51
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Fjell AM, Grydeland H, Wang Y, Amlien IK, Bartres-Faz D, Brandmaier AM, Düzel S, Elman J, Franz CE, Håberg AK, Kietzmann TC, Kievit RA, Kremen WS, Krogsrud SK, Kühn S, Lindenberger U, Macía D, Mowinckel AM, Nyberg L, Panizzon MS, Solé-Padullés C, Sørensen Ø, Westerhausen R, Walhovd KB. The genetic organization of longitudinal subcortical volumetric change is stable throughout the lifespan. eLife 2021; 10:66466. [PMID: 34180395 PMCID: PMC8260220 DOI: 10.7554/elife.66466] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 06/26/2021] [Indexed: 11/13/2022] Open
Abstract
Development and aging of the cerebral cortex show similar topographic organization and are governed by the same genes. It is unclear whether the same is true for subcortical regions, which follow fundamentally different ontogenetic and phylogenetic principles. We tested the hypothesis that genetically governed neurodevelopmental processes can be traced throughout life by assessing to which degree brain regions that develop together continue to change together through life. Analyzing over 6000 longitudinal MRIs of the brain, we used graph theory to identify five clusters of coordinated development, indexed as patterns of correlated volumetric change in brain structures. The clusters tended to follow placement along the cranial axis in embryonic brain development, suggesting continuity from prenatal stages, and correlated with cognition. Across independent longitudinal datasets, we demonstrated that developmental clusters were conserved through life. Twin-based genetic correlations revealed distinct sets of genes governing change in each cluster. Single-nucleotide polymorphisms-based analyses of 38,127 cross-sectional MRIs showed a similar pattern of genetic volume–volume correlations. In conclusion, coordination of subcortical change adheres to fundamental principles of lifespan continuity and genetic organization.
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Affiliation(s)
- Anders Martin Fjell
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway.,Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
| | - Hakon Grydeland
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Yunpeng Wang
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Inge K Amlien
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - David Bartres-Faz
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, and Institut de Neurociències, Universitat de Barcelona, Barcelona, Spain
| | - Andreas M Brandmaier
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.,Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
| | - Sandra Düzel
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Jeremy Elman
- Center for Behavioral Genomics Twin Research Laboratory, University of California, San Diego, La Jolla, United States
| | - Carol E Franz
- Center for Behavioral Genomics Twin Research Laboratory, University of California, San Diego, La Jolla, United States
| | - Asta K Håberg
- Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.,Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Tim C Kietzmann
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom.,Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Rogier Andrew Kievit
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - William S Kremen
- Center for Behavioral Genomics Twin Research Laboratory, University of California, San Diego, La Jolla, United States.,Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, La Jolla, United States
| | - Stine K Krogsrud
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Simone Kühn
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.,Lise Meitner Group for Environmental Neuroscience, Max Planck Institute for Human Development, Berlin, Germany
| | - Ulman Lindenberger
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany.,Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany
| | - Didac Macía
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, and Institut de Neurociències, Universitat de Barcelona, Barcelona, Spain
| | - Athanasia Monika Mowinckel
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Lars Nyberg
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway.,Department of Radiation Sciences, Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
| | - Matthew S Panizzon
- Center for Behavioral Genomics Twin Research Laboratory, University of California, San Diego, La Jolla, United States.,Department of Psychiatry, University of California, San Diego, La Jolla, United States
| | - Cristina Solé-Padullés
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, and Institut de Neurociències, Universitat de Barcelona, Barcelona, Spain
| | - Øystein Sørensen
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Rene Westerhausen
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Kristine Beate Walhovd
- Center for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway.,Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway
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52
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Kringelbach ML, Deco G. Brain States and Transitions: Insights from Computational Neuroscience. Cell Rep 2021; 32:108128. [PMID: 32905760 DOI: 10.1016/j.celrep.2020.108128] [Citation(s) in RCA: 94] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 07/22/2020] [Accepted: 08/19/2020] [Indexed: 11/25/2022] Open
Abstract
Within the field of computational neuroscience there are great expectations of finding new ways to rebalance the complex dynamic system of the human brain through controlled pharmacological or electromagnetic perturbation. Yet many obstacles remain between the ability to accurately predict how and where best to perturb to force a transition from one brain state to another. The foremost challenge is a commonly agreed definition of a given brain state. Recent progress in computational neuroscience has made it possible to robustly define brain states and force transitions between them. Here, we review the state of the art and propose a framework for determining the functional hierarchical organization describing any given brain state. We describe the latest advances in creating sophisticated whole-brain computational models with interacting neuronal and neurotransmitter systems that can be studied fully in silico to predict and design novel pharmacological and electromagnetic interventions to rebalance them in disease.
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Affiliation(s)
- Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK.
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Clayton, VIC 3800, Australia.
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53
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Popovych OV, Jung K, Manos T, Diaz-Pier S, Hoffstaedter F, Schreiber J, Yeo BTT, Eickhoff SB. Inter-subject and inter-parcellation variability of resting-state whole-brain dynamical modeling. Neuroimage 2021; 236:118201. [PMID: 34033913 PMCID: PMC8271096 DOI: 10.1016/j.neuroimage.2021.118201] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 05/21/2021] [Accepted: 05/22/2021] [Indexed: 11/05/2022] Open
Abstract
Modern approaches to investigate complex brain dynamics suggest to represent the brain as a functional network of brain regions defined by a brain atlas, while edges represent the structural or functional connectivity among them. This approach is also utilized for mathematical modeling of the resting-state brain dynamics, where the applied brain parcellation plays an essential role in deriving the model network and governing the modeling results. There is however no consensus and empirical evidence on how a given brain atlas affects the model outcome, and the choice of parcellation is still rather arbitrary. Accordingly, we explore the impact of brain parcellation on inter-subject and inter-parcellation variability of model fitting to empirical data. Our objective is to provide a comprehensive empirical evidence of potential influences of parcellation choice on resting-state whole-brain dynamical modeling. We show that brain atlases strongly influence the quality of model validation and propose several variables calculated from empirical data to account for the observed variability. A few classes of such data variables can be distinguished depending on their inter-subject and inter-parcellation explanatory power.
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Affiliation(s)
- Oleksandr V Popovych
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany.
| | - Kyesam Jung
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany
| | - Thanos Manos
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany; Laboratoire de Physique Théorique et Modélisation, CY Cergy Paris Université, CNRS, UMR 8089, Cergy-Pontoise cedex 95302, France
| | - Sandra Diaz-Pier
- Institute for Advanced Simulation, Juelich Supercomputing Centre (JSC), Research Centre Juelich, Juelich, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany
| | - Jan Schreiber
- Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, Juelich, Germany
| | - B T Thomas Yeo
- Centre for Sleep and Cognition, Centre for Translational MR Research & N.1 Institute for Health, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Integrative Sciences and Engineering Programme (ISEP), Singapore
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany
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54
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Jancke D, Herlitze S, Kringelbach ML, Deco G. Bridging the gap between single receptor type activity and whole-brain dynamics. FEBS J 2021; 289:2067-2084. [PMID: 33797854 DOI: 10.1111/febs.15855] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 03/15/2021] [Accepted: 03/31/2021] [Indexed: 02/05/2023]
Abstract
What is the effect of activating a single modulatory neuronal receptor type on entire brain network dynamics? Can such effect be isolated at all? These are important questions because characterizing elementary neuronal processes that influence network activity across the given anatomical backbone is fundamental to guide theories of brain function. Here, we introduce the concept of the cortical 'receptome' taking into account the distribution and densities of expression of different modulatory receptor types across the brain's anatomical connectivity matrix. By modelling whole-brain dynamics in silico, we suggest a bidirectional coupling between modulatory neurotransmission and neuronal connectivity hardware exemplified by the impact of single serotonergic (5-HT) receptor types on cortical dynamics. As experimental support of this concept, we show how optogenetic tools enable specific activation of a single 5-HT receptor type across the cortex as well as in vivo measurement of its distinct effects on cortical processing. Altogether, we demonstrate how the structural neuronal connectivity backbone and its modulation by a single neurotransmitter system allow access to a rich repertoire of different brain states that are fundamental for flexible behaviour. We further propose that irregular receptor expression patterns-genetically predisposed or acquired during a lifetime-may predispose for neuropsychiatric disorders like addiction, depression and anxiety along with distinct changes in brain state. Our long-term vision is that such diseases could be treated through rationally targeted therapeutic interventions of high specificity to eventually recover natural transitions of brain states.
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Affiliation(s)
- Dirk Jancke
- Optical Imaging Group, Institut für Neuroinformatik, Ruhr University Bochum, Germany.,International Graduate School of Neuroscience (IGSN), Ruhr University Bochum, Germany
| | - Stefan Herlitze
- Department of General Zoology and Neurobiology, Ruhr University, Bochum, Germany
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, UK.,Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Denmark.,Life and Health Sciences Research Institute, School of Medicine, University of Minho, Braga, Portugal.,Centre for Eudaimonia and Human Flourishing, University of Oxford, UK
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain.,Institució Catalana de la Recerca i Estudis Avançats, Barcelona, Spain.,Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,School of Psychological Sciences, Monash University, Clayton, Melbourne, Australia
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55
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Deco G, Vidaurre D, Kringelbach ML. Revisiting the global workspace orchestrating the hierarchical organization of the human brain. Nat Hum Behav 2021; 5:497-511. [PMID: 33398141 PMCID: PMC8060164 DOI: 10.1038/s41562-020-01003-6] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 10/12/2020] [Indexed: 12/31/2022]
Abstract
A central challenge in neuroscience is how the brain organizes the information necessary to orchestrate behaviour. Arguably, this whole-brain orchestration is carried out by a core subset of integrative brain regions, a 'global workspace', but its constitutive regions remain unclear. We quantified the global workspace as the common regions across seven tasks as well as rest, in a common 'functional rich club'. To identify this functional rich club, we determined the information flow between brain regions by means of a normalized directed transfer entropy framework applied to multimodal neuroimaging data from 1,003 healthy participants and validated in participants with retest data. This revealed a set of regions orchestrating information from perceptual, long-term memory, evaluative and attentional systems. We confirmed the causal significance and robustness of our results by systematically lesioning a generative whole-brain model. Overall, this framework describes a complex choreography of the functional hierarchical organization of the human brain.
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Affiliation(s)
- Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
- School of Psychological Sciences, Monash University, Clayton, Victoria, Australia.
| | - Diego Vidaurre
- Department of Psychiatry, University of Oxford, Oxford, UK
- Center for Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK.
- Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK.
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
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56
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Gryglewski G, Murgaš M, Klöbl M, Reed MB, Unterholzner J, Michenthaler P, Lanzenberger R. Enrichment of Disease-Associated Genes in Cortical Areas Defined by Transcriptome-Based Parcellation. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 7:10-23. [PMID: 33711548 DOI: 10.1016/j.bpsc.2021.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 02/05/2021] [Accepted: 02/23/2021] [Indexed: 10/21/2022]
Abstract
BACKGROUND Parcellation of the cerebral cortex serves the investigation of the emergence of uniquely human brain functions and disorders. Transcriptome data enable the characterization of the molecular properties of cortical areas in unprecedented detail. Previously, we predicted the expression of 18,686 genes in the entire human brain based on microarray data. Here, we employed these data to parcellate the cortex and study the regional enrichment of disease-associated genes. METHODS We performed agglomerative hierarchical clustering based on normalized transcriptome data to delineate areas with distinct gene expression profiles. Subsequently, we tested these profiles for the enrichment of gene sets associated with brain disorders by genome-wide association studies and expert-curated databases using gene set enrichment analysis. RESULTS Transcriptome-based parcellation identified borders in line with major anatomical landmarks and the functional differentiation of primary motor, somatosensory, visual, and auditory areas. Gene set enrichment analysis based on curated databases suggested new roles of specific areas in psychiatric and neurological disorders while reproducing well-established links for movement and neurodegenerative disorders, for example, amyotrophic lateral sclerosis (motor cortex) and Alzheimer's disease (entorhinal cortex). Meanwhile, gene sets derived from genome-wide association studies on psychiatric disorders exhibited similar enrichment patterns driven by pleiotropic genes expressed in the posterior fusiform gyrus and inferior parietal lobule. CONCLUSIONS The identified enrichment patterns suggest the vulnerability of specific cortical areas to various influences that might alter the risk of developing one or several brain disorders. For several diseases, specific genes were highlighted, which could lead to the discovery of novel disease mechanisms and urgently needed treatments.
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Affiliation(s)
- Gregor Gryglewski
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Matej Murgaš
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Manfred Klöbl
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Murray Bruce Reed
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Jakob Unterholzner
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Paul Michenthaler
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
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57
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Feilong M, Guntupalli JS, Haxby JV. The neural basis of intelligence in fine-grained cortical topographies. eLife 2021; 10:e64058. [PMID: 33683205 PMCID: PMC7993992 DOI: 10.7554/elife.64058] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 03/05/2021] [Indexed: 02/01/2023] Open
Abstract
Intelligent thought is the product of efficient neural information processing, which is embedded in fine-grained, topographically organized population responses and supported by fine-grained patterns of connectivity among cortical fields. Previous work on the neural basis of intelligence, however, has focused on coarse-grained features of brain anatomy and function because cortical topographies are highly idiosyncratic at a finer scale, obscuring individual differences in fine-grained connectivity patterns. We used a computational algorithm, hyperalignment, to resolve these topographic idiosyncrasies and found that predictions of general intelligence based on fine-grained (vertex-by-vertex) connectivity patterns were markedly stronger than predictions based on coarse-grained (region-by-region) patterns. Intelligence was best predicted by fine-grained connectivity in the default and frontoparietal cortical systems, both of which are associated with self-generated thought. Previous work overlooked fine-grained architecture because existing methods could not resolve idiosyncratic topographies, preventing investigation where the keys to the neural basis of intelligence are more likely to be found.
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Affiliation(s)
- Ma Feilong
- Center for Cognitive Neuroscience, Dartmouth CollegeHanover, NHUnited States
| | | | - James V Haxby
- Center for Cognitive Neuroscience, Dartmouth CollegeHanover, NHUnited States
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58
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Zunhammer M, Spisák T, Wager TD, Bingel U. Meta-analysis of neural systems underlying placebo analgesia from individual participant fMRI data. Nat Commun 2021; 12:1391. [PMID: 33654105 PMCID: PMC7925520 DOI: 10.1038/s41467-021-21179-3] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Accepted: 01/04/2021] [Indexed: 12/13/2022] Open
Abstract
The brain systems underlying placebo analgesia are insufficiently understood. Here we performed a systematic, participant-level meta-analysis of experimental functional neuroimaging studies of evoked pain under stimulus-intensity-matched placebo and control conditions, encompassing 603 healthy participants from 20 (out of 28 eligible) studies. We find that placebo vs. control treatments induce small, widespread reductions in pain-related activity, particularly in regions belonging to ventral attention (including mid-insula) and somatomotor networks (including posterior insula). Behavioral placebo analgesia correlates with reduced pain-related activity in these networks and the thalamus, habenula, mid-cingulate, and supplementary motor area. Placebo-associated activity increases occur mainly in frontoparietal regions, with high between-study heterogeneity. We conclude that placebo treatments affect pain-related activity in multiple brain areas, which may reflect changes in nociception and/or other affective and decision-making processes surrounding pain. Between-study heterogeneity suggests that placebo analgesia is a multi-faceted phenomenon involving multiple cerebral mechanisms that differ across studies.
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Affiliation(s)
- Matthias Zunhammer
- Center for Translational Neuro- and Behavioral Sciences, Dept. of Neurology, University Hospital Essen, Essen, Germany
| | - Tamás Spisák
- Center for Translational Neuro- and Behavioral Sciences, Dept. of Neurology, University Hospital Essen, Essen, Germany
| | - Tor D Wager
- Cognitive and Affective Neuroscience Laboratory, Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA.
| | - Ulrike Bingel
- Center for Translational Neuro- and Behavioral Sciences, Dept. of Neurology, University Hospital Essen, Essen, Germany.
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59
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Dohmatob E, Richard H, Pinho AL, Thirion B. Brain topography beyond parcellations: Local gradients of functional maps. Neuroimage 2021; 229:117706. [PMID: 33484851 DOI: 10.1016/j.neuroimage.2020.117706] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 11/09/2020] [Accepted: 12/19/2020] [Indexed: 01/21/2023] Open
Abstract
Functional neuroimaging provides the unique opportunity to characterize brain regions based on their response to tasks or ongoing activity. As such, it holds the premise to capture brain spatial organization. Yet, the conceptual framework to describe this organization has remained elusive: on the one hand, parcellations build implicitly on a piecewise constant organization, i.e. flat regions separated by sharp boundaries; on the other hand, the recently popularized concept of functional gradient hints instead at a smooth structure. Noting that both views converge to a topographic scheme that pieces together local variations of functional features, we perform a quantitative assessment of local gradient-based models. Using as a driving case the prediction of functional Magnetic Resonance Imaging (fMRI) data -concretely, the prediction of task-fMRI from rest-fMRI maps across subjects- we develop a parcel-wise linear regression model based on a dictionary of reference topographies. Our method uses multiple random parcellations -as opposed to a single fixed parcellation- and aggregates estimates across these parcellations to predict functional features in left-out subjects. Our experiments demonstrate the existence of an optimal cardinality of the parcellation to capture local gradients of functional maps.
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Affiliation(s)
- Elvis Dohmatob
- Inria, CEA, Université Paris-Saclay, Saclay, France; Criteo AI Lab, France
| | - Hugo Richard
- Inria, CEA, Université Paris-Saclay, Saclay, France
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60
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High-resolution connectomic fingerprints: Mapping neural identity and behavior. Neuroimage 2021; 229:117695. [PMID: 33422711 DOI: 10.1016/j.neuroimage.2020.117695] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Revised: 12/16/2020] [Accepted: 12/23/2020] [Indexed: 01/30/2023] Open
Abstract
Connectomes are typically mapped at low resolution based on a specific brain parcellation atlas. Here, we investigate high-resolution connectomes independent of any atlas, propose new methodologies to facilitate their mapping and demonstrate their utility in predicting behavior and identifying individuals. Using structural, functional and diffusion-weighted MRI acquired in 1000 healthy adults, we aimed to map the cortical correlates of identity and behavior at ultra-high spatial resolution. Using methods based on sparse matrix representations, we propose a computationally feasible high-resolution connectomic approach that improves neural fingerprinting and behavior prediction. Using this high-resolution approach, we find that the multimodal cortical gradients of individual uniqueness reside in the association cortices. Furthermore, our analyses identified a striking dichotomy between the facets of a person's neural identity that best predict their behavior and cognition, compared to those that best differentiate them from other individuals. Functional connectivity was one of the most accurate predictors of behavior, yet resided among the weakest differentiators of identity; whereas the converse was found for morphological properties, such as cortical curvature. This study provides new insights into the neural basis of personal identity and new tools to facilitate ultra-high-resolution connectomics.
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61
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Bellucci G, Camilleri JA, Eickhoff SB, Krueger F. Neural signatures of prosocial behaviors. Neurosci Biobehav Rev 2020; 118:186-195. [PMID: 32707344 PMCID: PMC7958651 DOI: 10.1016/j.neubiorev.2020.07.006] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 05/02/2020] [Accepted: 07/10/2020] [Indexed: 12/12/2022]
Abstract
Prosocial behaviors are hypothesized to require socio-cognitive and empathic abilities-engaging brain regions attributed to the mentalizing and empathy brain networks. Here, we tested this hypothesis with a coordinate-based meta-analysis of 600 neuroimaging studies on prosociality, mentalizing and empathy (∼12,000 individuals). We showed that brain areas recruited by prosocial behaviors only partially overlap with the mentalizing (dorsal posterior cingulate cortex) and empathy networks (middle cingulate cortex). Additionally, the dorsolateral and ventromedial prefrontal cortices were preferentially activated by prosocial behaviors. Analyses on the functional connectivity profile and functional roles of the neural patterns underlying prosociality revealed that in addition to socio-cognitive and empathic processes, prosocial behaviors further involve evaluation processes and action planning, likely to select the action sequence that best satisfies another person's needs. By characterizing the multidimensional construct of prosociality at the neural level, we provide insights that may support a better understanding of normal and abnormal social cognition (e.g., psychopathy).
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Affiliation(s)
- Gabriele Bellucci
- Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
| | - Julia A Camilleri
- Institute for Neuroscience and Medicine (INM-7), Research Center Jülich, Germany; Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute for Neuroscience and Medicine (INM-7), Research Center Jülich, Germany; Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany
| | - Frank Krueger
- School of Systems Biology, George Mason University, Fairfax, VA, USA; Department of Psychology, George Mason University, Fairfax, VA, USA
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62
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Bijsterbosch J, Harrison SJ, Jbabdi S, Woolrich M, Beckmann C, Smith S, Duff EP. Challenges and future directions for representations of functional brain organization. Nat Neurosci 2020; 23:1484-1495. [PMID: 33106677 DOI: 10.1038/s41593-020-00726-z] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 09/16/2020] [Indexed: 12/12/2022]
Abstract
A key principle of brain organization is the functional integration of brain regions into interconnected networks. Functional MRI scans acquired at rest offer insights into functional integration via patterns of coherent fluctuations in spontaneous activity, known as functional connectivity. These patterns have been studied intensively and have been linked to cognition and disease. However, the field is fractionated. Diverging analysis approaches have segregated the community into research silos, limiting the replication and clinical translation of findings. A primary source of this fractionation is the diversity of approaches used to reduce complex brain data into a lower-dimensional set of features for analysis and interpretation, which we refer to as brain representations. In this Primer, we provide an overview of different brain representations, lay out the challenges that have led to the fractionation of the field and that continue to form obstacles for convergence, and propose concrete guidelines to unite the field.
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Affiliation(s)
- Janine Bijsterbosch
- Mallinckrodt Institute of Radiology, Washington University in St Louis, Saint Louis, MO, USA. .,Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford. John Radcliffe Hospital, Oxford, UK.
| | - Samuel J Harrison
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford. John Radcliffe Hospital, Oxford, UK.,Translational Neuromodeling Unit, University of Zurich & ETH Zurich, Zurich, Switzerland
| | - Saad Jbabdi
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford. John Radcliffe Hospital, Oxford, UK
| | - Mark Woolrich
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Christian Beckmann
- Donders Institute and Department of Cognitive Neurosciences, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Stephen Smith
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford. John Radcliffe Hospital, Oxford, UK
| | - Eugene P Duff
- Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford. John Radcliffe Hospital, Oxford, UK. .,Department of Paediatrics, University of Oxford, John Radcliffe Hospital, Oxford, UK.
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63
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Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture. Nat Neurosci 2020; 23:1644-1654. [PMID: 33077948 DOI: 10.1038/s41593-020-00719-y] [Citation(s) in RCA: 101] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 09/03/2020] [Indexed: 12/18/2022]
Abstract
Network neuroscience has relied on a node-centric network model in which cells, populations and regions are linked to one another via anatomical or functional connections. This model cannot account for interactions of edges with one another. In this study, we developed an edge-centric network model that generates constructs 'edge time series' and 'edge functional connectivity' (eFC). Using network analysis, we show that, at rest, eFC is consistent across datasets and reproducible within the same individual over multiple scan sessions. We demonstrate that clustering eFC yields communities of edges that naturally divide the brain into overlapping clusters, with regions in sensorimotor and attentional networks exhibiting the greatest levels of overlap. We show that eFC is systematically modulated by variation in sensory input. In future work, the edge-centric approach could be useful for identifying novel biomarkers of disease, characterizing individual variation and mapping the architecture of highly resolved neural circuits.
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64
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Valk SL, Hoffstaedter F, Camilleri JA, Kochunov P, Yeo BT, Eickhoff SB. Personality and local brain structure: Their shared genetic basis and reproducibility. Neuroimage 2020; 220:117067. [DOI: 10.1016/j.neuroimage.2020.117067] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 06/12/2020] [Accepted: 06/15/2020] [Indexed: 12/16/2022] Open
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65
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Kaltenmark I, Deruelle C, Brun L, Lefèvre J, Coulon O, Auzias G. Group-level cortical surface parcellation with sulcal pits labeling. Med Image Anal 2020; 66:101749. [PMID: 32877840 DOI: 10.1016/j.media.2020.101749] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 04/09/2020] [Accepted: 06/03/2020] [Indexed: 11/16/2022]
Abstract
Sulcal pits are the points of maximal depth within the folds of the cortical surface. These shape descriptors give a unique opportunity to access to a rich, fine-scale representation of the geometry and the developmental milestones of the cortical surface. However, using sulcal pits analysis at group level requires new numerical tools to establish inter-subject correspondences. Here, we address this issue by taking advantage of the geometrical information carried by sulcal basins that are the local patches of surfaces surrounding each sulcal pit. Our framework consists in two phases. First, we present a new method to generate a population-specific atlas of this sulcal basins organi- zation as a fold-level parcellation of the cortical surface. Then, we address the labeling of individual sulcal pits and corresponding basins with respect to this atlas. To assess their validity, we applied these methodological advances on two different populations of healthy subjects. The first database of 137 adults allowed us to compare our method to the state-of-the-art and the second database of 209 children, aged between 0 and 18 years, illustrates the adaptability and relevance of our method in the context of pediatric data showing strong variations in cortical volume and folding.
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Affiliation(s)
- Irène Kaltenmark
- Institut de Neurosciences de la Timone UMR 7289, Aix-Marseille Université, CNRS Faculté de Médecine, 27 boulevard Faculté Jean Moulin, 13005 Marseille, France.
| | - Christine Deruelle
- Institut de Neurosciences de la Timone UMR 7289, Aix-Marseille Université, CNRS Faculté de Médecine, 27 boulevard Faculté Jean Moulin, 13005 Marseille, France
| | - Lucile Brun
- Institut de Neurosciences de la Timone UMR 7289, Aix-Marseille Université, CNRS Faculté de Médecine, 27 boulevard Faculté Jean Moulin, 13005 Marseille, France
| | - Julien Lefèvre
- Institut de Neurosciences de la Timone UMR 7289, Aix-Marseille Université, CNRS Faculté de Médecine, 27 boulevard Faculté Jean Moulin, 13005 Marseille, France
| | - Olivier Coulon
- Institut de Neurosciences de la Timone UMR 7289, Aix-Marseille Université, CNRS Faculté de Médecine, 27 boulevard Faculté Jean Moulin, 13005 Marseille, France
| | - Guillaume Auzias
- Institut de Neurosciences de la Timone UMR 7289, Aix-Marseille Université, CNRS Faculté de Médecine, 27 boulevard Faculté Jean Moulin, 13005 Marseille, France
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66
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Mahjoory K, Schoffelen JM, Keitel A, Gross J. The frequency gradient of human resting-state brain oscillations follows cortical hierarchies. eLife 2020; 9:e53715. [PMID: 32820722 PMCID: PMC7476753 DOI: 10.7554/elife.53715] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 08/20/2020] [Indexed: 12/20/2022] Open
Abstract
The human cortex is characterized by local morphological features such as cortical thickness, myelin content, and gene expression that change along the posterior-anterior axis. We investigated if some of these structural gradients are associated with a similar gradient in a prominent feature of brain activity - namely the frequency of oscillations. In resting-state MEG recordings from healthy participants (N = 187) using mixed effect models, we found that the dominant peak frequency in a brain area decreases significantly along the posterior-anterior axis following the global hierarchy from early sensory to higher order areas. This spatial gradient of peak frequency was significantly anticorrelated with that of cortical thickness, representing a proxy of the cortical hierarchical level. This result indicates that the dominant frequency changes systematically and globally along the spatial and hierarchical gradients and establishes a new structure-function relationship pertaining to brain oscillations as a core organization that may underlie hierarchical specialization in the brain.
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Affiliation(s)
- Keyvan Mahjoory
- Institute for Biomagnetism and Biosignalanalysis (IBB), University of MuensterMuensterGermany
| | - Jan-Mathijs Schoffelen
- Radboud University Nijmegen, Donders Institute for Brain, Cognition and BehaviourNijmegenNetherlands
| | - Anne Keitel
- Psychology, University of Dundee, Scrymgeour BuildingDundeeUnited Kingdom
| | - Joachim Gross
- Institute for Biomagnetism and Biosignalanalysis (IBB), University of MuensterMuensterGermany
- Centre for Cognitive Neuroimaging (CCNi), University of GlasgowGlasgowUnited Kingdom
- Otto-Creutzfeldt-Center for Cognitive and Behavioral Neuroscience, University of MuensterMuensterGermany
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67
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Di Plinio S, Ebisch SJH. Combining local and global evolutionary trajectories of brain-behaviour relationships through game theory. Eur J Neurosci 2020; 52:4198-4213. [PMID: 32594640 DOI: 10.1111/ejn.14883] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 06/15/2020] [Accepted: 06/20/2020] [Indexed: 01/05/2023]
Abstract
The study of the evolution of brain-behaviour relationships concerns understanding the causes and repercussions of cross- and within-species variability. Understanding such variability is a main objective of evolutionary and cognitive neuroscience, and it may help explaining the appearance of psychopathological phenotypes. Although brain evolution is related to the progressive action of selection and adaptation through multiple paths (e.g. mosaic vs. concerted evolution, metabolic vs. structural and functional constraints), a coherent, integrative framework is needed to combine evolutionary paths and neuroscientific evidence. Here, we review the literature on evolutionary pressures focusing on structural-functional changes and developmental constraints. Taking advantage of recent progress in neuroimaging and cognitive neuroscience, we propose a twofold hypothetical model of brain evolution. Within this model, global and local trajectories imply rearrangements of neural subunits and subsystems and of behavioural repertoires of a species, respectively. We incorporate these two processes in a game in which the global trajectory shapes the structural-functional neural substrates (i.e. players), while the local trajectory shapes the behavioural repertoires (i.e. stochastic payoffs).
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Affiliation(s)
- Simone Di Plinio
- Department of Neuroscience, Imaging, and Clinical Sciences, G D'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Sjoerd J H Ebisch
- Department of Neuroscience, Imaging, and Clinical Sciences, G D'Annunzio University of Chieti-Pescara, Chieti, Italy.,Institute for Advanced Biomedical Technologies (ITAB), G D'Annunzio University of Chieti Pescara, Chieti, Italy
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68
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Bellucci G, Camilleri JA, Iyengar V, Eickhoff SB, Krueger F. The emerging neuroscience of social punishment: Meta-analytic evidence. Neurosci Biobehav Rev 2020; 113:426-439. [PMID: 32302599 PMCID: PMC7291369 DOI: 10.1016/j.neubiorev.2020.04.011] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 04/08/2020] [Accepted: 04/09/2020] [Indexed: 02/07/2023]
Abstract
Social punishment (SOP)-third-party punishment (TPP) and second-party punishment (SPP)-sanctions norm-deviant behavior. The hierarchical punishment model (HPM) posits that TPP is an extension of SPP and both recruit common processes engaging large-scale domain-general brain networks. Here, we provided meta-analytic evidence to the HPM by combining the activation likelihood estimation approach with connectivity analyses and hierarchical clustering analyses. Although both forms of SOP engaged the dorsolateral prefrontal cortex and bilateral anterior insula (AI), a functional differentiation also emerged with TPP preferentially engaging social cognitive regions (temporoparietal junction) and SPP affective regions (AI). Further, although both TPP and SPP recruit domain-general networks (salience, default-mode, and central-executive networks), some specificity in network organization was observed. By revealing differences and commonalities of the neural networks consistently activated by different types of SOP, our findings contribute to a better understanding of the neuropsychological mechanisms of social punishment behavior--one of the most peculiar human behaviors.
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Affiliation(s)
- Gabriele Bellucci
- Department of Computational Neuroscience, Max Planck Institute for Biological Cybernetics, Tübingen, Germany.
| | - Julia A Camilleri
- Institute for Neuroscience and Medicine (INM-7), Research Center Jülich, Germany; Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Vijeth Iyengar
- Administration for Community Living/Administration on Aging, U.S. Department of Health and Human Services, Washington DC, USA
| | - Simon B Eickhoff
- Institute for Neuroscience and Medicine (INM-7), Research Center Jülich, Germany; Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
| | - Frank Krueger
- School of Systems Biology, George Mason University, Fairfax, VA, USA; Department of Psychology, George Mason University, Fairfax, VA, USA.
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69
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Chase HW, Grace AA, Fox PT, Phillips ML, Eickhoff SB. Functional differentiation in the human ventromedial frontal lobe: A data-driven parcellation. Hum Brain Mapp 2020; 41:3266-3283. [PMID: 32314470 PMCID: PMC7375078 DOI: 10.1002/hbm.25014] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 03/06/2020] [Accepted: 04/07/2020] [Indexed: 12/18/2022] Open
Abstract
Ventromedial regions of the frontal lobe (vmFL) are thought to play a key role in decision-making and emotional regulation. However, aspects of this area's functional organization, including the presence of a multiple subregions, their functional and anatomical connectivity, and the cross-species homologies of these subregions with those of other species, remain poorly understood. To address this uncertainty, we employed a two-stage parcellation of the region to identify six distinct structures within the region on the basis of data-driven classification of functional connectivity patterns obtained using the meta-analytic connectivity modeling (MACM) approach. From anterior to posterior, the derived subregions included two lateralized posterior regions, an intermediate posterior region, a dorsal and ventral central region, and a single anterior region. The regions were characterized further by functional connectivity derived using resting-state fMRI and functional decoding using the Brain Map database. In general, the regions could be differentiated on the basis of different patterns of functional connectivity with canonical "default mode network" regions and/or subcortical regions such as the striatum. Together, the findings suggest the presence of functionally distinct neural structures within vmFL, consistent with data from experimental animals as well prior demonstrations of anatomical differences within the region. Detailed correspondence with the anterior cingulate, medial orbitofrontal cortex, and rostroventral prefrontal cortex, as well as specific animal homologs are discussed. The findings may suggest future directions for resolving potential functional and structural correspondence of subregions within the frontal lobe across behavioral contexts, and across mammalian species.
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Affiliation(s)
- Henry W Chase
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Anthony A Grace
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.,Department of Neuroscience and Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center, San Antonio, Texas, USA.,Department of Radiology, University of Texas Health Science Center, San Antonio, Texas, USA.,Department of Psychiatry, University of Texas Health Science Center, San Antonio, Texas, USA.,Research and Development Service, South Texas Veterans Health Care System, San Antonio, Texas, USA
| | - Mary L Phillips
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
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70
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Abstract
Remarkable progress has come from whole-brain models linking anatomy and function. Paradoxically, it is not clear how a neuronal dynamical system running in the fixed human anatomical connectome can give rise to the rich changes in the functional repertoire associated with human brain function, which is impossible to explain through long-term plasticity. Neuromodulation evolved to allow for such flexibility by dynamically updating the effectivity of the fixed anatomical connectivity. Here, we introduce a theoretical framework modeling the dynamical mutual coupling between the neuronal and neurotransmitter systems. We demonstrate that this framework is crucial to advance our understanding of whole-brain dynamics by bidirectional coupling of the two systems through combining multimodal neuroimaging data (diffusion magnetic resonance imaging [dMRI], functional magnetic resonance imaging [fMRI], and positron electron tomography [PET]) to explain the functional effects of specific serotoninergic receptor (5-HT2AR) stimulation with psilocybin in healthy humans. This advance provides an understanding of why psilocybin is showing considerable promise as a therapeutic intervention for neuropsychiatric disorders including depression, anxiety, and addiction. Overall, these insights demonstrate that the whole-brain mutual coupling between the neuronal and the neurotransmission systems is essential for understanding the remarkable flexibility of human brain function despite having to rely on fixed anatomical connectivity.
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71
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Guo Z, Fan C, Li T, Gesang L, Yin W, Wang N, Weng X, Gong Q, Zhang J, Wang J. Neural network correlates of high-altitude adaptive genetic variants in Tibetans: A pilot, exploratory study. Hum Brain Mapp 2020; 41:2406-2430. [PMID: 32128935 PMCID: PMC7267913 DOI: 10.1002/hbm.24954] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 01/16/2020] [Accepted: 02/09/2020] [Indexed: 02/05/2023] Open
Abstract
Although substantial progress has been made in the identification of genetic substrates underlying physiology, neuropsychology, and brain organization, the genotype–phenotype associations remain largely unknown in the context of high‐altitude (HA) adaptation. Here, we related HA adaptive genetic variants in three gene loci (EGLN1, EPAS1, and PPARA) to interindividual variance in a set of physiological characteristics, neuropsychological tests, and topological attributes of large‐scale structural and functional brain networks in 135 indigenous Tibetan highlanders. Analyses of individual HA adaptive single‐nucleotide polymorphisms (SNPs) revealed that specific SNPs selectively modulated physiological characteristics (erythrocyte level, ratio between forced expiratory volume in the first second to forced vital capacity, arterial oxygen saturation, and heart rate) and structural network centrality (the left anterior orbital gyrus) with no effects on neuropsychology or functional brain networks. Further analyses of genetic adaptive scores, which summarized the overall degree of genetic adaptation to HA, revealed significant correlations only with structural brain networks with respect to local interconnectivity of the whole networks, intermodule communication between the right frontal and parietal module and the left occipital module, nodal centrality in several frontal regions, and connectivity strength of a subnetwork predominantly involving in intramodule edges in the right temporal and occipital module. Moreover, the associations were dependent on gene loci, weight types, or topological scales. Together, these findings shed new light on genotype–phenotype interactions under HA hypoxia and have important implications for developing new strategies to optimize organism and tissue responses to chronic hypoxia induced by extreme environments or diseases.
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Affiliation(s)
- Zhiyue Guo
- Institute of Brain Diseases and Cognition, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Cunxiu Fan
- Institute of Brain Diseases and Cognition, School of Medicine, Xiamen University, Xiamen, Fujian, China.,Department of Neurology, Shanghai Changhai Hospital, Navy Medical University, Shanghai, China
| | - Ting Li
- Institute of Brain Diseases and Cognition, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Luobu Gesang
- Institute of High Altitude Medicine, Tibet Autonomous Region People's Hospital, Lhasa, Tibet Autonomous Region, China
| | - Wu Yin
- Department of Radiology, Tibet Autonomous Region People's Hospital, Lhasa, Tibet Autonomous Region, China
| | - Ningkai Wang
- Department of Psychology, Hangzhou Normal University, Hangzhou, China
| | - Xuchu Weng
- Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Institute for Brain Research and Rehabilitation, Guangzhou, China
| | - Qiyong Gong
- Huaxi Magnetic Resonance Research Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jiaxing Zhang
- Institute of Brain Diseases and Cognition, School of Medicine, Xiamen University, Xiamen, Fujian, China
| | - Jinhui Wang
- Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Institute for Brain Research and Rehabilitation, Guangzhou, China
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72
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Akiki TJ, Abdallah CG. Determining the Hierarchical Architecture of the Human Brain Using Subject-Level Clustering of Functional Networks. Sci Rep 2019; 9:19290. [PMID: 31848397 PMCID: PMC6917755 DOI: 10.1038/s41598-019-55738-y] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 11/27/2019] [Indexed: 01/18/2023] Open
Abstract
Optimal integration and segregation of neuronal connections are necessary for efficient large-scale network communication between distributed cortical regions while allowing for modular specialization. This dynamic in the cortex is enabled at the network mesoscale by the organization of nodes into communities. Previous in vivo efforts to map the mesoscale architecture in humans had several limitations. Here we characterize a consensus multiscale community organization of the functional cortical network. We derive this consensus from the clustering of subject-level networks. We applied this analysis to magnetic resonance imaging data from 1003 healthy individuals part of the Human Connectome Project. The hierarchical atlas and code will be made publicly available for future investigators.
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Affiliation(s)
- Teddy J Akiki
- Clinical Neurosciences Division-National Center for PTSD, United States Department of Veterans Affairs, West Haven, CT, 06516, USA.,Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA
| | - Chadi G Abdallah
- Clinical Neurosciences Division-National Center for PTSD, United States Department of Veterans Affairs, West Haven, CT, 06516, USA. .,Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06511, USA.
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73
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Han M, Yang G, Li H, Zhou S, Xu B, Jiang J, Men W, Ge J, Gong G, Liu H, Gao JH. Individualized Cortical Parcellation Based on Diffusion MRI Tractography. Cereb Cortex 2019; 30:3198-3208. [PMID: 31814022 DOI: 10.1093/cercor/bhz303] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 10/24/2019] [Accepted: 11/11/2019] [Indexed: 12/29/2022] Open
Abstract
The spatial topological properties of cortical regions vary across individuals. Connectivity-based functional and anatomical cortical mapping in individuals will facilitate research on structure-function relationships. However, individual-specific cortical topographic properties derived from anatomical connectivity are less explored than those based on functional connectivity. We aimed to develop a novel individualized anatomical connectivity-based parcellation framework and investigate individual differences in spatial topographic features of cortical regions using diffusion magnetic resonance imaging (dMRI) tractography. Using a high-quality, repeated-session dMRI dataset (42 subjects, 2 sessions per subject), cortical parcels were derived through in vivo anatomical connectivity-based parcellation. These individual-specific parcels demonstrated good within-individual reproducibility and reflected interindividual differences in anatomical brain organization. Connectivity in these individual-specific parcels was significantly more homogeneous than that based on the group atlas. We found that the position, size, and topography of these anatomical parcels were highly variable across individuals and demonstrated nonredundant information about individual differences. Finally, we found that intersubject variability in anatomical connectivity was correlated with the diversity of anatomical connectivity patterns. Overall, we identified cortical parcels that show homogeneous anatomical connectivity patterns. These parcels displayed marked intersubject spatial variability, which may be used in future functional studies to reveal structure-function relationships in the human brain.
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Affiliation(s)
- Meizhen Han
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Guoyuan Yang
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Hai Li
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,McGovern Institute for Brain Research, Peking University, Beijing 100871, China.,Beijing Intelligent Brain Cloud Inc., Beijing 100036, China
| | - Sizhong Zhou
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Boyan Xu
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Jun Jiang
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Weiwei Men
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Jianqiao Ge
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Gaolang Gong
- National Key Laboratory of Cognitive Neuroscience and Learning, School of Brain and Cognitive Sciences, Beijing Normal University, Beijing 100875, China
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA.,Beijing Institute for Brain Disorders, Capital Medical University, Beijing 100069, China
| | - Jia-Hong Gao
- Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.,McGovern Institute for Brain Research, Peking University, Beijing 100871, China
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74
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Messé A. Parcellation influence on the connectivity-based structure-function relationship in the human brain. Hum Brain Mapp 2019; 41:1167-1180. [PMID: 31746083 PMCID: PMC7267927 DOI: 10.1002/hbm.24866] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 10/29/2019] [Accepted: 11/02/2019] [Indexed: 11/18/2022] Open
Abstract
One of the fundamental questions in neuroscience is how brain structure and function are intertwined. MRI‐based studies have demonstrated a close relationship between the physical wiring of the brain (structural connectivity) and the associated patterns of synchronization (functional connectivity). However, little is known about the spatial consistency of such a relationship and notably its potential dependence on brain parcellations. In the present study, we performed a comparison of a set of state‐of‐the‐art group‐wise brain atlases, with various spatial resolutions, to relate structural and functional connectivity derived from high quality MRI data. We aim to investigate if the definition of brain areas influences the relationship between structural and functional connectivity. We observed that there is a significant effect of brain parcellations, which is mainly driven by the number of areas; there are mixed differences in the SC–FC relationship when compared to purely random parcellations; the influence of the number of areas cannot be attributed solely to the reliability of the connectivity estimates; and beyond the influence of the number of regions, the spatial embedding of the brain (distance effect) can explain a large portion of the observed relationship. As such the choice of a brain parcellation for connectivity analyses remains most likely a matter of convenience.
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Affiliation(s)
- Arnaud Messé
- Department of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany
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75
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Salehi M, Greene AS, Karbasi A, Shen X, Scheinost D, Constable RT. There is no single functional atlas even for a single individual: Functional parcel definitions change with task. Neuroimage 2019; 208:116366. [PMID: 31740342 DOI: 10.1016/j.neuroimage.2019.116366] [Citation(s) in RCA: 135] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 11/07/2019] [Accepted: 11/13/2019] [Indexed: 12/30/2022] Open
Abstract
The goal of human brain mapping has long been to delineate the functional subunits in the brain and elucidate the functional role of each of these brain regions. Recent work has focused on whole-brain parcellation of functional Magnetic Resonance Imaging (fMRI) data to identify these subunits and create a functional atlas. Functional connectivity approaches to understand the brain at the network level require such an atlas to assess connections between parcels and extract network properties. While no single functional atlas has emerged as the dominant atlas to date, there remains an underlying assumption that such an atlas exists. Using fMRI data from a highly sampled subject as well as two independent replication data sets, we demonstrate that functional parcellations based on fMRI connectivity data reconfigure substantially and in a meaningful manner, according to brain state.
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Affiliation(s)
- Mehraveh Salehi
- Department of Electrical Engineering, Yale University, United States; Yale Institute for Network Science (YINS), Yale University, United States.
| | - Abigail S Greene
- Interdepartmental Neuroscience Program, Yale School of Medicine, United States
| | - Amin Karbasi
- Department of Electrical Engineering, Yale University, United States; Yale Institute for Network Science (YINS), Yale University, United States
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, United States
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, United States
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale School of Medicine, United States; Department of Radiology and Biomedical Imaging, Yale School of Medicine, United States; Department of Neurosurgery, Yale School of Medicine, United States
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76
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Kuo PC, Tseng YL, Zilles K, Suen S, Eickhoff SB, Lee JD, Cheng PE, Liou M. Brain dynamics and connectivity networks under natural auditory stimulation. Neuroimage 2019; 202:116042. [PMID: 31344485 DOI: 10.1016/j.neuroimage.2019.116042] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 07/17/2019] [Accepted: 07/20/2019] [Indexed: 02/03/2023] Open
Abstract
The analysis of functional magnetic resonance imaging (fMRI) data is challenging when subjects are under exposure to natural sensory stimulation. In this study, a two-stage approach was developed to enable the identification of connectivity networks involved in the processing of information in the brain under natural sensory stimulation. In the first stage, the degree of concordance between the results of inter-subject and intra-subject correlation analyses is assessed statistically. The microstructurally (i.e., cytoarchitectonically) defined brain areas are designated either as concordant in which the results of both correlation analyses are in agreement, or as discordant in which one analysis method shows a higher proportion of supra-threshold voxels than does the other. In the second stage, connectivity networks are identified using the time courses of supra-threshold voxels in brain areas contingent upon the classifications derived in the first stage. In an empirical study, fMRI data were collected from 40 young adults (19 males, average age 22.76 ± 3.25), who underwent auditory stimulation involving sound clips of human voices and animal vocalizations under two operational conditions (i.e., eyes-closed and eyes-open). The operational conditions were designed to assess confounding effects due to auditory instructions or visual perception. The proposed two-stage analysis demonstrated that stress modulation (affective) and language networks in the limbic and cortical structures were respectively engaged during sound stimulation, and presented considerable variability among subjects. The network involved in regulating visuomotor control was sensitive to the eyes-open instruction, and presented only small variations among subjects. A high degree of concordance was observed between the two analyses in the primary auditory cortex which was highly sensitive to the pitch of sound clips. Our results have indicated that brain areas can be identified as concordant or discordant based on the two correlation analyses. This may further facilitate the search for connectivity networks involved in the processing of information under natural sensory stimulation.
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Affiliation(s)
- Po-Chih Kuo
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Yi-Li Tseng
- Department of Electrical Engineering, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Karl Zilles
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
| | - Summit Suen
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| | - Juin-Der Lee
- Graduate Institute of Business Administration, National Chengchi University, Taipei, Taiwan
| | - Philip E Cheng
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Michelle Liou
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.
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77
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Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks. Brain Topogr 2019; 32:926-942. [PMID: 31707621 DOI: 10.1007/s10548-019-00744-6] [Citation(s) in RCA: 302] [Impact Index Per Article: 60.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 11/02/2019] [Indexed: 12/25/2022]
Abstract
The past decade has witnessed a proliferation of studies aimed at characterizing the human connectome. These projects map the brain regions comprising large-scale systems underlying cognition using non-invasive neuroimaging approaches and advanced analytic techniques adopted from network science. While the idea that the human brain is composed of multiple macro-scale functional networks has been gaining traction in cognitive neuroscience, the field has yet to reach consensus on several key issues regarding terminology. What constitutes a functional brain network? Are there "core" functional networks, and if so, what are their spatial topographies? What naming conventions, if universally adopted, will provide the most utility and facilitate communication amongst researchers? Can a taxonomy of functional brain networks be delineated? Here we survey the current landscape to identify six common macro-scale brain network naming schemes and conventions utilized in the literature, highlighting inconsistencies and points of confusion where appropriate. As a minimum recommendation upon which to build, we propose that a scheme incorporating anatomical terminology should provide the foundation for a taxonomy of functional brain networks. A logical starting point in this endeavor might delineate systems that we refer to here as "occipital", "pericentral", "dorsal frontoparietal", "lateral frontoparietal", "midcingulo-insular", and "medial frontoparietal" networks. We posit that as the field of network neuroscience matures, it will become increasingly imperative to arrive at a taxonomy such as that proposed here, that can be consistently referenced across research groups.
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78
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Wang F, Lian C, Wu Z, Wang L, Lin W, Gilmore JH, Shen D, Li G. Revealing Developmental Regionalization of Infant Cerebral Cortex Based on Multiple Cortical Properties. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2019; 11765:841-849. [PMID: 32190846 PMCID: PMC7079741 DOI: 10.1007/978-3-030-32245-8_93] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The human brain develops dynamically and regionally heterogeneously during the first two postnatal years. Cortical developmental regionalization, i.e., the landscape of cortical heterogeneity in development, reflects the organization of underlying microstructures, which are closely related to the functional principles of the cortex. Therefore, prospecting early cortical developmental regionalization can provide neurobiologically meaningful units for precise region localization, which will advance our understanding on brain development in this critical period. However, due to the absence of dedicated computational tools and large-scale datasets, our knowledge on early cortical developmental regionalization still remains intact. To fill both the methodological and knowledge gaps, we propose to explore the cortical developmental regionalization using a novel method based on nonnegative matrix factorization (NMF), due to its ability in analyzing complex high-dimensional data by representing data using several bases in a data-driven way. Specifically, a novel multi-view NMF (MV-NMF) method is proposed, in which multiple distinct and complementary cortical properties (i.e., multiple views) are jointly considered to provide comprehensive observation of cortical regionalization process. To ensure the sparsity of the discovered regions, an orthogonal constraint defined in Stiefel manifold is imposed in our MV-NMF method. Meanwhile, a graph-induced constraint is also included to improve the compactness of the discovered regions. Capitalizing on an unprecedentedly large dataset with 1,560 longitudinal MRI scans from 887 infants, we delineate the first neurobiologically meaningful representation of early cortical regionalization, providing a valuable reference for brain development studies.
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Affiliation(s)
- Fan Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Chunfeng Lian
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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79
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The brain’s default network: updated anatomy, physiology and evolving insights. Nat Rev Neurosci 2019; 20:593-608. [DOI: 10.1038/s41583-019-0212-7] [Citation(s) in RCA: 421] [Impact Index Per Article: 84.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/31/2019] [Indexed: 12/15/2022]
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80
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Abstract
A defining aspect of brain organization is its spatial heterogeneity, which gives rise to multiple topographies at different scales. Brain parcellation - defining distinct partitions in the brain, be they areas or networks that comprise multiple discontinuous but closely interacting regions - is thus fundamental for understanding brain organization and function. The past decade has seen an explosion of in vivo MRI-based approaches to identify and parcellate the brain on the basis of a wealth of different features, ranging from local properties of brain tissue to long-range connectivity patterns, in addition to structural and functional markers. Given the high diversity of these various approaches, assessing the convergence and divergence among these ensuing maps is a challenge. Inter-individual variability adds to this challenge but also provides new opportunities when coupled with cross-species and developmental parcellation studies.
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81
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Kong R, Li J, Orban C, Sabuncu MR, Liu H, Schaefer A, Sun N, Zuo XN, Holmes AJ, Eickhoff SB, Yeo BTT. Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion. Cereb Cortex 2019; 29:2533-2551. [PMID: 29878084 PMCID: PMC6519695 DOI: 10.1093/cercor/bhy123] [Citation(s) in RCA: 312] [Impact Index Per Article: 62.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Indexed: 01/28/2023] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangement) is behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific cortical networks and investigate whether individual-specific network topography can predict human behavior. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. By ignoring intra-subject variability, previous network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects. More specifically, MS-HBM parcellations estimated from a single rs-fMRI session (10 min) showed comparable generalizability as parcellations estimated by 2 state-of-the-art methods using 5 sessions (50 min). We also showed that behavioral phenotypes across cognition, personality, and emotion could be predicted by individual-specific network topography with modest accuracy, comparable to previous reports predicting phenotypes based on connectivity strength. Network topography estimated by MS-HBM was more effective for behavioral prediction than network size, as well as network topography estimated by other parcellation approaches. Thus, similar to connectivity strength, individual-specific network topography might also serve as a fingerprint of human behavior.
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Affiliation(s)
- Ru Kong
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
| | - Jingwei Li
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
| | - Csaba Orban
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
| | - Mert R Sabuncu
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA
| | - Hesheng Liu
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Alexander Schaefer
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
| | - Nanbo Sun
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
| | - Xi-Nian Zuo
- CAS Key Laboratory of Behavioral Sciences and Research Center for Lifespan Development of Brain and Mind (CLIMB), Institute of Psychology, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Avram J Holmes
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Centre for Cognitive Neuroscience, Duke-NUS Medical School, Singapore
- NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore
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82
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Palomero-Gallagher N, Hoffstaedter F, Mohlberg H, Eickhoff SB, Amunts K, Zilles K. Human Pregenual Anterior Cingulate Cortex: Structural, Functional, and Connectional Heterogeneity. Cereb Cortex 2019; 29:2552-2574. [PMID: 29850806 PMCID: PMC6519696 DOI: 10.1093/cercor/bhy124] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Indexed: 12/21/2022] Open
Abstract
The human pregenual anterior cingulate cortex (pACC) encompasses 7 distinct cyto- and receptorarchitectonic areas. We lack a detailed understanding of the functions in which they are involved, and stereotaxic maps are not available. We present an integrated structural/functional map of pACC based on probabilistic cytoarchitectonic mapping and meta-analytic connectivity modeling and quantitative functional decoding. Due to the restricted spatial resolution of functional imaging data relative to the microstructural parcellation, areas p24a of the callosal sulcus and p24b on the surface of the cingulate gyrus were merged into a "gyral component" (p24ab) of area p24, and areas pv24c, pd24cv, and pd24cd, located within the cingulate sulcus were merged into a "sulcal component" (p24c) for meta-analytic analysis. Area p24ab was specifically associated with interoception, p24c with the inhibition of action, and p32, which was also activated by emotion induction tasks pertaining negatively valenced stimuli, with the ability to experience empathy. Thus, area p32 could be classified as cingulate association cortex playing a crucial role in the cognitive regulation of emotion. By this spectrum of functions, pACC is a structurally and functionally heterogeneous region, clearly differing from other parts of the anterior and middle cingulate cortex.
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Affiliation(s)
- Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425 Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen, 52074 Aachen, Germany
| | - Felix Hoffstaedter
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf 40225, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, 52425 Jülich, Germany
| | - Hartmut Mohlberg
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425 Jülich, Germany
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf 40225, Düsseldorf, Germany
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, 52425 Jülich, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425 Jülich, Germany
- C. & O. Vogt Institute for Brain Research, Heinrich-Heine-University, 40225 Düsseldorf, Germany
- JARA-BRAIN, Jülich-Aachen Research Alliance, 52425 Jülich, Germany
| | - Karl Zilles
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425 Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen, 52074 Aachen, Germany
- JARA-BRAIN, Jülich-Aachen Research Alliance, 52425 Jülich, Germany
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83
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Yoo K, Rosenberg MD, Noble S, Scheinost D, Constable RT, Chun MM. Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors. Neuroimage 2019; 197:212-223. [PMID: 31039408 DOI: 10.1016/j.neuroimage.2019.04.060] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 04/17/2019] [Accepted: 04/23/2019] [Indexed: 10/26/2022] Open
Abstract
Brain functional connectivity features can predict cognition and behavior at the level of the individual. Most studies measure univariate signals, correlating timecourses from the average of constituent voxels in each node. While straightforward, this approach overlooks the spatial patterns of voxel-wise signals within individual nodes. Given that multivariate spatial activity patterns across voxels can improve fMRI measures of mental representations, here we asked whether using voxel-wise timecourses can better characterize region-by-region interactions relative to univariate approaches. Using two fMRI datasets, the Human Connectome Project sample and a local test-retest sample, we measured multivariate functional connectivity with multivariate distance correlation and univariate connectivity with Pearson's correlation. We compared multivariate and univariate connectivity estimates, demonstrating that relative to univariate estimates, multivariate estimates exhibited higher reliability at both the edge-level and connectome-level, stronger prediction of individual differences, and greater sensitivity to brain states within individuals. Our findings suggest that multivariate estimates reliably provide more powerful information about an individual's functional brain organization and its relation to cognitive skills.
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Affiliation(s)
| | | | - Stephanie Noble
- Interdepartmental Neuroscience Program, Yale University, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Neurosurgery, Yale School of Medicine, USA
| | - Marvin M Chun
- Department of Psychology, Yale University, USA; Interdepartmental Neuroscience Program, Yale University, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT, 06520, USA
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84
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Bolt T, Nomi JS, Bainter SA, Cole MW, Uddin LQ. The situation or the person? Individual and task-evoked differences in BOLD activity. Hum Brain Mapp 2019; 40:2943-2954. [PMID: 30919517 DOI: 10.1002/hbm.24570] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 02/06/2019] [Accepted: 03/01/2019] [Indexed: 11/10/2022] Open
Abstract
Investigations of between-person variability are enjoying a recent resurgence in functional magnetic resonance imaging (fMRI) research. Several recent studies have found persistent between-person differences in blood-oxygenated-level dependent (BOLD) activation patterns and resting-state functional connectivity. Conflicting findings have been reported regarding the extent to which (a) between-person or (b) within-person cognitive state differences explain differences in BOLD activation patterns. These discrepancies may arise due to statistical analysis choices, parcellation resolution, and limited sampling of task-fMRI datasets. We attempt to address these issues in a large-scale analysis of several task-fMRI paradigms. Using a novel application of multivariate distance matrix regression, we examine between-person and task-condition variability estimates across varying levels of "resolution", from a coarse region-of-interest level to the vertex-level, and across different distance metrics. These analyses revealed that under most circumstances, differences in task conditions explained a greater amount of variance in activation map differences than between-person differences. However, this finding was reversed when comparing activation maps at a "high-resolution" vertex level. More generally, we observed that when moving from "low" to "high" resolutions, the variance explained by between-person differences increased while variance explained by task conditions decreased. We further analyzed the relationships among subject-level activation maps across all task-conditions using an unsupervised clustering approach and identified a superordinate task structure. This structure went beyond conventional task labels and highlighted those experimental manipulations across task conditions that produce contrasting versus similar whole-brain activation patterns. Overall, these analyses suggest that the question of the subject- versus task-effects on BOLD activation patterns is nontrivial, and depends on the comparison "resolution," choice of distance metric, and the coding of task-conditions.
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Affiliation(s)
- Taylor Bolt
- Gallup, Data Science Division, Washington, DC
| | - Jason S Nomi
- Department of Psychology, University of Miami, Coral Gables, Florida
| | - Sierra A Bainter
- Department of Psychology, University of Miami, Coral Gables, Florida
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, USA
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, Florida.,Neuroscience Program, University of Miami Miller School of Medicine, Miami, Florida
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85
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Urchs S, Armoza J, Moreau C, Benhajali Y, St-Aubin J, Orban P, Bellec P. MIST: A multi-resolution parcellation of functional brain networks. ACTA ACUST UNITED AC 2019. [DOI: 10.12688/mniopenres.12767.2] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The functional architecture of the brain is organized across multiple levels of spatial resolutions, from distributed networks to the localized areas they are made of. A brain parcellation that defines functional nodes at multiple resolutions is required to investigate the functional connectome across these scales. Here we present the Multiresolution Intrinsic Segmentation Template (MIST), a multi-resolution group level parcellation of the cortical, subcortical and cerebellar gray matter. The individual MIST parcellations match other published group parcellations in internal homogeneity and reproducibility and perform very well in real-world application benchmarks. In addition, the MIST parcellations are fully annotated and provide a hierarchical decomposition of functional brain networks across nine resolutions (7 to 444 functional parcels). We hope that the MIST parcellation will accelerate research in brain connectivity across resolutions. Because visualizing multiresolution parcellations is challenging, we provide an interactive web interface to explore the MIST. The MIST is also available through the popular nilearn toolbox.
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86
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Braga RM, Van Dijk KRA, Polimeni JR, Eldaief MC, Buckner RL. Parallel distributed networks resolved at high resolution reveal close juxtaposition of distinct regions. J Neurophysiol 2019; 121:1513-1534. [PMID: 30785825 PMCID: PMC6485740 DOI: 10.1152/jn.00808.2018] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
Examination of large-scale distributed networks within the individual reveals details of cortical network organization that are absent in group-averaged studies. One recent discovery is that a distributed transmodal network, often referred to as the “default network,” comprises two closely interdigitated networks, only one of which is coupled to posterior parahippocampal cortex. Not all studies of individuals have identified the same networks, and questions remain about the degree to which the two networks are separate, particularly within regions hypothesized to be interconnected hubs. In this study we replicate the observation of network separation across analytical (seed-based connectivity and parcellation) and data projection (volume and surface) methods in two individuals each scanned 31 times. Additionally, three individuals were examined with high-resolution (7T; 1.35 mm) functional magnetic resonance imaging to gain further insight into the anatomical details. The two networks were identified with separate regions localized to adjacent portions of the cortical ribbon, sometimes inside the same sulcus. Midline regions previously implicated as hubs revealed near complete spatial separation of the two networks, displaying a complex spatial topography in the posterior cingulate and precuneus. The network coupled to parahippocampal cortex also revealed a separate region directly within the hippocampus, at or near the subiculum. These collective results support that the default network is composed of at least two spatially juxtaposed networks. Fine spatial details and juxtapositions of the two networks can be identified within individuals at high resolution, providing insight into the network organization of association cortex and placing further constraints on interpretation of group-averaged neuroimaging data. NEW & NOTEWORTHY Recent evidence has emerged that canonical large-scale networks such as the “default network” fractionate into parallel distributed networks when defined within individuals. This research uses high-resolution imaging to show that the networks possess juxtapositions sometimes evident inside the same sulcus and within regions that have been previously hypothesized to be network hubs. Distinct circumscribed regions of one network were also resolved in the hippocampal formation, at or near the parahippocampal cortex and subiculum.
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Affiliation(s)
- Rodrigo M Braga
- Department of Psychology, Center for Brain Science, Harvard University , Cambridge, Massachusetts.,The Computational, Cognitive & Clinical Neuroimaging Laboratory, Hammersmith Hospital Campus, Imperial College London , London , United Kingdom.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Koene R A Van Dijk
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School , Boston, Massachusetts.,Division of Health Sciences and Technology, Massachusetts Institute of Technology , Cambridge, Massachusetts
| | - Mark C Eldaief
- Department of Psychology, Center for Brain Science, Harvard University , Cambridge, Massachusetts.,Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts
| | - Randy L Buckner
- Department of Psychology, Center for Brain Science, Harvard University , Cambridge, Massachusetts.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts.,Department of Radiology, Harvard Medical School , Boston, Massachusetts.,Department of Psychiatry, Massachusetts General Hospital, Charlestown, Massachusetts
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87
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Popovych OV, Manos T, Hoffstaedter F, Eickhoff SB. What Can Computational Models Contribute to Neuroimaging Data Analytics? Front Syst Neurosci 2019; 12:68. [PMID: 30687028 PMCID: PMC6338060 DOI: 10.3389/fnsys.2018.00068] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Accepted: 12/17/2018] [Indexed: 01/12/2023] Open
Abstract
Over the past years, nonlinear dynamical models have significantly contributed to the general understanding of brain activity as well as brain disorders. Appropriately validated and optimized mathematical models can be used to mechanistically explain properties of brain structure and neuronal dynamics observed from neuroimaging data. A thorough exploration of the model parameter space and hypothesis testing with the methods of nonlinear dynamical systems and statistical physics can assist in classification and prediction of brain states. On the one hand, such a detailed investigation and systematic parameter variation are hardly feasible in experiments and data analysis. On the other hand, the model-based approach can establish a link between empirically discovered phenomena and more abstract concepts of attractors, multistability, bifurcations, synchronization, noise-induced dynamics, etc. Such a mathematical description allows to compare and differentiate brain structure and dynamics in health and disease, such that model parameters and dynamical regimes may serve as additional biomarkers of brain states and behavioral modes. In this perspective paper we first provide very brief overview of the recent progress and some open problems in neuroimaging data analytics with emphasis on the resting state brain activity. We then focus on a few recent contributions of mathematical modeling to our understanding of the brain dynamics and model-based approaches in medicine. Finally, we discuss the question stated in the title. We conclude that incorporating computational models in neuroimaging data analytics as well as in translational medicine could significantly contribute to the progress in these fields.
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Affiliation(s)
- Oleksandr V. Popovych
- Institute of Neuroscience and Medicine - Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Thanos Manos
- Institute of Neuroscience and Medicine - Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine - Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Simon B. Eickhoff
- Institute of Neuroscience and Medicine - Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
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88
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Han L, Savalia NK, Chan MY, Agres PF, Nair AS, Wig GS. Functional Parcellation of the Cerebral Cortex Across the Human Adult Lifespan. Cereb Cortex 2018; 28:4403-4423. [PMID: 30307480 PMCID: PMC6215466 DOI: 10.1093/cercor/bhy218] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2018] [Revised: 08/03/2018] [Indexed: 12/26/2022] Open
Abstract
Adult aging is associated with differences in structure, function, and connectivity of brain areas. Age-based brain comparisons have typically rested on the assumption that brain areas exhibit a similar spatial organization across age; we evaluate this hypothesis directly. Area parcellation methods that identify locations where resting-state functional correlations (RSFC) exhibit abrupt transitions (boundary-mapping) are used to define cortical areas in cohorts of individuals sampled across a large range of the human adult lifespan (20-93 years). Most of the strongest areal boundaries are spatially consistent across age. Differences in parcellation boundaries are largely explained by differences in cortical thickness and anatomical alignment in older relative to younger adults. Despite the parcellation similarities, age-specific parcellations exhibit better internal validity relative to a young-adult parcellation applied to older adults' data, and age-specific parcels are better able to capture variability in task-evoked functional activity. Incorporating age-specific parcels as nodes in RSFC network analysis reveals that the spatial topography of the brain's large-scale system organization is comparable throughout aging, but confirms that the segregation of systems declines with increasing age. These observations demonstrate that many features of areal organization are consistent across adulthood, and reveal sources of age-related brain variation that contribute to the differences.
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Affiliation(s)
- Liang Han
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
| | - Neil K Savalia
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
- Yale University School of Medicine, New Haven, CT, USA
| | - Micaela Y Chan
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
| | - Phillip F Agres
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
| | - Anupama S Nair
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
| | - Gagan S Wig
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX, USA
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89
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Cheng C, Fan L, Xia X, Eickhoff SB, Li H, Li H, Chen J, Jiang T. Rostro-caudal organization of the human posterior superior temporal sulcus revealed by connectivity profiles. Hum Brain Mapp 2018; 39:5112-5125. [PMID: 30273447 DOI: 10.1002/hbm.24349] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Revised: 07/20/2018] [Accepted: 07/30/2018] [Indexed: 01/01/2023] Open
Abstract
The posterior superior temporal sulcus (pSTS) plays an important role in biological motion perception but is also thought to be essential for speech and facial processing. However, although there are many previous investigations of distinct functional modules within the pSTS, the functional organization of the pSTS in its full functional heterogeneity has not yet been established. Here we applied a connectivity-based parcellation strategy to delineate the human pSTS subregions based on distinct anatomical connectivity profiles and divided it into rostral and caudal subregions using diffusion tensor imaging. Subsequent multimodal connection pattern analyses revealed distinct subregional connectivity profiles. From this we inferred that the two subregions are involved in distinct functional circuits, the language processing loop and the cognition attention network. These results indicate a convergent functional architecture of the pSTS that can be revealed based on different types of connectivity and is reflected in different functions and interactions. In addition, when the subregions were performing their processing in the different functional circuits, we found asymmetry in the bilateral pSTS. Our findings may improve the understanding of the functional organization of the pSTS and provide new insights into its interactions and integration of information at the subregional level.
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Affiliation(s)
- Chen Cheng
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoluan Xia
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Hai Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Haifang Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Junjie Chen
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
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90
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Deco G, Cruzat J, Cabral J, Knudsen GM, Carhart-Harris RL, Whybrow PC, Logothetis NK, Kringelbach ML. Whole-Brain Multimodal Neuroimaging Model Using Serotonin Receptor Maps Explains Non-linear Functional Effects of LSD. Curr Biol 2018; 28:3065-3074.e6. [PMID: 30270185 DOI: 10.1016/j.cub.2018.07.083] [Citation(s) in RCA: 103] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 06/21/2018] [Accepted: 07/31/2018] [Indexed: 12/20/2022]
Abstract
Understanding the underlying mechanisms of the human brain in health and disease will require models with necessary and sufficient details to explain how function emerges from the underlying anatomy and is shaped by neuromodulation. Here, we provide such a detailed causal explanation using a whole-brain model integrating multimodal imaging in healthy human participants undergoing manipulation of the serotonin system. Specifically, we combined anatomical data from diffusion magnetic resonance imaging (dMRI) and functional magnetic resonance imaging (fMRI) with neurotransmitter data obtained with positron emission tomography (PET) of the detailed serotonin 2A receptor (5-HT2AR) density map. This allowed us to model the resting state (with and without concurrent music listening) and mechanistically explain the functional effects of 5-HT2AR stimulation with lysergic acid diethylamide (LSD) on healthy participants. The whole-brain model used a dynamical mean-field quantitative description of populations of excitatory and inhibitory neurons as well as the associated synaptic dynamics, where the neuronal gain function of the model is modulated by the 5-HT2AR density. The model identified the causative mechanisms for the non-linear interactions between the neuronal and neurotransmitter system, which are uniquely linked to (1) the underlying anatomical connectivity, (2) the modulation by the specific brainwide distribution of neurotransmitter receptor density, and (3) the non-linear interactions between the two. Taking neuromodulatory activity into account when modeling global brain dynamics will lead to novel insights into human brain function in health and disease and opens exciting possibilities for drug discovery and design in neuropsychiatric disorders.
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Affiliation(s)
- Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, 08018 Barcelona, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, 08010 Barcelona, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Clayton VIC 3800, Australia.
| | - Josephine Cruzat
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, 08018 Barcelona, Spain
| | - Joana Cabral
- Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057 Braga, Portugal
| | - Gitte M Knudsen
- Neurobiology Research Unit and Center for Integrated Molecular Brain Imaging, Rigshospitalet, Copenhagen, Denmark; Faculty of Health and Medical Sciences, Copenhagen University, DK-2100 Copenhagen, Denmark
| | - Robin L Carhart-Harris
- Psychedelic Research Group, Centre for Psychiatry, Division of Brain Sciences, Imperial College London, London, UK
| | - Peter C Whybrow
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Nikos K Logothetis
- Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany; Imaging Science and Biomedical Engineering, University of Manchester, Manchester M13 9PT, UK
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057 Braga, Portugal; Institut d'études avancées de Paris, Paris, France.
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91
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Bellucci G, Feng C, Camilleri J, Eickhoff SB, Krueger F. The role of the anterior insula in social norm compliance and enforcement: Evidence from coordinate-based and functional connectivity meta-analyses. Neurosci Biobehav Rev 2018; 92:378-389. [DOI: 10.1016/j.neubiorev.2018.06.024] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2017] [Revised: 03/29/2018] [Accepted: 06/25/2018] [Indexed: 12/12/2022]
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92
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Van Essen DC, Glasser MF. Parcellating Cerebral Cortex: How Invasive Animal Studies Inform Noninvasive Mapmaking in Humans. Neuron 2018; 99:640-663. [PMID: 30138588 PMCID: PMC6149530 DOI: 10.1016/j.neuron.2018.07.002] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Revised: 06/25/2018] [Accepted: 07/02/2018] [Indexed: 10/28/2022]
Abstract
The cerebral cortex in mammals contains a mosaic of cortical areas that differ in function, architecture, connectivity, and/or topographic organization. A combination of local connectivity (within-area microcircuitry) and long-distance (between-area) connectivity enables each area to perform a unique set of computations. Some areas also have characteristic within-area mesoscale organization, reflecting specialized representations of distinct types of information. Cortical areas interact with one another to form functional networks that mediate behavior, and each area may be a part of multiple, partially overlapping networks. Given their importance to the understanding of brain organization, mapping cortical areas across species is a major objective of systems neuroscience and has been a century-long challenge. Here, we review recent progress in multi-modal mapping of mouse and nonhuman primate cortex, mainly using invasive experimental methods. These studies also provide a neuroanatomical foundation for mapping human cerebral cortex using noninvasive neuroimaging, including a new map of human cortical areas that we generated using a semiautomated analysis of high-quality, multimodal neuroimaging data. We contrast our semiautomated approach to human multimodal cortical mapping with various extant fully automated human brain parcellations that are based on only a single imaging modality and offer suggestions on how to best advance the noninvasive brain parcellation field. We discuss the limitations as well as the strengths of current noninvasive methods of mapping brain function, architecture, connectivity, and topography and of current approaches to mapping the brain's functional networks.
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Affiliation(s)
- David C Van Essen
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, USA.
| | - Matthew F Glasser
- Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, USA; St. Luke's Hospital, St. Louis, MO 63107, USA.
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93
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Neil JJ, Smyser CD. Recent advances in the use of MRI to assess early human cortical development. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2018; 293:56-69. [PMID: 29894905 PMCID: PMC6047926 DOI: 10.1016/j.jmr.2018.05.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Revised: 05/17/2018] [Accepted: 05/21/2018] [Indexed: 05/18/2023]
Abstract
Over the past decade, a number of advanced magnetic resonance-based methods have been brought to bear on questions related to early development of the human cerebral cortex. Herein, we describe studies employing analysis of cortical surface folding (cortical cartography), cortical microstructure (diffusion anisotropy), and cortically-based functional networks (resting state-functional connectivity MRI). The fundamentals of each MR method are described, followed by a discussion of application of the method to developing cortex and potential clinical uses. We use premature birth as an exemplar of how these modalities can be used to investigate the effects of medical and environmental variables on early cortical development.
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Affiliation(s)
- Jeffrey J Neil
- Department of Pediatric Neurology, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA 02115, United States.
| | - Christopher D Smyser
- Departments of Neurology, Pediatrics and Radiology, Washington University School of Medicine, 660 S. Euclid Ave., Campus Box 8111, St. Louis, MO 63110, United States.
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94
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Isaacs BR, Forstmann BU, Temel Y, Keuken MC. The Connectivity Fingerprint of the Human Frontal Cortex, Subthalamic Nucleus, and Striatum. Front Neuroanat 2018; 12:60. [PMID: 30072875 PMCID: PMC6060372 DOI: 10.3389/fnana.2018.00060] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Accepted: 07/02/2018] [Indexed: 11/13/2022] Open
Abstract
Within the cortico basal ganglia (BG)-thalamic network, the direct and indirect pathways comprise of projections from the cortex to the striatum (STR), whereas the hyperdirect pathway(s) consist of cortical projections toward the subthalamic nucleus (STN). Each pathway possesses a functionally distinct role for action selection. The current study quantified and compared the structural connectivity between 17 distinct cortical areas with the STN and STR using 7 Tesla diffusion weighted magnetic resonance imaging (dMRI) and resting-state functional MRI (rs-fMRI) in healthy young subjects. The selection of these cortical areas was based on a literature search focusing on animal tracer studies. The results indicate that, relative to other cortical areas, both the STN and STR showed markedly weaker structural connections to areas assumed to be essential for action inhibition such as the inferior frontal cortex pars opercularis. Additionally, the cortical connectivity fingerprint of the STN and STR indicated relatively strong connections to areas related to voluntary motor initiation such as the cingulate motor area and supplementary motor area. Overall the results indicated that the cortical-STN connections were sparser compared to the STR. There were two notable exceptions, namely for the orbitofrontal cortex and ventral medial prefrontal cortex, where a higher tract strength was found for the STN. These two areas are thought to be involved in reward processing and action bias.
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Affiliation(s)
- Bethany R. Isaacs
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, Netherlands
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, Netherlands
| | - Birte U. Forstmann
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, Netherlands
| | - Yasin Temel
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, Netherlands
- Department of Neuroscience, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Max C. Keuken
- Integrative Model-Based Cognitive Neuroscience Research Unit, University of Amsterdam, Amsterdam, Netherlands
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95
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Trevisi G, Eickhoff SB, Chowdhury F, Jha A, Rodionov R, Nowell M, Miserocchi A, McEvoy AW, Nachev P, Diehl B. Probabilistic electrical stimulation mapping of human medial frontal cortex. Cortex 2018; 109:336-346. [PMID: 30057247 PMCID: PMC6259584 DOI: 10.1016/j.cortex.2018.06.015] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Revised: 04/27/2018] [Accepted: 06/25/2018] [Indexed: 01/08/2023]
Abstract
The medial frontal cortex remains functionally ill-understood; this is reflected by the heterogeneity of behavioural outcomes following damage to the region. We aim to use the rich information provided by extraoperative direct electrical cortical stimulation to enhance our understanding of its functional anatomy. Examining a cohort of 38 epilepsy patients undergoing direct electrical cortical stimulation in the context of presurgical evaluation, we reviewed stimulation findings and classified them in a behavioural framework (positive motor, negative motor, somatosensory, speech disturbances, and “other”). The spatially discrete cortical stimulation-derived data points were then transformed into continuous probabilistic maps, thereby enabling the voxel-wise spatial inference widely used in the analysis of functional and structural imaging data. A functional map of stimulation findings of the medial wall emerged. Positive motor responses occurred in 141 stimulations (31.2%), anatomically located on the paracentral lobule (threshold at p<.05), extending no further than the vertical anterior commissure (VCA) line. Thirty negative motor responses were observed (6.6%), localised to the VCA line (at p < .001 uncorrected). In 43 stimulations (9.5%) a somatosensory response localised to the caudal cingulate zone (at p < .001 uncorrected), with a second region posterior to central sulcus. Speech disturbances were elicited in 38 stimulations (8.4%), more commonly but not exclusively from the language fMRI dominant side, just anterior to VCA (p < .001 uncorrected). In only 2 stimulations, the patient experienced a subjective “urge” to move in the absence of overt movement. Classifying motor behaviour along the dimensions of effector, and movement vs arrest, we derive a wholly data-driven stimulation map of the medial wall, powered by the largest number of stimulations of the region reported (n = 452) in patients imaged with MRI. This model and the underlying data provide a robust framework for understanding the architecture of the region through the joint analysis of disruptive and correlative anatomical maps.
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Affiliation(s)
- Gianluca Trevisi
- Neurosurgery Department, Fondazione Policlinico Universitario A. Gemelli, Catholic University Medical School, Rome, Italy
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | | | - Ashwani Jha
- National Hospital for Neurology and Neurosurgery, London, UK; Institute of Neurology, UCL, London, UK
| | | | | | - Anna Miserocchi
- National Hospital for Neurology and Neurosurgery, London, UK; Institute of Neurology, UCL, London, UK
| | - Andrew W McEvoy
- National Hospital for Neurology and Neurosurgery, London, UK; Institute of Neurology, UCL, London, UK
| | - Parashkev Nachev
- National Hospital for Neurology and Neurosurgery, London, UK; Institute of Neurology, UCL, London, UK
| | - Beate Diehl
- National Hospital for Neurology and Neurosurgery, London, UK; Institute of Neurology, UCL, London, UK.
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96
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Varikuti DP, Genon S, Sotiras A, Schwender H, Hoffstaedter F, Patil KR, Jockwitz C, Caspers S, Moebus S, Amunts K, Davatzikos C, Eickhoff SB. Evaluation of non-negative matrix factorization of grey matter in age prediction. Neuroimage 2018; 173:394-410. [PMID: 29518572 PMCID: PMC5911196 DOI: 10.1016/j.neuroimage.2018.03.007] [Citation(s) in RCA: 58] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Revised: 02/28/2018] [Accepted: 03/03/2018] [Indexed: 11/24/2022] Open
Abstract
The relationship between grey matter volume (GMV) patterns and age can be captured by multivariate pattern analysis, allowing prediction of individuals' age based on structural imaging. Raw data, voxel-wise GMV and non-sparse factorization (with Principal Component Analysis, PCA) show good performance but do not promote relatively localized brain components for post-hoc examinations. Here we evaluated a non-negative matrix factorization (NNMF) approach to provide a reduced, but also interpretable representation of GMV data in age prediction frameworks in healthy and clinical populations. This examination was performed using three datasets: a multi-site cohort of life-span healthy adults, a single site cohort of older adults and clinical samples from the ADNI dataset with healthy subjects, participants with Mild Cognitive Impairment and patients with Alzheimer's disease (AD) subsamples. T1-weighted images were preprocessed with VBM8 standard settings to compute GMV values after normalization, segmentation and modulation for non-linear transformations only. Non-negative matrix factorization was computed on the GM voxel-wise values for a range of granularities (50-690 components) and LASSO (Least Absolute Shrinkage and Selection Operator) regression were used for age prediction. First, we compared the performance of our data compression procedure (i.e., NNMF) to various other approaches (i.e., uncompressed VBM data, PCA-based factorization and parcellation-based compression). We then investigated the impact of the granularity on the accuracy of age prediction, as well as the transferability of the factorization and model generalization across datasets. We finally validated our framework by examining age prediction in ADNI samples. Our results showed that our framework favorably compares with other approaches. They also demonstrated that the NNMF based factorization derived from one dataset could be efficiently applied to compress VBM data of another dataset and that granularities between 300 and 500 components give an optimal representation for age prediction. In addition to the good performance in healthy subjects our framework provided relatively localized brain regions as the features contributing to the prediction, thereby offering further insights into structural changes due to brain aging. Finally, our validation in clinical populations showed that our framework is sensitive to deviance from normal structural variations in pathological aging.
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Affiliation(s)
- Deepthi P Varikuti
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
| | - Sarah Genon
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Aristeidis Sotiras
- Section for Biomedical Image Analysis, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Holger Schwender
- Mathematical Institute, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany
| | - Christiane Jockwitz
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; C. & O. Vogt Institute for Brain Research, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany; JARA-BRAIN, Juelich-Aachen Research Alliance, Juelich, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; C. & O. Vogt Institute for Brain Research, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany; JARA-BRAIN, Juelich-Aachen Research Alliance, Juelich, Germany
| | - Susanne Moebus
- Institute of Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Essen, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; C. & O. Vogt Institute for Brain Research, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Christos Davatzikos
- Section for Biomedical Image Analysis, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-1, INM-7), Research Centre Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
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97
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On testing for spatial correspondence between maps of human brain structure and function. Neuroimage 2018; 178:540-551. [PMID: 29860082 DOI: 10.1016/j.neuroimage.2018.05.070] [Citation(s) in RCA: 274] [Impact Index Per Article: 45.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2018] [Revised: 04/23/2018] [Accepted: 05/30/2018] [Indexed: 01/28/2023] Open
Abstract
A critical issue in many neuroimaging studies is the comparison between brain maps. Nonetheless, it remains unclear how one should test hypotheses focused on the overlap or spatial correspondence between two or more brain maps. This "correspondence problem" affects, for example, the interpretation of comparisons between task-based patterns of functional activation, resting-state networks or modules, and neuroanatomical landmarks. To date, this problem has been addressed with remarkable variability in terms of methodological approaches and statistical rigor. In this paper, we address the correspondence problem using a spatial permutation framework to generate null models of overlap by applying random rotations to spherical representations of the cortical surface, an approach for which we also provide a theoretical statistical foundation. We use this method to derive clusters of cognitive functions that are correlated in terms of their functional neuroatomical substrates. In addition, using publicly available data, we formally demonstrate the correspondence between maps of task-based functional activity, resting-state fMRI networks and gyral-based anatomical landmarks. We provide open-access code to implement the methods presented for two commonly-used tools for surface based cortical analysis (https://www.github.com/spin-test). This spatial permutation approach constitutes a useful advance over widely-used methods for the comparison of cortical maps, thereby opening new possibilities for the integration of diverse neuroimaging data.
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98
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Wang J, Hao Z, Wang H. Generation of Individual Whole-Brain Atlases With Resting-State fMRI Data Using Simultaneous Graph Computation and Parcellation. Front Hum Neurosci 2018; 12:166. [PMID: 29780309 PMCID: PMC5945868 DOI: 10.3389/fnhum.2018.00166] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Accepted: 04/10/2018] [Indexed: 11/13/2022] Open
Abstract
The human brain can be characterized as functional networks. Therefore, it is important to subdivide the brain appropriately in order to construct reliable networks. Resting-state functional connectivity-based parcellation is a commonly used technique to fulfill this goal. Here we propose a novel individual subject-level parcellation approach based on whole-brain resting-state functional magnetic resonance imaging (fMRI) data. We first used a supervoxel method known as simple linear iterative clustering directly on resting-state fMRI time series to generate supervoxels, and then combined similar supervoxels to generate clusters using a clustering method known as graph-without-cut (GWC). The GWC approach incorporates spatial information and multiple features of the supervoxels by energy minimization, simultaneously yielding an optimal graph and brain parcellation. Meanwhile, it theoretically guarantees that the actual cluster number is exactly equal to the initialized cluster number. By comparing the results of the GWC approach and those of the random GWC approach, we demonstrated that GWC does not rely heavily on spatial structures, thus avoiding the challenges encountered in some previous whole-brain parcellation approaches. In addition, by comparing the GWC approach to two competing approaches, we showed that GWC achieved better parcellation performances in terms of different evaluation metrics. The proposed approach can be used to generate individualized brain atlases for applications related to cognition, development, aging, disease, personalized medicine, etc. The major source codes of this study have been made publicly available at https://github.com/yuzhounh/GWC.
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Affiliation(s)
- J Wang
- School of Mathematics and Big Data, Foshan University, Foshan, China.,Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, China
| | - Z Hao
- School of Mathematics and Big Data, Foshan University, Foshan, China
| | - H Wang
- School of Mathematics and Big Data, Foshan University, Foshan, China
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Computational neuroanatomy of baby brains: A review. Neuroimage 2018; 185:906-925. [PMID: 29574033 DOI: 10.1016/j.neuroimage.2018.03.042] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 02/23/2018] [Accepted: 03/19/2018] [Indexed: 12/12/2022] Open
Abstract
The first postnatal years are an exceptionally dynamic and critical period of structural, functional and connectivity development of the human brain. The increasing availability of non-invasive infant brain MR images provides unprecedented opportunities for accurate and reliable charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, infant brain MR images typically exhibit reduced tissue contrast (especially around 6 months of age), large within-tissue intensity variations, and regionally-heterogeneous, dynamic changes, in comparison with adult brain MR images. Consequently, the existing computational tools developed typically for adult brains are not suitable for infant brain MR image processing. To address these challenges, many infant-tailored computational methods have been proposed for computational neuroanatomy of infant brains. In this review paper, we provide a comprehensive review of the state-of-the-art computational methods for infant brain MRI processing and analysis, which have advanced our understanding of early postnatal brain development. We also summarize publically available infant-dedicated resources, including MRI datasets, computational tools, grand challenges, and brain atlases. Finally, we discuss the limitations in current research and suggest potential future research directions.
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100
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Xu T, Rolf Jäger H, Husain M, Rees G, Nachev P. High-dimensional therapeutic inference in the focally damaged human brain. Brain 2018; 141:48-54. [PMID: 29149245 PMCID: PMC5837627 DOI: 10.1093/brain/awx288] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Revised: 08/22/2017] [Accepted: 09/18/2017] [Indexed: 01/28/2023] Open
Abstract
See Thiebaut de Schotten and Foulon (doi:10.1093/brain/awx332) for a scientific commentary on this article.Though consistency across the population renders the extraordinarily complex functional anatomy of the human brain surveyable, the inverse inference-from common functional maps to individual behaviour-is constrained by marked individual deviation from the population mean. Such inference is fundamental to the evaluation of therapeutic interventions in focal brain injury, where the impact of an induced structural change in the brain is quantified by its behavioural consequences, inevitably refracted through the lens of lesion-outcome relations. Current therapeutic evaluations do not incorporate inferences to the individual outcome derived from a detailed specification of the lesion anatomy, relying only on reductive parameters such as lesion volume and crudely discretised location. Examining 1172 patients with anatomically registered focal brain lesions, here we show that such low-dimensional models are highly insensitive to therapeutic effects. In contrast, high-dimensional models supported by machine learning dramatically improve sensitivity by leveraging complex individuating patterns in the functional architecture of the brain. The failure to replicate in humans positive interventional effects in experimental animals is thus revealed to have a remediable inferential cause, forcing a radical re-evaluation of therapeutic inference in the human brain.
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Affiliation(s)
- Tianbo Xu
- Institute of Neurology, UCL, London, WC1N 3BG, UK
| | | | - Masud Husain
- Department of Clinical Neurology, University of Oxford, Oxford OX3 9DU, UK
- Institute of Cognitive Neuroscience, UCL, London WC1N 3AR, UK
| | - Geraint Rees
- Institute of Neurology, UCL, London, WC1N 3BG, UK
- Institute of Cognitive Neuroscience, UCL, London WC1N 3AR, UK
- Faculty of Life Sciences, UCL, London, WC1E 6BT, UK
- Wellcome Trust Centre for Neuroimaging, UCL, London WC1N 3BG, UK
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