Overall, these analyses establish that joint embedding can simultaneously capture individual neural representations in a common connectivity space aligning functional data across participants and populations and preserve individual specificity.
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Affiliation(s)
- Karl-Heinz Nenning
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA.
| | - Ernst Schwartz
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Jesus Arroyo
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA
| | - Adelheid Woehrer
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Alexandre R Franco
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA; Department of Psychiatry, NYU Langone School of Medicine, New York, NY, USA
| | - Joshua T Vogelstein
- Department of Biomedical Engineering, Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique, Frontlab, Institut du Cerveau et de la Moelle Epinière, Paris, France
| | - Hesheng Liu
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
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29
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Wang X, Margulies DS, Smallwood J, Jefferies E. A gradient from long-term memory to novel cognition: Transitions through default mode and executive cortex. Neuroimage 2020; 220:117074. [PMID: 32574804 PMCID: PMC7573535 DOI: 10.1016/j.neuroimage.2020.117074] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 05/21/2020] [Accepted: 06/17/2020] [Indexed: 12/15/2022] Open
Abstract
Human cognition flexibly guides decision-making in familiar and novel situations. Although these decisions are often treated as dichotomous, in reality, situations are neither completely familiar, nor entirely new. Contemporary accounts of brain organization suggest that neural function is organized along a connectivity gradient from unimodal regions of sensorimotor cortex, through executive regions to transmodal default mode network. We examined whether this graded view of neural organization helps to explain how decision-making changes across situations that vary in their alignment with long-term knowledge. We used a semantic judgment task, which parametrically varied the global semantic similarity of items within a feature matching task to create a 'task gradient', from conceptual combinations that were highly overlapping in long-term memory to trials that only shared the goal-relevant feature. We found the brain's response to the task gradient varied systematically along the connectivity gradient, with the strongest response in default mode network when the probe and target items were highly overlapping conceptually. This graded functional change was seen in multiple brain regions and within individual brains, and was not readily explained by task difficulty. Moreover, the gradient captured the spatial layout of networks involved in semantic processing, providing an organizational principle for controlled semantic cognition across the cortex. In this way, the cortex is organized to support semantic decision-making in both highly familiar and less familiar situations.
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Affiliation(s)
- Xiuyi Wang
- Department of Psychology, University of York, Heslington, York, YO10 5DD, United Kingdom.
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique (CNRS) UMR 7225, Frontlab, Institut du Cerveau et de la Moelle Épinière, Paris, France
| | - Jonathan Smallwood
- Department of Psychology, University of York, Heslington, York, YO10 5DD, United Kingdom
| | - Elizabeth Jefferies
- Department of Psychology, University of York, Heslington, York, YO10 5DD, United Kingdom.
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30
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Park BY, Vos de Wael R, Paquola C, Larivière S, Benkarim O, Royer J, Tavakol S, Cruces RR, Li Q, Valk SL, Margulies DS, Mišić B, Bzdok D, Smallwood J, Bernhardt BC. Signal diffusion along connectome gradients and inter-hub routing differentially contribute to dynamic human brain function. Neuroimage 2020; 224:117429. [PMID: 33038538 DOI: 10.1016/j.neuroimage.2020.117429] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 09/13/2020] [Accepted: 09/30/2020] [Indexed: 12/14/2022] Open
Abstract
Human cognition is dynamic, alternating over time between externally-focused states and more abstract, often self-generated, patterns of thought. Although cognitive neuroscience has documented how networks anchor particular modes of brain function, mechanisms that describe transitions between distinct functional states remain poorly understood. Here, we examined how time-varying changes in brain function emerge within the constraints imposed by macroscale structural network organization. Studying a large cohort of healthy adults (n = 326), we capitalized on manifold learning techniques that identify low dimensional representations of structural connectome organization and we decomposed neurophysiological activity into distinct functional states and their transition patterns using Hidden Markov Models. Structural connectome organization predicted dynamic transitions anchored in sensorimotor systems and those between sensorimotor and transmodal states. Connectome topology analyses revealed that transitions involving sensorimotor states traversed short and intermediary distances and adhered strongly to communication mechanisms of network diffusion. Conversely, transitions between transmodal states involved spatially distributed hubs and increasingly engaged long-range routing. These findings establish that the structure of the cortex is optimized to allow neural states the freedom to vary between distinct modes of processing, and so provides a key insight into the neural mechanisms that give rise to the flexibility of human cognition.
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Affiliation(s)
- Bo-Yong Park
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Casey Paquola
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Sara Larivière
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Oualid Benkarim
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Jessica Royer
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Shahin Tavakol
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Raul R Cruces
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Qiongling Li
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Sofie L Valk
- Institute of Neuroscience and Medicine (INM-7: Brain & Behaviour), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Daniel S Margulies
- Frontlab, Institut du Cerveau et de la Moelle épinière, UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - Bratislav Mišić
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Danilo Bzdok
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Mila - Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada
| | - Jonathan Smallwood
- Department of Psychology, York Neuroimaging Centre, University of York, New York, United Kingdom
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
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31
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Tian Y, Margulies DS, Breakspear M, Zalesky A. Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nat Neurosci 2020; 23:1421-1432. [PMID: 32989295 DOI: 10.1038/s41593-020-00711-6] [Citation(s) in RCA: 202] [Impact Index Per Article: 50.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Accepted: 08/21/2020] [Indexed: 12/14/2022]
Abstract
Brain atlases are fundamental to understanding the topographic organization of the human brain, yet many contemporary human atlases cover only the cerebral cortex, leaving the subcortex a terra incognita. We use functional MRI (fMRI) to map the complex topographic organization of the human subcortex, revealing large-scale connectivity gradients and new areal boundaries. We unveil four scales of subcortical organization that recapitulate well-known anatomical nuclei at the coarsest scale and delineate 27 new bilateral regions at the finest. Ultrahigh field strength fMRI corroborates and extends this organizational structure, enabling the delineation of finer subdivisions of the hippocampus and the amygdala, while task-evoked fMRI reveals a subtle subcortical reorganization in response to changing cognitive demands. A new subcortical atlas is delineated, personalized to represent individual differences and used to uncover reproducible brain-behavior relationships. Linking cortical networks to subcortical regions recapitulates a task-positive to task-negative axis. This new atlas enables holistic connectome mapping and characterization of cortico-subcortical connectivity.
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Affiliation(s)
- Ye Tian
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia.
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique (CNRS) UMR 8002, Integrative Neuroscience and Cognition Center, Université de Paris, Paris, France
| | - Michael Breakspear
- Discipline of Psychiatry, Faculty of Health and Medicine, University of Newcastle, Newcastle, New South Wales, Australia.,School of Psychology, Faculty of Science, University of Newcastle, Newcastle, New South Wales, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne, Parkville, Victoria, Australia. .,Department of Biomedical Engineering, Melbourne School of Engineering, The University of Melbourne, Parkville, Victoria, Australia.
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32
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Mandonnet E, Vincent M, Valero-Cabré A, Facque V, Barberis M, Bonnetblanc F, Rheault F, Volle E, Descoteaux M, Margulies DS. Network-level causal analysis of set-shifting during trail making test part B: A multimodal analysis of a glioma surgery case. Cortex 2020; 132:238-249. [PMID: 33007639 DOI: 10.1016/j.cortex.2020.08.021] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 05/18/2020] [Accepted: 08/24/2020] [Indexed: 02/04/2023]
Abstract
The trail making test part B (TMT-B) is one of the most widely used task for the assessment of set-shifting ability in patients. However, the set of brain regions impacting TMT-B performance when lesioned is still poorly known. In this case report, we provide a multimodal analysis of a patient operated on while awake for a diffuse low-grade glioma located in the right supramarginal gyrus. TMT-B performance was probed intraoperatively. Direct electrical stimulation of the white matter in the depth of the resection generated shifting errors. Using the recent methodology of axono-cortical-evoked potentials (ACEP), we demonstrated that the eloquent fibers were connected to the posterior end of the middle temporal gyrus (MTG). This was further confirmed by a tractography analysis of the postoperative diffusion MRI. Finally, the functional connectivity maps of this MTG seed were assessed in both pre- and post-operative resting state MRI. These maps matched with the Control network B (13th) and Default B (17th) from the 17-networks parcellation of (Yeo et al., 2011). Last but not least, we showed that the dorsal attention B (6th), the control A & B networks (12th and 13th) and the default A (16th) have been preserved here but disconnected after a more extensive resection in a previous glioma case within the same area, and in whom TMT-B was definitively impaired. Taken together, these data support the need of a network-level approach to identify the neural basis of the TMT-B and point to the Control network B as playing an important role in set-shifting.
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Affiliation(s)
- Emmanuel Mandonnet
- Department of Neurosurgery, Lariboisière Hospital, APHP, Paris, France; University Paris 7, Paris, France; Frontlab, CNRS UMR 7225, Inserm U1127, Sorbonne Université ICM, Paris, France.
| | - Marion Vincent
- University of Lille, CNRS, CHU Lille, UMR 9193, SCALab - Affectives and Cognitives Sciences Lab, Lille, France; INRIA, University of Montpellier, LIRMM, CAMIN Team, Montpellier, France
| | - Antoni Valero-Cabré
- Frontlab, CNRS UMR 7225, Inserm U1127, Sorbonne Université ICM, Paris, France; Cognitive Neuroscience and Information Technology Research Program, Open University of Catalonia (UOC), Barcelona, Catalunya, Spain
| | - Valentine Facque
- Department of Neurosurgery, Lariboisière Hospital, APHP, Paris, France; Frontlab, CNRS UMR 7225, Inserm U1127, Sorbonne Université ICM, Paris, France
| | - Marion Barberis
- Department of Neurosurgery, Lariboisière Hospital, APHP, Paris, France
| | | | - François Rheault
- Cognitive Neuroscience and Information Technology Research Program, Open University of Catalonia (UOC), Barcelona, Catalunya, Spain
| | - Emmanuelle Volle
- Frontlab, CNRS UMR 7225, Inserm U1127, Sorbonne Université ICM, Paris, France
| | - Maxime Descoteaux
- Sherbrook Connectivity Imaging Lab, Department of Computer Science, Faculty of Sciences, Université de Sherbrook, Sherbrook, Canada
| | - Daniel S Margulies
- Frontlab, CNRS UMR 7225, Inserm U1127, Sorbonne Université ICM, Paris, France
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33
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Xu T, Nenning KH, Schwartz E, Hong SJ, Vogelstein JT, Goulas A, Fair DA, Schroeder CE, Margulies DS, Smallwood J, Milham MP, Langs G. Cross-species functional alignment reveals evolutionary hierarchy within the connectome. Neuroimage 2020; 223:117346. [PMID: 32916286 PMCID: PMC7871099 DOI: 10.1016/j.neuroimage.2020.117346] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [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: 04/20/2020] [Revised: 08/04/2020] [Accepted: 08/31/2020] [Indexed: 11/22/2022] Open
Abstract
Evolution provides an important window into how cortical organization
shapes function and vice versa. The complex mosaic of changes in brain
morphology and functional organization that have shaped the mammalian cortex
during evolution, complicates attempts to chart cortical differences across
species. It limits our ability to fully appreciate how evolution has shaped our
brain, especially in systems associated with unique human cognitive capabilities
that lack anatomical homologues in other species. Here, we develop a
function-based method for cross-species alignment that enables the
quantification of homologous regions between humans and rhesus macaques, even
when their location is decoupled from anatomical landmarks. Critically, we find
cross-species similarity in functional organization reflects a gradient of
evolutionary change that decreases from unimodal systems and culminates with the
most pronounced changes in posterior regions of the default mode network
(angular gyrus, posterior cingulate and middle temporal cortices). Our findings
suggest that the establishment of the default mode network, as the apex of a
cognitive hierarchy, has changed in a complex manner during human evolution
– even within subnetworks.
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Affiliation(s)
- Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA.
| | - Karl-Heinz Nenning
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Ernst Schwartz
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria
| | - Seok-Jun Hong
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Joshua T Vogelstein
- Department of Biomedical Engineering, Institute for Computational Medicine, Kavli Neuroscience Discovery Institute, Johns Hopkins University, MD, USA
| | - Alexandros Goulas
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Hamburg, Germany
| | - Damien A Fair
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA
| | - Charles E Schroeder
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA; Departments of neurosurgery and Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY, USA
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique (CNRS) UMR 7225, Frontlab, Institut du Cerveau et de la Moelle Epinière, Paris, France
| | - Jonny Smallwood
- Department of Psychology, Queen's University, Kingston, Ontario, Canada; Psychology Department, University of York, York, UK
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, Orangeburg, NY, USA
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
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34
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Valk SL, Xu T, Margulies DS, Masouleh SK, Paquola C, Goulas A, Kochunov P, Smallwood J, Yeo BTT, Bernhardt BC, Eickhoff SB. Shaping brain structure: Genetic and phylogenetic axes of macroscale organization of cortical thickness. Sci Adv 2020; 6:eabb3417. [PMID: 32978162 PMCID: PMC7518868 DOI: 10.1126/sciadv.abb3417] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 08/04/2020] [Indexed: 05/16/2023]
Abstract
The topology of the cerebral cortex has been proposed to provide an important source of constraint for the organization of cognition. In a sample of twins (n = 1113), we determined structural covariance of thickness to be organized along both a posterior-to-anterior and an inferior-to-superior axis. Both organizational axes were present when investigating the genetic correlation of cortical thickness, suggesting a strong genetic component in humans, and had a comparable organization in macaques, demonstrating they are phylogenetically conserved in primates. In both species, the inferior-superior dimension of cortical organization aligned with the predictions of dual-origin theory, and in humans, we found that the posterior-to-anterior axis related to a functional topography describing a continuum of functions from basic processes involved in perception and action to more abstract features of human cognition. Together, our study provides important insights into how functional and evolutionary patterns converge at the level of macroscale cortical structural organization.
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Affiliation(s)
- Sofie L Valk
- Institute of Neuroscience and Medicine (INM-7: Brain and Behavior), Research Centre Jülich, Jülich, Germany.
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
| | - Daniel S Margulies
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
- Frontlab, Centre National de la Recherche Scientifique Institut du Cerveau et de la Moelle Épinière, Paris, France
| | - Shahrzad Kharabian Masouleh
- Institute of Neuroscience and Medicine (INM-7: Brain and Behavior), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Alexandros Goulas
- Institute of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Hamburg, Germany
| | - Peter Kochunov
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, Centre for Sleep and Cognition, Centre for Translational MR Research and N.1 Institute for Health, National University of Singapore, Singapore, Singapore
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7: Brain and Behavior), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
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35
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Lifshitz M, Sacchet MD, Huntenburg JM, Thiery T, Fan Y, Gärtner M, Grimm S, Winnebeck E, Fissler M, Schroeter TA, Margulies DS, Barnhofer T. Mindfulness-Based Therapy Regulates Brain Connectivity in Major Depression. Psychother Psychosom 2020; 88:375-377. [PMID: 31509824 DOI: 10.1159/000501170] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Accepted: 05/26/2019] [Indexed: 11/19/2022]
Affiliation(s)
- Michael Lifshitz
- Integrated Program in Neuroscience, McGill University, Montreal, Québec, Canada, .,Department of Anthropology, Stanford University, Stanford, California, USA,
| | - Matthew D Sacchet
- Center for Depression, Anxiety, and Stress Research, McLean Hospital, Harvard Medical School, Belmont, Massachusetts, USA
| | - Julia M Huntenburg
- Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Neurocomputation and Neuroimaging Unit, Department of Education and Psychology, Free University of Berlin, Berlin, Germany
| | - Thomas Thiery
- Department of Psychology, Université de Montréal, Montreal, Québec, Canada
| | - Yan Fan
- Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors, Dortmund, Germany.,Clinic for Psychiatry and Psychotherapy, Charité University Medicine Berlin, Berlin, Germany
| | - Matti Gärtner
- Clinic for Psychiatry and Psychotherapy, Charité University Medicine Berlin, Berlin, Germany
| | - Simone Grimm
- Clinic for Psychiatry and Psychotherapy, Charité University Medicine Berlin, Berlin, Germany
| | - Emilia Winnebeck
- Clinic for Psychiatry and Psychotherapy, Charité University Medicine Berlin, Berlin, Germany.,Dahlem Institute for Neuroimaging of Emotion, Freie Universität, Berlin, Germany
| | - Maria Fissler
- Clinic for Psychiatry and Psychotherapy, Charité University Medicine Berlin, Berlin, Germany.,Dahlem Institute for Neuroimaging of Emotion, Freie Universität, Berlin, Germany
| | - Titus A Schroeter
- Dahlem Institute for Neuroimaging of Emotion, Freie Universität, Berlin, Germany
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique (CNRS) UMR 7225, Frontlab, Institut du Cerveau et de la Moelle Épinière, Paris, France
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36
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Wang HT, Ho NSP, Bzdok D, Bernhardt BC, Margulies DS, Jefferies E, Smallwood J. Neurocognitive patterns dissociating semantic processing from executive control are linked to more detailed off-task mental time travel. Sci Rep 2020; 10:11904. [PMID: 32681101 PMCID: PMC7368037 DOI: 10.1038/s41598-020-67605-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Accepted: 06/08/2020] [Indexed: 12/14/2022] Open
Abstract
Features of ongoing experience are common across individuals and cultures. However, certain people express specific patterns of thought to a greater extent than others. Contemporary psychological theory assumes that individual differences in thought patterns occur because different types of experience depend on the expression of different neurocognitive processes. Consequently, individual variation in the underlying neurocognitive architecture is hypothesised to determine the ease with which certain thought patterns are generated or maintained. Our study (N = 178) tested this hypothesis using multivariate pattern analysis to infer shared variance among measures of cognitive function and neural organisation and examined whether these latent variables explained reports of the patterns of on-going thoughts people experienced in the lab. We found that relatively better performance on tasks relying primarily on semantic knowledge, rather than executive control, was linked to a neural functional organisation associated, via meta-analysis, with task labels related to semantic associations (sentence processing, reading and verbal semantics). Variability of this functional mode predicted significant individual variation in the types of thoughts that individuals experienced in the laboratory: neurocognitive patterns linked to better performance at tasks that required guidance from semantic representation, rather than those dependent on executive control, were associated with patterns of thought characterised by greater subjective detail and a focus on time periods other than the here and now. These relationships were consistent across different days and did not vary with level of task demands, indicating they are relatively stable features of an individual’s cognitive profile. Together these data confirm that individual variation in aspects of ongoing experience can be inferred from hidden neurocognitive architecture and demonstrate that performance trade-offs between executive control and long-term semantic knowledge are linked to a person’s tendency to imagine situations that transcend the here and now.
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Affiliation(s)
- Hao-Ting Wang
- Sackler Centre for Consciousness Science, University of Sussex, Brighton, UK.
| | | | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imagine Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada.,Mila-Quebec Artificial Intelligence Institute, Montreal, Canada
| | - Boris C Bernhardt
- Department of Neurology and Neurosurgery, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
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37
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Fox KCR, Shi L, Baek S, Raccah O, Foster BL, Saha S, Margulies DS, Kucyi A, Parvizi J. Intrinsic network architecture predicts the effects elicited by intracranial electrical stimulation of the human brain. Nat Hum Behav 2020; 4:1039-1052. [PMID: 32632334 DOI: 10.1038/s41562-020-0910-1] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 06/04/2020] [Indexed: 12/12/2022]
Abstract
Intracranial electrical stimulation (iES) of the human brain has long been known to elicit a remarkable variety of perceptual, motor and cognitive effects, but the functional-anatomical basis of this heterogeneity remains poorly understood. We conducted a whole-brain mapping of iES-elicited effects, collecting first-person reports following iES at 1,537 cortical sites in 67 participants implanted with intracranial electrodes. We found that intrinsic network membership and the principal gradient of functional connectivity strongly predicted the type and frequency of iES-elicited effects in a given brain region. While iES in unimodal brain networks at the base of the cortical hierarchy elicited frequent and simple effects, effects became increasingly rare, heterogeneous and complex in heteromodal and transmodal networks higher in the hierarchy. Our study provides a comprehensive exploration of the relationship between the hierarchical organization of intrinsic functional networks and the causal modulation of human behaviour and experience with iES.
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Affiliation(s)
- Kieran C R Fox
- Stanford Human Intracranial Cognitive Electrophysiology Program, Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA. .,School of Medicine, Stanford University, Stanford, CA, USA.
| | - Lin Shi
- Stanford Human Intracranial Cognitive Electrophysiology Program, Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA.,Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Sori Baek
- Stanford Human Intracranial Cognitive Electrophysiology Program, Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Omri Raccah
- Stanford Human Intracranial Cognitive Electrophysiology Program, Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Brett L Foster
- Departments of Neurosurgery and Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Srijani Saha
- Stanford Human Intracranial Cognitive Electrophysiology Program, Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique (CNRS), UMR 7225, Frontlab, Institut du Cerveau et de la Moelle Épinière, Paris, France
| | - Aaron Kucyi
- Stanford Human Intracranial Cognitive Electrophysiology Program, Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Josef Parvizi
- Stanford Human Intracranial Cognitive Electrophysiology Program, Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA.
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38
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Vatansever D, Karapanagiotidis T, Margulies DS, Jefferies E, Smallwood J. Distinct patterns of thought mediate the link between brain functional connectomes and well-being. Netw Neurosci 2020; 4:637-657. [PMID: 32885119 PMCID: PMC7462429 DOI: 10.1162/netn_a_00137] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [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: 10/18/2019] [Accepted: 03/04/2020] [Indexed: 12/29/2022] Open
Abstract
Ongoing thought patterns constitute important aspects of both healthy and abnormal human cognition. However, the neural mechanisms behind these daily experiences and their contribution to well-being remain a matter of debate. Here, using resting-state fMRI and retrospective thought sampling in a large neurotypical cohort (n = 211), we identified two distinct patterns of thought, broadly describing the participants' current concerns and future plans, that significantly explained variability in the individual functional connectomes. Consistent with the view that ongoing thoughts are an emergent property of multiple neural systems, network-based analysis highlighted the central importance of both unimodal and transmodal cortices in the generation of these experiences. Importantly, while state-dependent current concerns predicted better psychological health, mediating the effect of functional connectomes, trait-level future plans were related to better social health, yet with no mediatory influence. Collectively, we show that ongoing thoughts can influence the link between brain physiology and well-being.
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Affiliation(s)
- Deniz Vatansever
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | | | - Daniel S Margulies
- Brain and Spine Institute, French National Centre for Scientific Research, Paris, France
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39
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Milham M, Petkov CI, Margulies DS, Schroeder CE, Basso MA, Belin P, Fair DA, Fox A, Kastner S, Mars RB, Messinger A, Poirier C, Vanduffel W, Van Essen DC, Alvand A, Becker Y, Ben Hamed S, Benn A, Bodin C, Boretius S, Cagna B, Coulon O, El-Gohary SH, Evrard H, Forkel SJ, Friedrich P, Froudist-Walsh S, Garza-Villarreal EA, Gao Y, Gozzi A, Grigis A, Hartig R, Hayashi T, Heuer K, Howells H, Ardesch DJ, Jarraya B, Jarrett W, Jedema HP, Kagan I, Kelly C, Kennedy H, Klink PC, Kwok SC, Leech R, Liu X, Madan C, Madushanka W, Majka P, Mallon AM, Marche K, Meguerditchian A, Menon RS, Merchant H, Mitchell A, Nenning KH, Nikolaidis A, Ortiz-Rios M, Pagani M, Pareek V, Prescott M, Procyk E, Rajimehr R, Rautu IS, Raz A, Roe AW, Rossi-Pool R, Roumazeilles L, Sakai T, Sallet J, García-Saldivar P, Sato C, Sawiak S, Schiffer M, Schwiedrzik CM, Seidlitz J, Sein J, Shen ZM, Shmuel A, Silva AC, Simone L, Sirmpilatze N, Sliwa J, Smallwood J, Tasserie J, Thiebaut de Schotten M, Toro R, Trapeau R, Uhrig L, Vezoli J, Wang Z, Wells S, Williams B, Xu T, Xu AG, Yacoub E, Zhan M, Ai L, Amiez C, Balezeau F, Baxter MG, Blezer EL, Brochier T, Chen A, Croxson PL, Damatac CG, Dehaene S, Everling S, Fleysher L, Freiwald W, Griffiths TD, Guedj C, Hadj-Bouziane F, Harel N, Hiba B, Jung B, Koo B, Laland KN, Leopold DA, Lindenfors P, Meunier M, Mok K, Morrison JH, Nacef J, Nagy J, Pinsk M, Reader SM, Roelfsema PR, Rudko DA, Rushworth MF, Russ BE, Schmid MC, Sullivan EL, Thiele A, Todorov OS, Tsao D, Ungerleider L, Wilson CR, Ye FQ, Zarco W, Zhou YD. Accelerating the Evolution of Nonhuman Primate Neuroimaging. Neuron 2020; 105:600-603. [PMID: 32078795 PMCID: PMC7610430 DOI: 10.1016/j.neuron.2019.12.023] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 12/13/2019] [Accepted: 12/17/2019] [Indexed: 11/17/2022]
Abstract
Nonhuman primate neuroimaging is on the cusp of a transformation, much in the same way its human counterpart was in 2010, when the Human Connectome Project was launched to accelerate progress. Inspired by an open data-sharing initiative, the global community recently met and, in this article, breaks through obstacles to define its ambitions.
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40
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Paquola C, Bethlehem RAI, Seidlitz J, Wagstyl K, Romero-Garcia R, Whitaker KJ, Vos de Wael R, Williams GB, Vértes PE, Margulies DS, Bernhardt B, Bullmore ET. Shifts in myeloarchitecture characterise adolescent development of cortical gradients. eLife 2019; 8:e50482. [PMID: 31724948 PMCID: PMC6855802 DOI: 10.7554/elife.50482] [Citation(s) in RCA: 60] [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: 07/24/2019] [Accepted: 11/02/2019] [Indexed: 11/21/2022] Open
Abstract
We studied an accelerated longitudinal cohort of adolescents and young adults (n = 234, two time points) to investigate dynamic reconfigurations in myeloarchitecture. Intracortical profiles were generated using magnetization transfer (MT) data, a myelin-sensitive magnetic resonance imaging contrast. Mixed-effect models of depth specific intracortical profiles demonstrated two separate processes i) overall increases in MT, and ii) flattening of the MT profile related to enhanced signal in mid-to-deeper layers, especially in heteromodal and unimodal association cortices. This development was independent of morphological changes. Enhanced MT in mid-to-deeper layers was found to spatially co-localise specifically with gene expression markers of oligodendrocytes. Interregional covariance analysis revealed that these intracortical changes contributed to a gradual differentiation of higher-order from lower-order systems. Depth-dependent trajectories of intracortical myeloarchitectural development contribute to the maturation of structural hierarchies in the human neocortex, providing a model for adolescent development that bridges microstructural and macroscopic scales of brain organisation.
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Affiliation(s)
- Casey Paquola
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and HospitalMcGill UniversityMontrealCanada
| | - Richard AI Bethlehem
- Department of PsychiatryUniversity of CambridgeCambridgeUnited Kingdom
- Autism Research Centre, Department of PsychiatryUniversity of CambridgeCambridgeUnited Kingdom
| | - Jakob Seidlitz
- Department of PsychiatryUniversity of CambridgeCambridgeUnited Kingdom
- Developmental Neurogenomics UnitNational Institute of Mental HealthBethesdaUnited States
| | - Konrad Wagstyl
- Department of PsychiatryUniversity of CambridgeCambridgeUnited Kingdom
| | | | - Kirstie J Whitaker
- Department of PsychiatryUniversity of CambridgeCambridgeUnited Kingdom
- The Alan Turing InstituteLondonUnited Kingdom
| | - Reinder Vos de Wael
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and HospitalMcGill UniversityMontrealCanada
| | - Guy B Williams
- Department of Clinical Neurosciences, Wolfson Brain Imaging CentreUniversity of CambridgeCambridgeUnited Kingdom
| | | | - Petra E Vértes
- Department of PsychiatryUniversity of CambridgeCambridgeUnited Kingdom
- The Alan Turing InstituteLondonUnited Kingdom
| | - Daniel S Margulies
- FrontlabInstitut du Cerveau et de la Moelle épinière, UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225ParisFrance
| | - Boris Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute and HospitalMcGill UniversityMontrealCanada
| | - Edward T Bullmore
- Department of PsychiatryUniversity of CambridgeCambridgeUnited Kingdom
- Department of Clinical Neurosciences, Wolfson Brain Imaging CentreUniversity of CambridgeCambridgeUnited Kingdom
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41
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Murphy C, Poerio G, Sormaz M, Wang HT, Vatansever D, Allen M, Margulies DS, Jefferies E, Smallwood J. Hello, is that me you are looking for? A re-examination of the role of the DMN in social and self relevant aspects of off-task thought. PLoS One 2019; 14:e0216182. [PMID: 31697677 PMCID: PMC6837379 DOI: 10.1371/journal.pone.0216182] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2019] [Accepted: 10/15/2019] [Indexed: 11/21/2022] Open
Abstract
Neural activity within the default mode network (DMN) is widely assumed to relate to processing during off-task states, however it remains unclear whether this association emerges from a shared role in self or social content that is common in these conditions. In the current study, we examine the possibility that the role of the DMN in ongoing thought emerges from contributions to specific features of off-task experience such as self-relevant or social content. A group of participants described their experiences while performing a laboratory task over a period of days. In a different session, neural activity was measured while participants performed Self/Other judgements (e.g., Does the word ‘Honest’ apply to you (Self condition) or Barack Obama (Other condition)). Despite the prominence of social and personal content in off-task reports, there was no association with neural activity during off-task trait adjective judgements. Instead, during both Self and Other judgements we found recruitment of caudal posterior cingulate cortex—a core DMN hub—was above baseline for individuals whose laboratory experiences were characterised as detailed. These data provide little support for a role of the DMN in self or other content in the off-task state and instead suggest a role in how on-going thought is represented.
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Affiliation(s)
- Charlotte Murphy
- Department of Psychology, University of York, York England, United Kingdom
- * E-mail:
| | - Giulia Poerio
- Department of Psychology, The University of Sheffield, Sheffield, England, United Kingdom
| | - Mladen Sormaz
- Department of Psychology, University of York, York England, United Kingdom
| | - Hao-Ting Wang
- Department of Psychology, University of York, York England, United Kingdom
| | - Deniz Vatansever
- Department of Psychology, University of York, York England, United Kingdom
| | - Micah Allen
- Cambridge Psychiatry, Cambridge University, Cambridge, United Kingdom
| | | | | | - Jonathan Smallwood
- Department of Psychology, University of York, York England, United Kingdom
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42
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Zhang M, Savill N, Margulies DS, Smallwood J, Jefferies E. Distinct individual differences in default mode network connectivity relate to off-task thought and text memory during reading. Sci Rep 2019; 9:16220. [PMID: 31700143 PMCID: PMC6838089 DOI: 10.1038/s41598-019-52674-9] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [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: 03/25/2019] [Accepted: 10/21/2019] [Indexed: 01/08/2023] Open
Abstract
Often, as we read, we find ourselves thinking about something other than the text; this tendency to mind-wander is linked to poor comprehension and reduced subsequent memory for texts. Contemporary accounts argue that periods of off-task thought are related to the tendency for attention to be decoupled from external input. We used fMRI to understand the neural processes that underpin this phenomenon. First, we found that individuals with poorer text-based memory tend to show reduced recruitment of left middle temporal gyrus in response to orthographic input, within a region located at the intersection of default mode, dorsal attention and frontoparietal networks. Voxels within these networks were taken as seeds in a subsequent resting-state study. The default mode network region (i) had greater connectivity with medial prefrontal cortex, falling within the same network, for individuals with better text-based memory, and (ii) was more decoupled from medial visual regions in participants who mind-wandered more frequently. These findings suggest that stronger intrinsic connectivity within the default mode network is linked to better text processing, while reductions in default mode network coupling to the visual system may underpin individual variation in the tendency for our attention to become disengaged from what we are reading.
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Affiliation(s)
- Meichao Zhang
- Department of Psychology, University of York, Heslington, York, YO10 5DD, UK.
| | - Nicola Savill
- School of Psychological and Social Sciences, York St John University, York, YO31 7EX, UK
| | - Daniel S Margulies
- Frontlab, Centre National de la Recherche Scientifique (CNRS) UMR 7225, Institut du cerveau et de la moelle épinière (ICM), 75013, Paris, France
| | - Jonathan Smallwood
- Department of Psychology, University of York, Heslington, York, YO10 5DD, UK
| | - Elizabeth Jefferies
- Department of Psychology, University of York, Heslington, York, YO10 5DD, UK
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43
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Alves PN, Foulon C, Karolis V, Bzdok D, Margulies DS, Volle E, Thiebaut de Schotten M. An improved neuroanatomical model of the default-mode network reconciles previous neuroimaging and neuropathological findings. Commun Biol 2019; 2:370. [PMID: 31633061 PMCID: PMC6787009 DOI: 10.1038/s42003-019-0611-3] [Citation(s) in RCA: 160] [Impact Index Per Article: 32.0] [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: 01/24/2019] [Accepted: 09/16/2019] [Indexed: 12/16/2022] Open
Abstract
The brain is constituted of multiple networks of functionally correlated brain areas, out of which the default-mode network (DMN) is the largest. Most existing research into the DMN has taken a corticocentric approach. Despite its resemblance with the unitary model of the limbic system, the contribution of subcortical structures to the DMN may be underappreciated. Here, we propose a more comprehensive neuroanatomical model of the DMN including subcortical structures such as the basal forebrain, cholinergic nuclei, anterior and mediodorsal thalamic nuclei. Additionally, tractography of diffusion-weighted imaging was employed to explore the structural connectivity, which revealed that the thalamus and basal forebrain are of central importance for the functioning of the DMN. The contribution of these neurochemically diverse brain nuclei reconciles previous neuroimaging with neuropathological findings in diseased brains and offers the potential for identifying a conserved homologue of the DMN in other mammalian species.
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Affiliation(s)
- Pedro Nascimento Alves
- Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris, France
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225 Paris, France
- Department of Neurosciences and Mental Health, Neurology, Hospital de Santa Maria, CHULN, Lisbon, Portugal
- Language Research Laboratory, Faculty of Medicine, Universidade de Lisboa, Lisbon, Portugal
| | - Chris Foulon
- Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris, France
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225 Paris, France
- Computational Neuroimaging Laboratory, Department of Diagnostic Medicine, The University of Texas at Austin Dell Medical School, Austin, TX USA
| | - Vyacheslav Karolis
- Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris, France
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225 Paris, France
- FMRIB centre, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Danilo Bzdok
- INRIA, Parietal Team, Saclay, France
- Neurospin, CEA, Gif-sur-Yvette, France
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Aachen, Germany
- JARA-BRAIN, Jülich-Aachen Research Alliance, Jülich, Germany
| | - Daniel S. Margulies
- Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris, France
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225 Paris, France
| | - Emmanuelle Volle
- Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris, France
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225 Paris, France
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Laboratory, BCBlab, Sorbonne Universities, Paris, France
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225 Paris, France
- Centre de Neuroimagerie de Recherche CENIR, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
- Groupe d’Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France
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Goulas A, Margulies DS, Bezgin G, Hilgetag CC. The architecture of mammalian cortical connectomes in light of the theory of the dual origin of the cerebral cortex. Cortex 2019; 118:244-261. [DOI: 10.1016/j.cortex.2019.03.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 01/04/2019] [Accepted: 03/05/2019] [Indexed: 12/14/2022]
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Ho NSP, Wang X, Vatansever D, Margulies DS, Bernhardt B, Jefferies E, Smallwood J. Individual variation in patterns of task focused, and detailed, thought are uniquely associated within the architecture of the medial temporal lobe. Neuroimage 2019; 202:116045. [PMID: 31349068 DOI: 10.1016/j.neuroimage.2019.116045] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 06/26/2019] [Accepted: 07/21/2019] [Indexed: 11/29/2022] Open
Abstract
Understanding the neural processes that support different patterns of ongoing thought is an important goal of contemporary cognitive neuroscience. Early accounts assumed the default mode network (DMN) was especially important for conscious attention to task-irrelevant/personally relevant materials. However, simple task-negative accounts of the DMN are incompatible with more recent evidence that neural patterns within the system can be related to ongoing processing during active task states. To better characterise the contribution of the DMN to ongoing thought, we conducted a cross-sectional analysis of the relationship between the structural organisation of the brain, as indexed by cortical thickness, and patterns of experience, identified using experience sampling in the cognitive laboratory. In a sample of 181 healthy individuals (mean age 20 years, 117 females) we identified an association between cortical thickness in the anterior parahippocampus and patterns of task focused thought, as well as an adjacent posterior region in which cortical thickness was associated with experiences with higher levels of subjective detail. Both regions fell within regions of medial temporal lobe associated with the DMN, yet varied in their functional connectivity: the time series of signals in the 'on-task' region were more correlated with systems important for external task-relevant processing (as determined by meta-analysis) including the dorsal and ventral attention, and fronto-parietal networks. In contrast, connectivity within the region linked to subjective 'detail' was more correlated with the medial core of the DMN (posterior cingulate and the medial pre-frontal cortex) and regions of primary visual cortex. These results provide cross-sectional evidence that confirms a role of the DMN in how detailed experiences are and so provide further evidence that the role of this system in experience is not simply task-irrelevant. Our results also highlight processes within the medial temporal lobe, and their interactions with other regions of cortex, as important in determining multiple aspects of how human cognition unfolds.
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Affiliation(s)
| | - Xiuyi Wang
- Department of Psychology, University of York, England, UK
| | - Deniz Vatansever
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, PR China
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique (CNRS) UMR 7225, Institut du Cerveau et de la Moelle épinière, Paris, France
| | - Boris Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
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Bayrak Ş, Khalil AA, Villringer K, Fiebach JB, Villringer A, Margulies DS, Ovadia-Caro S. The impact of ischemic stroke on connectivity gradients. Neuroimage Clin 2019; 24:101947. [PMID: 31376644 PMCID: PMC6676042 DOI: 10.1016/j.nicl.2019.101947] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [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: 04/16/2019] [Revised: 07/08/2019] [Accepted: 07/17/2019] [Indexed: 11/19/2022]
Abstract
The functional organization of the brain can be represented as a low-dimensional space that reflects its macroscale hierarchy. The dimensions of this space, described as connectivity gradients, capture the similarity of areas' connections along a continuous space. Studying how pathological perturbations with known effects on functional connectivity affect these connectivity gradients provides support for their biological relevance. Previous work has shown that localized lesions cause widespread functional connectivity alterations in structurally intact areas, affecting a network of interconnected regions. By using acute stroke as a model of the effects of focal lesions on the connectome, we apply the connectivity gradient framework to depict how functional reorganization occurs throughout the brain, unrestricted by traditional definitions of functional network boundaries. We define a three-dimensional connectivity space template based on functional connectivity data from healthy controls. By projecting lesion locations into this space, we demonstrate that ischemic strokes result in dimension-specific alterations in functional connectivity over the first week after symptom onset. Specifically, changes in functional connectivity were captured along connectivity Gradients 1 and 3. The degree of functional connectivity change was associated with the distance from the lesion along these connectivity gradients (a measure of functional similarity) regardless of the anatomical distance from the lesion. Together, these results provide support for the biological validity of connectivity gradients and suggest a novel framework to characterize connectivity alterations after stroke.
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Affiliation(s)
- Şeyma Bayrak
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Cognitive Neurology, University Hospital Leipzig and Faculty of Medicine, University of Leipzig, Leipzig, Germany
| | - Ahmed A Khalil
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Kersten Villringer
- Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jochen B Fiebach
- Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Department of Cognitive Neurology, University Hospital Leipzig and Faculty of Medicine, University of Leipzig, Leipzig, Germany; Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique (CNRS) UMR 7225, Frontlab, Institut du Cerveau et de la Moelle épinière, Paris, France.
| | - Smadar Ovadia-Caro
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany; Department of Neurology, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Berlin, Germany.
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47
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Tang R, Ketcha M, Badea A, Calabrese ED, Margulies DS, Vogelstein JT, Priebe CE, Sussman DL. Connectome Smoothing via Low-Rank Approximations. IEEE Trans Med Imaging 2019; 38:1446-1456. [PMID: 30530318 PMCID: PMC6554071 DOI: 10.1109/tmi.2018.2885968] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
In brain imaging and connectomics, the study of brain networks, estimating the mean of a population of graphs based on a sample is a core problem. Often, this problem is especially difficult because the sample or cohort size is relatively small, sometimes even a single subject, while the number of nodes can be very large with noisy estimates of connectivity. While the element-wise sample mean of the adjacency matrices is a common approach, this method does not exploit the underlying structural properties of the graphs. We propose using a low-rank method that incorporates dimension selection and diagonal augmentation to smooth the estimates and improve performance over the naïve methodology for small sample sizes. Theoretical results for the stochastic block model show that this method offers major improvements when there are many vertices. Similarly, we demonstrate that the low-rank methods outperform the standard sample mean for a variety of independent edge distributions as well as human connectome data derived from the magnetic resonance imaging, especially when the sample sizes are small. Moreover, the low-rank methods yield "eigen-connectomes," which correlate with the lobe-structure of the human brain and superstructures of the mouse brain. These results indicate that the low-rank methods are the important parts of the toolbox for researchers studying populations of graphs in general and statistical connectomics in particular.
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Affiliation(s)
- Runze Tang
- Department of Applied Math & Statistics, The Johns Hopkins University, Baltimore, MD
| | - Michael Ketcha
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD
| | - Alexandra Badea
- Department of Radiology, and Department of Biomedical Engineering, Duke University, Durham, NC
| | - Evan D. Calabrese
- Department of Radiology, and Department of Biomedical Engineering, Duke University, Durham, NC
| | - Daniel S. Margulies
- Max Planck Research Group for Neuroanatomy & Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Joshua T. Vogelstein
- Department of Biomedical Engineering, The Johns Hopkins University, Baltimore, MD
- Child Mind Institute, New York, NY
| | - Carey E. Priebe
- Department of Applied Math & Statistics, The Johns Hopkins University, Baltimore, MD
| | - Daniel L. Sussman
- Department of Mathematics & Statistics, Boston University, Boston, MA
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48
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van den Heuvel MP, Scholtens LH, van der Burgh HK, Agosta F, Alloza C, Arango C, Auyeung B, Baron-Cohen S, Basaia S, Benders MJNL, Beyer F, Booij L, Braun KPJ, Filho GB, Cahn W, Cannon DM, Chaim-Avancini TM, Chan SSM, Chen EYH, Crespo-Facorro B, Crone EA, Dannlowski U, de Zwarte SMC, Dietsche B, Donohoe G, Plessis SD, Durston S, Díaz-Caneja CM, Díaz-Zuluaga AM, Emsley R, Filippi M, Frodl T, Gorges M, Graff B, Grotegerd D, Gąsecki D, Hall JM, Holleran L, Holt R, Hopman HJ, Jansen A, Janssen J, Jodzio K, Jäncke L, Kaleda VG, Kassubek J, Masouleh SK, Kircher T, Koevoets MGJC, Kostic VS, Krug A, Lawrie SM, Lebedeva IS, Lee EHM, Lett TA, Lewis SJG, Liem F, Lombardo MV, Lopez-Jaramillo C, Margulies DS, Markett S, Marques P, Martínez-Zalacaín I, McDonald C, McIntosh AM, McPhilemy G, Meinert SL, Menchón JM, Montag C, Moreira PS, Morgado P, Mothersill DO, Mérillat S, Müller HP, Nabulsi L, Najt P, Narkiewicz K, Naumczyk P, Oranje B, Ortiz-Garcia de la Foz V, Peper JS, Pineda JA, Rasser PE, Redlich R, Repple J, Reuter M, Rosa PGP, Ruigrok ANV, Sabisz A, Schall U, Seedat S, Serpa MH, Skouras S, Soriano-Mas C, Sousa N, Szurowska E, Tomyshev AS, Tordesillas-Gutierrez D, Valk SL, van den Berg LH, van Erp TGM, van Haren NEM, van Leeuwen JMC, Villringer A, Vinkers CH, Vollmar C, Waller L, Walter H, Whalley HC, Witkowska M, Witte AV, Zanetti MV, Zhang R, de Lange SC. 10Kin1day: A Bottom-Up Neuroimaging Initiative. Front Neurol 2019; 10:425. [PMID: 31133958 PMCID: PMC6524614 DOI: 10.3389/fneur.2019.00425] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2018] [Accepted: 04/08/2019] [Indexed: 01/11/2023] Open
Abstract
We organized 10Kin1day, a pop-up scientific event with the goal to bring together neuroimaging groups from around the world to jointly analyze 10,000+ existing MRI connectivity datasets during a 3-day workshop. In this report, we describe the motivation and principles of 10Kin1day, together with a public release of 8,000+ MRI connectome maps of the human brain.
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Affiliation(s)
- Martijn P. van den Heuvel
- Connectome Lab, CTG, CNCR, VU Amsterdam, Amsterdam, Netherlands
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Lianne H. Scholtens
- Connectome Lab, CTG, CNCR, VU Amsterdam, Amsterdam, Netherlands
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Hannelore K. van der Burgh
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Federica Agosta
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Clara Alloza
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
- Department of Child and Adolescent Psychiatry, IiSGM, CIBERSAM, School of Medicine, Hospital General Universitario Gregorio Marañón, Universidad Complutense, Madrid, Spain
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, IiSGM, CIBERSAM, School of Medicine, Hospital General Universitario Gregorio Marañón, Universidad Complutense, Madrid, Spain
| | - Bonnie Auyeung
- Department of Psychiatry, Autism Research Centre, University of Cambridge, Cambridge, United Kingdom
| | - Simon Baron-Cohen
- Department of Psychiatry, Autism Research Centre, University of Cambridge, Cambridge, United Kingdom
| | - Silvia Basaia
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Manon J. N. L. Benders
- Department of Neonatology, UMC Utrecht Brain Center, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, Netherlands
| | - Frauke Beyer
- Department of Neurology, CRC “Obesity Mechanisms”, Subproject A1, Max Planck Institute for Human Cognitive and Brain Sciences, University of Leipzig, Leipzig, Germany
| | - Linda Booij
- Department of Psychology, Concordia University, Montreal, QC, Canada
| | - Kees P. J. Braun
- Department of Child Neurology, UMC Utrecht Brain Center, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, Netherlands
| | - Geraldo Busatto Filho
- Laboratory of Psychiatric Neuroimaging (LIM21), Faculdade de Medicina, Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Wiepke Cahn
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Dara M. Cannon
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics (NICOG), NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Tiffany M. Chaim-Avancini
- Laboratory of Psychiatric Neuroimaging (LIM21), Faculdade de Medicina, Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Sandra S. M. Chan
- Department of Psychiatry, Faculty of Medicine, Chinese University of Hong Kong, Hong Kong, China
| | - Eric Y. H. Chen
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - Benedicto Crespo-Facorro
- Psychiatry Unit, Department of Medicine and Psychiatry, Hospital Universitario Marques de Valdecilla, IDIVAL, CIBERSAM, Hosptial Universitario Virgen del Rocío, Universidad de Seville, Seville, Spain
| | - Eveline A. Crone
- Brain and Development Research Center, Leiden University, Leiden, Netherlands
| | - Udo Dannlowski
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Sonja M. C. de Zwarte
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Bruno Dietsche
- Department of Psychiatry, University of Marburg, Marburg, Germany
| | - Gary Donohoe
- Cognitive Genetics and Cognitive Therapy Group, Neuroimaging and Cognitive Genomics Centre and NCBES Galway Neuroscience Centre, School of Psychology and Discipline of Biochemistry, National University of Ireland, Galway, Ireland
| | - Stefan Du Plessis
- Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
| | - Sarah Durston
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Covadonga M. Díaz-Caneja
- Department of Child and Adolescent Psychiatry, IiSGM, CIBERSAM, School of Medicine, Hospital General Universitario Gregorio Marañón, Universidad Complutense, Madrid, Spain
| | - Ana M. Díaz-Zuluaga
- Research Group in Psychiatry GIPSI, Department of Psychiatry, Faculty of Medicine, Universidad de Antioquia, Medellín, Colombia
| | - Robin Emsley
- Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Thomas Frodl
- Department of Psychiatry and Psychotherapy, University Hospital, Otto von Guericke University, Magdeburg, Germany
| | - Martin Gorges
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Beata Graff
- Department of Hypertension and Diabetology, Medical University of Gdańsk, Gdańsk, Poland
| | | | - Dariusz Gąsecki
- Department of Neurology of Adults, Medical University of Gdańsk, Gdańsk, Poland
| | - Julie M. Hall
- Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - Laurena Holleran
- Cognitive Genetics and Cognitive Therapy Group, Neuroimaging and Cognitive Genomics Centre and NCBES Galway Neuroscience Centre, School of Psychology and Discipline of Biochemistry, National University of Ireland, Galway, Ireland
| | - Rosemary Holt
- Department of Psychiatry, Autism Research Centre, University of Cambridge, Cambridge, United Kingdom
| | - Helene J. Hopman
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
| | - Andreas Jansen
- Department of Psychiatry and Center for Mind, Brain and Behaviour, University of Marburg, Marburg, Germany
| | - Joost Janssen
- Department of Child and Adolescent Psychiatry, IiSGM, CIBERSAM, School of Medicine, Hospital General Universitario Gregorio Marañón, Universidad Complutense, Madrid, Spain
| | | | - Lutz Jäncke
- Division of Neuropsychology, University of Zurich, Zurich, Switzerland
| | - Vasiliy G. Kaleda
- Department of Endogenous Mental Disorders, Mental Health Research Center, Moscow, Russia
| | - Jan Kassubek
- Department of Neurology, University of Ulm, Ulm, Germany
| | | | - Tilo Kircher
- Department of Psychiatry and Center for Mind, Brain and Behaviour, University of Marburg, Marburg, Germany
| | - Martijn G. J. C. Koevoets
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Vladimir S. Kostic
- Clinic of Neurology, School of Medicine, University of Belgrade, Belgrade, Serbia
| | - Axel Krug
- Department of Psychiatry and Center for Mind, Brain and Behaviour, University of Marburg, Marburg, Germany
| | - Stephen M. Lawrie
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Irina S. Lebedeva
- Laboratory of Neuroimaging and Multimodal Analysis, Mental Health Research Center, Moscow, Russia
| | - Edwin H. M. Lee
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - Tristram A. Lett
- Department of Psychiatry and Psychotherapy, Division of Mind and Brain Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Simon J. G. Lewis
- Parkinson's Disease Research Clinic, Brain and Mind Centre, University of Sydney, Sydney, NSW, Australia
| | - Franziskus Liem
- University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Zurich, Switzerland
| | - Michael V. Lombardo
- Department of Psychiatry, Autism Research Centre, University of Cambridge, Cambridge, United Kingdom
| | - Carlos Lopez-Jaramillo
- Mood Disorders Program, Research Group in Psychiatry GIPSI, Department of Psychiatry, Faculty of Medicine, Hospital Universitario San Vicente Fundación, Universidad de Antioquia, Medellín, Colombia
| | - Daniel S. Margulies
- Frontlab, Centre National de la Recherche Scientifique, Institut du Cerveau et de la Moelle Épinière, UMR 7225, Paris, France
| | - Sebastian Markett
- Department of Psychology, Humboldt Universität zu Berlin, Berlin, Germany
| | - Paulo Marques
- School of Medicine, Life and Health Sciences Research Institute (ICVS), University of Minho, Braga, Portugal
| | - Ignacio Martínez-Zalacaín
- Department of Psychiatry, Bellvitge Biomedical Research Institute-IDIBELL and CIBERSAM, Barcelona, Spain
| | - Colm McDonald
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics (NICOG), NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Andrew M. McIntosh
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Genevieve McPhilemy
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics (NICOG), NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | | | - José M. Menchón
- Department of Psychiatry, Bellvitge Biomedical Research Institute-IDIBELL and CIBERSAM, Barcelona, Spain
| | - Christian Montag
- Department of Molecular Psychology, Institute of Psychology and Education, Ulm University, Ulm, Germany
| | - Pedro S. Moreira
- School of Medicine, Life and Health Sciences Research Institute (ICVS), University of Minho, Braga, Portugal
| | - Pedro Morgado
- School of Medicine, Life and Health Sciences Research Institute (ICVS), University of Minho, Braga, Portugal
| | - David O. Mothersill
- Cognitive Genetics and Cognitive Therapy Group, Neuroimaging and Cognitive Genomics Centre and NCBES Galway Neuroscience Centre, School of Psychology and Discipline of Biochemistry, National University of Ireland, Galway, Ireland
| | - Susan Mérillat
- University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Zurich, Switzerland
| | | | - Leila Nabulsi
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics (NICOG), NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Pablo Najt
- Clinical Neuroimaging Laboratory, Centre for Neuroimaging and Cognitive Genomics (NICOG), NCBES Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, National University of Ireland Galway, Galway, Ireland
| | - Krzysztof Narkiewicz
- Department of Hypertension and Diabetology, Medical University of Gdańsk, Gdańsk, Poland
| | | | - Bob Oranje
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
| | - Victor Ortiz-Garcia de la Foz
- Psychiatry Unit, Department of Medicine and Psychiatry, IDIVAL, CIBERSAM, Hospital Universitario Marques de Valdecilla, Santander, Spain
| | - Jiska S. Peper
- Brain and Development Research Center, Leiden University, Leiden, Netherlands
| | - Julian A. Pineda
- Research Group, Instituto de Alta Tecnología Médica, Universidad de Antioquia, Medellín, Colombia
| | - Paul E. Rasser
- Priority Centre for Brain and Mental Health Research, The University of Newcastle, Newcastle, NSW, Australia
| | - Ronny Redlich
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Jonathan Repple
- Department of Psychiatry, University of Münster, Münster, Germany
| | - Martin Reuter
- Department of Psychology, Humboldt Universität zu Berlin, Berlin, Germany
| | - Pedro G. P. Rosa
- Laboratory of Psychiatric Neuroimaging (LIM21), Faculdade de Medicina, Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, Universidade de São Paulo, São Paulo, Brazil
| | - Amber N. V. Ruigrok
- Department of Psychiatry, Autism Research Centre, University of Cambridge, Cambridge, United Kingdom
| | - Agnieszka Sabisz
- 2nd Department of Radiology, Medical University of Gdańsk, Gdańsk, Poland
| | - Ulrich Schall
- Priority Centre for Brain and Mental Health Research, The University of Newcastle, Newcastle, NSW, Australia
| | - Soraya Seedat
- Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
| | - Mauricio H. Serpa
- Laboratory of Psychiatric Neuroimaging (LIM21), Departamento de Psiquiatria, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Stavros Skouras
- BarcelonaBeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain
| | - Carles Soriano-Mas
- Department of Psychiatry (IDIBELL and CIBERSAM) and Department of Psychobiology and Methodology in Health Sciences (UAB), Bellvitge Biomedical Research Institute-IDIBELL, CIBERSAM and Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Nuno Sousa
- School of Medicine, Life and Health Sciences Research Institute (ICVS), University of Minho, Braga, Portugal
| | - Edyta Szurowska
- 2nd Department of Radiology, Medical University of Gdańsk, Gdańsk, Poland
| | - Alexander S. Tomyshev
- Laboratory of Neuroimaging and Multimodal Analysis, Mental Health Research Center, Moscow, Russia
| | - Diana Tordesillas-Gutierrez
- Neuroimaging Unit, Technological Facilities, Valdecilla Biomedical Research Institute IDIVAL, CIBERSAM, Santander, Spain
| | - Sofie L. Valk
- Institute for Neuroscience and Medicine 7/Institute of Systems Neuroscience, Forschungszentrum Jülich - Heinrich Heine Universitaet Duesseldorf, Jülich, Germany
| | - Leonard H. van den Berg
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, Netherlands
| | - Theo G. M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
| | - Neeltje E. M. van Haren
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus Medical Center, Rotterdam, Netherlands
| | - Judith M. C. van Leeuwen
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Arno Villringer
- Departments of Neurology, Cognitive Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, University of Leipzig, Leipzig, Germany
| | - Christiaan H. Vinkers
- Departments of Psychiatry, Anatomy and Neurosciences, Amsterdam UMC, Amsterdam, Netherlands
| | - Christian Vollmar
- Department of Neurology, Epilepsy Centre, University of Munich Hospital, Munich, Germany
| | - Lea Waller
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité Universitätsmedizin Berlin, Corporate Member of Berlin Institute of Health, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Henrik Walter
- Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy CCM, Charité - Universitätsmedizin Berlin, Corporate Member of Berlin Institute of Health, Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Heather C. Whalley
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Marta Witkowska
- Institute of Psychology, University of Gdańsk, Gdańsk, Poland
| | - A. Veronica Witte
- Department of Neurology, CRC “Obesity Mechanisms”, Subproject A1, Max Planck Institute for Human Cognitive and Brain Sciences, University of Leipzig, Leipzig, Germany
| | - Marcus V. Zanetti
- Laboratory of Psychiatric Neuroimaging (LIM21), Faculdade de Medicina, Instituto de Psiquiatria, Hospital das Clinicas HCFMUSP, São Paulo, Brazil
- Instituto de Ensino e Pesquisa, Hospital Sírio-Libanês, Universidade de São Paulo, São Paulo, Brazil
| | - Rui Zhang
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Siemon C. de Lange
- Connectome Lab, CTG, CNCR, VU Amsterdam, Amsterdam, Netherlands
- UMC Utrecht Brain Center, Department of Psychiatry, University Medical Center Utrecht, Utrecht, Netherlands
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Paquola C, Vos De Wael R, Wagstyl K, Bethlehem RAI, Hong SJ, Seidlitz J, Bullmore ET, Evans AC, Misic B, Margulies DS, Smallwood J, Bernhardt BC. Microstructural and functional gradients are increasingly dissociated in transmodal cortices. PLoS Biol 2019; 17:e3000284. [PMID: 31107870 PMCID: PMC6544318 DOI: 10.1371/journal.pbio.3000284] [Citation(s) in RCA: 210] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2018] [Revised: 05/31/2019] [Accepted: 05/08/2019] [Indexed: 01/10/2023] Open
Abstract
While the role of cortical microstructure in organising neural function is well established, it remains unclear how structural constraints can give rise to more flexible elements of cognition. While nonhuman primate research has demonstrated a close structure-function correspondence, the relationship between microstructure and function remains poorly understood in humans, in part because of the reliance on post mortem analyses, which cannot be directly related to functional data. To overcome this barrier, we developed a novel approach to model the similarity of microstructural profiles sampled in the direction of cortical columns. Our approach was initially formulated based on an ultra-high-resolution 3D histological reconstruction of an entire human brain and then translated to myelin-sensitive magnetic resonance imaging (MRI) data in a large cohort of healthy adults. This novel method identified a system-level gradient of microstructural differentiation traversing from primary sensory to limbic regions that followed shifts in laminar differentiation and cytoarchitectural complexity. Importantly, while microstructural and functional gradients described a similar hierarchy, they became increasingly dissociated in transmodal default mode and fronto-parietal networks. Meta-analytic decoding of these topographic dissociations highlighted involvement in higher-level aspects of cognition, such as cognitive control and social cognition. Our findings demonstrate a relative decoupling of macroscale functional from microstructural gradients in transmodal regions, which likely contributes to the flexible role these regions play in human cognition.
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Affiliation(s)
- Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Reinder Vos De Wael
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Konrad Wagstyl
- McGill Centre for Integrative Neuroscience, McGill University, Montreal, Canada
| | - Richard A. I. Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Seok-Jun Hong
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | - Jakob Seidlitz
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, Maryland, United States of America
- Brain Mapping Unit, University of Cambridge, Department of Psychiatry, Cambridge, United Kingdom
| | - Edward T. Bullmore
- Brain Mapping Unit, University of Cambridge, Department of Psychiatry, Cambridge, United Kingdom
| | - Alan C. Evans
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
- McGill Centre for Integrative Neuroscience, McGill University, Montreal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
| | | | | | - Boris C. Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Canada
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Abstract
Networks of widely distributed regions populate human association cortex. One network, often called the default network, is positioned at the apex of a gradient of sequential networks that radiate outward from primary cortex. Here, extensive anatomical data made available through the Marmoset Brain Architecture Project are explored to show a homologue exists in marmoset. Results reveal that a gradient of networks extend outward from primary cortex to progressively higher-order transmodal association cortex in both frontal and temporal cortex. The apex transmodal network comprises frontopolar and rostral temporal association cortex, parahippocampal areas TH / TF, the ventral posterior midline, and lateral parietal association cortex. The positioning of this network in the gradient and its composition of areas make it a candidate homologue to the human default network. That the marmoset, a physiologically- and genetically-accessible primate, might possess a default-network-like candidate creates opportunities for study of higher cognitive and social functions.
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Affiliation(s)
- Randy L Buckner
- Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, 02138, USA.
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, 02114, USA.
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, USA.
| | - Daniel S Margulies
- Centre National de la Recherche Scientifique, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, Paris, 75013, France
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