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Petersen SE, Seitzman BA, Nelson SM, Wig GS, Gordon EM. Principles of cortical areas and their implications for neuroimaging. Neuron 2024; 112:2837-2853. [PMID: 38834069 DOI: 10.1016/j.neuron.2024.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 04/11/2024] [Accepted: 05/08/2024] [Indexed: 06/06/2024]
Abstract
Cortical organization should constrain the study of how the brain performs behavior and cognition. A fundamental concept in cortical organization is that of arealization: that the cortex is parceled into discrete areas. In part one of this report, we review how non-human animal studies have illuminated principles of cortical arealization by revealing: (1) what defines a cortical area, (2) how cortical areas are formed, (3) how cortical areas interact with one another, and (4) what "computations" or "functions" areas perform. In part two, we discuss how these principles apply to neuroimaging research. In doing so, we highlight several examples where the commonly accepted interpretation of neuroimaging observations requires assumptions that violate the principles of arealization, including nonstationary areas that move on short time scales, large-scale gradients as organizing features, and cortical areas with singular functionality that perfectly map psychological constructs. Our belief is that principles of neurobiology should strongly guide the nature of computational explanations.
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Affiliation(s)
- Steven E Petersen
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Benjamin A Seitzman
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Steven M Nelson
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55455, USA
| | - Gagan S Wig
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, USA; Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Evan M Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA.
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2
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Tu JC, Wang Y, Wang X, Dierker D, Sobolewski CM, Day TKM, Kardan O, Miranda-Domínguez Ó, Moore LA, Elison JT, Gordon EM, Laumann TO, Eggebrecht AT, Wheelock MD. A subset of brain regions within adult functional connectivity networks demonstrate high reliability across early development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.31.606025. [PMID: 39131337 PMCID: PMC11312607 DOI: 10.1101/2024.07.31.606025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
The human cerebral cortex contains groups of areas that support sensory, motor, cognitive, and affective functions, often categorized as functional networks. These areas show stronger internal and weaker external functional connectivity (FC) and exhibit similar FC profiles within rather than between networks. Previous studies have demonstrated the development of these networks from nascent forms present before birth to their mature, adult-like topography in childhood. However, analyses often still use definitions based on adult functional networks. We aim to assess how this might lead to the misidentification of functional networks and explore potential consequences and solutions. Our findings suggest that even though adult networks provide only a marginally better than-chance description of the infant FC organization, misidentification was largely driven by specific areas. By restricting functional networks to areas showing adult-like network clustering, we observed consistent within-network FC both within and across scans and throughout development. Additionally, these areas were spatially closer to locations with low variability in network identity among adults. Our analysis aids in understanding the potential consequences of using adult networks "as is" and provides guidance for future research on selecting and utilizing functional network models based on the research question and scenario.
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Affiliation(s)
| | - Yu Wang
- Department of Mathematics and Statistics, Washington University in St. Louis
| | - Xintian Wang
- Department of Radiology, Washington University in St. Louis
| | - Donna Dierker
- Department of Radiology, Washington University in St. Louis
| | - Chloe M. Sobolewski
- Department of Radiology, Washington University in St. Louis
- Department of Psychology, Virginia Commonwealth University
| | - Trevor K. M. Day
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
- Center for Brain Plasticity and Recovery, Georgetown University
| | - Omid Kardan
- Department of Psychiatry, University of Michigan
| | | | - Lucille A. Moore
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Jed T. Elison
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
| | - Evan M. Gordon
- Department of Radiology, Washington University in St. Louis
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3
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Luppi AI, Mediano PAM, Rosas FE, Allanson J, Pickard J, Carhart-Harris RL, Williams GB, Craig MM, Finoia P, Owen AM, Naci L, Menon DK, Bor D, Stamatakis EA. A synergistic workspace for human consciousness revealed by Integrated Information Decomposition. eLife 2024; 12:RP88173. [PMID: 39022924 PMCID: PMC11257694 DOI: 10.7554/elife.88173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024] Open
Abstract
How is the information-processing architecture of the human brain organised, and how does its organisation support consciousness? Here, we combine network science and a rigorous information-theoretic notion of synergy to delineate a 'synergistic global workspace', comprising gateway regions that gather synergistic information from specialised modules across the human brain. This information is then integrated within the workspace and widely distributed via broadcaster regions. Through functional MRI analysis, we show that gateway regions of the synergistic workspace correspond to the human brain's default mode network, whereas broadcasters coincide with the executive control network. We find that loss of consciousness due to general anaesthesia or disorders of consciousness corresponds to diminished ability of the synergistic workspace to integrate information, which is restored upon recovery. Thus, loss of consciousness coincides with a breakdown of information integration within the synergistic workspace of the human brain. This work contributes to conceptual and empirical reconciliation between two prominent scientific theories of consciousness, the Global Neuronal Workspace and Integrated Information Theory, while also advancing our understanding of how the human brain supports consciousness through the synergistic integration of information.
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Affiliation(s)
- Andrea I Luppi
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
| | - Pedro AM Mediano
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
| | - Fernando E Rosas
- Center for Psychedelic Research, Department of Brain Science, Imperial College LondonLondonUnited Kingdom
- Center for Complexity Science, Imperial College LondonLondonUnited Kingdom
- Data Science Institute, Imperial College LondonLondonUnited Kingdom
| | - Judith Allanson
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Department of Neurosciences, Cambridge University Hospitals NHS Foundation, Addenbrooke's HospitalCambridgeUnited Kingdom
| | - John Pickard
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
- Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Addenbrooke's HospitalCambridgeUnited Kingdom
| | - Robin L Carhart-Harris
- Center for Psychedelic Research, Department of Brain Science, Imperial College LondonLondonUnited Kingdom
- Psychedelics Division - Neuroscape, Department of Neurology, University of CaliforniaSan FranciscoUnited States
| | - Guy B Williams
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
| | - Michael M Craig
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
| | - Paola Finoia
- Department of Clinical Neurosciences, University of CambridgeCambridgeUnited Kingdom
| | - Adrian M Owen
- Department of Psychology and Department of Physiology and Pharmacology, The Brain and Mind Institute, University of Western OntarioLondonCanada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Lloyd Building, Trinity CollegeDublinIreland
| | - David K Menon
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
- Wolfson Brain Imaging Centre, University of CambridgeCambridgeUnited Kingdom
| | - Daniel Bor
- Department of Psychology, University of CambridgeCambridgeUnited Kingdom
| | - Emmanuel A Stamatakis
- University Division of Anaesthesia, School of Clinical Medicine, University of CambridgeCambridgeUnited Kingdom
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4
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Li J, Jin S, Li Z, Zeng X, Yang Y, Luo Z, Xu X, Cui Z, Liu Y, Wang J. Morphological Brain Networks of White Matter: Mapping, Evaluation, Characterization, and Application. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2400061. [PMID: 39005232 DOI: 10.1002/advs.202400061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 06/27/2024] [Indexed: 07/16/2024]
Abstract
Although white matter (WM) accounts for nearly half of adult brain, its wiring diagram is largely unknown. Here, an approach is developed to construct WM networks by estimating interregional morphological similarity based on structural magnetic resonance imaging. It is found that morphological WM networks showed nontrivial topology, presented good-to-excellent test-retest reliability, accounted for phenotypic interindividual differences in cognition, and are under genetic control. Through integration with multimodal and multiscale data, it is further showed that morphological WM networks are able to predict the patterns of hamodynamic coherence, metabolic synchronization, gene co-expression, and chemoarchitectonic covariance, and associated with structural connectivity. Moreover, the prediction followed WM functional connectomic hierarchy for the hamodynamic coherence, is related to genes enriched in the forebrain neuron development and differentiation for the gene co-expression, and is associated with serotonergic system-related receptors and transporters for the chemoarchitectonic covariance. Finally, applying this approach to multiple sclerosis and neuromyelitis optica spectrum disorders, it is found that both diseases exhibited morphological dysconnectivity, which are correlated with clinical variables of patients and are able to diagnose and differentiate the diseases. Altogether, these findings indicate that morphological WM networks provide a reliable and biologically meaningful means to explore WM architecture in health and disease.
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Affiliation(s)
- Junle Li
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Suhui Jin
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Zhen Li
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Xiangli Zeng
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Yuping Yang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Zhenzhen Luo
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Xiaoyu Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Beijing, 100070, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
- Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
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5
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Zhu H, Michalak AJ, Merricks EM, Agopyan-Miu AHCW, Jacobs J, Hamberger MJ, Sheth SA, McKhann GM, Feldstein N, Schevon CA, Hillman EMC. Spectral-switching analysis reveals real-time neuronal network representations of concurrent spontaneous naturalistic behaviors in human brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.08.600416. [PMID: 39026706 PMCID: PMC11257469 DOI: 10.1101/2024.07.08.600416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Despite abundant evidence of functional networks in the human brain, their neuronal underpinnings, and relationships to real-time behavior have been challenging to resolve. Analyzing brain-wide intracranial-EEG recordings with video monitoring, acquired in awake subjects during clinical epilepsy evaluation, we discovered the tendency of each brain region to switch back and forth between 2 distinct power spectral densities (PSDs 2-55Hz). We further recognized that this 'spectral switching' occurs synchronously between distant sites, even between regions with differing baseline PSDs, revealing long-range functional networks that would be obscured in analysis of individual frequency bands. Moreover, the real-time PSD-switching dynamics of specific networks exhibited striking alignment with activities such as conversation and hand movements, revealing a multi-threaded functional network representation of concurrent naturalistic behaviors. Network structures and their relationships to behaviors were stable across days, but were altered during N3 sleep. Our results provide a new framework for understanding real-time, brain-wide neural-network dynamics.
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Affiliation(s)
- Hongkun Zhu
- Department of Biomedical Engineering, Columbia University
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027
| | - Andrew J Michalak
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Edward M Merricks
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | | | - Joshua Jacobs
- Department of Biomedical Engineering, Columbia University
- Department of Neurological Surgery, Columbia University Medical Center, New York, 10032, New York, USA
| | - Marla J Hamberger
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Sameer A Sheth
- Department of Neurological Surgery, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Guy M McKhann
- Department of Neurological Surgery, Columbia University Medical Center, New York, 10032, New York, USA
| | - Neil Feldstein
- Department of Neurological Surgery, Columbia University Medical Center, New York, 10032, New York, USA
| | - Catherine A Schevon
- Department of Neurology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Elizabeth M C Hillman
- Department of Biomedical Engineering, Columbia University
- Department of Radiology, Columbia University Medical Center, New York, 10032, New York, USA
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027
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6
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Luppi AI, Rosas FE, Mediano PAM, Demertzi A, Menon DK, Stamatakis EA. Unravelling consciousness and brain function through the lens of time, space, and information. Trends Neurosci 2024; 47:551-568. [PMID: 38824075 DOI: 10.1016/j.tins.2024.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 04/29/2024] [Accepted: 05/09/2024] [Indexed: 06/03/2024]
Abstract
Disentangling how cognitive functions emerge from the interplay of brain dynamics and network architecture is among the major challenges that neuroscientists face. Pharmacological and pathological perturbations of consciousness provide a lens to investigate these complex challenges. Here, we review how recent advances about consciousness and the brain's functional organisation have been driven by a common denominator: decomposing brain function into fundamental constituents of time, space, and information. Whereas unconsciousness increases structure-function coupling across scales, psychedelics may decouple brain function from structure. Convergent effects also emerge: anaesthetics, psychedelics, and disorders of consciousness can exhibit similar reconfigurations of the brain's unimodal-transmodal functional axis. Decomposition approaches reveal the potential to translate discoveries across species, with computational modelling providing a path towards mechanistic integration.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Montreal Neurological Institute, McGill University, Montreal, QC, Canada; St John's College, University of Cambridge, Cambridge, UK; Center for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK.
| | - Fernando E Rosas
- Center for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK; Department of Informatics, University of Sussex, Brighton, UK; Center for Psychedelic Research, Imperial College London, London, UK
| | | | - Athena Demertzi
- Physiology of Cognition Lab, GIGA-Cyclotron Research Center In Vivo Imaging, University of Liège, Liège 4000, Belgium; Psychology and Neuroscience of Cognition Research Unit, University of Liège, Liège 4000, Belgium; National Fund for Scientific Research (FNRS), Brussels 1000, Belgium
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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7
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Salvalaggio A, Pini L, Bertoldo A, Corbetta M. Glioblastoma and brain connectivity: the need for a paradigm shift. Lancet Neurol 2024; 23:740-748. [PMID: 38876751 DOI: 10.1016/s1474-4422(24)00160-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 02/29/2024] [Accepted: 04/03/2024] [Indexed: 06/16/2024]
Abstract
Despite substantial advances in cancer treatment, for patients with glioblastoma prognosis remains bleak. The emerging field of cancer neuroscience reveals intricate functional interplays between glioblastoma and the cellular architecture of the brain, encompassing neurons, glia, and vessels. New findings underscore the role of structural and functional connections within hierarchical networks, known as the connectome. These connections contribute to the location, spread, and recurrence of a glioblastoma, and a patient's overall survival, revealing a complex interplay between the tumour and the CNS. This mounting evidence prompts a paradigm shift, challenging the perception of glioblastomas as mere foreign bodies within the brain. Instead, these tumours are intricately woven into the structural and functional fabric of the brain. This radical change in thinking holds profound implications for the understanding and treatment of glioblastomas, which could unveil new prognostic factors and surgical strategies and optimise radiotherapy. Additionally, a connectivity approach suggests that non-invasive brain stimulation could disrupt pathological neuron-glioma interactions within specific networks.
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Affiliation(s)
- Alessandro Salvalaggio
- Clinica Neurologica, Azienda Ospedale Università Padova, Padova, Italy; Department of Neuroscience, University of Padova, Padova, Italy; Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Lorenzo Pini
- Department of Neuroscience, University of Padova, Padova, Italy
| | - Alessandra Bertoldo
- Padova Neuroscience Center, University of Padova, Padova, Italy; Department of Information Engineering, University of Padova, Padova, Italy
| | - Maurizio Corbetta
- Clinica Neurologica, Azienda Ospedale Università Padova, Padova, Italy; Department of Neuroscience, University of Padova, Padova, Italy; Padova Neuroscience Center, University of Padova, Padova, Italy; Veneto Institute of Molecular Medicine, Fondazione Biomedica, Padova, Italy.
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8
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Sun L, Zhang A, Liang F. Time-varying dynamic Bayesian network learning for an fMRI study of emotion processing. Stat Med 2024; 43:2713-2733. [PMID: 38690642 PMCID: PMC11195441 DOI: 10.1002/sim.10096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 04/01/2024] [Accepted: 04/19/2024] [Indexed: 05/02/2024]
Abstract
This article presents a novel method for learning time-varying dynamic Bayesian networks. The proposed method breaks down the dynamic Bayesian network learning problem into a sequence of regression inference problems and tackles each problem using the Markov neighborhood regression technique. Notably, the method demonstrates scalability concerning data dimensionality, accommodates time-varying network structure, and naturally handles multi-subject data. The proposed method exhibits consistency and offers superior performance compared to existing methods in terms of estimation accuracy and computational efficiency, as supported by extensive numerical experiments. To showcase its effectiveness, we apply the proposed method to an fMRI study investigating the effective connectivity among various regions of interest (ROIs) during an emotion-processing task. Our findings reveal the pivotal role of the subcortical-cerebellum in emotion processing.
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Affiliation(s)
- Lizhe Sun
- Beijing International Center for Mathematical Research, Peking University and Department of Statistics, Purdue University
| | | | - Faming Liang
- Department of Statistics, Purdue University, West Lafayette, IN 47907
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9
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Combrisson E, Basanisi R, Gueguen MCM, Rheims S, Kahane P, Bastin J, Brovelli A. Neural interactions in the human frontal cortex dissociate reward and punishment learning. eLife 2024; 12:RP92938. [PMID: 38941238 PMCID: PMC11213568 DOI: 10.7554/elife.92938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2024] Open
Abstract
How human prefrontal and insular regions interact while maximizing rewards and minimizing punishments is unknown. Capitalizing on human intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning is better disentangled by interactions compared to local representations. Prefrontal and insular cortices display non-selective neural populations to rewards and punishments. Non-selective responses, however, give rise to context-specific interareal interactions. We identify a reward subsystem with redundant interactions between the orbitofrontal and ventromedial prefrontal cortices, with a driving role of the latter. In addition, we find a punishment subsystem with redundant interactions between the insular and dorsolateral cortices, with a driving role of the insula. Finally, switching between reward and punishment learning is mediated by synergistic interactions between the two subsystems. These results provide a unifying explanation of distributed cortical representations and interactions supporting reward and punishment learning.
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Affiliation(s)
- Etienne Combrisson
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
| | - Ruggero Basanisi
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
| | - Maelle CM Gueguen
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut NeurosciencesGrenobleFrance
| | - Sylvain Rheims
- Department of Functional Neurology and Epileptology, Hospices Civils de Lyon and University of LyonLyonFrance
| | - Philippe Kahane
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut NeurosciencesGrenobleFrance
| | - Julien Bastin
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut NeurosciencesGrenobleFrance
| | - Andrea Brovelli
- Institut de Neurosciences de la Timone, Aix Marseille UniversitéMarseilleFrance
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10
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Luppi AI, Gellersen HM, Liu ZQ, Peattie ARD, Manktelow AE, Adapa R, Owen AM, Naci L, Menon DK, Dimitriadis SI, Stamatakis EA. Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics. Nat Commun 2024; 15:4745. [PMID: 38834553 PMCID: PMC11150439 DOI: 10.1038/s41467-024-48781-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 05/10/2024] [Indexed: 06/06/2024] Open
Abstract
Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, University of Cambridge, Cambridge, UK.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- St John's College, University of Cambridge, Cambridge, UK.
- Montreal Neurological Institute, McGill University, Montreal, Canada.
| | - Helena M Gellersen
- German Center for Neurodegenerative Diseases, Magdeburg, Germany
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Zhen-Qi Liu
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Alexander R D Peattie
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Anne E Manktelow
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Ram Adapa
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Adrian M Owen
- Department of Psychology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
- Department of Physiology and Pharmacology, Western Institute for Neuroscience (WIN), Western University, London, ON, Canada
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - David K Menon
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
| | - Stavros I Dimitriadis
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
- Institut de Neurociències, University of Barcelona, Barcelona, Spain
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, College of Biomedical and Life Sciences, Cardiff, Wales, UK
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Neuroscience and Mental Health Research Institute, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, Cardiff, Wales, UK
- Integrative Neuroimaging Lab, Thessaloniki, Greece
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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11
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Pigareva Y, Gladkov A, Kolpakov V, Kazantsev VB, Mukhina I, Pimashkin A. The Profile of Network Spontaneous Activity and Functional Organization Interplay in Hierarchically Connected Modular Neural Networks In Vitro. MICROMACHINES 2024; 15:732. [PMID: 38930702 PMCID: PMC11205292 DOI: 10.3390/mi15060732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 05/23/2024] [Accepted: 05/29/2024] [Indexed: 06/28/2024]
Abstract
Modern microtechnology methods are widely used to create neural networks on a chip with a connection architecture demonstrating properties of modularity and hierarchy similar to brain networks. Such in vitro networks serve as a valuable model for studying the interplay of functional architecture within modules, their activity, and the effectiveness of inter-module interaction. In this study, we use a two-chamber microfluidic platform to investigate functional connectivity and global activity in hierarchically connected modular neural networks. We found that the strength of functional connections within the module and the profile of network spontaneous activity determine the effectiveness of inter-modular interaction and integration activity in the network. The direction of intermodular activity propagation configures the different densities of inhibitory synapses in the network. The developed microfluidic platform holds the potential to explore function-structure relationships and efficient information processing in two- or multilayer neural networks, in both healthy and pathological states.
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Affiliation(s)
- Yana Pigareva
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
- Central Research Laboratory, Cell Technology Department, Privolzhsky Research Medical University, Nizhny Novgorod 603005, Russia
| | - Arseniy Gladkov
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
- Central Research Laboratory, Cell Technology Department, Privolzhsky Research Medical University, Nizhny Novgorod 603005, Russia
| | - Vladimir Kolpakov
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
- Central Research Laboratory, Cell Technology Department, Privolzhsky Research Medical University, Nizhny Novgorod 603005, Russia
| | - Victor B. Kazantsev
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
- Central Research Laboratory, Cell Technology Department, Privolzhsky Research Medical University, Nizhny Novgorod 603005, Russia
| | - Irina Mukhina
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
- Central Research Laboratory, Cell Technology Department, Privolzhsky Research Medical University, Nizhny Novgorod 603005, Russia
| | - Alexey Pimashkin
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod 603950, Russia
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12
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Li J, Qin Y, Zhong Z, Meng L, Huang L, Li B. Pain experience reduces social avoidance to others in pain: a c-Fos-based functional connectivity network study in mice. Cereb Cortex 2024; 34:bhae207. [PMID: 38798004 DOI: 10.1093/cercor/bhae207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/29/2024] Open
Abstract
Pain experience increases individuals' perception and contagion of others' pain, but whether pain experience affects individuals' affiliative or antagonistic responses to others' pain is largely unknown. Additionally, the neural mechanisms underlying how pain experience modulates individuals' responses to others' pain remain unclear. In this study, we explored the effects of pain experience on individuals' responses to others' pain and the underlying neural mechanisms. By comparing locomotion, social, exploration, stereotyped, and anxiety-like behaviors of mice without any pain experience (naïve observers) and mice with a similar pain experience (experienced observers) when they observed the pain-free demonstrator with intraperitoneal injection of normal saline and the painful demonstrator with intraperitoneal injection of acetic acid, we found that pain experience of the observers led to decreased social avoidance to the painful demonstrator. Through whole-brain c-Fos quantification, we discovered that pain experience altered neuronal activity and enhanced functional connectivity in the mouse brain. The analysis of complex network and graph theory exhibited that functional connectivity networks and activated hub regions were altered by pain experience. Together, these findings reveal that neuronal activity and functional connectivity networks are involved in the modulation of individuals' responses to others' pain by pain experience.
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Affiliation(s)
- Jiali Li
- Guangdong Provincial Key Laboratory of Brain Function and Disease, Neuroscience Program, Zhongshan School of Medicine and the Fifth Affiliated Hospital, Sun Yat-sen University, 74 Zhongshan Second Road, Yuexiu District, 510080 Guangzhou, China
| | - Yuxin Qin
- Guangdong Provincial Key Laboratory of Brain Function and Disease, Neuroscience Program, Zhongshan School of Medicine and the Fifth Affiliated Hospital, Sun Yat-sen University, 74 Zhongshan Second Road, Yuexiu District, 510080 Guangzhou, China
| | - Zifeng Zhong
- Guangdong Provincial Key Laboratory of Brain Function and Disease, Neuroscience Program, Zhongshan School of Medicine and the Fifth Affiliated Hospital, Sun Yat-sen University, 74 Zhongshan Second Road, Yuexiu District, 510080 Guangzhou, China
| | - Linjie Meng
- Guangdong Provincial Key Laboratory of Brain Function and Disease, Neuroscience Program, Zhongshan School of Medicine and the Fifth Affiliated Hospital, Sun Yat-sen University, 74 Zhongshan Second Road, Yuexiu District, 510080 Guangzhou, China
| | - Lianyan Huang
- Guangdong Provincial Key Laboratory of Brain Function and Disease, Neuroscience Program, Zhongshan School of Medicine and the Fifth Affiliated Hospital, Sun Yat-sen University, 74 Zhongshan Second Road, Yuexiu District, 510080 Guangzhou, China
| | - Boxing Li
- Guangdong Provincial Key Laboratory of Brain Function and Disease, Neuroscience Program, Zhongshan School of Medicine and the Fifth Affiliated Hospital, Sun Yat-sen University, 74 Zhongshan Second Road, Yuexiu District, 510080 Guangzhou, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan Second Road, Yuexiu District, 510080 Guangzhou, China
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13
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Xin H, Liang C, Fu Y, Feng M, Wang S, Gao Y, Sui C, Zhang N, Guo L, Wen H. Disrupted brain structural networks associated with depression and cognitive dysfunction in cerebral small vessel disease with microbleeds. Prog Neuropsychopharmacol Biol Psychiatry 2024; 131:110944. [PMID: 38246218 DOI: 10.1016/j.pnpbp.2024.110944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 12/26/2023] [Accepted: 01/14/2024] [Indexed: 01/23/2024]
Abstract
Emerging evidence highlights cerebral microbleeds (CMBs) as hallmarks of cerebral small vessel disease (CSVD) underlying depression and cognitive dysfunction. This study aimed to reveal how depression and cognition-related white matter (WM) abnormalities are topologically presented, and the network-level structural disruptions associated with CMBs in CSVD. We used probabilistic diffusion tractography and graph theory to investigate brain WM network topology in CSVD patients with (n = 64, CSVD-c) and without (n = 138, CSVD-n) CMBs and 90 healthy controls. Then we evaluated the Pearson's correlations between disrupted network metrics and neuropsychological parameters. For global topology, the CSVD-c group exhibited significantly decreased global (Eglob) and local (Eloc) efficiency and increased shortest path length compared with the controls, while no significant difference was found between the CSVD-c and CSVD-n groups. For regional topology, although all groups showed highly similar hub distributions, compare with control group, the CSVD-c group exhibited significantly decreased nodal efficiency mainly in the bilateral supplementary motor area (SMA), median cingulate gyrus (DCG) and right orbital middle frontal gyrus, while the CSVD-n group showed significantly decreased nodal efficiency only in the right SMA. Notably, Eglob, Eloc and nodal efficiency of the right anterior cingulate gyrus, DCG, middle temporal gyrus and left insula showed significantly negative correlations with depression score, significantly positive correlations with Rey auditory verbal learning test and symbol digit modalities test scores in CSVD-n group, as well as significantly negative correlations with Stroop color-word test scores in CSVD-c group. The WM networks of CSVD patients are characterized by decreased global integration and local specialization, and decreased nodal efficiency highly related to depression and cognitive dysfunction in the attention, default mode network and sensorimotor regions. These findings provide new insight into the neurobiological mechanisms of CSVD and concomitant affective and cognitive disorders.
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Affiliation(s)
- Haotian Xin
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jing-wu Road No. 324, Jinan, Shandong 250021, China; Department of Radiology and Nuclear medicine, Xuanwu Hospital, Capital Medical University, No. 45 Chang-chun St, Xicheng District, Beijing, China
| | - Changhu Liang
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Yajie Fu
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jing-wu Road No. 324, Jinan, Shandong 250021, China; Department of Medical Ultrasound, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Abdominal Medical Imaging, 16766 Jing-shi Road,Jinan 250014,China
| | - Mengmeng Feng
- Department of Radiology, Shandong Provincial Hospital, Shandong University, Jing-wu Road No. 324, Jinan, Shandong 250021, China; Department of Radiology and Nuclear medicine, Xuanwu Hospital, Capital Medical University, No. 45 Chang-chun St, Xicheng District, Beijing, China
| | - Shengpei Wang
- Research Center for Brain-inspired Intelligence Institute of Automation, Chinese Academy of Sciences, ZhongGuanCun East Rd. 95#, Beijing 100190, China
| | - Yian Gao
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Chaofan Sui
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Nan Zhang
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China
| | - Lingfei Guo
- Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, China.
| | - Hongwei Wen
- Key Laboratory of Cognition and Personality (Ministry of Education), Faculty of Psychology, Southwest University, Chongqing 400715, China.
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14
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Wang Z, Xiang L, Zhang R. P300 intention recognition based on phase lag index (PLI)-rich-club brain functional network. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2024; 95:045116. [PMID: 38624364 DOI: 10.1063/5.0202770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 03/28/2024] [Indexed: 04/17/2024]
Abstract
Brain-computer interface (BCI) technology based on P300 signals has a broad application prospect in the assessment and diagnosis of clinical diseases and game control. The paper of selecting key electrodes to realize a wearable intention recognition system has become a hotspot for scholars at home and abroad. In this paper, based on the rich-club phenomenon that exists in the process of intention generation, a phase lag index (PLI)-rich-club-based intention recognition method for P300 is proposed. The rich-club structure is a network consisting of electrodes that are highly connected with other electrodes in the process of P300 generation. To construct the rich-club network, this paper uses PLI to construct the brain functional network, calculates rich-club coefficients of the network in the range of k degrees, initially identifies rich-club nodes based on the feature of node degree, and then performs a descending order of betweenness centrality and identifies the nodes with larger betweenness centrality as the specific rich-club nodes, extracts the non-linear features and frequency domain features of Rich-club nodes, and finally uses support vector machine for classification. The experimental results show that the range of rich-club coefficients is smaller with intent compared to that without intent. Validation was performed on the BCI Competition III dataset by reducing the number of channels to 17 and 16 for subject A and subject B, with recognition quasi-departure rates of 96.93% and 94.93%, respectively, and on the BCI Competition II dataset by reducing the number of channels to 17 for subjects, with a recognition accuracy of 95.50%.
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Affiliation(s)
- Zhongmin Wang
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China
- Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China
- Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an 710121, Shaanxi, China
| | - Leihua Xiang
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China
| | - Rong Zhang
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China
- Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China
- Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an 710121, Shaanxi, China
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15
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Zhuo L, Jin Z, Xie K, Li S, Lin F, Zhang J, Li L. Identifying individual's distractor suppression using functional connectivity between anatomical large-scale brain regions. Neuroimage 2024; 289:120552. [PMID: 38387742 DOI: 10.1016/j.neuroimage.2024.120552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 02/19/2024] [Accepted: 02/20/2024] [Indexed: 02/24/2024] Open
Abstract
Distractor suppression (DS) is crucial in goal-oriented behaviors, referring to the ability to suppress irrelevant information. Current evidence points to the prefrontal cortex as an origin region of DS, while subcortical, occipital, and temporal regions are also implicated. The present study aimed to examine the contribution of communications between these brain regions to visual DS. To do it, we recruited two independent cohorts of participants for the study. One cohort participated in a visual search experiment where a salient distractor triggering distractor suppression to measure their DS and the other cohort filled out a Cognitive Failure Questionnaire to assess distractibility in daily life. Both cohorts collected resting-state functional magnetic resonance imaging (rs-fMRI) data to investigate function connectivity (FC) underlying DS. First, we generated predictive models of the DS measured in visual search task using resting-state functional connectivity between large anatomical regions. It turned out that the models could successfully predict individual's DS, indicated by a significant correlation between the actual and predicted DS (r = 0.32, p < 0.01). Importantly, Prefrontal-Temporal, Insula-Limbic and Parietal-Occipital connections contributed to the prediction model. Furthermore, the model could also predict individual's daily distractibility in the other independent cohort (r = -0.34, p < 0.05). Our findings showed the efficiency of the predictive models of distractor suppression encompassing connections between large anatomical regions and highlighted the importance of the communications between attention-related and visual information processing regions in distractor suppression. Current findings may potentially provide neurobiological markers of visual distractor suppression.
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Affiliation(s)
- Lei Zhuo
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Zhenlan Jin
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.
| | - Ke Xie
- Multimodal Imaging and Connectome Analysis Laboratory, McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC H3A 2B4, Canada
| | - Simeng Li
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Feng Lin
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Junjun Zhang
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China
| | - Ling Li
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.
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16
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Chinichian N, Lindner M, Yanchuk S, Schwalger T, Schöll E, Berner R. Modeling brain network flexibility in networks of coupled oscillators: a feasibility study. Sci Rep 2024; 14:5713. [PMID: 38459077 PMCID: PMC10923875 DOI: 10.1038/s41598-024-55753-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/27/2024] [Indexed: 03/10/2024] Open
Abstract
Modeling the functionality of the human brain is a major goal in neuroscience for which many powerful methodologies have been developed over the last decade. The impact of working memory and the associated brain regions on the brain dynamics is of particular interest due to their connection with many functions and malfunctions in the brain. In this context, the concept of brain flexibility has been developed for the characterization of brain functionality. We discuss emergence of brain flexibility that is commonly measured by the identification of changes in the cluster structure of co-active brain regions. We provide evidence that brain flexibility can be modeled by a system of coupled FitzHugh-Nagumo oscillators where the network structure is obtained from human brain Diffusion Tensor Imaging (DTI). Additionally, we propose a straightforward and computationally efficient alternative macroscopic measure, which is derived from the Pearson distance of functional brain matrices. This metric exhibits similarities to the established patterns of brain template flexibility that have been observed in prior investigations. Furthermore, we explore the significance of the brain's network structure and the strength of connections between network nodes or brain regions associated with working memory in the observation of patterns in networks flexibility. This work enriches our understanding of the interplay between the structure and function of dynamic brain networks and proposes a modeling strategy to study brain flexibility.
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Affiliation(s)
- Narges Chinichian
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany.
- Psychiatry Department, Charité-Universitätsmedizin, Berlin, Germany.
- Bernstein Center for Computational Neuroscience, Berlin, Germany.
| | - Michael Lindner
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
| | - Serhiy Yanchuk
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
- Institute of Mathematics, Humboldt Universität zu Berlin, Berlin, Germany
- School of Mathematical Sciences, University College Cork, Cork, Ireland
| | - Tilo Schwalger
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Institute of Mathematics, Technische Universität Berlin, Berlin, Germany
| | - Eckehard Schöll
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
- Potsdam Institute for Climate Impact Research, Potsdam, Germany
| | - Rico Berner
- Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
- Department of Physics, Humboldt Universität zu Berlin, Berlin, Germany
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17
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Jimenez-Marin A, Diez I, Erramuzpe A, Stramaglia S, Bonifazi P, Cortes JM. Open datasets and code for multi-scale relations on structure, function and neuro-genetics in the human brain. Sci Data 2024; 11:256. [PMID: 38424112 PMCID: PMC10904384 DOI: 10.1038/s41597-024-03060-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 02/12/2024] [Indexed: 03/02/2024] Open
Abstract
The human brain is an extremely complex network of structural and functional connections that operate at multiple spatial and temporal scales. Investigating the relationship between these multi-scale connections is critical to advancing our comprehension of brain function and disorders. However, accurately predicting structural connectivity from its functional counterpart remains a challenging pursuit. One of the major impediments is the lack of public repositories that integrate structural and functional networks at diverse resolutions, in conjunction with modular transcriptomic profiles, which are essential for comprehensive biological interpretation. To mitigate this limitation, our contribution encompasses the provision of an open-access dataset consisting of derivative matrices of functional and structural connectivity across multiple scales, accompanied by code that facilitates the investigation of their interrelations. We also provide additional resources focused on neuro-genetic associations of module-level network metrics, which present promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.
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Affiliation(s)
- Antonio Jimenez-Marin
- Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain
- Biomedical Research Doctorate Program, University of the Basque Country (UPV/EHU), Leioa, Spain
| | - Ibai Diez
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, United States of America
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, United States of America
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, United States of America
| | - Asier Erramuzpe
- Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain
- IKERBASQUE Basque Foundation for Science, Bilbao, Spain
| | - Sebastiano Stramaglia
- Dipartamento Interateneo di Fisica, Universita Degli Studi di Bari Aldo Moro, INFN, Bari, Italy
| | - Paolo Bonifazi
- Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain
- IKERBASQUE Basque Foundation for Science, Bilbao, Spain
| | - Jesus M Cortes
- Computational Neuroimaging Lab, Biobizkaia HRI, Barakaldo, Spain.
- IKERBASQUE Basque Foundation for Science, Bilbao, Spain.
- Department of Cell Biology and Histology, University of the Basque Country (UPV/EHU), Leioa, Spain.
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18
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Myers MJ, Labonte AK, Gordon EM, Laumann TO, Tu JC, Wheelock MD, Nielsen AN, Schwarzlose RF, Camacho MC, Alexopoulos D, Warner BB, Raghuraman N, Luby JL, Barch DM, Fair DA, Petersen SE, Rogers CE, Smyser CD, Sylvester CM. Functional parcellation of the neonatal cortical surface. Cereb Cortex 2024; 34:bhae047. [PMID: 38372292 PMCID: PMC10875653 DOI: 10.1093/cercor/bhae047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 01/23/2024] [Accepted: 01/26/2024] [Indexed: 02/20/2024] Open
Abstract
The cerebral cortex is organized into distinct but interconnected cortical areas, which can be defined by abrupt differences in patterns of resting state functional connectivity (FC) across the cortical surface. Such parcellations of the cortex have been derived in adults and older infants, but there is no widely used surface parcellation available for the neonatal brain. Here, we first demonstrate that existing parcellations, including surface-based parcels derived from older samples as well as volume-based neonatal parcels, are a poor fit for neonatal surface data. We next derive a set of 283 cortical surface parcels from a sample of n = 261 neonates. These parcels have highly homogenous FC patterns and are validated using three external neonatal datasets. The Infomap algorithm is used to assign functional network identities to each parcel, and derived networks are consistent with prior work in neonates. The proposed parcellation may represent neonatal cortical areas and provides a powerful tool for neonatal neuroimaging studies.
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Affiliation(s)
- Michael J Myers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Alyssa K Labonte
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Evan M Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Timothy O Laumann
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Jiaxin C Tu
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Muriah D Wheelock
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Ashley N Nielsen
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Rebecca F Schwarzlose
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - M Catalina Camacho
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Dimitrios Alexopoulos
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Barbara B Warner
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Nandini Raghuraman
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Joan L Luby
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Deanna M Barch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States
- Institute of Child Development, University of Minnesota, Minneapolis, MN 55455, United States
- Department of Pediatrics, University of Minnesota, Minneapolis, MN 55454, United States
| | - Steven E Petersen
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
| | - Christopher D Smyser
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, United States
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, United States
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, United States
- Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, MO 63110, United States
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19
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Koppang H, Wenstøp S, Pineda JA. Neural perspectives on morality due to beguiling mechanisms. Front Psychol 2024; 14:1151155. [PMID: 38362524 PMCID: PMC10867578 DOI: 10.3389/fpsyg.2023.1151155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 12/26/2023] [Indexed: 02/17/2024] Open
Abstract
We consider deception an example of behavior that challenges traditional explanations of moral behavior. Beguiling mechanisms, by which we mean deceptiveness with charming seduction for diversion, subtly influence moral sensitivity and judgment in moral dilemma situations. The duality of beguiling mechanisms is important to grasp, including how they relate to the ambiguity of situations. Further, we view moral behavior as quasi-adaptive, affectively based, and reliant on the processes of social cognition, arising out of a set of domain-general primitive predispositions that aggregate to produce moral "mindsets" and increasingly complex moral actions. Building on recent theoretical developments, contend that morality involves a complex heterarchical-hierarchical neurological architecture, where activity is dynamically and contextually dependent, as well as dependent on evolved brain structures and early life year socialization. We contribute to conceptualizing moral behavior from an integrated modern neural perspective. This provides a balance between moral decisions as situational, emotional, and genetically completed non-conscious processes, and the more traditional view of conscious reasoning. Beguiling mechanisms illustrate an integrative model of morality, consistent with emerging insights from affective and cognitive neuroscience.
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Affiliation(s)
| | - Søren Wenstøp
- Department of Economics, Innovation and Society, Østfold University College, Halden, Østfold, Norway
| | - Jaime A. Pineda
- University of California, San Diego, San Diego, CA, United States
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20
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Jun S, Malone SM, Iacono WG, Harper J, Wilson S, Sadaghiani S. Cognitive abilities are associated with rapid dynamics of electrophysiological connectome states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.15.575736. [PMID: 38293067 PMCID: PMC10827041 DOI: 10.1101/2024.01.15.575736] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, hold significant implications for cognition. However, connectome dynamics at fast (> 1Hz) timescales highly relevant to cognition are poorly understood due to the dominance of inherently slow fMRI in connectome studies. Here, we investigated the behavioral significance of rapid electrophysiological connectome dynamics using source-localized EEG connectomes during resting-state (N=926, 473 females). We focused on dynamic connectome features pertinent to individual differences, specifically those with established heritability: Fractional Occupancy (i.e., the overall duration spent in each recurrent connectome state) in beta and gamma bands, and Transition Probability (i.e., the frequency of state switches) in theta, alpha, beta, and gamma bands. Canonical correlation analysis found a significant relationship between the heritable phenotypes of sub-second connectome dynamics and cognition. Specifically, principal components of Transition Probabilities in alpha (followed by theta and gamma bands) and a cognitive factor representing visuospatial processing (followed by verbal and auditory working memory) most notably contributed to the relationship. We conclude that the specific order in which rapid connectome states are sequenced shapes individuals' cognitive abilities and traits. Such sub-second connectome dynamics may inform about behavioral function and dysfunction and serve as endophenotypes for cognitive abilities.
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Affiliation(s)
- Suhnyoung Jun
- Psychology Department, University of Illinois at Urbana-Champaign
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign
| | - Stephen M Malone
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota
| | - William G Iacono
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota
| | - Jeremy Harper
- Department of Psychology, University of Minnesota, Minneapolis, Minnesota
| | - Sylia Wilson
- Institute of Child Development, University of Minnesota, Twin Cities, USA
| | - Sepideh Sadaghiani
- Psychology Department, University of Illinois at Urbana-Champaign
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign
- Neuroscience Program, University of Illinois at Urbana-Champaign
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21
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Zhang Y, Han X, Ge X, Xu T, Wang Y, Mu J, Liu F. Modular brain network in volitional eyes closing: enhanced integration with a marked impact on hubs. Cereb Cortex 2024; 34:bhad464. [PMID: 38044477 DOI: 10.1093/cercor/bhad464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 11/12/2023] [Accepted: 11/13/2023] [Indexed: 12/05/2023] Open
Abstract
Volitional eyes closing would shift brain's information processing modes from the "exteroceptive" to "interoceptive" state. This transition induced by the eyes closing is underpinned by a large-scale reconfiguration of brain network, which is still not fully comprehended. Here, we investigated the eyes-closing-relevant network reconfiguration by examining the functional integration among intrinsic modules. Our investigation utilized a publicly available dataset with 48 subjects being scanned in both eyes closed and eyes open conditions. It was found that the modular integration was significantly enhanced during the eyes closing, including lower modularity index, higher participation coefficient, less provincial hubs, and more connector hubs. Moreover, the eyes-closing-enhanced integration was particularly noticeable in the hubs of network, mainly located in the default-mode network. Finally, the hub-dominant modular enhancement was positively correlated to the eyes-closing-reduced entropy of BOLD signal, suggesting a close connection to the diminished consciousness of individuals. Collectively, our findings strongly suggested that the enhanced modular integration with substantially reorganized hubs characterized the large-scale cortical underpinning of the volitional eyes closing.
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Affiliation(s)
- Yi Zhang
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou 310027, China
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Xiao Han
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Xuelian Ge
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Tianyong Xu
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Yanjie Wang
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Jiali Mu
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
| | - Fan Liu
- Bio-X Laboratory, Department of Physics, Zhejiang University, Hangzhou 310027, China
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22
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Liu X, Wang Z, Liu S, Gong L, Sosa PAV, Becker B, Jung TP, Dai XJ, Wan F. Activation network improves spatiotemporal modelling of human brain communication processes. Neuroimage 2024; 285:120472. [PMID: 38007187 DOI: 10.1016/j.neuroimage.2023.120472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 11/08/2023] [Accepted: 11/22/2023] [Indexed: 11/27/2023] Open
Abstract
Dynamic functional networks (DFN) have considerably advanced modelling of the brain communication processes. The prevailing implementation capitalizes on the system and network-level correlations between time series. However, this approach does not account for the continuous impact of non-dynamic dependencies within the statistical correlation, resulting in relatively stable connectivity patterns of DFN over time with limited sensitivity for communication dynamic between brain regions. Here, we propose an activation network framework based on the activity of functional connectivity (AFC) to extract new types of connectivity patterns during brain communication process. The AFC captures potential time-specific fluctuations associated with the brain communication processes by eliminating the non-dynamic dependency of the statistical correlation. In a simulation study, the positive correlation (r=0.966,p<0.001) between the extracted dynamic dependencies and the simulated "ground truth" validates the method's dynamic detection capability. Applying to autism spectrum disorders (ASD) and COVID-19 datasets, the proposed activation network extracts richer topological reorganization information, which is largely invisible to the DFN. Detailed, the activation network exhibits significant inter-regional connections between function-specific subnetworks and reconfigures more efficiently in the temporal dimension. Furthermore, the DFN fails to distinguish between patients and healthy controls. However, the proposed method reveals a significant decrease (p<0.05) in brain information processing abilities in patients. Finally, combining two types of networks successfully classifies ASD (83.636 % ± 11.969 %,mean±std) and COVID-19 (67.333 % ± 5.398 %). These findings suggest the proposed method could be a potential analytic framework for elucidating the neural mechanism of brain dynamics.
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Affiliation(s)
- Xucheng Liu
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China; Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau, 999078, China
| | - Ze Wang
- Macao Centre for Mathematical Sciences, and the Respiratory Disease AI Laboratory on Epidemic Intelligence and Medical Big Data Instrument Applications, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, 999078, China
| | - Shun Liu
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China; Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau, 999078, China
| | - Lianggeng Gong
- Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, China
| | - Pedro A Valdes Sosa
- The Clinical Hospital of Chengdu Brain Sciences Institute. University of Electronic Sciences and Technology of China, Chengdu, 611731, China; Cuban Neuroscience Center, La Habana 10200, Cuba
| | - Benjamin Becker
- State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong 999077, China; Department of Psychology, The University of Hong Kong, Hong Kong 999077, China
| | - Tzyy-Ping Jung
- Department of Bioengineering, University of California at San Diego, La Jolla 92092, United States; Swartz Center for Computational Neuroscience, Institute for Neural Computation, University of California at San Diego, La Jolla 92093, United States
| | - Xi-Jian Dai
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China; Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau, 999078, China; Department of Radiology, The Second Affiliated Hospital of Nanchang University, Nanchang, 330006, China.
| | - Feng Wan
- Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China; Centre for Cognitive and Brain Sciences, Institute of Collaborative Innovation, University of Macau, Macau, 999078, China.
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23
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Dai R, Huang Z, Larkin TE, Tarnal V, Picton P, Vlisides PE, Janke E, McKinney A, Hudetz AG, Harris RE, Mashour GA. Psychedelic concentrations of nitrous oxide reduce functional differentiation in frontoparietal and somatomotor cortical networks. Commun Biol 2023; 6:1284. [PMID: 38114805 PMCID: PMC10730842 DOI: 10.1038/s42003-023-05678-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 12/05/2023] [Indexed: 12/21/2023] Open
Abstract
Despite the longstanding use of nitrous oxide and descriptions of its psychological effects more than a century ago, there is a paucity of neurobiological investigation of associated psychedelic experiences. We measure the brain's functional geometry (through analysis of cortical gradients) and temporal dynamics (through analysis of co-activation patterns) using human resting-state functional magnetic resonance imaging data acquired before and during administration of 35% nitrous oxide. Both analyses demonstrate that nitrous oxide reduces functional differentiation in frontoparietal and somatomotor networks. Importantly, the subjective psychedelic experience induced by nitrous oxide is inversely correlated with the degree of functional differentiation. Thus, like classical psychedelics acting on serotonin receptors, nitrous oxide flattens the functional geometry of the cortex and disrupts temporal dynamics in association with psychoactive effects.
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Affiliation(s)
- Rui Dai
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Zirui Huang
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, 48109, USA.
| | - Tony E Larkin
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Chronic Pain and Fatigue Research Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Vijay Tarnal
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Paul Picton
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Phillip E Vlisides
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Ellen Janke
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Amy McKinney
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Anthony G Hudetz
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Richard E Harris
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, 48109, USA
- Chronic Pain and Fatigue Research Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - George A Mashour
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Center for Consciousness Science, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Michigan Psychedelic Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
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24
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Simpson SL, Shappell HM, Bahrami M. Statistical Brain Network Analysis. ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION 2023; 11:505-531. [PMID: 39184922 PMCID: PMC11343573 DOI: 10.1146/annurev-statistics-040522-020722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
The recent fusion of network science and neuroscience has catalyzed a paradigm shift in how we study the brain and led to the field of brain network analysis. Brain network analyses hold great potential in helping us understand normal and abnormal brain function by providing profound clinical insight into links between system-level properties and health and behavioral outcomes. Nonetheless, methods for statistically analyzing networks at the group and individual levels have lagged behind. We have attempted to address this need by developing three complementary statistical frameworks-a mixed modeling framework, a distance regression framework, and a hidden semi-Markov modeling framework. These tools serve as synergistic fusions of statistical approaches with network science methods, providing needed analytic foundations for whole-brain network data. Here we delineate these approaches, briefly survey related tools, and discuss potential future avenues of research. We hope this review catalyzes further statistical interest and methodological development in the field.
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Affiliation(s)
- Sean L Simpson
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
- Laboratory for Complex Brain Networks, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Heather M Shappell
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
- Laboratory for Complex Brain Networks, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Mohsen Bahrami
- Laboratory for Complex Brain Networks, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
- Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
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25
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Zhang Z, Chan MY, Han L, Carreno CA, Winter-Nelson E, Wig GS. Dissociable Effects of Alzheimer's Disease-Related Cognitive Dysfunction and Aging on Functional Brain Network Segregation. J Neurosci 2023; 43:7879-7892. [PMID: 37714710 PMCID: PMC10648516 DOI: 10.1523/jneurosci.0579-23.2023] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 09/03/2023] [Accepted: 09/11/2023] [Indexed: 09/17/2023] Open
Abstract
Alzheimer's disease (AD) is associated with changes in large-scale functional brain network organization. Individuals with AD exhibit less segregated resting-state brain networks compared with individuals without dementia. However, declines in brain network segregation are also evident as adult individuals grow older. Determining whether these observations reflect unique or overlapping alterations on the functional connectome of the brain is essential for understanding the impact of AD on network organization and incorporating measures of functional brain network organization toward AD characterization. Relationships between AD dementia severity and participant's age on resting-state brain system segregation were examined in 326 cognitively healthy and 275 cognitively impaired human individuals recruited through the Alzheimer's Disease Neuroimaging Initiative (ADNI) (N = 601; age range, 55-96 years; 320 females). Greater dementia severity and increasing age were independently associated with lower brain system segregation. Further, dementia versus age relationships with brain network organization varied according to the processing roles of brain systems and types of network interactions. Aging was associated with alterations to association systems, primarily among within-system relationships. Conversely, dementia severity was associated with alterations that included both association systems and sensory-motor systems and was most prominent among cross-system interactions. Dementia-related network alterations were evident regardless of the presence of cortical amyloid burden, revealing that the measures of functional network organization are unique from this marker of AD-related pathology. Collectively, these observations demonstrate the specific and widespread alterations in the topological organization of large-scale brain networks that accompany AD and highlight functionally dissociable brain network vulnerabilities associated with AD-related cognitive dysfunction versus aging.SIGNIFICANCE STATEMENT Alzheimer's disease (AD)-associated cognitive dysfunction is hypothesized to be a consequence of brain network damage. It is unclear exactly how brain network alterations vary with dementia severity and whether they are distinct from alterations associated with aging. We evaluated functional brain network organization measured at rest among individuals who varied in age and dementia status. AD and aging exerted dissociable impacts on the brain's functional connectome. AD-associated brain network alterations were widespread and involved systems that subserve not only higher-order cognitive operations, but also sensory and motor operations. Notably, AD-related network alterations were independent of amyloid pathology. The research furthers our understanding of AD-related brain dysfunction and motivates refining existing frameworks of dementia characterization with measures of functional network organization.
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Affiliation(s)
- Ziwei Zhang
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas 75235
| | - Micaela Y Chan
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas 75235
| | - Liang Han
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas 75235
| | - Claudia A Carreno
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas 75235
| | - Ezra Winter-Nelson
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas 75235
| | - Gagan S Wig
- Center for Vital Longevity and School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas 75235
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas 75390
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26
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Myers MJ, Labonte AK, Gordon EM, Laumann TO, Tu JC, Wheelock MD, Nielsen AN, Schwarzlose R, Camacho MC, Warner BB, Raghuraman N, Luby JL, Barch DM, Fair DA, Petersen SE, Rogers CE, Smyser CD, Sylvester CM. Functional parcellation of the neonatal brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.10.566629. [PMID: 37986902 PMCID: PMC10659431 DOI: 10.1101/2023.11.10.566629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
The cerebral cortex is organized into distinct but interconnected cortical areas, which can be defined by abrupt differences in patterns of resting state functional connectivity (FC) across the cortical surface. Such parcellations of the cortex have been derived in adults and older infants, but there is no widely used surface parcellation available for the neonatal brain. Here, we first demonstrate that adult- and older infant-derived parcels are a poor fit with neonatal data, emphasizing the need for neonatal-specific parcels. We next derive a set of 283 cortical surface parcels from a sample of n=261 neonates. These parcels have highly homogenous FC patterns and are validated using three external neonatal datasets. The Infomap algorithm is used to assign functional network identities to each parcel, and derived networks are consistent with prior work in neonates. The proposed parcellation may represent neonatal cortical areas and provides a powerful tool for neonatal neuroimaging studies.
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Affiliation(s)
- Michael J Myers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Alyssa K Labonte
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, MO USA
| | - Evan M Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
| | - Timothy O Laumann
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Jiaxin Cindy Tu
- Neurosciences Graduate Program, Washington University in St. Louis, St. Louis, MO USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
| | - Muriah D Wheelock
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
| | - Ashley N Nielsen
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Rebecca Schwarzlose
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - M Catalina Camacho
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Barbara B Warner
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
| | - Nandini Raghuraman
- Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO, USA
| | - Joan L Luby
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Deanna M Barch
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Steven E Petersen
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Cynthia E Rogers
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Christopher D Smyser
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Chad M Sylvester
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO USA
- Taylor Family Institute for Innovative Psychiatric Research, Washington University School of Medicine, St. Louis, MO, USA
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27
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Betzel RF, Faskowitz J, Sporns O. Living on the edge: network neuroscience beyond nodes. Trends Cogn Sci 2023; 27:1068-1084. [PMID: 37716895 PMCID: PMC10592364 DOI: 10.1016/j.tics.2023.08.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/14/2023] [Accepted: 08/10/2023] [Indexed: 09/18/2023]
Abstract
Network neuroscience has emphasized the connectional properties of neural elements - cells, populations, and regions. This has come at the expense of the anatomical and functional connections that link these elements to one another. A new perspective - namely one that emphasizes 'edges' - may prove fruitful in addressing outstanding questions in network neuroscience. We highlight one recently proposed 'edge-centric' method and review its current applications, merits, and limitations. We also seek to establish conceptual and mathematical links between this method and previously proposed approaches in the network science and neuroimaging literature. We conclude by presenting several avenues for future work to extend and refine existing edge-centric analysis.
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Affiliation(s)
- Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA; Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA; Network Science Institute, Indiana University, Bloomington, IN 47405, USA.
| | - Joshua Faskowitz
- Section on Functional Imaging Methods, National Institute of Mental Health, Bethesda, MD, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA; Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA; Network Science Institute, Indiana University, Bloomington, IN 47405, USA
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28
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Feitosa JA, Casseb RF, Camargo A, Brandao AF, Li LM, Castellano G. Graph analysis of cortical reorganization after virtual reality-based rehabilitation following stroke: a pilot randomized study. Front Neurol 2023; 14:1241639. [PMID: 37869147 PMCID: PMC10587561 DOI: 10.3389/fneur.2023.1241639] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 09/22/2023] [Indexed: 10/24/2023] Open
Abstract
Introduction Stroke is the leading cause of functional disability worldwide. With the increase of the global population, motor rehabilitation of stroke survivors is of ever-increasing importance. In the last decade, virtual reality (VR) technologies for rehabilitation have been extensively studied, to be used instead of or together with conventional treatments such as physiotherapy or occupational therapy. The aim of this work was to evaluate the GestureCollection VR-based rehabilitation tool in terms of the brain changes and clinical outcomes of the patients. Methods Two groups of chronic patients underwent a rehabilitation treatment with (experimental) or without (control) complementation with GestureCollection. Functional magnetic resonance imaging exams and clinical assessments were performed before and after the treatment. A functional connectivity graph-based analysis was used to assess differences between the connections and in the network parameters strength and clustering coefficient. Results Patients in both groups showed improvement in clinical scales, but there were more increases in functional connectivity in the experimental group than in the control group. Discussion The experimental group presented changes in the connections between the frontoparietal and the somatomotor networks, associative cerebellum and basal ganglia, which are regions associated with reward-based motor learning. On the other hand, the control group also had results in the somatomotor network, in its ipsilateral connections with the thalamus and with the motor cerebellum, which are regions more related to a purely mechanical activity. Thus, the use of the GestureCollection system was successfully shown to promote neuroplasticity in several motor-related areas.
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Affiliation(s)
- Jamille Almeida Feitosa
- Gleb Wataghin Institute of Physics, University of Campinas – UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology – BRAINN, Campinas, Brazil
| | - Raphael Fernandes Casseb
- Brazilian Institute of Neuroscience and Neurotechnology – BRAINN, Campinas, Brazil
- Neuroimaging Laboratory, Department of Neurology, University of Campinas – UNICAMP, Campinas, Brazil
| | - Alline Camargo
- Neuroimaging Laboratory, Department of Neurology, University of Campinas – UNICAMP, Campinas, Brazil
| | - Alexandre Fonseca Brandao
- Gleb Wataghin Institute of Physics, University of Campinas – UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology – BRAINN, Campinas, Brazil
| | - Li Min Li
- Brazilian Institute of Neuroscience and Neurotechnology – BRAINN, Campinas, Brazil
- Neuroimaging Laboratory, Department of Neurology, University of Campinas – UNICAMP, Campinas, Brazil
| | - Gabriela Castellano
- Gleb Wataghin Institute of Physics, University of Campinas – UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology – BRAINN, Campinas, Brazil
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29
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Rajesh A, Seider NA, Newbold DJ, Adeyemo B, Marek S, Greene DJ, Snyder AZ, Shimony JS, Laumann TO, Dosenbach NUF, Gordon EM. Structure-Function Coupling in Highly Sampled Individual Brains. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.04.560909. [PMID: 37873167 PMCID: PMC10592963 DOI: 10.1101/2023.10.04.560909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Structural connections (SC) between distant regions of the brain support synchronized function known as functional connectivity (FC) and give rise to the large-scale brain networks that enable cognition and behavior. Understanding how SC enables FC is important to understand how injuries to structural connections may alter brain function and cognition. Previous work evaluating whole-brain SC-FC relationships showed that SC explained FC well in unimodal visual and motor areas, but only weakly in association areas, suggesting a unimodal-heteromodal gradient organization of SC-FC coupling. However, this work was conducted in group-averaged SC/FC data. Thus, it could not account for inter-individual variability in the locations of cortical areas and white matter tracts. We evaluated the correspondence of SC and FC within three highly sampled healthy participants. For each participant, we collected 78 minutes of diffusion-weighted MRI for SC and 360 minutes of resting state fMRI for FC. We found that FC was best explained by SC in visual and motor systems, as well as in anterior and posterior cingulate regions. A unimodal-to-heteromodal gradient could not fully explain SC-FC coupling. We conclude that the SC-FC coupling of the anterior-posterior cingulate circuit is more similar to unimodal areas than to heteromodal areas. SIGNIFICANCE STATEMENT Structural connections between distant regions of the human brain support networked function that enables cognition and behavior. Improving our understanding of how structure enables function could allow better insight into how brain disconnection injuries impair brain function.Previous work using neuroimaging suggested that structure-function relationships vary systematically across the brain, with structure better explaining function in basic visual/motor areas than in higher-order areas. However, this work was conducted in group-averaged data, which may obscure details of individual-specific structure-function relationships.Using individual-specific densely sampled neuroimaging data, we found that in addition to visual/motor regions, structure strongly predicts function in specific circuits of the higher-order cingulate gyrus. The cingulate's structure-function relationship suggests that its organization may be unique among higher-order cortical regions.
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Milisav F, Bazinet V, Iturria-Medina Y, Misic B. Resolving inter-regional communication capacity in the human connectome. Netw Neurosci 2023; 7:1051-1079. [PMID: 37781139 PMCID: PMC10473316 DOI: 10.1162/netn_a_00318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 04/03/2023] [Indexed: 10/03/2023] Open
Abstract
Applications of graph theory to the connectome have inspired several models of how neural signaling unfolds atop its structure. Analytic measures derived from these communication models have mainly been used to extract global characteristics of brain networks, obscuring potentially informative inter-regional relationships. Here we develop a simple standardization method to investigate polysynaptic communication pathways between pairs of cortical regions. This procedure allows us to determine which pairs of nodes are topologically closer and which are further than expected on the basis of their degree. We find that communication pathways delineate canonical functional systems. Relating nodal communication capacity to meta-analytic probabilistic patterns of functional specialization, we also show that areas that are most closely integrated within the network are associated with higher order cognitive functions. We find that these regions' proclivity towards functional integration could naturally arise from the brain's anatomical configuration through evenly distributed connections among multiple specialized communities. Throughout, we consider two increasingly constrained null models to disentangle the effects of the network's topology from those passively endowed by spatial embedding. Altogether, the present findings uncover relationships between polysynaptic communication pathways and the brain's functional organization across multiple topological levels of analysis and demonstrate that network integration facilitates cognitive integration.
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Affiliation(s)
- Filip Milisav
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Vincent Bazinet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Yasser Iturria-Medina
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
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de Pasquale F, Chiacchiaretta P, Pavone L, Sparano A, Capotosto P, Grillea G, Committeri G, Baldassarre A. Brain Topological Reorganization Associated with Visual Neglect After Stroke. Brain Connect 2023; 13:473-486. [PMID: 34269620 PMCID: PMC10618825 DOI: 10.1089/brain.2020.0969] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background/Purpose: To identify brain hubs that are behaviorally relevant for neglect after stroke as well as to characterize their functional architecture of communication. Methods: Twenty acute right hemisphere damaged patients underwent neuropsychological and resting-state functional magnetic resonance imaging sessions. Spatial neglect was assessed by means of the Center of Cancellation on the Bells Cancellation Test. For each patient, resting-state functional connectivity matrices were derived by adopting a brain parcellation scheme consisting of 153 nodes. For every node, we extracted its betweenness centrality (BC) defined as the portion of all shortest paths in the connectome involving such node. Then, neglect hubs were identified as those regions showing a high correlation between their BC and neglect scores. Results: A first set of neglect hubs was identified in multiple systems including dorsal attention and ventral attention, default mode, and frontoparietal executive-control networks within the damaged hemisphere as well as in the posterior and anterior cingulate cortex. Such cortical regions exhibited a loss of BC and increased (i.e., less efficient) weighted shortest path length (WSPL) related to severe neglect. Conversely, a second group of neglect hubs found in visual and motor networks, in the undamaged hemisphere, exhibited a pathological increase of BC and reduction of WSPL associated with severe neglect. Conclusion: The topological reorganization of the brain in neglect patients might reflect a maladaptive shift in processing spatial information from higher level associative-control systems to lower level visual and sensory-motor processing areas after a right hemisphere lesion.
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Affiliation(s)
| | - Piero Chiacchiaretta
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | | | | | - Paolo Capotosto
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | | | - Giorgia Committeri
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Antonello Baldassarre
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy
- IRCCS NEUROMED, Pozzilli, Italy
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Yang CC, Totzek JF, Lepage M, Lavigne KM. Sex differences in cognition and structural covariance-based morphometric connectivity: evidence from 28,000+ UK Biobank participants. Cereb Cortex 2023; 33:10341-10354. [PMID: 37557917 DOI: 10.1093/cercor/bhad286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 07/06/2023] [Accepted: 07/08/2023] [Indexed: 08/11/2023] Open
Abstract
There is robust evidence for sex differences in domain-specific cognition, where females typically show an advantage for verbal memory, whereas males tend to perform better in spatial memory. Sex differences in brain connectivity are well documented and may provide insight into these differences. In this study, we examined sex differences in cognition and structural covariance, as an index of morphometric connectivity, of a large healthy sample (n = 28,821) from the UK Biobank. Using T1-weighted magnetic resonance imaging scans and regional cortical thickness values, we applied jackknife bias estimation and graph theory to obtain subject-specific measures of structural covariance, hypothesizing that sex-related differences in brain network global efficiency, or overall covariance, would underlie cognitive differences. As predicted, females demonstrated better verbal memory and males showed a spatial memory advantage. Females also demonstrated faster processing speed, with no observed sex difference in executive functioning. Males showed higher global efficiency, as well as higher regional covariance (nodal strengths) in both hemispheres relative to females. Furthermore, higher global efficiency in males mediated sex differences in verbal memory and processing speed. Findings contribute to an improved understanding of how biological sex and differences in cognition are related to morphometric connectivity as derived from graph-theoretic methods.
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Affiliation(s)
- Crystal C Yang
- Department of Psychology, McGill University, Montréal, QC H4H 1R3, Canada
| | - Jana F Totzek
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, 6211 LK, Netherlands
- Department of Psychiatry, McGill University, Montréal, QC H4H 1R3, Canada
- Douglas Research Centre, Montréal, QC, H4H 1R3, Canada
| | - Martin Lepage
- Department of Psychology, McGill University, Montréal, QC H4H 1R3, Canada
- Department of Psychiatry, McGill University, Montréal, QC H4H 1R3, Canada
- Douglas Research Centre, Montréal, QC, H4H 1R3, Canada
| | - Katie M Lavigne
- Department of Psychiatry, McGill University, Montréal, QC H4H 1R3, Canada
- Douglas Research Centre, Montréal, QC, H4H 1R3, Canada
- Montreal Neurological Institute-Hospital, McGill University, Montréal, QC H4H 1R3, Canada
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Han Z, Liu T, Shi Z, Zhang J, Suo D, Wang L, Chen D, Wu J, Yan T. Investigating the heterogeneity within the somatosensory-motor network and its relationship with the attention and default systems. PNAS NEXUS 2023; 2:pgad276. [PMID: 37693210 PMCID: PMC10485902 DOI: 10.1093/pnasnexus/pgad276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 06/23/2023] [Accepted: 08/14/2023] [Indexed: 09/12/2023]
Abstract
The somatosensory-motor network (SMN) not only plays an important role in primary somatosensory and motor processing but is also central to many disorders. However, the SMN heterogeneity related to higher-order systems still remains unclear. Here, we investigated SMN heterogeneity from multiple perspectives. To characterize the SMN substructures in more detail, we used ultra-high-field functional MRI to delineate a finer-grained cortical parcellation containing 430 parcels that is more homogenous than the state-of-the-art parcellation. We personalized the new parcellation to account for individual differences and identified multiscale individual-specific brain structures. We found that the SMN subnetworks showed distinct resting-state functional connectivity (RSFC) patterns. The Hand subnetwork was central within the SMN and exhibited stronger RSFC with the attention systems than the other subnetworks, whereas the Tongue subnetwork exhibited stronger RSFC with the default systems. This two-fold differentiation was observed in the temporal ordering patterns within the SMN. Furthermore, we characterized how the distinct attention and default streams were carried forward into the functions of the SMN using dynamic causal modeling and identified two behavioral domains associated with this SMN fractionation using meta-analytic tools. Overall, our findings provided important insights into the heterogeneous SMN organization at the system level and suggested that the Hand subnetwork may be preferentially involved in exogenous processes, whereas the Tongue subnetwork may be more important in endogenous processes.
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Affiliation(s)
- Ziteng Han
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Tiantian Liu
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Zhongyan Shi
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Jian Zhang
- School of Mechatronical Engineering, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Dingjie Suo
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Li Wang
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Duanduan Chen
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Jinglong Wu
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
| | - Tianyi Yan
- School of Medical Technology, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China
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Mercante B, Enrico P, Deriu F. Cognitive Functions following Trigeminal Neuromodulation. Biomedicines 2023; 11:2392. [PMID: 37760833 PMCID: PMC10525298 DOI: 10.3390/biomedicines11092392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 08/13/2023] [Accepted: 08/25/2023] [Indexed: 09/29/2023] Open
Abstract
Vast scientific effort in recent years have been focused on the search for effective and safe treatments for cognitive decline. In this regard, non-invasive neuromodulation has gained increasing attention for its reported effectiveness in promoting the recovery of multiple cognitive domains after central nervous system damage. In this short review, we discuss the available evidence supporting a possible cognitive effect of trigeminal nerve stimulation (TNS). In particular, we ask that, while TNS has been widely and successfully used in the treatment of various neuropsychiatric conditions, as far as research in the cognitive field is concerned, where does TNS stand? The trigeminal nerve is the largest cranial nerve, conveying the sensory information from the face to the trigeminal sensory nuclei, and from there to the thalamus and up to the somatosensory cortex. On these bases, a bottom-up mechanism has been proposed, positing that TNS-induced modulation of the brainstem noradrenergic system may affect the function of the brain networks involved in cognition. Nevertheless, despite the promising theories, to date, the use of TNS for cognitive empowering and/or cognitive decline treatment has several challenges ahead of it, mainly due to little uniformity of the stimulation protocols. However, as the field continues to grow, standardization of practice will allow for data comparisons across studies, leading to optimized protocols targeting specific brain circuitries, which may, in turn, influence cognition in a designed manner.
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Affiliation(s)
- Beniamina Mercante
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy; (B.M.); (P.E.)
| | - Paolo Enrico
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy; (B.M.); (P.E.)
| | - Franca Deriu
- Department of Biomedical Sciences, University of Sassari, 07100 Sassari, Italy; (B.M.); (P.E.)
- AOU Sassari, Unit of Endocrinology, Nutritional and Metabolic Disorders, 07100 Sassari, Italy
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35
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Fan JM, Lee AM, Sellers KK, Woodworth K, Makhoul GS, Liu TX, Henderson C, Astudillo Maya DA, Martinez R, Zamanian H, Speidel BA, Khambhati AN, Rao VR, Sugrue LP, Scangos KW, Chang EF, Krystal AD. Intracranial electrical stimulation of corticolimbic sites modulates arousal in humans. Brain Stimul 2023; 16:1072-1082. [PMID: 37385540 PMCID: PMC10634663 DOI: 10.1016/j.brs.2023.06.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/23/2023] [Accepted: 06/26/2023] [Indexed: 07/01/2023] Open
Abstract
BACKGROUND Humans routinely shift their sleepiness and wakefulness levels in response to emotional factors. The diversity of emotional factors that modulates sleep-wake levels suggests that the ascending arousal network may be intimately linked with networks that mediate mood. Indeed, while animal studies have identified select limbic structures that play a role in sleep-wake regulation, the breadth of corticolimbic structures that directly modulates arousal in humans remains unknown. OBJECTIVE We investigated whether select regional activation of the corticolimbic network through direct electrical stimulation can modulate sleep-wake levels in humans, as measured by subjective experience and behavior. METHODS We performed intensive inpatient stimulation mapping in two human participants with treatment resistant depression, who underwent intracranial implantation with multi-site, bilateral depth electrodes. Stimulation responses of sleep-wake levels were measured by subjective surveys (i.e. Stanford Sleepiness Scale and visual-analog scale of energy) and a behavioral arousal score. Biomarker analyses of sleep-wake levels were performed by assessing spectral power features of resting-state electrophysiology. RESULTS Our findings demonstrated three regions whereby direct stimulation modulated arousal, including the orbitofrontal cortex (OFC), subgenual cingulate (SGC), and, most robustly, ventral capsule (VC). Modulation of sleep-wake levels was frequency-specific: 100Hz OFC, SGC, and VC stimulation promoted wakefulness, whereas 1Hz OFC stimulation increased sleepiness. Sleep-wake levels were correlated with gamma activity across broad brain regions. CONCLUSIONS Our findings provide evidence for the overlapping circuitry between arousal and mood regulation in humans. Furthermore, our findings open the door to new treatment targets and the consideration of therapeutic neurostimulation for sleep-wake disorders.
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Affiliation(s)
- Joline M Fan
- Department of Neurology, University of California, San Francisco, CA, USA; Weill Institute for Neurosciences, University of California, San Francisco, CA, USA.
| | - A Moses Lee
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Kristin K Sellers
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA; Department of Neurosurgery, University of California, San Francisco, CA, USA
| | - Kai Woodworth
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Ghassan S Makhoul
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Tony X Liu
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Catherine Henderson
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Daniela A Astudillo Maya
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Rebecca Martinez
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Hashem Zamanian
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Benjamin A Speidel
- Department of Neurology, University of California, San Francisco, CA, USA; Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Ankit N Khambhati
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA; Department of Neurosurgery, University of California, San Francisco, CA, USA
| | - Vikram R Rao
- Department of Neurology, University of California, San Francisco, CA, USA; Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Leo P Sugrue
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA
| | - Katherine W Scangos
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Edward F Chang
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA; Department of Neurosurgery, University of California, San Francisco, CA, USA
| | - Andrew D Krystal
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA; Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
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Shahsavarani S, Thibodeaux DN, Xu W, Kim SH, Lodgher F, Nwokeabia C, Cambareri M, Yagielski AJ, Zhao HT, Handwerker DA, Gonzalez-Castillo J, Bandettini PA, Hillman EMC. Cortex-wide neural dynamics predict behavioral states and provide a neural basis for resting-state dynamic functional connectivity. Cell Rep 2023; 42:112527. [PMID: 37243588 PMCID: PMC10592480 DOI: 10.1016/j.celrep.2023.112527] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/14/2023] [Accepted: 05/01/2023] [Indexed: 05/29/2023] Open
Abstract
Although resting-state functional magnetic resonance imaging (fMRI) studies have observed dynamically changing brain-wide networks of correlated activity, fMRI's dependence on hemodynamic signals makes results challenging to interpret. Meanwhile, emerging techniques for real-time recording of large populations of neurons have revealed compelling fluctuations in neuronal activity across the brain that are obscured by traditional trial averaging. To reconcile these observations, we use wide-field optical mapping to simultaneously record pan-cortical neuronal and hemodynamic activity in awake, spontaneously behaving mice. Some components of observed neuronal activity clearly represent sensory and motor function. However, particularly during quiet rest, strongly fluctuating patterns of activity across diverse brain regions contribute greatly to interregional correlations. Dynamic changes in these correlations coincide with changes in arousal state. Simultaneously acquired hemodynamics depict similar brain-state-dependent correlation shifts. These results support a neural basis for dynamic resting-state fMRI, while highlighting the importance of brain-wide neuronal fluctuations in the study of brain state.
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Affiliation(s)
- Somayeh Shahsavarani
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA; Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - David N Thibodeaux
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Weihao Xu
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Sharon H Kim
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Fatema Lodgher
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Chinwendu Nwokeabia
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Morgan Cambareri
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Alexis J Yagielski
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Hanzhi T Zhao
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Daniel A Handwerker
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Peter A Bandettini
- Section on Functional Imaging Methods, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA; Functional MRI Core Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Elizabeth M C Hillman
- Mortimer B. Zuckerman Mind Brain Behavior Institute and Department of Biomedical Engineering, Columbia University, New York, NY, USA; Department of Radiology, Columbia University Irving Medical Center, New York, NY, USA.
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Xu Y, Long X, Feng J, Gong P. Interacting spiral wave patterns underlie complex brain dynamics and are related to cognitive processing. Nat Hum Behav 2023:10.1038/s41562-023-01626-5. [PMID: 37322235 DOI: 10.1038/s41562-023-01626-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 05/12/2023] [Indexed: 06/17/2023]
Abstract
The large-scale activity of the human brain exhibits rich and complex patterns, but the spatiotemporal dynamics of these patterns and their functional roles in cognition remain unclear. Here by characterizing moment-by-moment fluctuations of human cortical functional magnetic resonance imaging signals, we show that spiral-like, rotational wave patterns (brain spirals) are widespread during both resting and cognitive task states. These brain spirals propagate across the cortex while rotating around their phase singularity centres, giving rise to spatiotemporal activity dynamics with non-stationary features. The properties of these brain spirals, such as their rotational directions and locations, are task relevant and can be used to classify different cognitive tasks. We also demonstrate that multiple, interacting brain spirals are involved in coordinating the correlated activations and de-activations of distributed functional regions; this mechanism enables flexible reconfiguration of task-driven activity flow between bottom-up and top-down directions during cognitive processing. Our findings suggest that brain spirals organize complex spatiotemporal dynamics of the human brain and have functional correlates to cognitive processing.
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Affiliation(s)
- Yiben Xu
- School of Physics, University of Sydney, Sydney, New South Wales, Australia
- ARC Centre of Excellence for Integrative Brain Function, University of Sydney, Sydney, New South Wales, Australia
| | - Xian Long
- School of Physics, University of Sydney, Sydney, New South Wales, Australia
- ARC Centre of Excellence for Integrative Brain Function, University of Sydney, Sydney, New South Wales, Australia
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Pulin Gong
- School of Physics, University of Sydney, Sydney, New South Wales, Australia.
- ARC Centre of Excellence for Integrative Brain Function, University of Sydney, Sydney, New South Wales, Australia.
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Mason SL, Junges L, Woldman W, Facer-Childs ER, de Campos BM, Bagshaw AP, Terry JR. Classification of human chronotype based on fMRI network-based statistics. Front Neurosci 2023; 17:1147219. [PMID: 37342462 PMCID: PMC10277557 DOI: 10.3389/fnins.2023.1147219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 05/17/2023] [Indexed: 06/23/2023] Open
Abstract
Chronotype-the relationship between the internal circadian physiology of an individual and the external 24-h light-dark cycle-is increasingly implicated in mental health and cognition. Individuals presenting with a late chronotype have an increased likelihood of developing depression, and can display reduced cognitive performance during the societal 9-5 day. However, the interplay between physiological rhythms and the brain networks that underpin cognition and mental health is not well-understood. To address this issue, we use rs-fMRI collected from 16 people with an early chronotype and 22 people with a late chronotype over three scanning sessions. We develop a classification framework utilizing the Network Based-Statistic methodology, to understand if differentiable information about chronotype is embedded in functional brain networks and how this changes throughout the day. We find evidence of subnetworks throughout the day that differ between extreme chronotypes such that high accuracy can occur, describe rigorous threshold criteria for achieving 97.3% accuracy in the Evening and investigate how the same conditions hinder accuracy for other scanning sessions. Revealing differences in functional brain networks based on extreme chronotype suggests future avenues of research that may ultimately better characterize the relationship between internal physiology, external perturbations, brain networks, and disease.
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Affiliation(s)
- Sophie L. Mason
- School of Mathematics, College of Engineering and Physical Sciences, University of Birmingham, Birmingham, United Kingdom
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
| | - Leandro Junges
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
| | - Wessel Woldman
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
| | - Elise R. Facer-Childs
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, VIC, Australia
- Danny Frawley Centre for Health and Wellbeing, Melbourne, VIC, Australia
- Centre for Human Brain Health, College of Life and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom
- Faculty of Health and Medical Sciences, University of Surrey, Surrey, United Kingdom
| | | | - Andrew P. Bagshaw
- Centre for Human Brain Health, College of Life and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom
| | - John R. Terry
- Centre for Systems Modelling and Quantitative Biomedicine, University of Birmingham, Birmingham, United Kingdom
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Demeter DV, Gordon EM, Nugiel T, Garza A, Larguinho TL, Church JA. Resting-state cortical hubs in youth organize into four categories. Cell Rep 2023; 42:112521. [PMID: 37200192 PMCID: PMC10281712 DOI: 10.1016/j.celrep.2023.112521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 02/13/2023] [Accepted: 05/02/2023] [Indexed: 05/20/2023] Open
Abstract
During childhood, neural systems supporting high-level cognitive processes undergo periods of rapid growth and refinement, which rely on the successful coordination of activation across the brain. Some coordination occurs via cortical hubs-brain regions that coactivate with functional networks other than their own. Adult cortical hubs map into three distinct profiles, but less is known about hub categories during development, when critical improvement in cognition occurs. We identify four distinct hub categories in a large youth sample (n = 567, ages 8.5-17.2), each exhibiting more diverse connectivity profiles than adults. Youth hubs integrating control-sensory processing split into two distinct categories (visual control and auditory/motor control), whereas adult hubs unite under one. This split suggests a need for segregating sensory stimuli while functional networks are experiencing rapid development. Functional coactivation strength for youth control-processing hubs are associated with task performance, suggesting a specialized role in routing sensory information to and from the brain's control system.
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Affiliation(s)
- Damion V Demeter
- Department of Cognitive Science, University of California San Diego, La Jolla, CA 92093, USA.
| | - Evan M Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Tehila Nugiel
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - AnnaCarolina Garza
- Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Tyler L Larguinho
- Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA
| | - Jessica A Church
- Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA
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40
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Zhang H, Hao Y, He H, Roberts N. EEG based brain functional connectivity analysis for post-autoimmune encephalitis (AE) patients with epilepsy. Epilepsy Res 2023; 193:107166. [PMID: 37216856 DOI: 10.1016/j.eplepsyres.2023.107166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/16/2023] [Accepted: 05/08/2023] [Indexed: 05/24/2023]
Abstract
Autoimmune Encephalitis (AE) refers to a group of conditions that occur when the body's immune system mistakenly attacks healthy brain cells, leading to inflammation of the brain. Seizures are a common symptom of AE and more than a third of patients experiencing seizures secondary to AE become epileptic over time. The objective of the present study is to identify biomarkers that can be used to identify those patients in whom AE will evolve into epilepsy. The bursts of abnormal electrical activity that occur during a seizure can be recorded by using Electroencephalography (EEG). In this work, common EEG (cEEG) and ambulatory EEG (aEEG) were recorded to compare the brain functional connectivity (FC) properties in post-AE patients with epilepsy patients and post-AE patients without epilepsy. The brain functional networks of spike waves were first constructed on the basis of Phase Locking Value (PLV). An analysis was then performed of the differences which existed in the FC properties of clustering coefficient, characteristic path length, global efficiency, local efficiency, and node degree between post-AE patients with epilepsy patients and post-AE patients without epilepsy. From the perspective of brain functional network analysis, post-AE patients with epilepsy showed a more complex network structure. Furthermore, the five FC properties have been found signification different, all FC property values of post-AE patients with epilepsy are higher than those of post-AE patients without epilepsy of cEEG and aEEG. Based on the extracted FC properties, five classifiers were used to classify them, and the results showed that all five FC properties could effectively distinguish between post-AE patients with epilepsy patients and post-AE patients without epilepsy in both cEEG and aEEG. These findings are potentially helpful for diagnosing whether a patient with AE will become epileptic.
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Affiliation(s)
- Huimin Zhang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yong Hao
- Department of Neurology, Shanghai Jiao Tong University School of Medicine Affiliated Renji Hospital, Shanghai 200127, China
| | - Hong He
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Neil Roberts
- Centre for Reproductive Health (CRH), School of Clinical Sciences, University of Edinburgh, Edinburgh EH16 4TJ, UK
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41
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Kaptan M, Horn U, Vannesjo SJ, Mildner T, Weiskopf N, Finsterbusch J, Brooks JCW, Eippert F. Reliability of resting-state functional connectivity in the human spinal cord: assessing the impact of distinct noise sources. Neuroimage 2023; 275:120152. [PMID: 37142169 DOI: 10.1016/j.neuroimage.2023.120152] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 04/20/2023] [Accepted: 05/01/2023] [Indexed: 05/06/2023] Open
Abstract
The investigation of spontaneous fluctuations of the blood-oxygen-level-dependent (BOLD) signal has recently been extended from the brain to the spinal cord, where it has stimulated interest from a clinical perspective. A number of resting-state functional magnetic resonance imaging (fMRI) studies have demonstrated robust functional connectivity between the time series of BOLD fluctuations in bilateral dorsal horns and between those in bilateral ventral horns, in line with the functional neuroanatomy of the spinal cord. A necessary step prior to extension to clinical studies is assessing the reliability of such resting-state signals, which we aimed to do here in a group of 45 healthy young adults at the clinically prevalent field strength of 3T. When investigating connectivity in the entire cervical spinal cord, we observed fair to good reliability for dorsal-dorsal and ventral-ventral connectivity, whereas reliability was poor for within- and between-hemicord dorsal-ventral connectivity. Considering how prone spinal cord fMRI is to noise, we extensively investigated the impact of distinct noise sources and made two crucial observations: removal of physiological noise led to a reduction in functional connectivity strength and reliability - due to the removal of stable and participant-specific noise patterns - whereas removal of thermal noise considerably increased the detectability of functional connectivity without a clear influence on reliability. Finally, we also assessed connectivity within spinal cord segments and observed that while the pattern of connectivity was similar to that of whole cervical cord, reliability at the level of single segments was consistently poor. Taken together, our results demonstrate the presence of reliable resting-state functional connectivity in the human spinal cord even after thoroughly accounting for physiological and thermal noise, but at the same time urge caution if focal changes in connectivity (e.g. due to segmental lesions) are to be studied, especially in a longitudinal manner.
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Affiliation(s)
- Merve Kaptan
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| | - Ulrike Horn
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - S Johanna Vannesjo
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
| | - Toralf Mildner
- Methods & Development Group Nuclear Magnetic Resonance, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, University of Leipzig, Leipzig, Germany
| | - Jürgen Finsterbusch
- Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jonathan C W Brooks
- School of Psychology, University of East Anglia Wellcome Wolfson Brain Imaging Centre (UWWBIC), Norwich, United Kingdom
| | - Falk Eippert
- Max Planck Research Group Pain Perception, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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42
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Wang Y, Zhang N, Qian S, Liu J, Yu S, Li N, Xia C. Classify patients with Moyamoya disease according to their cognitive performance might be helpful in clinical and practical with support vector machine based on hypergraph. Hum Brain Mapp 2023; 44:2407-2417. [PMID: 36799621 PMCID: PMC10028655 DOI: 10.1002/hbm.26218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 02/18/2023] Open
Abstract
Moyamoya disease (MMD) patients were now classified according to their cerebrovascular manifestations, with cognition and emotion ignored, which attenuated the therapy. The present study tried to classify them based on their cognitive and emotional performance and explored the neural basis underlying this classification using resting-state fMRI (rs-fMRI). Thirty-nine MMD patients were recruited, assessed mental function and MRI scanned. We adopted hierarchical analysis of their mental performance for new subtypes. Next, a three-step analysis, with each step consisting of 10 random cross validation, was conducted for robust brain regions in classifying the three subtypes of patients in a support vector machine (SVM) model with hypergraph of rs-fMRI. We found three new subtypes including high depression-high anxiety-low cognition (HE-LC, 50%), low depression-low anxiety-high cognition (LE-HC, 14%), and low depression-low anxiety-low cognition (LE-LC, 36%), and no hemorrhagic MMD patients fell into the LE-HC group. The temporal and the bilateral superior frontal cortex, and so forth were included in all 10 randomized SVM modeling. The classification accuracy of the final three-way classification model was 67.5% in average of 10 random cross validation. In addition, the S value between the frontal cortex and the angular cortex was positively correlated with the anxiety score and backward digit span (p < .05). Our results might provide a new perspective for MMD classification concerning patients' mental status, guide timely surgery and suggest angular cortex, and so forth should be protected in surgery for cognitive consideration.
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Affiliation(s)
- Ying Wang
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, People's Republic of China
- Anhui Provincial Stereotactic Neurosurgical Institute, Hefei, Anhui, People's Republic of China
- Anhui Key Laboratory of Brain Function and Brain Disease, Hefei, Anhui, People's Republic of China
| | - Nan Zhang
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, People's Republic of China
| | - Sheng Qian
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, People's Republic of China
| | - Jian Liu
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, People's Republic of China
| | - Shaojie Yu
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, People's Republic of China
| | - Nan Li
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, Anhui, People's Republic of China
| | - Chengyu Xia
- Department of Neurosurgery, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, People's Republic of China
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43
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Callow DD, Smith JC. Physical fitness, cognition, and structural network efficiency of brain connections across the lifespan. Neuropsychologia 2023; 182:108527. [PMID: 36871816 DOI: 10.1016/j.neuropsychologia.2023.108527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/19/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023]
Abstract
Inadequate levels of exercise is one of the most potent modifiable risk factors for preventing cognitive decline and dementia as we age. Meanwhile, network science-based measures of structural brain network global and local efficiency show promise as robust biomarkers of aging, cognitive decline, and pathological disease progression. Despite this, little to no work has established how maintaining physical activity (PA) and physical fitness might relate to cognition and network efficiency measures across the lifespan. Therefore the purpose of this study was to determine the relationship between (1) PA and fitness and cognition, (2) fitness and network efficiency, and (3) how network efficiency measures relate to cognition. To accomplish this, we analyzed a large cross-sectional data set (n = 720; 36-100 years) from the aging human connectome project, which included the Trail Making Task (TMT) A and B, a measure of fitness (2-min walk test), physical activity (International Physical Activity Questionnaire), and high-resolution diffusion imaging data. Our analysis consisted of employing multiple linear regression while controlling for age, sex, and education. Age was associated with lower global and local brain network efficiency and poorer Trail A & B performance. Meanwhile, fitness, but not physical activity, was related to better Trail A and B performance and fitness, and was positively associated with local and global brain efficiency. Finally, local efficiency was related to better TMT B performance and partially mediated the relationship between fitness and TMT B performance. These results indicate aging may be associated with a shift towards less efficient local and global neural networks and that maintaining physical fitness might protect against age-related cognitive performance deterioration by bolstering structural network efficiency.
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Affiliation(s)
- Daniel D Callow
- Department of Kinesiology, University of Maryland, College Park, MD, USA; Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA
| | - J Carson Smith
- Department of Kinesiology, University of Maryland, College Park, MD, USA; Program in Neuroscience and Cognitive Science, University of Maryland, College Park, MD, USA.
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44
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Chadwick W, Maudsley S, Hull W, Havolli E, Boshoff E, Hill MDW, Goetghebeur PJD, Harrison DC, Nizami S, Bedford DC, Coope G, Real K, Thiemermann C, Maycox P, Carlton M, Cole SL. The oDGal Mouse: A Novel, Physiologically Relevant Rodent Model of Sporadic Alzheimer's Disease. Int J Mol Sci 2023; 24:ijms24086953. [PMID: 37108119 PMCID: PMC10138655 DOI: 10.3390/ijms24086953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 03/17/2023] [Accepted: 03/30/2023] [Indexed: 04/29/2023] Open
Abstract
Sporadic Alzheimer's disease (sAD) represents a serious and growing worldwide economic and healthcare burden. Almost 95% of current AD patients are associated with sAD as opposed to patients presenting with well-characterized genetic mutations that lead to AD predisposition, i.e., familial AD (fAD). Presently, the use of transgenic (Tg) animals overexpressing human versions of these causative fAD genes represents the dominant research model for AD therapeutic development. As significant differences in etiology exist between sAD and fAD, it is perhaps more appropriate to develop novel, more sAD-reminiscent experimental models that would expedite the discovery of effective therapies for the majority of AD patients. Here we present the oDGal mouse model, a novel model of sAD that displays a range of AD-like pathologies as well as multiple cognitive deficits reminiscent of AD symptomology. Hippocampal cognitive impairment and pathology were delayed with N-acetyl-cysteine (NaC) treatment, which strongly suggests that reactive oxygen species (ROS) are the drivers of downstream pathologies such as elevated amyloid beta and hyperphosphorylated tau. These features demonstrate a desired pathophenotype that distinguishes our model from current transgenic rodent AD models. A preclinical model that presents a phenotype of non-genetic AD-like pathologies and cognitive deficits would benefit the sAD field, particularly when translating therapeutics from the preclinical to the clinical phase.
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Affiliation(s)
- Wayne Chadwick
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Stuart Maudsley
- Receptor Biology Lab, University of Antwerp, 2000 Antwerp, Belgium
| | - William Hull
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Centre for Translational Medicine and Therapeutics, Queen Mary University of London, London E1 4NS, UK
| | - Enes Havolli
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Eugene Boshoff
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Mark D W Hill
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | | | - David C Harrison
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Sohaib Nizami
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - David C Bedford
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Gareth Coope
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Katia Real
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Christoph Thiemermann
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Centre for Translational Medicine and Therapeutics, Queen Mary University of London, London E1 4NS, UK
| | - Peter Maycox
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Mark Carlton
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
| | - Sarah L Cole
- Takeda Cambridge, 418 Cambridge Science Park, Cambridge CB4 0PZ, UK
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45
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Luppi AI, Mediano PAM, Rosas FE, Allanson J, Pickard JD, Williams GB, Craig MM, Finoia P, Peattie ARD, Coppola P, Menon DK, Bor D, Stamatakis EA. Reduced emergent character of neural dynamics in patients with a disrupted connectome. Neuroimage 2023; 269:119926. [PMID: 36740030 PMCID: PMC9989666 DOI: 10.1016/j.neuroimage.2023.119926] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 01/23/2023] [Accepted: 02/02/2023] [Indexed: 02/05/2023] Open
Abstract
High-level brain functions are widely believed to emerge from the orchestrated activity of multiple neural systems. However, lacking a formal definition and practical quantification of emergence for experimental data, neuroscientists have been unable to empirically test this long-standing conjecture. Here we investigate this fundamental question by leveraging a recently proposed framework known as "Integrated Information Decomposition," which establishes a principled information-theoretic approach to operationalise and quantify emergence in dynamical systems - including the human brain. By analysing functional MRI data, our results show that the emergent and hierarchical character of neural dynamics is significantly diminished in chronically unresponsive patients suffering from severe brain injury. At a functional level, we demonstrate that emergence capacity is positively correlated with the extent of hierarchical organisation in brain activity. Furthermore, by combining computational approaches from network control theory and whole-brain biophysical modelling, we show that the reduced capacity for emergent and hierarchical dynamics in severely brain-injured patients can be mechanistically explained by disruptions in the patients' structural connectome. Overall, our results suggest that chronic unresponsiveness resulting from severe brain injury may be related to structural impairment of the fundamental neural infrastructures required for brain dynamics to support emergence.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Leverhulme Centre for the Future of Intelligence, Cambridge, UK; The Alan Turing Institute, London, UK.
| | - Pedro A M Mediano
- Department of Computing, Imperial College London, London, UK; Department of Psychology, University of Cambridge, Cambridge, UK
| | - Fernando E Rosas
- Department of Brain Science, Center for Psychedelic Research, Imperial College London, London, UK; Data Science Institute, Imperial College London, London, UK; Centre for Complexity Science, Imperial College London, London, UK; Center for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK; Department of Informatics, University of Sussex, Brighton, UK
| | - Judith Allanson
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Department of Neurosciences, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation, Cambridge, UK
| | - John D Pickard
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Guy B Williams
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Michael M Craig
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Paola Finoia
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Alexander R D Peattie
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Peter Coppola
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Daniel Bor
- Department of Psychology, University of Cambridge, Cambridge, UK; Department of Psychology, Queen Mary University of London, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, UK; Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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46
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Zhang L, Pini L, Kim D, Shulman GL, Corbetta M. Spontaneous Activity Patterns in Human Attention Networks Code for Hand Movements. J Neurosci 2023; 43:1976-1986. [PMID: 36788030 PMCID: PMC10027113 DOI: 10.1523/jneurosci.1601-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 01/18/2023] [Accepted: 02/03/2023] [Indexed: 02/16/2023] Open
Abstract
Recent evidence suggests that, in the absence of any task, spontaneous brain activity patterns and connectivity in the visual and motor cortex code for natural stimuli and actions, respectively. These "resting-state" activity patterns may underlie the maintenance and consolidation (replay) of information states coding for ecological stimuli and behaviors. In this study, we examine whether replay patterns occur in resting-state activity in association cortex grouped into high-order cognitive networks not directly processing sensory inputs or motor outputs. Fifteen participants (7 females) performed four hand movements during an fMRI study. Three movements were ecological. The fourth movement as control was less ecological. Before and after the task scans, we acquired resting-state fMRI scans. The analysis examined whether multivertex task activation patterns for the four movements computed at the cortical surface in different brain networks resembled spontaneous activity patterns measured at rest. For each movement, we computed a vector of r values indicating the strength of the similarity between the mean task activation pattern and frame-by-frame resting-state patterns. We computed a cumulative distribution function of r 2 values and used the 90th percentile cutoff value for comparison. In the dorsal attention network, resting-state patterns were more likely to match task patterns for the ecological movements than the control movement. In contrast, rest-task pattern correlation was more likely for less ecological movement in the ventral attention network. These findings show that spontaneous activity patterns in human attention networks code for hand movements.SIGNIFICANCE STATEMENT fMRI indirectly measures neural activity noninvasively. Resting-state (spontaneous) fMRI signals measured in the absence of any task resemble signals evoked by task performance both in topography and inter-regional (functional) connectivity. However, the function of spontaneous brain activity is unknown. We recently showed that spatial activity patterns evoked by visual and motor tasks in visual and motor cortex, respectively, occur at rest in the absence of any stimulus or response. Here we show that activity patterns related to hand movements replay at rest in frontoparietal regions of the human attention system. These findings show that spontaneous activity in the human cortex may mediate the maintenance and consolidation of information states coding for ecological stimuli and behaviors.
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Affiliation(s)
- Lu Zhang
- Padova Neuroscience Center, University of Padova, Padova, 35131, Italy
| | - Lorenzo Pini
- Padova Neuroscience Center, University of Padova, Padova, 35131, Italy
| | - DoHyun Kim
- Departments of Neurology and Radiology, Washington University-St Louis, St Louis, Missouri 63110
| | - Gordon L Shulman
- Departments of Neurology and Radiology, Washington University-St Louis, St Louis, Missouri 63110
| | - Maurizio Corbetta
- Padova Neuroscience Center, University of Padova, Padova, 35131, Italy
- Departments of Neurology and Radiology, Washington University-St Louis, St Louis, Missouri 63110
- Department of Neuroscience, University of Padova, Padova, 35131, Italy
- Venetian Institute of Molecular Medicine, Padova, 35129, Italy
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47
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Chiou R, Cox CR, Lambon Ralph MA. Bipartite functional fractionation within the neural system for social cognition supports the psychological continuity of self versus other. Cereb Cortex 2023; 33:1277-1299. [PMID: 35394005 PMCID: PMC9930627 DOI: 10.1093/cercor/bhac135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 02/10/2022] [Accepted: 03/14/2022] [Indexed: 11/12/2022] Open
Abstract
Research of social neuroscience establishes that regions in the brain's default-mode network (DN) and semantic network (SN) are engaged by socio-cognitive tasks. Research of the human connectome shows that DN and SN regions are both situated at the transmodal end of a cortical gradient but differ in their loci along this gradient. Here we integrated these 2 bodies of research, used the psychological continuity of self versus other as a "test-case," and used functional magnetic resonance imaging to investigate whether these 2 networks would encode social concepts differently. We found a robust dissociation between the DN and SN-while both networks contained sufficient information for decoding broad-stroke distinction of social categories, the DN carried more generalizable information for cross-classifying across social distance and emotive valence than did the SN. We also found that the overarching distinction of self versus other was a principal divider of the representational space while social distance was an auxiliary factor (subdivision, nested within the principal dimension), and this representational landscape was more manifested in the DN than in the SN. Taken together, our findings demonstrate how insights from connectome research can benefit social neuroscience and have implications for clarifying the 2 networks' differential contributions to social cognition.
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Affiliation(s)
| | - Christopher R Cox
- Department of Psychology, Louisiana State University, LA 70803, Baton Rouge, United States
| | - Matthew A Lambon Ralph
- MRC Cognition & Brain Science Unit, University of Cambridge, Cambridge, CB2 7EF, United Kingdom
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48
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Sinha R, Masina R, Morales C, Burton K, Wan Y, Joannides A, Mair RJ, Morris RC, Santarius T, Manly T, Price SJ. A Prospective Study of Longitudinal Risks of Cognitive Deficit for People Undergoing Glioblastoma Surgery Using a Tablet Computer Cognition Testing Battery: Towards Personalized Understanding of Risks to Cognitive Function. J Pers Med 2023; 13:jpm13020278. [PMID: 36836511 PMCID: PMC9967594 DOI: 10.3390/jpm13020278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/26/2023] [Accepted: 01/30/2023] [Indexed: 02/04/2023] Open
Abstract
Glioblastoma and the surgery to remove it pose high risks to the cognitive function of patients. Little reliable data exist about these risks, especially postoperatively before radiotherapy. We hypothesized that cognitive deficit risks detected before surgery will be exacerbated by surgery in patients with glioblastoma undergoing maximal treatment regimens. We used longitudinal electronic cognitive testing perioperatively to perform a prospective, longitudinal, observational study of 49 participants with glioblastoma undergoing surgery. Before surgery (A1), the participant risk of deficit in 5/6 cognitive domains was increased compared to normative data. Of these, the risks to Attention (OR = 31.19), Memory (OR = 97.38), and Perception (OR = 213.75) were markedly increased. These risks significantly increased in the early period after surgery (A2) when patients were discharged home or seen in the clinic to discuss histology results. For participants tested at 4-6 weeks after surgery (A3) before starting radiotherapy, there was evidence of risk reduction towards A1. The observed risks of cognitive deficit were independent of patient-specific, tumour-specific, and surgery-specific co-variates. These results reveal a timeframe of natural recovery in the first 4-6 weeks after surgery based on personalized deficit profiles for each participant. Future research in this period could investigate personalized rehabilitation tools to aid the recovery process found.
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Affiliation(s)
- Rohitashwa Sinha
- Department of Neurosurgery, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
- Leeds Institute of Medical Research, University of Leeds, Leeds LS9 7TF, UK
- Correspondence:
| | - Riccardo Masina
- Department of Neurosurgery, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
| | - Cristina Morales
- Department of Neurosurgery, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
| | - Katherine Burton
- Department of Oncology, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
| | - Yizhou Wan
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Alexis Joannides
- Department of Neurosurgery, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Richard J. Mair
- Department of Neurosurgery, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Robert C. Morris
- Department of Neurosurgery, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
| | - Thomas Santarius
- Department of Neurosurgery, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
| | - Tom Manly
- MRC Cognition and Brain Sciences Unit, Cambridge CB2 7EF, UK
| | - Stephen J. Price
- Department of Neurosurgery, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
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49
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Luppi AI, Vohryzek J, Kringelbach ML, Mediano PAM, Craig MM, Adapa R, Carhart-Harris RL, Roseman L, Pappas I, Peattie ARD, Manktelow AE, Sahakian BJ, Finoia P, Williams GB, Allanson J, Pickard JD, Menon DK, Atasoy S, Stamatakis EA. Distributed harmonic patterns of structure-function dependence orchestrate human consciousness. Commun Biol 2023; 6:117. [PMID: 36709401 PMCID: PMC9884288 DOI: 10.1038/s42003-023-04474-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 01/11/2023] [Indexed: 01/29/2023] Open
Abstract
A central question in neuroscience is how consciousness arises from the dynamic interplay of brain structure and function. Here we decompose functional MRI signals from pathological and pharmacologically-induced perturbations of consciousness into distributed patterns of structure-function dependence across scales: the harmonic modes of the human structural connectome. We show that structure-function coupling is a generalisable indicator of consciousness that is under bi-directional neuromodulatory control. We find increased structure-function coupling across scales during loss of consciousness, whether due to anaesthesia or brain injury, capable of discriminating between behaviourally indistinguishable sub-categories of brain-injured patients, tracking the presence of covert consciousness. The opposite harmonic signature characterises the altered state induced by LSD or ketamine, reflecting psychedelic-induced decoupling of brain function from structure and correlating with physiological and subjective scores. Overall, connectome harmonic decomposition reveals how neuromodulation and the network architecture of the human connectome jointly shape consciousness and distributed functional activation across scales.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK.
- Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, CB2 1SB, UK.
| | - Jakub Vohryzek
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, 08005, Spain
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
| | - Pedro A M Mediano
- Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, UK
- Department of Computing, Imperial College London, London, W12 0NN, UK
| | - Michael M Craig
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Ram Adapa
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Robin L Carhart-Harris
- Center for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, W12 0NN, UK
- Psychedelics Division - Neuroscape, Department of Neurology, University of California San Francisco, San Francisco, CA, 94143, USA
| | - Leor Roseman
- Center for Psychedelic Research, Department of Brain Sciences, Imperial College London, London, W12 0NN, UK
| | - Ioannis Pappas
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Alexander R D Peattie
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Anne E Manktelow
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Barbara J Sahakian
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Psychiatry, MRC/Wellcome Trust Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, CB2 3EB, UK
| | - Paola Finoia
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
- Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Guy B Williams
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Judith Allanson
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Neurosciences, Cambridge University Hospitals NHS Foundation, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK
| | - John D Pickard
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, CB2 0QQ, UK
- Division of Neurosurgery, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Selen Atasoy
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
- Center for Music in the Brain, Aarhus University, Aarhus, Denmark
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, CB2 0QQ, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, CB2 0QQ, UK
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50
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Fukuda T, Tominaga T, Tominaga Y, Kanayama H, Kato N, Yoshimura H. Alternative strategy for driving voltage-oscillator in neocortex of rats. Neurosci Res 2023; 191:28-37. [PMID: 36642104 DOI: 10.1016/j.neures.2023.01.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 01/11/2023] [Accepted: 01/11/2023] [Indexed: 01/15/2023]
Abstract
Information integration in the brain requires functional connectivity between local neural networks. Here, we investigated the interregional coupling mechanism from the viewpoint of oscillations using optical recording methods. Low-frequency electrical stimulation of rat neocortical slices in a caffeine-containing medium induced oscillatory activity between the primary visual cortex (Oc1) and medial secondary visual cortex (Oc2M), in which the oscillation generator was located in the Oc2M and was triggered by a feedforward signal. During to-and-fro oscillatory activity, neural excitation was marked in layer II/III. When the upper layer was disrupted between Oc1 and Oc2M, feedforward signals could propagate through the deep layer and switch on the oscillator in the Oc2M. When the lower layer was disrupted between Oc1 and Oc2M, feedforward signals could propagate through the upper layer and switch on the oscillator in the Oc2M. In the backward direction, neither the upper layer cut nor the lower layer cut disrupted the propagation of the oscillations. In all cases, the horizontal and vertical pathways were used as needed. Fluctuations in the oscillatory waveforms of the local field potential at the upper and lower layers in the Oc2M were reversed, suggesting that the oscillation originated between the two layers. Thus, the neocortex may work as a safety device for interregional communications in an alternative way to drive voltage oscillators in the neocortex.
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Affiliation(s)
- Takako Fukuda
- Department of Molecular Oral Physiology, Institute of Biomedical Sciences, Tokushima University Graduate School, Kuramoto, Tokushima 770-8504, Japan
| | - Takashi Tominaga
- Institute of Neuroscience, Tokushima Bunri University, Shido, Kagawa 769-2123, Japan
| | - Yoko Tominaga
- Institute of Neuroscience, Tokushima Bunri University, Shido, Kagawa 769-2123, Japan
| | - Hiroyuki Kanayama
- Department of Molecular Oral Physiology, Institute of Biomedical Sciences, Tokushima University Graduate School, Kuramoto, Tokushima 770-8504, Japan; Department of Oral and Maxillofacial Surgery, National Hospital Organization Osaka National Hospital, Osaka 540-0006, Japan
| | - Nobuo Kato
- Department of Physiology, Kanazawa Medical University, Uchinada-cho, Ishikawa 920-0293, Japan
| | - Hiroshi Yoshimura
- Department of Molecular Oral Physiology, Institute of Biomedical Sciences, Tokushima University Graduate School, Kuramoto, Tokushima 770-8504, Japan.
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