451
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Wang R, Lin P, Liu M, Wu Y, Zhou T, Zhou C. Hierarchical Connectome Modes and Critical State Jointly Maximize Human Brain Functional Diversity. PHYSICAL REVIEW LETTERS 2019; 123:038301. [PMID: 31386449 DOI: 10.1103/physrevlett.123.038301] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Revised: 06/05/2019] [Indexed: 06/10/2023]
Abstract
The brain requires diverse segregated and integrated processing to perform normal functions in terms of anatomical structure and self-organized dynamics with critical features, but the fundamental relationships between the complex structural connectome, critical state, and functional diversity remain unknown. Herein, we extend the eigenmode analysis to investigate the joint contribution of hierarchical modular structural organization and critical state to brain functional diversity. We show that the structural modes inherent to the hierarchical modular structural connectome allow a nested functional segregation and integration across multiple spatiotemporal scales. The real brain hierarchical modular organization provides large structural capacity for diverse functional interactions, which are generated by sequentially activating and recruiting the hierarchical connectome modes, and the critical state can best explore the capacity to maximize the functional diversity. Our results reveal structural and dynamical mechanisms that jointly support a balanced segregated and integrated brain processing with diverse functional interactions, and they also shed light on dysfunctional segregation and integration in neurodegenerative diseases and neuropsychiatric disorders.
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Affiliation(s)
- Rong Wang
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Shaanxi Engineering Laboratory for Vibration Control of Aerospace Structures, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
- College of Science, Xi'an University of Science and Technology, Xi'an 710054, China
| | - Pan Lin
- Key Laboratory of Cognitive Science, College of Biomedical Engineering, South-Central University for Nationalities, Wuhan 430074, China
| | - Mianxin Liu
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
| | - Ying Wu
- State Key Laboratory for Strength and Vibration of Mechanical Structures, Shaanxi Engineering Laboratory for Vibration Control of Aerospace Structures, School of Aerospace Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Tao Zhou
- Complex Lab, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Kowloon Tong, Hong Kong
- Research Centre, HKBU Institute of Research and Continuing Education, Shenzhen 518057, China
- Beijing Computational Science Research Center, Beijing 100084, China
- Department of Physics, Zhejiang University, Hangzhou 310058, China
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452
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Takagi Y, Hirayama JI, Tanaka SC. State-unspecific patterns of whole-brain functional connectivity from resting and multiple task states predict stable individual traits. Neuroimage 2019; 201:116036. [PMID: 31326571 DOI: 10.1016/j.neuroimage.2019.116036] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Revised: 06/27/2019] [Accepted: 07/17/2019] [Indexed: 10/26/2022] Open
Abstract
An increasing number of functional magnetic resonance imaging (fMRI) studies have revealed potential neural substrates of individual differences in diverse types of brain function and dysfunction. Although most previous studies have inherently focused on state-specific characterizations of brain networks and their functions, several recent studies reported on the potential state-unspecific nature of functional brain networks, such as global similarities across different experimental conditions or states, including both task and resting states. However, no previous studies have carried out direct, systematic characterizations of state-unspecific brain networks, or their functional implications. Here, we quantitatively identified several modes of state-unspecific individual variations in whole-brain functional connectivity patterns, called "Common Neural Modes" (CNMs), from a large-scale fMRI database including eight task/resting states. Furthermore, we tested how CNMs accounted for variability in individual cognitive measures. The results revealed that three CNMs were robustly extracted under various dimensions of features used. Each of these CNMs was preferentially correlated with different aspects of representative cognitive measures, reflecting stable individual traits. Importantly, the association between CNMs and cognitive measures emerged from brain connectivity data alone ("unsupervised"), whereas previous related studies have explicitly used both connectivity and cognitive measures to build their prediction models ("supervised"). The three CNMs were also able to predict several life outcomes, including income and life satisfaction, and achieved the highest level of performance when combined with a conventional cognitive measure. Our findings highlight the importance of state-unspecific brain networks in characterizing fundamental individual variation.
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Affiliation(s)
- Yu Takagi
- ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan; Department of Psychiatry, Oxford Centre for Human Brain Activity, University of Oxford, Oxford, UK; Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Graduate School of Information Science, Nara Institute of Science and Technology, Nara, 630-0192, Japan; Japan Society for the Promotion of Science, Tokyo, 102-0083, Japan.
| | - Jun-Ichiro Hirayama
- RIKEN Center for Advanced Intelligence Project, Tokyo, 103-0027, Japan; ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan.
| | - Saori C Tanaka
- ATR Brain Information Communication Research Laboratory Group, Kyoto, Japan.
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453
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Horien C, Greene AS, Constable RT, Scheinost D. Regions and Connections: Complementary Approaches to Characterize Brain Organization and Function. Neuroscientist 2019; 26:117-133. [PMID: 31304866 PMCID: PMC7079335 DOI: 10.1177/1073858419860115] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Functional magnetic resonance imaging has proved to be a powerful tool to characterize spatiotemporal patterns of human brain activity. Analysis methods broadly fall into two camps: those summarizing properties of a region and those measuring interactions among regions. Here we pose an unappreciated question in the field: What are the strengths and limitations of each approach to study fundamental neural processes? We explore the relative utility of region- and connection-based measures in the context of three topics of interest: neurobiological relevance, brain-behavior relationships, and individual differences in brain organization. In each section, we offer illustrative examples. We hope that this discussion offers a novel and useful framework to support efforts to better understand the macroscale functional organization of the brain and how it relates to behavior.
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Affiliation(s)
- Corey Horien
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA
| | - Abigail S Greene
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA.,Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA.,Department of Neurosurgery, Yale University School of Medicine, New Haven, CT, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA.,The Child Study Center, Yale University School of Medicine, New Haven, CT, USA.,Department of Statistics and Data Science, Yale University, USA
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454
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Kudela MA, Dzemidzic M, Oberlin BG, Lin Z, Goñi J, Kareken DA, Harezlak J. Semiparametric Estimation of Task-Based Dynamic Functional Connectivity on the Population Level. Front Neurosci 2019; 13:583. [PMID: 31293367 PMCID: PMC6598619 DOI: 10.3389/fnins.2019.00583] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2018] [Accepted: 05/23/2019] [Indexed: 12/13/2022] Open
Abstract
Dynamic functional connectivity (dFC) estimates time-dependent associations between pairs of brain region time series as typically acquired during functional MRI. dFC changes are most commonly quantified by pairwise correlation coefficients between the time series within a sliding window. Here, we applied a recently developed bootstrap-based technique (Kudela et al., 2017) to robustly estimate subject-level dFC and its confidence intervals in a task-based fMRI study (24 subjects who tasted their most frequently consumed beer and Gatorade as an appetitive control). We then combined information across subjects and scans utilizing semiparametric mixed models to obtain a group-level dFC estimate for each pair of brain regions, flavor, and the difference between flavors. The proposed approach relies on the estimated group-level dFC accounting for complex correlation structures of the fMRI data, multiple repeated observations per subject, experimental design, and subject-specific variability. It also provides condition-specific dFC and confidence intervals for the whole brain at the group level. As a summary dFC metric, we used the proportion of time when the estimated associations were either significantly positive or negative. For both flavors, our fully-data driven approach yielded regional associations that reflected known, biologically meaningful brain organization as shown in prior work, as well as closely resembled resting state networks (RSNs). Specifically, beer flavor-potentiated associations were detected between several reward-related regions, including the right ventral striatum (VST), lateral orbitofrontal cortex, and ventral anterior insular cortex (vAIC). The enhancement of right VST-vAIC association by a taste of beer independently validated the main activation-based finding (Oberlin et al., 2016). Most notably, our novel dFC methodology uncovered numerous associations undetected by the traditional static FC analysis. The data-driven, novel dFC methodology presented here can be used for a wide range of task-based fMRI designs to estimate the dFC at multiple levels—group-, individual-, and task-specific, utilizing a combination of well-established statistical methods.
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Affiliation(s)
- Maria A Kudela
- Safety and Observational Statistics, Takeda R&D Data Science Institute, Takeda Pharmaceuticals, Cambridge, MA, United States
| | - Mario Dzemidzic
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, United States.,Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Brandon G Oberlin
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, United States.,Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Zikai Lin
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Joaquín Goñi
- School of Industrial Engineering, Purdue University, West Lafayette, IN, United States.,Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, United States.,Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States
| | - David A Kareken
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, United States.,Department of Radiology, Indiana University School of Medicine, Indianapolis, IN, United States.,Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Jaroslaw Harezlak
- Department of Epidemiology and Biostatistics, School of Public Health, Indiana University, Bloomington, IN, United States
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455
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Escrichs A, Sanjuán A, Atasoy S, López-González A, Garrido C, Càmara E, Deco G. Characterizing the Dynamical Complexity Underlying Meditation. Front Syst Neurosci 2019; 13:27. [PMID: 31354439 PMCID: PMC6637306 DOI: 10.3389/fnsys.2019.00027] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Accepted: 06/27/2019] [Indexed: 01/24/2023] Open
Abstract
Over the past 2,500 years, contemplative traditions have explored the nature of the mind using meditation. More recently, neuroimaging research on meditation has revealed differences in brain function and structure in meditators. Nevertheless, the underlying neural mechanisms are still unclear. In order to understand how meditation shapes global activity through the brain, we investigated the spatiotemporal dynamics across the whole-brain functional network using the Intrinsic Ignition Framework. Recent neuroimaging studies have demonstrated that different states of consciousness differ in their underlying dynamical complexity, i.e., how the broadness of communication is elicited and distributed through the brain over time and space. In this work, controls and experienced meditators were scanned using functional magnetic resonance imaging (fMRI) during resting-state and meditation (focused attention on breathing). Our results evidenced that the dynamical complexity underlying meditation shows less complexity than during resting-state in the meditator group but not in the control group. Furthermore, we report that during resting-state, the brain activity of experienced meditators showed higher metastability (i.e., a wider dynamical regime over time) than the one observed in the control group. Overall, these results indicate that the meditation state operates in a different dynamical regime compared to the resting-state.
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Affiliation(s)
- Anira Escrichs
- Computational Neuroscience Group, Department of Information and Communication Technologies, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain.,Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
| | - Ana Sanjuán
- Computational Neuroscience Group, Department of Information and Communication Technologies, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - Selen Atasoy
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Ane López-González
- Computational Neuroscience Group, Department of Information and Communication Technologies, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain
| | - César Garrido
- Radiology Unit, Hospital Clínic Barcelona, Barcelona, Spain
| | - Estela Càmara
- Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain.,Department of Cognition, Development and Educational Psychology, University of Barcelona, Barcelona, Spain
| | - Gustavo Deco
- Computational Neuroscience Group, Department of Information and Communication Technologies, Center for Brain and Cognition, Universitat Pompeu Fabra, Barcelona, Spain.,Institució Catalana de la Recerca i Estudis Avançats, Barcelona, Spain
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456
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Zhou Q, Zhang L, Feng J, Lo CYZ. Tracking the Main States of Dynamic Functional Connectivity in Resting State. Front Neurosci 2019; 13:685. [PMID: 31338016 PMCID: PMC6629909 DOI: 10.3389/fnins.2019.00685] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 06/17/2019] [Indexed: 01/22/2023] Open
Abstract
Dynamical changes have recently been tracked in functional connectivity (FC) calculated from resting-state functional magnetic resonance imaging (R-fMRI), when a person is conscious but not carrying out a directed task during scanning. Diverse dynamical FC states (dFC) are believed to represent different internal states of the brain, in terms of brain-regional interactions. In this paper, we propose a novel protocol, the signed community clustering with the optimized modularity by two-step procedures, to track dynamical whole brain functional connectivity (dWFC) states. This protocol is assumption free without a priori threshold for the number of clusters. By applying our method on sliding window based dWFC’s with automated anatomical labeling 2 (AAL2), three main dWFC states were extracted from R-fMRI datasets in Human Connectome Project, that are independent on window size. Through extracting the FC features of these states, we found the functional links in state 1 (WFC-C1) mainly involved visual, somatomotor, attention and cerebellar (posterior lobe) modules. State 2 (WFC-C2) was similar to WFC-C1, but more FC’s linking limbic, default mode, and frontoparietal modules and less linking the cerebellum, sensory and attention modules. State 3 had more FC’s linking default mode, limbic, and cerebellum, compared to WFC-C1 and WFC-C2. With tests of robustness and stability, our work provides a solid, hypothesis-free tool to detect dWFC states for the possibility of tracking rapid dynamical change in FCs among large data sets.
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Affiliation(s)
- Qunjie Zhou
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
| | - Lu Zhang
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China.,Oxford Centre for Computational Neuroscience, Oxford, United Kingdom.,Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Chun-Yi Zac Lo
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
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457
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Malone PS, Eberhardt SP, Wimmer K, Sprouse C, Klein R, Glomb K, Scholl CA, Bokeria L, Cho P, Deco G, Jiang X, Bernstein LE, Riesenhuber M. Neural mechanisms of vibrotactile categorization. Hum Brain Mapp 2019; 40:3078-3090. [PMID: 30920706 PMCID: PMC6865665 DOI: 10.1002/hbm.24581] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 02/18/2019] [Accepted: 03/12/2019] [Indexed: 11/11/2022] Open
Abstract
The grouping of sensory stimuli into categories is fundamental to cognition. Previous research in the visual and auditory systems supports a two-stage processing hierarchy that underlies perceptual categorization: (a) a "bottom-up" perceptual stage in sensory cortices where neurons show selectivity for stimulus features and (b) a "top-down" second stage in higher level cortical areas that categorizes the stimulus-selective input from the first stage. In order to test the hypothesis that the two-stage model applies to the somatosensory system, 14 human participants were trained to categorize vibrotactile stimuli presented to their right forearm. Then, during an fMRI scan, participants actively categorized the stimuli. Representational similarity analysis revealed stimulus selectivity in areas including the left precentral and postcentral gyri, the supramarginal gyrus, and the posterior middle temporal gyrus. Crucially, we identified a single category-selective region in the left ventral precentral gyrus. Furthermore, an estimation of directed functional connectivity delivered evidence for robust top-down connectivity from the second to first stage. These results support the validity of the two-stage model of perceptual categorization for the somatosensory system, suggesting common computational principles and a unified theory of perceptual categorization across the visual, auditory, and somatosensory systems.
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Affiliation(s)
- Patrick S. Malone
- Department of NeuroscienceGeorgetown University Medical CenterWashingtonDistrict of Columbia
| | - Silvio P. Eberhardt
- Department of Speech, Language, and Hearing SciencesGeorge Washington UniversityWashingtonDistrict of Columbia
| | - Klaus Wimmer
- Center for Brain and Cognition, Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain
- Centre de Recerca MatemàticaBarcelonaSpain
- Barcelona Graduate School of MathematicsBarcelonaSpain
| | - Courtney Sprouse
- Department of NeuroscienceGeorgetown University Medical CenterWashingtonDistrict of Columbia
| | - Richard Klein
- Department of NeuroscienceGeorgetown University Medical CenterWashingtonDistrict of Columbia
| | - Katharina Glomb
- Center for Brain and Cognition, Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain
- Department of RadiologyCentre Hospitalier Universitaire VaudoisLausanneSwitzerland
| | - Clara A. Scholl
- Department of NeuroscienceGeorgetown University Medical CenterWashingtonDistrict of Columbia
| | - Levan Bokeria
- Department of NeuroscienceGeorgetown University Medical CenterWashingtonDistrict of Columbia
| | - Philip Cho
- Department of NeuroscienceGeorgetown University Medical CenterWashingtonDistrict of Columbia
| | - Gustavo Deco
- Center for Brain and Cognition, Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA)BarcelonaSpain
- Department of NeuropsychologyMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- School of Psychological SciencesMonash UniversityMelbourneVictoriaAustralia
| | - Xiong Jiang
- Department of NeuroscienceGeorgetown University Medical CenterWashingtonDistrict of Columbia
| | - Lynne E. Bernstein
- Department of Speech, Language, and Hearing SciencesGeorge Washington UniversityWashingtonDistrict of Columbia
| | - Maximilian Riesenhuber
- Department of NeuroscienceGeorgetown University Medical CenterWashingtonDistrict of Columbia
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458
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Ren Y, Guo L, Guo CC. A connectivity-based parcellation improved functional representation of the human cerebellum. Sci Rep 2019; 9:9115. [PMID: 31235754 PMCID: PMC6591283 DOI: 10.1038/s41598-019-45670-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 06/06/2019] [Indexed: 11/23/2022] Open
Abstract
The cerebellum is traditionally well known for its role in motor learning and coordination. Recently, it is recognized that the function of the cerebellum is highly diverse and extends to non-motor domains, such as working memory, emotion and language. The diversity of the cerebellum can be appreciated by examining its extensive connectivity to the cerebral regions selective for both motor and cognitive functions. Importantly, the pattern of cerebro-cerebellar connectivity is specific and distinct to different cerebellar subregions. Therefore, to understand the cerebellum and the various functions it involves, it is essential to identify and differentiate its subdivisions. However, most studies are still referring the cerebellum as one brain structure or by its gross anatomical subdivisions, which does not necessarily reflect the functional mapping of the cerebellum. We here employed a data-driven method to generate a functional connectivity-based parcellation of the cerebellum. Our results demonstrated that functional connectivity-based atlas is superior to existing atlases in regards to cluster homogeneity, accuracy of functional connectivity representation and individual identification. Furthermore, our functional atlas improves statistical results of task fMRI analyses, as compared to the standard voxel-based approach and existing atlases. Our detailed functional parcellation provides a valuable tool for elucidating the functional diversity and connectivity of the cerebellum as well as its network relationships with the whole brain.
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Affiliation(s)
- Yudan Ren
- School of Automation, Northwestern Polytechnical University, Xi'an, China.,QIMR Berghofer Medical Research Institute, Brisbane, Australia.,School of Information Science and Technology, Northwest University, Xi'an, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, China
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459
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Nawani H, Smith ML, Wheeler AL, Widjaja E. Functional Connectivity Associated with Health-Related Quality of Life in Children with Focal Epilepsy. AJNR Am J Neuroradiol 2019; 40:1213-1220. [PMID: 31221633 DOI: 10.3174/ajnr.a6106] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Accepted: 05/16/2019] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Although functional connectivity has been linked to cognitive function in epilepsy, its relationship with physical, psychological, or social dysfunction is unknown. This study aimed to assess the relationship between network architecture from resting-state fMRI and health-related quality of life in children with medically intractable focal epilepsy. MATERIALS AND METHODS Forty-seven children with nonlesional focal epilepsy were included; 22 had frontal lobe epilepsy and 15 had temporal lobe epilepsy. We computed graph metrics of functional connectivity, including network segregation (clustering coefficient and modularity) and integration (characteristic path length and participation coefficient). Health-related quality of life was measured using the Quality of Life in Childhood Epilepsy questionnaire. We examined the associations between graph metrics and the Quality of Life in Childhood Epilepsy total and domains scores, with age, sex, age at seizure onset, fMRI motion, and network density as covariates. RESULTS There was a negative relationship between the clustering coefficient and total Quality of Life in Childhood Epilepsy score [t(40) = -2.0; P = .04] and social function [t(40) = -2.9; P = .005]. There was a positive association between the mean participation coefficient and total Quality of Life in Childhood Epilepsy score [t(40) = 2.2; P = .03] and cognition [t(40) = 3.8; P = .0004]. In temporal lobe epilepsy, there was a negative relationship between the clustering coefficient and total Quality of Life in Childhood Epilepsy score [t(8) = -2.8; P = .02] and social function [t(8) = -3.6; P = .0075] and between modularity and total Quality of Life in Childhood Epilepsy score [t(8) = -2.5; P = .04] and social function [t(8) = -4.4; P = .0021]. In frontal lobe epilepsy, there was no association between network segregation and integration and Quality of Life in Childhood Epilepsy total or domain scores. CONCLUSIONS Our findings indicate that there are other higher order brain functions beyond cognition, which may be linked with functional connectivity of the brain.
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Affiliation(s)
- H Nawani
- From Neurosciences and Mental Health (H.N., M.L.S., A.L.W., E.W.)
| | - M L Smith
- From Neurosciences and Mental Health (H.N., M.L.S., A.L.W., E.W.).,Departments of Psychology (M.L.S.)
| | - A L Wheeler
- From Neurosciences and Mental Health (H.N., M.L.S., A.L.W., E.W.) .,Physiology (A.L.W.), University of Toronto, Toronto, Ontario, Canada
| | - E Widjaja
- From Neurosciences and Mental Health (H.N., M.L.S., A.L.W., E.W.).,Diagnostic Imaging (E.W.).,Division of Neurology (E.W.), Hospital for Sick Children, Toronto, Ontario, Canada
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460
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Jollans L, Boyle R, Artiges E, Banaschewski T, Desrivières S, Grigis A, Martinot JL, Paus T, Smolka MN, Walter H, Schumann G, Garavan H, Whelan R. Quantifying performance of machine learning methods for neuroimaging data. Neuroimage 2019; 199:351-365. [PMID: 31173905 DOI: 10.1016/j.neuroimage.2019.05.082] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2018] [Revised: 05/21/2019] [Accepted: 05/30/2019] [Indexed: 01/18/2023] Open
Abstract
Machine learning is increasingly being applied to neuroimaging data. However, most machine learning algorithms have not been designed to accommodate neuroimaging data, which typically has many more data points than subjects, in addition to multicollinearity and low signal-to-noise. Consequently, the relative efficacy of different machine learning regression algorithms for different types of neuroimaging data are not known. Here, we sought to quantify the performance of a variety of machine learning algorithms for use with neuroimaging data with various sample sizes, feature set sizes, and predictor effect sizes. The contribution of additional machine learning techniques - embedded feature selection and bootstrap aggregation (bagging) - to model performance was also quantified. Five machine learning regression methods - Gaussian Process Regression, Multiple Kernel Learning, Kernel Ridge Regression, the Elastic Net and Random Forest, were examined with both real and simulated MRI data, and in comparison to standard multiple regression. The different machine learning regression algorithms produced varying results, which depended on sample size, feature set size, and predictor effect size. When the effect size was large, the Elastic Net, Kernel Ridge Regression and Gaussian Process Regression performed well at most sample sizes and feature set sizes. However, when the effect size was small, only the Elastic Net made accurate predictions, but this was limited to analyses with sample sizes greater than 400. Random Forest also produced a moderate performance for small effect sizes, but could do so across all sample sizes. Machine learning techniques also improved prediction accuracy for multiple regression. These data provide empirical evidence for the differential performance of various machines on neuroimaging data, which are dependent on number of sample size, features and effect size.
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Affiliation(s)
- Lee Jollans
- School of Psychology, Trinity College Dublin, Dublin, Ireland; Department of Translational Research in Psychiatry, Max-Planck Institute of Psychiatry, Munich, Germany
| | - Rory Boyle
- School of Psychology, Trinity College Dublin, Dublin, Ireland
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 "Neuroimaging & Psychiatry", University Paris Sud, University Paris Descartes - Sorbonne Paris Cité, and Psychiatry Department 91G16, Orsay Hospital, France
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159, Mannheim, Germany
| | - Sylvane Desrivières
- Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Antoine Grigis
- NeuroSpin, CEA, Université Paris-Saclay, F-91191, Gif-sur-Yvette, France
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 "Neuroimaging & Psychiatry", University Paris Sud, University Paris Descartes - Sorbonne Paris Cité, and Maison de Solenn, Paris, France
| | - Tomáš Paus
- Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital and Departments of Psychology and Psychiatry, University of Toronto, Toronto, Ontario, M6A 2E1, Canada
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
| | - Gunter Schumann
- Medical Research Council - Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Hugh Garavan
- Department of Psychiatry, University of Vermont, Burlington, USA
| | - Robert Whelan
- School of Psychology, Trinity College Dublin, Dublin, Ireland; Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland.
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461
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Lee YB, Yoo K, Roh JH, Moon WJ, Jeong Y. Brain-State Extraction Algorithm Based on the State Transition (BEST): A Dynamic Functional Brain Network Analysis in fMRI Study. Brain Topogr 2019; 32:897-913. [DOI: 10.1007/s10548-019-00719-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Accepted: 05/28/2019] [Indexed: 12/23/2022]
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462
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Ji G, Chen X, Bai T, Wang L, Wei Q, Gao Y, Tao L, He K, Li D, Dong Y, Hu P, Yu F, Zhu C, Tian Y, Yu Y, Wang K. Classification of schizophrenia by intersubject correlation in functional connectome. Hum Brain Mapp 2019; 40:2347-2357. [PMID: 30663853 PMCID: PMC6865403 DOI: 10.1002/hbm.24527] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 12/07/2018] [Accepted: 01/08/2019] [Indexed: 01/16/2023] Open
Abstract
Functional connectomes have been suggested as fingerprinting for individual identification. Accordingly, we hypothesized that subjects in the same phenotypic group have similar functional connectome features, which could help to discriminate schizophrenia (SCH) patients from healthy controls (HCs) and from depression patients. To this end, we included resting-state functional magnetic resonance imaging data of SCH, depression patients, and HCs from three centers. We first investigated the characteristics of connectome similarity between individuals, and found higher similarity between subjects belonging to the same group (i.e., SCH-SCH) than different groups (i.e., HC-SCH). These findings suggest that the average connectome within group (termed as group-specific functional connectome [GFC]) may help in individual classification. Consistently, significant accuracy (75-77%) and area under curve (81-86%) were found in discriminating SCH from HC or depression patients by GFC-based leave-one-out cross-validation. Cross-center classification further suggests a good generalizability of the GFC classification. We additionally included normal aging data (255 young and 242 old subjects with different scanning sequences) to show factors could be improved for better classification performance, and the findings emphasized the importance of increasing sample size but not temporal resolution during scanning. In conclusion, our findings suggest that the average functional connectome across subjects contained group-specific biological features and may be helpful in clinical diagnosis for schizophrenia.
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Affiliation(s)
- Gong‐Jun Ji
- Department of Medical PsychologyChaohu Clinical Medical College, Anhui Medical UniversityHefeiChina
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Xingui Chen
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Tongjian Bai
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Lu Wang
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Qiang Wei
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Yaxiang Gao
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Longxiang Tao
- Department of RadiologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Kongliang He
- Anhui Mental Health CenterHefeiChina
- The Fourth People's Hospital of HefeiHefeiChina
| | - Dandan Li
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Yi Dong
- Anhui Mental Health CenterHefeiChina
- The Second Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Panpan Hu
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Fengqiong Yu
- Department of Medical PsychologyChaohu Clinical Medical College, Anhui Medical UniversityHefeiChina
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Chunyan Zhu
- Department of Medical PsychologyChaohu Clinical Medical College, Anhui Medical UniversityHefeiChina
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Yanghua Tian
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Yongqiang Yu
- Department of RadiologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Kai Wang
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
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463
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Lord LD, Expert P, Atasoy S, Roseman L, Rapuano K, Lambiotte R, Nutt DJ, Deco G, Carhart-Harris RL, Kringelbach ML, Cabral J. Dynamical exploration of the repertoire of brain networks at rest is modulated by psilocybin. Neuroimage 2019; 199:127-142. [PMID: 31132450 DOI: 10.1016/j.neuroimage.2019.05.060] [Citation(s) in RCA: 96] [Impact Index Per Article: 19.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 05/20/2019] [Accepted: 05/23/2019] [Indexed: 12/11/2022] Open
Abstract
Growing evidence from the dynamical analysis of functional neuroimaging data suggests that brain function can be understood as the exploration of a repertoire of metastable connectivity patterns ('functional brain networks'), which potentially underlie different mental processes. The present study characterizes how the brain's dynamical exploration of resting-state networks is rapidly modulated by intravenous infusion of psilocybin, a tryptamine psychedelic found in "magic mushrooms". We employed a data-driven approach to characterize recurrent functional connectivity patterns by focusing on the leading eigenvector of BOLD phase coherence at single-TR resolution. Recurrent BOLD phase-locking patterns (PL states) were assessed and statistically compared pre- and post-infusion of psilocybin in terms of their probability of occurrence and transition profiles. Results were validated using a placebo session. Recurrent BOLD PL states revealed high spatial overlap with canonical resting-state networks. Notably, a PL state forming a frontoparietal subsystem was strongly destabilized after psilocybin injection, with a concomitant increase in the probability of occurrence of another PL state characterized by global BOLD phase coherence. These findings provide evidence of network-specific neuromodulation by psilocybin and represent one of the first attempts at bridging molecular pharmacodynamics and whole-brain network dynamics.
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Affiliation(s)
| | - Paul Expert
- Centre for Mathematics of Precision Healthcare, Imperial College London, UK; Department of Mathematics, Imperial College London, UK
| | - Selen Atasoy
- Department of Psychiatry, University of Oxford, UK
| | - Leor Roseman
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, UK
| | | | | | - David J Nutt
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, UK
| | - Gustavo Deco
- Center for Brain and Cognition, Universitat Pompeu Fabra, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Spain
| | - Robin L Carhart-Harris
- Centre for Psychedelic Research, Department of Brain Sciences, Imperial College London, UK
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, UK; Centre for Music in the Brain, Department of Clinical Medicine, Aarhus University, Denmark; Institut d'Études Avancées de Paris, France; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Portugal
| | - Joana Cabral
- Department of Psychiatry, University of Oxford, UK; Centre for Music in the Brain, Department of Clinical Medicine, Aarhus University, Denmark; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Portugal.
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464
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Ge R, Kot P, Liu X, Lang DJ, Wang JZ, Honer WG, Vila-Rodriguez F. Parcellation of the human hippocampus based on gray matter volume covariance: Replicable results on healthy young adults. Hum Brain Mapp 2019; 40:3738-3752. [PMID: 31115118 DOI: 10.1002/hbm.24628] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 03/25/2019] [Accepted: 04/29/2019] [Indexed: 12/31/2022] Open
Abstract
The hippocampus is a key brain region that participates in a range of cognitive and affective functions, and is involved in the etiopathogenesis of numerous neuropsychiatric disorders. The structural complexity and functional diversity of the hippocampus suggest the existence of structural and functional subdivisions within this structure. For the first time, we parcellated the human hippocampus with two independent data sets, each of which consisted of 198 T1-weighted structural magnetic resonance imaging (sMRI) images of healthy young subjects. The method was based on gray matter volume (GMV) covariance, which was quantified by a bivariate voxel-to-voxel linear correlation approach, as well as a multivariate masked independent component analysis approach. We subsequently interrogated the relationship between the GMV covariance patterns and the functional connectivity patterns of the hippocampal subregions using sMRI and resting-state functional MRI (fMRI) data from the same participants. Seven distinct GMV covariance-based subregions were identified for bilateral hippocampi, with robust reproducibility across the two data sets. We further demonstrated that the structural covariance patterns of the hippocampal subregions had a correspondence with the intrinsic functional connectivity patterns of these subregions. Together, our results provide a topographical configuration of the hippocampus with converging structural and functional support. The resulting subregions may improve our understanding of the hippocampal connectivity and functions at a subregional level, which provides useful parcellations and masks for future neuroscience and clinical research on the structural and/or functional connectivity of the hippocampus.
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Affiliation(s)
- Ruiyang Ge
- Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Paul Kot
- Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Xiang Liu
- Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Donna J Lang
- Department of Radiology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Jane Z Wang
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, British Columbia, Canada
| | - William G Honer
- Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
| | - Fidel Vila-Rodriguez
- Non-Invasive Neurostimulation Therapies (NINET) Laboratory, Department of Psychiatry, University of British Columbia, Vancouver, British Columbia, Canada
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465
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Brain functional connectivity correlates of coping styles. COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2019; 18:495-508. [PMID: 29572771 DOI: 10.3758/s13415-018-0583-7] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Coping abilities represent the individual set of mental and behavioral strategies adopted when facing stress or traumatic experiences. Coping styles related to avoidance have been linked to a disposition to develop psychiatric disorders such as PTSD, anxiety, and major depression, whereas problem-oriented coping skills have been positively correlated with well-being and high quality of life. Even though coping styles constitute an important determinant of resilience and can impact many aspects of everyday living, no study has investigated their brain functional connectivity underpinnings in humans. Here we analyzed both psychometric scores of coping and resting-state fMRI data from 102 healthy adult participants. Controlling for personality and problem-solving abilities, we identified significant links between the propensity to adopt different coping styles and the functional connectivity profiles of regions belonging to the default mode (DMN) and anterior salience (AS) networks-namely, the anterior cingulate cortex, left frontopolar cortex, and left angular gyrus. Also, a reduced negative correlation between AS and DMN nodes explained variability in one specific coping style, related to avoiding problems while focusing on the emotional component of the stressor at hand, instead of relying on cognitive resources. These results might be integrated with current neurophysiological models of resilience and individual responses to stress, in order to understand the propensity to develop clinical conditions (e.g., PTSD) and predict the outcomes of psychotherapeutic interventions.
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466
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Chumin EJ, Grecco GG, Dzemidzic M, Cheng H, Finn P, Sporns O, Newman SD, Yoder KK. Alterations in White Matter Microstructure and Connectivity in Young Adults with Alcohol Use Disorder. Alcohol Clin Exp Res 2019; 43:1170-1179. [PMID: 30977902 DOI: 10.1111/acer.14048] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 03/28/2019] [Indexed: 12/12/2022]
Abstract
BACKGROUND Magnetic resonance imaging (MRI) studies have shown differences in volume and structure in the brains of individuals with alcohol use disorder (AUD). Most research has focused on neuropathological effects of alcohol that appear after years of chronic alcohol misuse. However, few studies have investigated white matter (WM) microstructure and diffusion MRI-based (DWI) connectivity during early stages of AUD. Therefore, the goal of this work was to investigate WM integrity and structural connectivity in emerging adulthood AUD subjects using both conventional DWI metrics and a novel connectomics approach. METHODS Twenty-two AUD and 18 controls (CON) underwent anatomic and diffusion MRI. Outcome measures were scalar diffusion metrics and structural network connectomes. Tract-Based Spatial Statistics was used to investigate group differences in diffusion measures. Structural connectomes were used as input into a community structure procedure to obtain a coclassification index matrix (an indicator of community association strength) for each subject. Differences in coclassification and structural connectivity (indexed by streamline density) were assessed via the Network Based Statistics Toolbox. RESULTS AUD had higher fractional anisotropy (FA) values throughout the major WM tracts, but also had lower FA values in WM tracts in the cerebellum and right insula (pTFCE < 0.05). Mean diffusivity was generally lower in the AUD group (pTFCE < 0.05). AUD had lower coclassification of nodes between ventral attention and default mode networks and higher coclassification between nodes of visual, default mode, and somatomotor networks. Additionally, AUD had higher fiber density between an adjacent pair of nodes within the default mode network. CONCLUSIONS Our results indicate that emerging adulthood AUD subjects may have differential patterns of FA and distinct differences in structural connectomes compared with CON. These data suggest that such alterations in microstructure and structural connectivity may uniquely characterize early stages of AUD and/or a predisposition for development of AUD.
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Affiliation(s)
- Evgeny J Chumin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana.,Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Indiana
| | - Gregory G Grecco
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana.,Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Indiana.,Medical Scientist Training Program, Indiana University School of Medicine, Indianapolis, Indiana
| | - Mario Dzemidzic
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana.,Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Indiana.,Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana
| | - Hu Cheng
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana.,Program in Neuroscience, Indiana University, Bloomington, Indiana
| | - Peter Finn
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana.,Program in Neuroscience, Indiana University, Bloomington, Indiana
| | - Sharlene D Newman
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana.,Program in Neuroscience, Indiana University, Bloomington, Indiana
| | - Karmen K Yoder
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana.,Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, Indiana.,Department of Psychology, Indiana University-Purdue University Indianapolis, Indianapolis, Indiana
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467
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Yamashita A, Yahata N, Itahashi T, Lisi G, Yamada T, Ichikawa N, Takamura M, Yoshihara Y, Kunimatsu A, Okada N, Yamagata H, Matsuo K, Hashimoto R, Okada G, Sakai Y, Morimoto J, Narumoto J, Shimada Y, Kasai K, Kato N, Takahashi H, Okamoto Y, Tanaka SC, Kawato M, Yamashita O, Imamizu H. Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias. PLoS Biol 2019; 17:e3000042. [PMID: 30998673 PMCID: PMC6472734 DOI: 10.1371/journal.pbio.3000042] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2018] [Accepted: 03/14/2019] [Indexed: 01/07/2023] Open
Abstract
When collecting large amounts of neuroimaging data associated with psychiatric disorders, images must be acquired from multiple sites because of the limited capacity of a single site. However, site differences represent a barrier when acquiring multisite neuroimaging data. We utilized a traveling-subject dataset in conjunction with a multisite, multidisorder dataset to demonstrate that site differences are composed of biological sampling bias and engineering measurement bias. The effects on resting-state functional MRI connectivity based on pairwise correlations because of both bias types were greater than or equal to psychiatric disorder differences. Furthermore, our findings indicated that each site can sample only from a subpopulation of participants. This result suggests that it is essential to collect large amounts of neuroimaging data from as many sites as possible to appropriately estimate the distribution of the grand population. Finally, we developed a novel harmonization method that removed only the measurement bias by using a traveling-subject dataset and achieved the reduction of the measurement bias by 29% and improvement of the signal-to-noise ratios by 40%. Our results provide fundamental knowledge regarding site effects, which is important for future research using multisite, multidisorder resting-state functional MRI data.
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Affiliation(s)
- Ayumu Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- * E-mail: (HI); (OY); or (AY)
| | - Noriaki Yahata
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Molecular Imaging and Theranostics, National Institute of Radiological Sciences, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Takashi Itahashi
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Giuseppe Lisi
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
| | - Takashi Yamada
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Naho Ichikawa
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Masahiro Takamura
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Yujiro Yoshihara
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Akira Kunimatsu
- Department of Radiology, IMSUT Hospital, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Naohiro Okada
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN) at the University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
| | - Hirotaka Yamagata
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi, Japan
| | - Koji Matsuo
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi, Japan
- Department of Psychiatry, Faculty of Medicine, Saitama Medical University, Saitama, Japan
| | - Ryuichiro Hashimoto
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
- Department of Language Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Go Okada
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Yuki Sakai
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Jun Morimoto
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
| | - Jin Narumoto
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Department of Psychiatry, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yasuhiro Shimada
- Brain Activity Imaging Center, ATR-Promotions Inc., Kyoto, Japan
| | - Kiyoto Kasai
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- The International Research Center for Neurointelligence (WPI-IRCN) at the University of Tokyo Institutes for Advanced Study (UTIAS), Tokyo, Japan
| | - Nobumasa Kato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Medical Institute of Developmental Disabilities Research, Showa University, Tokyo, Japan
| | - Hidehiko Takahashi
- Department of Psychiatry, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan
| | - Saori C. Tanaka
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
| | - Mitsuo Kawato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
| | - Okito Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
- * E-mail: (HI); (OY); or (AY)
| | - Hiroshi Imamizu
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International, Kyoto, Japan
- Department of Psychology, Graduate School of Humanities and Sociology, The University of Tokyo, Tokyo, Japan
- * E-mail: (HI); (OY); or (AY)
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468
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Lu X, Li T, Xia Z, Zhu R, Wang L, Luo Y, Feng C, Krueger F. Connectome-based model predicts individual differences in propensity to trust. Hum Brain Mapp 2019; 40:1942-1954. [PMID: 30633429 PMCID: PMC6865671 DOI: 10.1002/hbm.24503] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 11/15/2018] [Accepted: 12/02/2018] [Indexed: 12/12/2022] Open
Abstract
Trust constitutes a fundamental basis of human society and plays a pivotal role in almost every aspect of human relationships. Although enormous interest exists in determining the neuropsychological underpinnings of a person's propensity to trust utilizing task-based fMRI; however, little progress has been made in predicting its variations by task-free fMRI based on whole-brain resting-state functional connectivity (RSFC). Here, we combined a one-shot trust game with a connectome-based predictive modeling approach to predict propensity to trust from whole-brain RSFC. We demonstrated that individual variations in the propensity to trust were primarily predicted by RSFC rooted in the functional integration of distributed key nodes-caudate, amygdala, lateral prefrontal cortex, temporal-parietal junction, and the temporal pole-which are part of domain-general large-scale networks essential for the motivational, affective, and cognitive aspects of trust. We showed, further, that the identified brain-behavior associations were only evident for trust but not altruistic preferences and that propensity to trust (and its underlying neural underpinnings) were modulated according to the extent to which a person emphasizes general social preferences (i.e., horizontal collectivism) rather than general risk preferences (i.e., trait impulsiveness). In conclusion, the employed data-driven approach enables to predict propensity to trust from RSFC and highlights its potential use as an objective neuromarker of trust impairment in mental disorders.
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Affiliation(s)
- Xiaping Lu
- Center for Brain Disorders and Cognitive SciencesShenzhen UniveristyShenzhenChina
- Brain, Mind & Markets Laboratory, Department of FinanceThe University of MelbourneMelbourneVictoriaAustralia
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Ting Li
- Collaborative Innovation Center of Assessment toward Basic Education QualityBeijing Normal UniversityBeijingChina
| | - Zhichao Xia
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Ruida Zhu
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Li Wang
- Collaborative Innovation Center of Assessment toward Basic Education QualityBeijing Normal UniversityBeijingChina
| | - Yue‐Jia Luo
- Center for Brain Disorders and Cognitive SciencesShenzhen UniveristyShenzhenChina
- Center for Emotion and BrainShenzhen Institute of NeuroscienceShenzhenChina
- Medical SchoolKunming University of Science and TechnologyKunmingChina
| | - Chunliang Feng
- Center for Brain Disorders and Cognitive SciencesShenzhen UniveristyShenzhenChina
- College of Information Science and TechnologyBeijing Normal UniversityBeijingChina
| | - Frank Krueger
- School of Systems BiologyGeorge Mason UniversityFairfaxVirginia
- Department of PsychologyUniversity of MannheimMannheimGermany
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469
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Figueroa CA, Cabral J, Mocking RJT, Rapuano KM, van Hartevelt TJ, Deco G, Expert P, Schene AH, Kringelbach ML, Ruhé HG. Altered ability to access a clinically relevant control network in patients remitted from major depressive disorder. Hum Brain Mapp 2019; 40:2771-2786. [PMID: 30864248 PMCID: PMC6865599 DOI: 10.1002/hbm.24559] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2018] [Revised: 01/30/2019] [Accepted: 02/21/2019] [Indexed: 01/01/2023] Open
Abstract
Neurobiological models to explain vulnerability of major depressive disorder (MDD) are scarce and previous functional magnetic resonance imaging studies mostly examined “static” functional connectivity (FC). Knowing that FC constantly evolves over time, it becomes important to assess how FC dynamically differs in remitted‐MDD patients vulnerable for new depressive episodes. Using a recently developed method to examine dynamic FC, we characterized re‐emerging FC states during rest in 51 antidepressant‐free MDD patients at high risk of recurrence (≥2 previous episodes), and 35 healthy controls. We examined differences in occurrence, duration, and switching profiles of FC states after neutral and sad mood induction. Remitted MDD patients showed a decreased probability of an FC state (p < 0.005) consisting of an extensive network connecting frontal areas—important for cognitive control—with default mode network, striatum, and salience areas, involved in emotional and self‐referential processing. Even when this FC state was observed in patients, it lasted shorter (p < 0.005) and was less likely to switch to a smaller prefrontal–striatum network (p < 0.005). Differences between patients and controls decreased after sad mood induction. Further, the duration of this FC state increased in remitted patients after sad mood induction but not in controls (p < 0.05). Our findings suggest reduced ability of remitted‐MDD patients, in neutral mood, to access a clinically relevant control network involved in the interplay between externally and internally oriented attention. When recovering from sad mood, remitted recurrent MDD appears to employ a compensatory mechanism to access this FC state. This study provides a novel neurobiological profile of MDD vulnerability.
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Affiliation(s)
- Caroline A Figueroa
- Department of Psychiatry, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.,Brain Imaging Center, Academic Medical Center, Amsterdam, The Netherlands.,School of Social Welfare, University of California Berkeley, Berkeley, California
| | - Joana Cabral
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom.,Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,Center for Music in the Brain, Aarhus University, Aarhus, Denmark
| | - Roel J T Mocking
- Department of Psychiatry, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.,Brain Imaging Center, Academic Medical Center, Amsterdam, The Netherlands
| | - Kristina M Rapuano
- Department of Psychological and Brain Sciences, Dartmouth College, Hanover, New Hampshire
| | | | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain.,Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Paul Expert
- Centre for Mathematics of Precision Healthcare, Imperial College London, London, United Kingdom.,Department of Mathematics, Imperial College London, London, United Kingdom
| | - Aart H Schene
- Department of Psychiatry, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.,Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands.,Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Morten L Kringelbach
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom.,Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,Center for Music in the Brain, Aarhus University, Aarhus, Denmark
| | - Henricus G Ruhé
- Department of Psychiatry, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.,Department of Psychiatry, University of Oxford, Oxford, United Kingdom.,Department of Psychiatry, Radboud University Medical Center, Nijmegen, The Netherlands.,Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
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470
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Malagurski B, Péran P, Sarton B, Vinour H, Naboulsi E, Riu B, Bounes F, Seguin T, Lotterie JA, Fourcade O, Minville V, Ferré F, Achard S, Silva S. Topological disintegration of resting state functional connectomes in coma. Neuroimage 2019; 195:354-361. [PMID: 30862533 DOI: 10.1016/j.neuroimage.2019.03.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 03/01/2019] [Accepted: 03/06/2019] [Indexed: 01/10/2023] Open
Abstract
Graph theory has been playing an increasingly important role in understanding the organizational properties of brain networks, subsequently providing new tools for the search of neural correlates of consciousness, particularly in the context of patients recovering from severe brain injury. However, this approach is not without challenges, as it usually relies on arbitrarily fixing a threshold in order to retain the strongest connections proportionally equal across subjects. This method increases the comparability between individuals or groups but it risks the inclusion of false positive and therefore spurious connections, especially in the context of brain disorders. Resting state data acquired in 25 coma patients and 22 healthy subjects was compared. We obtained a representative fixed density of significant connections by first applying a p-value-based threshold on healthy subjects' networks and then choosing a threshold at which all individuals exhibited meaningful connections. The obtained threshold (i.e. 10%) was used to construct graphs in the patient group. The findings showed that coma patients have lower number of significant connections with approximately 50% of them not fulfilling the criteria of the fixed density threshold. The remaining patients with relatively preserved global functional connectivity had sufficient significant connections between regions, but showed signs of major whole-brain network reorganization. These results warrant careful consideration in the construction of functional connectomes in patients with disorders of consciousness and set the scene for future studies investigating potential clinical implications of such an approach.
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Affiliation(s)
- Brigitta Malagurski
- University Research Priority Program "Dynamics of Healthy Aging", University of Zürich, Zürich, Switzerland; Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France.
| | - Patrice Péran
- Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France
| | - Benjamine Sarton
- Critical Care Unit. University Teaching Hospital of Purpan, Place du Dr Baylac, F-31059, Toulouse Cedex 9, France
| | - Hélène Vinour
- Critical Care Unit. University Teaching Hospital of Purpan, Place du Dr Baylac, F-31059, Toulouse Cedex 9, France
| | - Edouard Naboulsi
- Critical Care Unit. University Teaching Hospital of Purpan, Place du Dr Baylac, F-31059, Toulouse Cedex 9, France
| | - Béatrice Riu
- Critical Care Unit. University Teaching Hospital of Purpan, Place du Dr Baylac, F-31059, Toulouse Cedex 9, France
| | - Fanny Bounes
- Critical Care Unit. University Teaching Hospital of Rangueil, F-31060, Toulouse Cedex 9, France
| | - Thierry Seguin
- Critical Care Unit. University Teaching Hospital of Rangueil, F-31060, Toulouse Cedex 9, France
| | | | - Olivier Fourcade
- Critical Care Unit. University Teaching Hospital of Rangueil, F-31060, Toulouse Cedex 9, France
| | - Vincent Minville
- Critical Care Unit. University Teaching Hospital of Rangueil, F-31060, Toulouse Cedex 9, France
| | - Fabrice Ferré
- Critical Care Unit. University Teaching Hospital of Purpan, Place du Dr Baylac, F-31059, Toulouse Cedex 9, France
| | - Sophie Achard
- Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000, Grenoble, France
| | - Stein Silva
- Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France; Critical Care Unit. University Teaching Hospital of Purpan, Place du Dr Baylac, F-31059, Toulouse Cedex 9, France
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471
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Chén OY, Cao H, Reinen JM, Qian T, Gou J, Phan H, De Vos M, Cannon TD. Resting-state brain information flow predicts cognitive flexibility in humans. Sci Rep 2019; 9:3879. [PMID: 30846746 PMCID: PMC6406001 DOI: 10.1038/s41598-019-40345-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Accepted: 02/07/2019] [Indexed: 11/25/2022] Open
Abstract
The human brain is a dynamic system, where communication between spatially distinct areas facilitates complex cognitive functions and behaviors. How information transfers between brain regions and how it gives rise to human cognition, however, are unclear. In this article, using resting-state functional magnetic resonance imaging (fMRI) data from 783 healthy adults in the Human Connectome Project (HCP) dataset, we map the brain's directed information flow architecture through a Granger-Geweke causality prism. We demonstrate that the information flow profiles in the general population primarily involve local exchanges within specialized functional systems, long-distance exchanges from the dorsal brain to the ventral brain, and top-down exchanges from the higher-order systems to the primary systems. Using an information flow map discovered from 550 subjects, the individual directed information flow profiles can significantly predict cognitive flexibility scores in 233 novel individuals. Our results provide evidence for directed information network architecture in the cerebral cortex, and suggest that features of the information flow configuration during rest underpin cognitive ability in humans.
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Affiliation(s)
- Oliver Y Chén
- Department of Psychology, Yale University, New Haven, CT, USA.
- Department of Engineering Science, University of Oxford, Oxford, UK.
| | - Hengyi Cao
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Jenna M Reinen
- Department of Psychology, Yale University, New Haven, CT, USA
- IBM Watson Research, New York, NY, USA
| | - Tianchen Qian
- Department of Statistics, Harvard University, Cambridge, MA, USA
| | - Jiangtao Gou
- Department of Mathematics and Statistics, The City University of New York, New York, NY, USA
- Fox Chase Cancer Center, Philadelphia, PA, USA
| | - Huy Phan
- Department of Engineering Science, University of Oxford, Oxford, UK
- School of Computing, University of Kent, Canterbury, UK
| | - Maarten De Vos
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Tyrone D Cannon
- Department of Psychology, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
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472
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Urchs S, Armoza J, Moreau C, Benhajali Y, St-Aubin J, Orban P, Bellec P. MIST: A multi-resolution parcellation of functional brain networks. ACTA ACUST UNITED AC 2019. [DOI: 10.12688/mniopenres.12767.2] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The functional architecture of the brain is organized across multiple levels of spatial resolutions, from distributed networks to the localized areas they are made of. A brain parcellation that defines functional nodes at multiple resolutions is required to investigate the functional connectome across these scales. Here we present the Multiresolution Intrinsic Segmentation Template (MIST), a multi-resolution group level parcellation of the cortical, subcortical and cerebellar gray matter. The individual MIST parcellations match other published group parcellations in internal homogeneity and reproducibility and perform very well in real-world application benchmarks. In addition, the MIST parcellations are fully annotated and provide a hierarchical decomposition of functional brain networks across nine resolutions (7 to 444 functional parcels). We hope that the MIST parcellation will accelerate research in brain connectivity across resolutions. Because visualizing multiresolution parcellations is challenging, we provide an interactive web interface to explore the MIST. The MIST is also available through the popular nilearn toolbox.
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473
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Zhu H, Huang J, Deng L, He N, Cheng L, Shu P, Yan F, Tong S, Sun J, Ling H. Abnormal Dynamic Functional Connectivity Associated With Subcortical Networks in Parkinson's Disease: A Temporal Variability Perspective. Front Neurosci 2019; 13:80. [PMID: 30837825 PMCID: PMC6389716 DOI: 10.3389/fnins.2019.00080] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Accepted: 01/25/2019] [Indexed: 01/08/2023] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disease characterized by dysfunction in distributed functional brain networks. Previous studies have reported abnormal changes in static functional connectivity using resting-state functional magnetic resonance imaging (fMRI). However, the dynamic characteristics of brain networks in PD is still poorly understood. This study aimed to quantify the characteristics of dynamic functional connectivity in PD patients at nodal, intra- and inter-subnetwork levels. Resting-state fMRI data of a total of 42 PD patients and 40 normal controls (NCs) were investigated from the perspective of the temporal variability on the connectivity profiles across sliding windows. The results revealed that PD patients had greater nodal variability in precentral and postcentral area (in sensorimotor network, SMN), middle occipital gyrus (in visual network), putamen (in subcortical network) and cerebellum, compared with NCs. Furthermore, at the subnetwork level, PD patients had greater intra-network variability for the subcortical network, salience network and visual network, and distributed changes of inter-network variability across several subnetwork pairs. Specifically, the temporal variability within and between subcortical network and other cortical subnetworks involving SMN, visual, ventral and dorsal attention networks as well as cerebellum was positively associated with the severity of clinical symptoms in PD patients. Additionally, the increased inter-network variability of cerebellum-auditory pair was also correlated with clinical severity of symptoms in PD patients. These observations indicate that temporal variability can detect the distributed abnormalities of dynamic functional network of PD patients at nodal, intra- and inter-subnetwork scales, and may provide new insights into understanding PD.
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Affiliation(s)
- Hong Zhu
- Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Juan Huang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lifu Deng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lin Cheng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Pin Shu
- Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shanbao Tong
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Junfeng Sun
- Shanghai Med-X Engineering Research Center, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Huawei Ling
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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474
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Abstract
OBJECTIVE The authors sought to identify a brain-based predictor of cocaine abstinence by using connectome-based predictive modeling (CPM), a recently developed machine learning approach. CPM is a predictive tool and a method of identifying networks that underlie specific behaviors ("neural fingerprints"). METHODS Fifty-three individuals participated in neuroimaging protocols at the start of treatment for cocaine use disorder, and again at the end of 12 weeks of treatment. CPM with leave-one-out cross-validation was conducted to identify pretreatment networks that predicted abstinence (percent cocaine-negative urine samples during treatment). Networks were applied to posttreatment functional MRI data to assess changes over time and ability to predict abstinence during follow-up. The predictive ability of identified networks was then tested in a separate, heterogeneous sample of individuals who underwent scanning before treatment for cocaine use disorder (N=45). RESULTS CPM predicted abstinence during treatment, as indicated by a significant correspondence between predicted and actual abstinence values (r=0.42, df=52). Identified networks included connections within and between canonical networks implicated in cognitive/executive control (frontoparietal, medial frontal) and in reward responsiveness (subcortical, salience, motor/sensory). Connectivity strength did not change with treatment, and strength at posttreatment assessment also significantly predicted abstinence during follow-up (r=0.34, df=39). Network strength in the independent sample predicted treatment response with 64% accuracy by itself and 71% accuracy when combined with baseline cocaine use. CONCLUSIONS These data demonstrate that individual differences in large-scale neural networks contribute to variability in treatment outcomes for cocaine use disorder, and they identify specific abstinence networks that may be targeted in novel interventions.
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Affiliation(s)
- Sarah W. Yip
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510,Child Study Center, Yale School of Medicine, New Haven, CT, 06510,Location of work and address for correspondence: Sarah W. Yip, 1 Church Street, Suite 731, New Haven, CT, 06510, USA; Tel: (203) 704-7588;
| | - Dustin Scheinost
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510,Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510
| | - Marc N. Potenza
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510,Child Study Center, Yale School of Medicine, New Haven, CT, 06510,Department of Neuroscience, Yale School of Medicine, New Haven, CT, 06510,Connecticut Mental Health Center, New Haven, CT, 06519
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475
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Rasero J, Diez I, Cortes JM, Marinazzo D, Stramaglia S. Connectome sorting by consensus clustering increases separability in group neuroimaging studies. Netw Neurosci 2019; 3:325-343. [PMID: 30793085 PMCID: PMC6370473 DOI: 10.1162/netn_a_00074] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Accepted: 11/09/2018] [Indexed: 01/27/2023] Open
Abstract
A fundamental challenge in preprocessing pipelines for neuroimaging datasets is to increase the signal-to-noise ratio for subsequent analyses. In the same line, we suggest here that the application of the consensus clustering approach to brain connectivity matrices can be a valid additional step for connectome processing to find subgroups of subjects with reduced intragroup variability and therefore increasing the separability of the distinct subgroups when connectomes are used as a biomarker. Moreover, by partitioning the data with consensus clustering before any group comparison (for instance, between a healthy population vs. a pathological one), we demonstrate that unique regions within each cluster arise and bring new information that could be relevant from a clinical point of view.
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Affiliation(s)
- Javier Rasero
- Biocruces Health Research Institute, Hospital Universitario de Cruces, Barakaldo, Spain
| | - Ibai Diez
- Functional Neurology Research Group, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Gordon Center, Department of Nuclear Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Neurotechnology Laboratory, Tecnalia Health Department, Derio, Spain
| | - Jesus M. Cortes
- Biocruces Health Research Institute, Hospital Universitario de Cruces, Barakaldo, Spain
- Department of Cell Biology and Histology, University of the Basque Country, Leioa, Spain
- Ikerbasque, The Basque Foundation for Science, Bilbao, Spain
| | - Daniele Marinazzo
- Faculty of Psychology and Educational Sciences, Department of Data Analysis, Ghent University, Ghent, Belgium
| | - Sebastiano Stramaglia
- Dipartimento di Fisica, Universitá degli Studi “Aldo Moro” Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Italy
- TIRES-Center of Innovative Technologies for Signal Detection and Processing, Universitá degli Studi “Aldo Moro” Bari, Italy
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476
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Bartholomew ME, Yee CM, Heller W, Miller GA, Spielberg JM. Reconfiguration of brain networks supporting inhibition of emotional challenge. Neuroimage 2019; 186:350-357. [PMID: 30394327 PMCID: PMC6372757 DOI: 10.1016/j.neuroimage.2018.10.066] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Revised: 09/25/2018] [Accepted: 10/24/2018] [Indexed: 01/28/2023] Open
Abstract
Reacting to the salient emotional features of a stimulus is adaptive unless the information is irrelevant or interferes with goal-directed behavior. The ability to ignore salient but otherwise extraneous information involves restructuring of brain networks and is a key impairment in several psychological disorders. Despite the importance of understanding inhibitory control of emotional response, the associated brain network mechanisms remain unknown. Utilizing functional magnetic resonance imaging (fMRI) data obtained from 103 participants performing an emotion-word Stroop (EWS) task, the present study applied graph-theory analysis to identify how brain regions subserving emotion processing and cognitive control are integrated within the global brain network to promote more specialized and efficient processing during successful inhibition of response to emotional distractors. The present study identified two sub-networks associated with emotion inhibition, one involving hyper-connectivity to prefrontal cortex and one involving hyper-connectivity to thalamus. Brain regions typically associated with identifying emotion salience were more densely connected with the thalamic hub, consistent with thalamic amplification of prefrontal cortex control of these regions. Additionally, stimuli high in emotional arousal prompted restructuring of the global network to increase clustered processing and overall communication efficiency. These results provide evidence that inhibition of emotion relies on interactions between cognitive control and emotion salience sub-networks.
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Affiliation(s)
- Morgan E Bartholomew
- Department of Psychology, UCLA, 1285 Franz Hall, Box 951563, Los Angeles, CA, 90095-1563, USA.
| | - Cindy M Yee
- Department of Psychology, UCLA, 1285 Franz Hall, Box 951563, Los Angeles, CA, 90095-1563, USA; Department of Psychiatry and Biobehavioral Sciences, UCLA, 760 Westwood Plaza, Los Angeles, CA, 90095, USA.
| | - Wendy Heller
- Department of Psychology, University of Illinois at Urbana-Champaign, 603 East Daniel Street, Champaign, IL, 61820, USA.
| | - Gregory A Miller
- Department of Psychology, UCLA, 1285 Franz Hall, Box 951563, Los Angeles, CA, 90095-1563, USA; Department of Psychiatry and Biobehavioral Sciences, UCLA, 760 Westwood Plaza, Los Angeles, CA, 90095, USA; Department of Psychology, University of Illinois at Urbana-Champaign, 603 East Daniel Street, Champaign, IL, 61820, USA.
| | - Jeffrey M Spielberg
- Department of Psychological and Brain Sciences, University of Delaware, 105 the Green, Newark, DE, 19716, USA.
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477
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Contreras JA, Avena-Koenigsberger A, Risacher SL, West JD, Tallman E, McDonald BC, Farlow MR, Apostolova LG, Goñi J, Dzemidzic M, Wu YC, Kessler D, Jeub L, Fortunato S, Saykin AJ, Sporns O. Resting state network modularity along the prodromal late onset Alzheimer's disease continuum. Neuroimage Clin 2019; 22:101687. [PMID: 30710872 PMCID: PMC6357852 DOI: 10.1016/j.nicl.2019.101687] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 12/12/2018] [Accepted: 01/20/2019] [Indexed: 01/01/2023]
Abstract
Alzheimer's disease is considered a disconnection syndrome, motivating the use of brain network measures to detect changes in whole-brain resting state functional connectivity (FC). We investigated changes in FC within and among resting state networks (RSN) across four different stages in the Alzheimer's disease continuum. FC changes were examined in two independent cohorts of individuals (84 and 58 individuals, respectively) each comprising control, subjective cognitive decline, mild cognitive impairment and Alzheimer's dementia groups. For each participant, FC was computed as a matrix of Pearson correlations between pairs of time series from 278 gray matter brain regions. We determined significant differences in FC modular organization with two distinct approaches, network contingency analysis and multiresolution consensus clustering. Network contingency analysis identified RSN sub-blocks that differed significantly across clinical groups. Multiresolution consensus clustering identified differences in the stability of modules across multiple spatial scales. Significant modules were further tested for statistical association with memory and executive function cognitive domain scores. Across both analytic approaches and in both participant cohorts, the findings converged on a pattern of FC that varied systematically with diagnosis within the frontoparietal network (FP) and between the FP network and default mode network (DMN). Disturbances of modular organization were manifest as greater internal coherence of the FP network and stronger coupling between FP and DMN, resulting in less segregation of these two networks. Our findings suggest that the pattern of interactions within and between specific RSNs offers new insight into the functional disruption that occurs across the Alzheimer's disease spectrum.
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Affiliation(s)
- Joey A Contreras
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA; Indiana Alzheimer Disease Center, IUSM, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA; Program in Medical Neuroscience, Paul and Carole Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA; Indiana Alzheimer Disease Center, IUSM, Indianapolis, IN, USA
| | - John D West
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA; Indiana Alzheimer Disease Center, IUSM, Indianapolis, IN, USA
| | - Eileen Tallman
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA; Indiana Alzheimer Disease Center, IUSM, Indianapolis, IN, USA
| | - Brenna C McDonald
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA; Indiana Alzheimer Disease Center, IUSM, Indianapolis, IN, USA; Program in Medical Neuroscience, Paul and Carole Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Martin R Farlow
- Indiana Alzheimer Disease Center, IUSM, Indianapolis, IN, USA; Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Liana G Apostolova
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA; Indiana Alzheimer Disease Center, IUSM, Indianapolis, IN, USA; Program in Medical Neuroscience, Paul and Carole Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Joaquín Goñi
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA; Indiana Alzheimer Disease Center, IUSM, Indianapolis, IN, USA; College of Engineering, Purdue University, West Lafayette, IN, USA
| | - Mario Dzemidzic
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA; Indiana Alzheimer Disease Center, IUSM, Indianapolis, IN, USA; Program in Medical Neuroscience, Paul and Carole Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Neurology, IUSM, Indianapolis, IN, USA
| | - Yu-Chien Wu
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA; Indiana Alzheimer Disease Center, IUSM, Indianapolis, IN, USA
| | - Daniel Kessler
- Departments of Statistics and Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Lucas Jeub
- Indiana University Network Science Institute, Bloomington, IN, USA; School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA
| | - Santo Fortunato
- Indiana University Network Science Institute, Bloomington, IN, USA; School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA; Indiana Alzheimer Disease Center, IUSM, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA; Program in Medical Neuroscience, Paul and Carole Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Neurology, IUSM, Indianapolis, IN, USA.
| | - Olaf Sporns
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine (IUSM), Indianapolis, IN, USA; Indiana Alzheimer Disease Center, IUSM, Indianapolis, IN, USA; Indiana University Network Science Institute, Bloomington, IN, USA; School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA; Program in Medical Neuroscience, Paul and Carole Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA.
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478
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Lindquist MA, Geuter S, Wager TD, Caffo BS. Modular preprocessing pipelines can reintroduce artifacts into fMRI data. Hum Brain Mapp 2019; 40:2358-2376. [PMID: 30666750 DOI: 10.1002/hbm.24528] [Citation(s) in RCA: 109] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 12/06/2018] [Accepted: 01/08/2019] [Indexed: 12/20/2022] Open
Abstract
The preprocessing pipelines typically used in both task and resting-state functional magnetic resonance imaging (rs-fMRI) analysis are modular in nature: They are composed of a number of separate filtering/regression steps, including removal of head motion covariates and band-pass filtering, performed sequentially and in a flexible order. In this article, we illustrate the shortcomings of this approach, as we show how later preprocessing steps can reintroduce artifacts previously removed from the data in prior preprocessing steps. We show that each regression step is a geometric projection of data onto a subspace, and that performing a sequence of projections can move the data into subspaces no longer orthogonal to those previously removed, reintroducing signal related to nuisance covariates. Thus, linear filtering operations are not commutative, and the order in which the preprocessing steps are performed is critical. These issues can arise in practice when any combination of standard preprocessing steps including motion regression, scrubbing, component-based correction, physiological correction, global signal regression, and temporal filtering are performed sequentially. In this work, we focus primarily on rs-fMRI. We illustrate the problem both theoretically and empirically through application to a test-retest rs-fMRI data set, and suggest remedies. These include (a) combining all steps into a single linear filter, or (b) sequential orthogonalization of covariates/linear filters performed in series.
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Affiliation(s)
- Martin A Lindquist
- Biostatistics, Johns Hopkins School of Public Health, Baltimore, Maryland
| | - Stephan Geuter
- Biostatistics, Johns Hopkins School of Public Health, Baltimore, Maryland.,Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado
| | - Tor D Wager
- Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado
| | - Brian S Caffo
- Biostatistics, Johns Hopkins School of Public Health, Baltimore, Maryland
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479
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Zhang H, Shen D, Lin W. Resting-state functional MRI studies on infant brains: A decade of gap-filling efforts. Neuroimage 2019; 185:664-684. [PMID: 29990581 PMCID: PMC6289773 DOI: 10.1016/j.neuroimage.2018.07.004] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 05/19/2018] [Accepted: 07/02/2018] [Indexed: 12/16/2022] Open
Abstract
Resting-state functional MRI (rs-fMRI) is one of the most prevalent brain functional imaging modalities. Previous rs-fMRI studies have mainly focused on adults and elderly subjects. Recently, infant rs-fMRI studies have become an area of active research. After a decade of gap filling studies, many facets of the brain functional development from early infancy to toddler has been uncovered. However, infant rs-fMRI is still in its infancy. The image analysis tools for neonates and young infants can be quite different from those for adults. From data analysis to result interpretation, more questions and issues have been raised, and new hypotheses have been formed. With the anticipated availability of unprecedented high-resolution rs-fMRI and dedicated analysis pipelines from the Baby Connectome Project (BCP), it is important now to revisit previous findings and hypotheses, discuss and comment existing issues and problems, and make a "to-do-list" for the future studies. This review article aims to comprehensively review a decade of the findings, unveiling hidden jewels of the fields of developmental neuroscience and neuroimage computing. Emphases will be given to early infancy, particularly the first few years of life. In this review, an end-to-end summary, from infant rs-fMRI experimental design to data processing, and from the development of individual functional systems to large-scale brain functional networks, is provided. A comprehensive summary of the rs-fMRI findings in developmental patterns is highlighted. Furthermore, an extensive summary of the neurodevelopmental disorders and the effects of other hazardous factors is provided. Finally, future research trends focusing on emerging dynamic functional connectivity and state-of-the-art functional connectome analysis are summarized. In next decade, early infant rs-fMRI and developmental connectome study could be one of the shining research topics.
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Affiliation(s)
- Han Zhang
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, 27599, USA
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, 27599, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, 27599, USA.
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480
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Interactive effects of OXTR and GAD1 on envy-associated behaviors and neural responses. PLoS One 2019; 14:e0210493. [PMID: 30633779 PMCID: PMC6329522 DOI: 10.1371/journal.pone.0210493] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Accepted: 12/23/2018] [Indexed: 12/26/2022] Open
Abstract
Inequity aversion (negative feelings induced by outcome differences between the self and other) plays a key role in human social behaviors. The neurotransmitters oxytocin and GABA have been implicated in neural responses to inequity. However, it remains poorly understood not only how individual genetic factors related to oxytocin and GABA affect the neural mechanisms behind inequity aversion, but also how these genes interact. To address these issues, we examined relationships between genotypes, behavioral decisions and brain activities during the ultimatum game. We identified interactive effects between the polymorphisms of the oxytocin receptor gene (OXTR) and glutamate decarboxylase 1 gene for GABA synthesis (GAD1) on envy aversion (i.e., disadvantageous inequity aversion) and on envy-induced activity in the dorsal ACC (dACC). Thus, our integrated approach suggested interactive genetic effects between OXTR and GAD1 on envy aversion and the underlying neural substrates.
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481
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Cheng W, Rolls ET, Robbins TW, Gong W, Liu Z, Lv W, Du J, Wen H, Ma L, Quinlan EB, Garavan H, Artiges E, Papadopoulos Orfanos D, Smolka MN, Schumann G, Kendrick K, Feng J. Decreased brain connectivity in smoking contrasts with increased connectivity in drinking. eLife 2019; 8:e40765. [PMID: 30616717 PMCID: PMC6336408 DOI: 10.7554/elife.40765] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2018] [Accepted: 12/20/2018] [Indexed: 01/01/2023] Open
Abstract
In a group of 831 participants from the general population in the Human Connectome Project, smokers exhibited low overall functional connectivity, and more specifically of the lateral orbitofrontal cortex which is associated with non-reward mechanisms, the adjacent inferior frontal gyrus, and the precuneus. Participants who drank a high amount had overall increases in resting state functional connectivity, and specific increases in reward-related systems including the medial orbitofrontal cortex and the cingulate cortex. Increased impulsivity was found in smokers, associated with decreased functional connectivity of the non-reward-related lateral orbitofrontal cortex; and increased impulsivity was found in high amount drinkers, associated with increased functional connectivity of the reward-related medial orbitofrontal cortex. The main findings were cross-validated in an independent longitudinal dataset with 1176 participants, IMAGEN. Further, the functional connectivities in 14-year-old non-smokers (and also in female low-drinkers) were related to who would smoke or drink at age 19. An implication is that these differences in brain functional connectivities play a role in smoking and drinking, together with other factors.
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Affiliation(s)
- Wei Cheng
- Institute of Science and Technology for Brain-inspired IntelligenceFudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University)Ministry of EducationShanghaiChina
- Department of Computer ScienceUniversity of WarwickCoventryUnited Kingdom
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-inspired IntelligenceFudan UniversityShanghaiChina
- Department of Computer ScienceUniversity of WarwickCoventryUnited Kingdom
- Oxford Centre for Computational NeuroscienceOxfordUnited Kingdom
| | - Trevor W Robbins
- Behavioural and Clinical Neuroscience InstituteUniversity of CambridgeCambridgeUnited Kingdom
- Department of PsychologyUniversity of CambridgeCambridgeUnited Kingdom
| | - Weikang Gong
- Institute of Science and Technology for Brain-inspired IntelligenceFudan UniversityShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
| | - Zhaowen Liu
- School of Computer Science and TechnologyXidian UniversityXi’anChina
| | - Wujun Lv
- School of MathematicsShanghai University Finance and EconomicsShanghaiChina
| | - Jingnan Du
- Institute of Science and Technology for Brain-inspired IntelligenceFudan UniversityShanghaiChina
| | - Hongkai Wen
- Department of Computer ScienceUniversity of WarwickCoventryUnited Kingdom
| | - Liang Ma
- Beijing Institute of Genomics, Chinese Academy of SciencesBeijingChina
| | - Erin Burke Quinlan
- Centre for Population Neuroscience and Stratified Medicine (PONS) and MRC-SGDP Centre, Institute of Psychiatry, Psychology and NeuroscienceKing’s College LondonLondonUnited Kingdom
| | - Hugh Garavan
- Department of PsychiatryUniversity of VermontVermontUnited States
- Department of Psychiatry PsychologyUniversity of VermontVermontUnited States
| | - Eric Artiges
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 'Neuroimaging & Psychiatry', University Paris Sud – Paris Saclay, University Paris Descartes, Service Hospitalier Frédéric Joliot and GH Nord Essonne Psychiatry Department 91G16OrsayFrance
| | | | - Michael N Smolka
- Department of Psychiatry and Neuroimaging CenterTechnische Universität DresdenDresdenGermany
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS) and MRC-SGDP Centre, Institute of Psychiatry, Psychology and NeuroscienceKing’s College LondonLondonUnited Kingdom
| | - Keith Kendrick
- Key Laboratory for Neuroinformation of the Ministry of Education, School of Life Science and Technology, Center for Information in MedicineUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired IntelligenceFudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University)Ministry of EducationShanghaiChina
- Department of Computer ScienceUniversity of WarwickCoventryUnited Kingdom
- School of Mathematical Sciences and Centre for Computational Systems BiologyFudan UniversityShanghaiChina
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482
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Takami K, Haruno M. Behavioral and functional connectivity basis for peer-influenced bystander participation in bullying. Soc Cogn Affect Neurosci 2019; 14:23-33. [PMID: 30481351 PMCID: PMC6348439 DOI: 10.1093/scan/nsy109] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Revised: 11/06/2018] [Accepted: 11/21/2018] [Indexed: 12/19/2022] Open
Abstract
Recent studies have shown that the reactions of bystanders who witness bullying significantly affect whether the bullying persists. However, the underlying behavioral and neural mechanisms that determine a peer-influenced bystander's participation in bullying remain largely unknown. Here, we designed a new 'catch-ball' task where four players choose to throw a sequence of normal or strong (aggressive) balls in turn and examined whether the players (n = 43) participated in other players' bullying. We analyzed behaviors with a computational model that quantifies the tendencies of a participant's (i) baseline propensity for bullying, (ii) reactive revenge, (iii) conformity to bullying, and (iv) capitulation to threat and estimated these effects on the choice of balls. We found only conformity had a positive effect on the throwing of strong balls. Furthermore, we identified a correlation between a participant's conformity and social anxiety. Our mediation analysis of resting-state functional magnetic resonance imaging revealed that there were significant relationships of each participant's functional connectivity between the amygdala and right temporoparietal junction (TPJ) and social anxiety to the participant's conformity to bullying. We also found that amygdala-TPJ connectivity partially mediated the relationship between social anxiety and conformity. These results highlighted the anxiety-based conformity and amygdala network on peer-influenced bystander participation in bullying.
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Affiliation(s)
- Kyosuke Takami
- Center for Information and Neural Networks, NICT, Suita, Osaka, Japan
- Graduate School of Frontier Biosciences, Osaka University, Suita, Osaka, Japan
| | - Masahiko Haruno
- Center for Information and Neural Networks, NICT, Suita, Osaka, Japan
- Graduate School of Frontier Biosciences, Osaka University, Suita, Osaka, Japan
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483
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Jahanian H, Holdsworth S, Christen T, Wu H, Zhu K, Kerr AB, Middione MJ, Dougherty RF, Moseley M, Zaharchuk G. Advantages of short repetition time resting-state functional MRI enabled by simultaneous multi-slice imaging. J Neurosci Methods 2019; 311:122-132. [DOI: 10.1016/j.jneumeth.2018.09.033] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Revised: 09/17/2018] [Accepted: 09/28/2018] [Indexed: 01/15/2023]
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484
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Hawco C, Viviano JD, Chavez S, Dickie EW, Calarco N, Kochunov P, Argyelan M, Turner JA, Malhotra AK, Buchanan RW, Voineskos AN. A longitudinal human phantom reliability study of multi-center T1-weighted, DTI, and resting state fMRI data. Psychiatry Res Neuroimaging 2018; 282:134-142. [PMID: 29945740 PMCID: PMC6482446 DOI: 10.1016/j.pscychresns.2018.06.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 06/06/2018] [Accepted: 06/06/2018] [Indexed: 12/31/2022]
Abstract
Multi-center MRI studies can enhance power, generalizability, and discovery for clinical neuroimaging research in brain disorders. Here, we sought to establish the utility of a clustering algorithm as an alternative to more traditional intra-class correlation coefficient approaches in a longitudinal multi-center human phantom study. We completed annual reliability scans on 'travelling human phantoms'. Acquisitions across sites were harmonized prospectively. Twenty-seven MRI sessions were available across four participants, scanned on five scanners, across three years. For each scan, three metrics were extracted: cortical thickness (CT), white matter fractional anisotropy (FA), and resting state functional connectivity (FC). For each metric, hierarchical clustering (Ward's method) was performed. The cluster solutions were compared to participant and scanner using the adjusted Rand index (ARI). For all metrics, data clustered by participant rather than by scanner (ARI > 0.8 comparing clusters to participants, ARI < 0.2 comparing clusters to scanners). These results demonstrate that hierarchical clustering can reliably identify structural and functional scans from different participants imaged on different scanners across time. With increasing interest in data-driven approaches in psychiatric and neurologic brain imaging studies, our findings provide a framework for multi-center analytic approaches aiming to identify subgroups of participants based on brain structure or function.
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Affiliation(s)
- Colin Hawco
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Joseph D Viviano
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, Canada
| | - Sofia Chavez
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Erin W Dickie
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, Canada
| | - Navona Calarco
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, Canada
| | - Peter Kochunov
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, P.O. Box 21247, Baltimore, MD, United States
| | - Miklos Argyelan
- Zucker Hillside Hospital, 75-59 263rd St, Glen Oaks, NY, United States
| | - Jessica A Turner
- Department of Psychology, Georgia State University, 33 Gilmer Street SE, Atlanta, GA, United States
| | - Anil K Malhotra
- Zucker Hillside Hospital, 75-59 263rd St, Glen Oaks, NY, United States; The Zucker School of Medicine at Hofstra/Northwell
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, P.O. Box 21247, Baltimore, MD, United States
| | - Aristotle N Voineskos
- Campbell Family Mental Health Institute, Centre for Addiction and Mental Health, 250 College St., Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
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485
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Mokhtari F, Laurienti PJ, Rejeski WJ, Ballard G. Dynamic Functional Magnetic Resonance Imaging Connectivity Tensor Decomposition: A New Approach to Analyze and Interpret Dynamic Brain Connectivity. Brain Connect 2018; 9:95-112. [PMID: 30318906 DOI: 10.1089/brain.2018.0605] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
There is a growing interest in using so-called dynamic functional connectivity, as the conventional static brain connectivity models are being questioned. Brain network analyses yield complex network data that are difficult to analyze and interpret. To deal with the complex structures, decomposition/factorization techniques that simplify the data are often used. For dynamic network analyses, data simplification is of even greater importance, as dynamic connectivity analyses result in a time series of complex networks. A new challenge that must be faced when using these decomposition/factorization techniques is how to interpret the resulting connectivity patterns. Connectivity patterns resulting from decomposition analyses are often visualized as networks in brain space, in the same way that pairwise correlation networks are visualized. This elevates the risk of conflating connections between nodes that represent correlations between nodes' time series with connections between nodes that result from decomposition analyses. Moreover, dynamic connectivity data may be represented with three-dimensional or four-dimensional (4D) tensors and decomposition results require unique interpretations. Thus, the primary goal of this article is to (1) address the issues that must be considered when interpreting the connectivity patterns from decomposition techniques and (2) show how the data structure and decomposition method interact to affect this interpretation. The outcome of our analyses is summarized as follows. (1) The edge strength in decomposition connectivity patterns represents complex relationships not pairwise interactions between the nodes. (2) The structure of the data significantly alters the connectivity patterns, for example, 4D data result in connectivity patterns with higher regional connections. (3) Orthogonal decomposition methods outperform in feature reduction applications, whereas nonorthogonal decomposition methods are better for mechanistic interpretation.
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Affiliation(s)
- Fatemeh Mokhtari
- 1 Laboratory for Complex Brain Networks, Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina.,2 Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Paul J Laurienti
- 1 Laboratory for Complex Brain Networks, Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina.,3 Translational Science Center, Wake Forest University, Winston-Salem, North Carolina
| | - W Jack Rejeski
- 1 Laboratory for Complex Brain Networks, Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina.,3 Translational Science Center, Wake Forest University, Winston-Salem, North Carolina.,4 Department of Health and Exercise Science, Wake Forest University, Winston-Salem, North Carolina
| | - Grey Ballard
- 5 Department of Computer Science, Wake Forest University, Winston-Salem, North Carolina
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486
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Fong AHC, Yoo K, Rosenberg MD, Zhang S, Li CSR, Scheinost D, Constable RT, Chun MM. Dynamic functional connectivity during task performance and rest predicts individual differences in attention across studies. Neuroimage 2018; 188:14-25. [PMID: 30521950 DOI: 10.1016/j.neuroimage.2018.11.057] [Citation(s) in RCA: 88] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 11/26/2018] [Accepted: 11/30/2018] [Indexed: 11/30/2022] Open
Abstract
Dynamic functional connectivity (DFC) aims to maximize resolvable information from functional brain scans by considering temporal changes in network structure. Recent work has demonstrated that static, i.e. time-invariant resting-state and task-based FC predicts individual differences in behavior, including attention. Here, we show that DFC predicts attention performance across individuals. Sliding-window FC matrices were generated from fMRI data collected during rest and attention task performance by calculating Pearson's r between every pair of nodes of a whole-brain atlas within overlapping 10-60s time segments. Next, variance in r values across windows was taken to quantify temporal variability in the strength of each connection, resulting in a DFC connectome for each individual. In a leave-one-subject-out-cross-validation approach, partial-least-square-regression (PLSR) models were then trained to predict attention task performance from DFC matrices. Predicted and observed attention scores were significantly correlated, indicating successful out-of-sample predictions across rest and task conditions. Combining DFC and static FC features numerically improves predictions over either model alone, but the improvement was not statistically significant. Moreover, dynamic and combined models generalized to two independent data sets (participants performing the Attention Network Task and the stop-signal task). Edges with significant PLSR coefficients concentrated in visual, motor, and executive-control brain networks; moreover, most of these coefficients were negative. Thus, better attention may rely on more stable, i.e. less variable, information flow between brain regions.
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Affiliation(s)
| | | | | | - Sheng Zhang
- Department of Psychiatry, Yale School of Medicine, USA
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale School of Medicine, USA; Department of Neuroscience, Yale School of Medicine, USA; Interdepartmental Neuroscience Program, Yale University, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA
| | - R Todd Constable
- Interdepartmental Neuroscience Program, Yale University, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, USA; Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06520, USA
| | - Marvin M Chun
- Department of Psychology, Yale University, USA; Department of Neuroscience, Yale School of Medicine, USA; Interdepartmental Neuroscience Program, Yale University, USA
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487
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Tobyne SM, Somers DC, Brissenden JA, Michalka SW, Noyce AL, Osher DE. Prediction of individualized task activation in sensory modality-selective frontal cortex with 'connectome fingerprinting'. Neuroimage 2018; 183:173-185. [PMID: 30092348 PMCID: PMC6292512 DOI: 10.1016/j.neuroimage.2018.08.007] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 08/01/2018] [Accepted: 08/05/2018] [Indexed: 11/25/2022] Open
Abstract
The human cerebral cortex is estimated to comprise 200-300 distinct functional regions per hemisphere. Identification of the precise anatomical location of an individual's unique set of functional regions is a challenge for neuroscience that has broad scientific and clinical utility. Recent studies have demonstrated the existence of four interleaved regions in lateral frontal cortex (LFC) that are part of broader visual attention and auditory attention networks (Michalka et al., 2015; Noyce et al., 2017; Tobyne et al., 2017). Due to a large degree of inter-subject anatomical variability, identification of these regions depends critically on within-subject analyses. Here, we demonstrate that, for both sexes, an individual's unique pattern of resting-state functional connectivity can accurately identify their specific pattern of visual- and auditory-selective working memory and attention task activation in lateral frontal cortex (LFC) using "connectome fingerprinting." Building on prior techniques (Saygin et al., 2011; Osher et al., 2016; Tavor et al., 2016; Smittenaar et al., 2017; Wang et al., 2017; Parker Jones et al., 2017), we demonstrate here that connectome fingerprint predictions are far more accurate than group-average predictions and match the accuracy of within-subject task-based functional localization, while requiring less data. These findings are robust across brain parcellations and are improved with penalized regression methods. Because resting-state data can be easily and rapidly collected, these results have broad implications for both clinical and research investigations of frontal lobe function. Our findings also provide a set of recommendations for future research.
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Affiliation(s)
- Sean M Tobyne
- Graduate Program for Neuroscience, Boston University, Boston, MA, 02215, USA
| | - David C Somers
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, 02215, USA.
| | - James A Brissenden
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, 02215, USA
| | | | - Abigail L Noyce
- Department of Psychological and Brain Sciences, Boston University, Boston, MA, 02215, USA
| | - David E Osher
- Department of Psychology, The Ohio State University, Columbus, OH, 43210, USA.
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488
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Wang C, Ng B, Garbi R. Multimodal Brain Parcellation Based on Functional and Anatomical Connectivity. Brain Connect 2018; 8:604-617. [PMID: 30499336 DOI: 10.1089/brain.2017.0576] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Brain parcellation is often a prerequisite for network analysis due to the statistical challenges, computational burdens, and interpretation difficulties arising from the high dimensionality of neuroimaging data. Predominant approaches are largely unimodal with functional magnetic resonance imaging (fMRI) being the primary modality used. These approaches thus neglect other brain attributes that relate to brain organization. In this paper, we propose an approach for integrating fMRI and diffusion MRI (dMRI) data. Our approach introduces a nonlinear mapping between the connectivity values of two modalities, and adaptively balances their weighting based on their voxel-wise test-retest reliability. An efficient region level extension that additionally incorporates structural information on gyri and sulci is further presented. To validate, we compare multimodal parcellations with unimodal parcellations and existing atlases on the Human Connectome Project data. We show that multimodal parcellations achieve higher reproducibility, comparable/higher functional homogeneity, and comparable/higher leftout data likelihood. The boundaries of multimodal parcels are observed to align to those based on cyto-architecture, and subnetworks extracted from multimodal parcels matched well with established brain systems. Our results thus show that multimodal information improves brain parcellation.
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Affiliation(s)
- Chendi Wang
- University of British Columbia, Electrical and Computer Engineering , ICICS x421-2366 Main Mall , Vancouver, British Columbia, Canada , V6T 1Z4 ;
| | - Bernard Ng
- University of British Columbia, Department of Statistics , Vancouver, British Columbia, Canada ;
| | - Rafeef Garbi
- University of British Columbia, Electrical and Computer Engineering, Vancouver, British Columbia, Canada ;
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489
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Hsu WT, Rosenberg MD, Scheinost D, Constable RT, Chun MM. Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals. Soc Cogn Affect Neurosci 2018; 13:224-232. [PMID: 29373729 PMCID: PMC5827338 DOI: 10.1093/scan/nsy002] [Citation(s) in RCA: 104] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Accepted: 01/11/2018] [Indexed: 12/17/2022] Open
Abstract
The personality dimensions of neuroticism and extraversion are strongly associated with emotional experience and affective disorders. Previous studies reported functional magnetic resonance imaging (fMRI) activity correlates of these traits, but no study has used brain-based measures to predict them. Here, using a fully cross-validated approach, we predict novel individuals’ neuroticism and extraversion from functional connectivity (FC) data observed as they simply rested during fMRI scanning. We applied a data-driven technique, connectome-based predictive modeling (CPM), to resting-state FC data and neuroticism and extraversion scores (self-reported NEO Five Factor Inventory) from 114 participants of the Nathan Kline Institute Rockland sample. After dividing the whole brain into 268 nodes using a predefined functional atlas, we defined each individual’s FC matrix as the set of correlations between the activity timecourses of every pair of nodes. CPM identified networks consisting of functional connections correlated with neuroticism and extraversion scores, and used strength in these networks to predict a left-out individual’s scores. CPM predicted neuroticism and extraversion in novel individuals, demonstrating that patterns in resting-state FC reveal trait-level measures of personality. CPM also revealed predictive networks that exhibit some anatomical patterns consistent with past studies and potential new brain areas of interest in personality.
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Affiliation(s)
- Wei-Ting Hsu
- Department of Psychology, Yale University, New Haven, CT 06520, USA
| | | | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA
| | - R Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT 06520, USA.,Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA.,Department of Neurosurgery, Yale School of Medicine, New Haven, CT 06520, USA
| | - Marvin M Chun
- Department of Psychology, Yale University, New Haven, CT 06520, USA.,Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA.,Department of Neuroscience, Yale School of Medicine, New Haven, CT 06520, USA
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490
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Rasero J, Aerts H, Ontivero Ortega M, Cortes JM, Stramaglia S, Marinazzo D. Predicting functional networks from region connectivity profiles in task-based versus resting-state fMRI data. PLoS One 2018; 13:e0207385. [PMID: 30419063 PMCID: PMC6231684 DOI: 10.1371/journal.pone.0207385] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 10/29/2018] [Indexed: 11/23/2022] Open
Abstract
Intrinsic Connectivity Networks, patterns of correlated activity emerging from “resting-state” BOLD time series, are increasingly being associated with cognitive, clinical, and behavioral aspects, and compared with patterns of activity elicited by specific tasks. We study the reconfiguration of brain networks between task and resting-state conditions by a machine learning approach, to highlight the Intrinsic Connectivity Networks (ICNs) which are more affected by the change of network configurations in task vs. rest. To this end, we use a large cohort of publicly available data in both resting and task-based fMRI paradigms. By applying a battery of different supervised classifiers relying only on task-based measurements, we show that the highest accuracy to predict ICNs is reached with a simple neural network of one hidden layer. In addition, when testing the fitted model on resting state measurements, such architecture yields a performance close to 90% for areas connected to the task performed, which mainly involve the visual and sensorimotor cortex, whilst a relevant decrease of the performance is observed in the other ICNs. On one hand, our results confirm the correspondence of ICNs in both paradigms (task and resting) thus opening a window for future clinical applications to subjects whose participation in a required task cannot be guaranteed. On the other hand it is shown that brain areas not involved in the task display different connectivity patterns in the two paradigms.
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Affiliation(s)
- Javier Rasero
- Biocruces Health Research Institute. Hospital Universitario de Cruces. E-48903, Barakaldo, Spain
- Dipartimento di Fisica, Universitá degli Studi “Aldo Moro” Bari, Italy
| | - Hannelore Aerts
- Faculty of Psychology and Educational Sciences, Department of Data Analysis, Ghent University, Henri Dunantlaan 2, B-9000 Ghent, Belgium
| | - Marlis Ontivero Ortega
- Faculty of Psychology and Educational Sciences, Department of Data Analysis, Ghent University, Henri Dunantlaan 2, B-9000 Ghent, Belgium
- Neuroinformatics Department, Cuban Center for Neuroscience (CNeuro), La Habana, Cuba
| | - Jesus M. Cortes
- Biocruces Health Research Institute. Hospital Universitario de Cruces. E-48903, Barakaldo, Spain
- Ikerbasque, The Basque Foundation for Science, E-48011, Bilbao, Spain
| | - Sebastiano Stramaglia
- Dipartimento di Fisica, Universitá degli Studi “Aldo Moro” Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
- * E-mail:
| | - Daniele Marinazzo
- Faculty of Psychology and Educational Sciences, Department of Data Analysis, Ghent University, Henri Dunantlaan 2, B-9000 Ghent, Belgium
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491
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On the relationship between instantaneous phase synchrony and correlation-based sliding windows for time-resolved fMRI connectivity analysis. Neuroimage 2018; 181:85-94. [DOI: 10.1016/j.neuroimage.2018.06.020] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2017] [Revised: 04/30/2018] [Accepted: 06/05/2018] [Indexed: 11/22/2022] Open
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492
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Suo X, Lei D, Li L, Li W, Dai J, Wang S, He M, Zhu H, Kemp GJ, Gong Q. Psychoradiological patterns of small-world properties and a systematic review of connectome studies of patients with 6 major psychiatric disorders. J Psychiatry Neurosci 2018; 43:427. [PMID: 30375837 PMCID: PMC6203546 DOI: 10.1503/jpn.170214] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Revised: 01/07/2018] [Accepted: 01/28/2018] [Indexed: 02/05/2023] Open
Abstract
Background Brain connectome research based on graph theoretical analysis shows that small-world topological properties play an important role in the structural and functional alterations observed in patients with psychiatric disorders. However, the reported global topological alterations in small-world properties are controversial, are not consistently conceptualized according to agreed-upon criteria, and are not critically examined for consistent alterations in patients with each major psychiatric disorder. Methods Based on a comprehensive PubMed search, we systematically reviewed studies using noninvasive neuroimaging data and graph theoretical approaches for 6 major psychiatric disorders: schizophrenia, major depressive disorder (MDD), attention-deficit/hyperactivity disorder (ADHD), bipolar disorder (BD), obsessive–compulsive disorder (OCD) and posttraumatic stress disorder (PTSD). Here, we describe the main patterns of altered small-world properties and then systematically review the evidence for these alterations in the structural and functional connectome in patients with these disorders. Results We selected 40 studies of schizophrenia, 33 studies of MDD, 5 studies of ADHD, 5 studies of BD, 7 studies of OCD and 5 studies of PTSD. The following 4 patterns of altered small-world properties are defined from theperspectives of segregation and integration: "regularization," "randomization," "stronger small-worldization" and "weaker small-worldization." Although more differences than similarities are noted in patients with these disorders, a prominent trend is the structural regularization versus functional randomization in patients with schizophrenia. Limitations Differences in demographic and clinical characteristics, preprocessing steps and analytical methods can produce contradictory results, increasing the difficulty of integrating results across different studies. Conclusion Four psychoradiological patterns of altered small-world properties are proposed. The analysis of altered smallworld properties may provide novel insights into the pathophysiological mechanisms underlying psychiatric disorders from a connectomic perspective. In future connectome studies, the global network measures of both segregation and integration should be calculated to fully evaluate altered small-world properties in patients with a particular disease.
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Affiliation(s)
- Xueling Suo
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041 China (Suo, Lei, Li, Gong); the Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK (Lei); the Department of Psychoradiology, Chengdu Mental Health Center, Chengdu, Sichuan, China (Dai, Wang, He); the Laboratory of Stem Cell Biology, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Zhu); the Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK (Kemp); and the Department of Psychology, School of Public Administration, Sichuan University, Chengdu, Sichuan, China (Gong)
| | - Du Lei
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041 China (Suo, Lei, Li, Gong); the Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK (Lei); the Department of Psychoradiology, Chengdu Mental Health Center, Chengdu, Sichuan, China (Dai, Wang, He); the Laboratory of Stem Cell Biology, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Zhu); the Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK (Kemp); and the Department of Psychology, School of Public Administration, Sichuan University, Chengdu, Sichuan, China (Gong)
| | - Lei Li
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041 China (Suo, Lei, Li, Gong); the Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK (Lei); the Department of Psychoradiology, Chengdu Mental Health Center, Chengdu, Sichuan, China (Dai, Wang, He); the Laboratory of Stem Cell Biology, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Zhu); the Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK (Kemp); and the Department of Psychology, School of Public Administration, Sichuan University, Chengdu, Sichuan, China (Gong)
| | - Wenbin Li
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041 China (Suo, Lei, Li, Gong); the Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK (Lei); the Department of Psychoradiology, Chengdu Mental Health Center, Chengdu, Sichuan, China (Dai, Wang, He); the Laboratory of Stem Cell Biology, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Zhu); the Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK (Kemp); and the Department of Psychology, School of Public Administration, Sichuan University, Chengdu, Sichuan, China (Gong)
| | - Jing Dai
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041 China (Suo, Lei, Li, Gong); the Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK (Lei); the Department of Psychoradiology, Chengdu Mental Health Center, Chengdu, Sichuan, China (Dai, Wang, He); the Laboratory of Stem Cell Biology, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Zhu); the Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK (Kemp); and the Department of Psychology, School of Public Administration, Sichuan University, Chengdu, Sichuan, China (Gong)
| | - Song Wang
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041 China (Suo, Lei, Li, Gong); the Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK (Lei); the Department of Psychoradiology, Chengdu Mental Health Center, Chengdu, Sichuan, China (Dai, Wang, He); the Laboratory of Stem Cell Biology, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Zhu); the Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK (Kemp); and the Department of Psychology, School of Public Administration, Sichuan University, Chengdu, Sichuan, China (Gong)
| | - Manxi He
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041 China (Suo, Lei, Li, Gong); the Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK (Lei); the Department of Psychoradiology, Chengdu Mental Health Center, Chengdu, Sichuan, China (Dai, Wang, He); the Laboratory of Stem Cell Biology, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Zhu); the Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK (Kemp); and the Department of Psychology, School of Public Administration, Sichuan University, Chengdu, Sichuan, China (Gong)
| | - Hongyan Zhu
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041 China (Suo, Lei, Li, Gong); the Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK (Lei); the Department of Psychoradiology, Chengdu Mental Health Center, Chengdu, Sichuan, China (Dai, Wang, He); the Laboratory of Stem Cell Biology, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Zhu); the Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK (Kemp); and the Department of Psychology, School of Public Administration, Sichuan University, Chengdu, Sichuan, China (Gong)
| | - Graham J. Kemp
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041 China (Suo, Lei, Li, Gong); the Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK (Lei); the Department of Psychoradiology, Chengdu Mental Health Center, Chengdu, Sichuan, China (Dai, Wang, He); the Laboratory of Stem Cell Biology, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Zhu); the Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK (Kemp); and the Department of Psychology, School of Public Administration, Sichuan University, Chengdu, Sichuan, China (Gong)
| | - Qiyong Gong
- From the Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, 610041 China (Suo, Lei, Li, Gong); the Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK (Lei); the Department of Psychoradiology, Chengdu Mental Health Center, Chengdu, Sichuan, China (Dai, Wang, He); the Laboratory of Stem Cell Biology, State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, Sichuan, China (Zhu); the Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Ageing and Chronic Disease, University of Liverpool, Liverpool, UK (Kemp); and the Department of Psychology, School of Public Administration, Sichuan University, Chengdu, Sichuan, China (Gong)
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493
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Fountain-Zaragoza S, Samimy S, Rosenberg MD, Prakash RS. Connectome-based models predict attentional control in aging adults. Neuroimage 2018; 186:1-13. [PMID: 30394324 DOI: 10.1016/j.neuroimage.2018.10.074] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Revised: 10/24/2018] [Accepted: 10/26/2018] [Indexed: 12/23/2022] Open
Abstract
There are well-characterized age-related differences in behavioral and neural responses to tasks of attentional control. However, there is also increasing recognition of individual variability in the process of neurocognitive aging. Using connectome-based predictive modeling, a method for predicting individual-level behaviors from whole-brain functional connectivity, a sustained attention connectome-based prediction model (saCPM) has been derived in young adults. The saCPM consists of two large-scale functional networks: a high-attention network whose strength predicts better attention and a low-attention network whose strength predicts worse attention. Here we examined the generalizability of the saCPM for predicting inhibitory control in an aging sample. Forty-two healthy young adults (n = 21, ages 18-30) and older adults (n = 21, ages 60-80) performed a modified Stroop task, on which older adults exhibited poorer performance, indexed by higher reaction time cost between incongruent and congruent trials. The saCPM generalized to predict reaction time cost across age groups, but did not account for age-related differences in performance. Exploratory analyses were conducted to characterize the effects of age on functional connectivity and behavior. We identified subnetworks of the saCPM that exhibited age-related differences in strength. The strength of two low-attention subnetworks, consisting of frontoparietal, medial frontal, default mode, and motor nodes that were more strongly connected in older adults, mediated the effect of age group on performance. These results support the saCPM's ability to capture attention-related patterns reflected in each individual's functional connectivity signature across both task context and age. However, older and younger adults exhibit functional connectivity differences within components of the saCPM networks, and it is these connections that better account for age-related deficits in attentional control.
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Affiliation(s)
| | - Shaadee Samimy
- Department of Psychology, The Ohio State University, USA
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494
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Liu A, Lin SJ, Mi T, Chen X, Chan P, Wang ZJ, McKeown MJ. Decreased subregional specificity of the putamen in Parkinson's Disease revealed by dynamic connectivity-derived parcellation. Neuroimage Clin 2018; 20:1163-1175. [PMID: 30388599 PMCID: PMC6214880 DOI: 10.1016/j.nicl.2018.10.022] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Revised: 10/08/2018] [Accepted: 10/21/2018] [Indexed: 12/16/2022]
Abstract
Parkinson's Disease (PD) is associated with decreased ability to perform habitual tasks, relying instead on goal-directed behaviour subserved by different cortical/subcortical circuits, including parts of the putamen. We explored the functional subunits in the putamen in PD using novel dynamic connectivity features derived from resting state fMRI recorded from thirty PD subjects and twenty-eight age-matched healthy controls (HC). Dynamic functional segmentation of the putamina was obtained by determining the correlation between each voxel in each putamen along a moving window and applying a joint temporal clustering algorithm to establish cluster membership of each voxel at each window. Contiguous voxels that had consistent cluster membership across all windows were then considered to be part of a homogeneous functional subunit. As PD subjects robustly had two homogenous clusters in the putamina, we also segmented the putamina in HC into two dynamic clusters for a fair comparison. We then estimated the dynamic connectivity using sliding windowed correlation between the mean signal from the identified homogenous subunits and 56 other predefined cortical and subcortical ROIs. Specifically, the mean dynamic connectivity strength and connectivity deviation were then compared to evaluate subregional differences. HC subjects had significant differences in mean dynamic connectivity and connectivity deviation between the two putaminal subunits. The posterior subunit connected strongly to sensorimotor areas, the cerebellum, as well as the middle frontal gyrus. The anterior subunit had strong mean dynamic connectivity to the nucleus accumbens, hippocampus, amygdala, caudate and cingulate. In contrast, PD subjects had fewer differences in mean dynamic connectivity between subunits, indicating a degradation of subregional specificity. Overall UPDRS III and MoCA scores could be predicted using mean dynamic connectivity strength and connectivity deviation. Side of onset of the disease was also jointly related with functional connectivity features. Our results suggest a robust loss of specificity of mean dynamic connectivity and connectivity deviation in putaminal subunits in PD that is sensitive to disease severity. In addition, altered mean dynamic connectivity and connectivity deviation features in PD suggest that looking at connectivity dynamics offers an additional dimension for assessment of neurodegenerative disorders.
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Affiliation(s)
- Aiping Liu
- Pacific Parkinson's Research Centre, Vancouver, Canada; Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada.
| | - Sue-Jin Lin
- Pacific Parkinson's Research Centre, Vancouver, Canada; Graduate Program in Neuroscience, University of British Columbia, Vancouver, Canada
| | - Taomian Mi
- Department of Neurology, Neurobiology and Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing Institute of Brain Disorders, Beijing, China
| | - Xun Chen
- Department of Electronic Science and Technology, University of Science and Technology of China, Hefei, China.
| | - Piu Chan
- Department of Neurology, Neurobiology and Geriatrics, Xuanwu Hospital of Capital Medical University, Beijing Institute of Brain Disorders, Beijing, China
| | - Z Jane Wang
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada
| | - Martin J McKeown
- Pacific Parkinson's Research Centre, Vancouver, Canada; Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, Canada; Graduate Program in Neuroscience, University of British Columbia, Vancouver, Canada; Department of Medicine (Neurology), University of British Columbia, Vancouver, Canada
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495
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Spechler PA, Allgaier N, Chaarani B, Whelan R, Watts R, Orr C, Albaugh MD, D'Alberto N, Higgins ST, Hudson KE, Mackey S, Potter A, Banaschewski T, Bokde ALW, Bromberg U, Büchel C, Cattrell A, Conrod PJ, Desrivières S, Flor H, Frouin V, Gallinat J, Gowland P, Heinz A, Ittermann B, Martinot JL, Paillère Martinot ML, Nees F, Papadopoulos Orfanos D, Paus T, Poustka L, Smolka MN, Walter H, Schumann G, Althoff RR, Garavan H. The initiation of cannabis use in adolescence is predicted by sex-specific psychosocial and neurobiological features. Eur J Neurosci 2018; 50:2346-2356. [PMID: 29889330 DOI: 10.1111/ejn.13989] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Revised: 05/03/2018] [Accepted: 05/21/2018] [Indexed: 12/14/2022]
Abstract
Cannabis use initiated during adolescence might precipitate negative consequences in adulthood. Thus, predicting adolescent cannabis use prior to any exposure will inform the aetiology of substance abuse by disentangling predictors from consequences of use. In this prediction study, data were drawn from the IMAGEN sample, a longitudinal study of adolescence. All selected participants (n = 1,581) were cannabis-naïve at age 14. Those reporting any cannabis use (out of six ordinal use levels) by age 16 were included in the outcome group (N = 365, males n = 207). Cannabis-naïve participants at age 14 and 16 were included in the comparison group (N = 1,216, males n = 538). Psychosocial, brain and genetic features were measured at age 14 prior to any exposure. Cross-validated regularized logistic regressions for each use level by sex were used to perform feature selection and obtain prediction error statistics on independent observations. Predictors were probed for sex- and drug-specificity using post-hoc logistic regressions. Models reliably predicted use as indicated by satisfactory prediction error statistics, and contained psychosocial features common to both sexes. However, males and females exhibited distinct brain predictors that failed to predict use in the opposite sex or predict binge drinking in independent samples of same-sex participants. Collapsed across sex, genetic variation on catecholamine and opioid receptors marginally predicted use. Using machine learning techniques applied to a large multimodal dataset, we identified a risk profile containing psychosocial and sex-specific brain prognostic markers, which were likely to precede and influence cannabis initiation.
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Affiliation(s)
- Philip A Spechler
- Vermont Center on Behavior and Health, University of Vermont, Burlington, VT, USA.,Department of Psychological Science, University of Vermont, Burlington, VT, 05401, USA.,Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - Nicholas Allgaier
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - Bader Chaarani
- Vermont Center on Behavior and Health, University of Vermont, Burlington, VT, USA.,Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Richard Watts
- Department of Radiology, University of Vermont, Burlington, VT, USA
| | - Catherine Orr
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - Matthew D Albaugh
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | | | - Stephen T Higgins
- Vermont Center on Behavior and Health, University of Vermont, Burlington, VT, USA.,Department of Psychological Science, University of Vermont, Burlington, VT, 05401, USA.,Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - Kelsey E Hudson
- Department of Psychological Science, University of Vermont, Burlington, VT, 05401, USA
| | - Scott Mackey
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - Alexandra Potter
- Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - Tobias Banaschewski
- Medical Faculty Mannheim, Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neurosciences, Trinity College Dublin, Dublin, Ireland
| | - Uli Bromberg
- University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | | | - Anna Cattrell
- Centre for Population Neuroscience and Stratified Medicine (PONS) and MRC-SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Patricia J Conrod
- Department of Psychiatry, Universite de Montreal, CHU Ste Justine Hospital, Montreal, Canada
| | - Sylvane Desrivières
- Centre for Population Neuroscience and Stratified Medicine (PONS) and MRC-SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Herta Flor
- Medical Faculty Mannheim, Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany.,Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany
| | - Vincent Frouin
- NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Jürgen Gallinat
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - Andreas Heinz
- Department of Psychiatry and Psychotherapy, Charité, Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany
| | - Bernd Ittermann
- Physikalisch-Technische Bundesanstalt (PTB), Braunschweig, Germany
| | - Jean-Luc Martinot
- DIGITEO Labs, Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 "Neuroimaging & Psychiatry", University Paris Sud - University Paris Saclay, Gif sur Yvette, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 "Neuroimaging & Psychiatry", University Paris Sud - Paris Saclay, University Paris Descartes, Paris, France.,Department of Adolescent Psychopathology and Medicine, AP-HP, Maison de Solenn, Cochin Hospital, Paris, France
| | - Frauke Nees
- Medical Faculty Mannheim, Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany.,Medical Faculty Mannheim, Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Heidelberg University, Mannheim, Germany
| | | | - Tomáš Paus
- Baycrest and Departments of Psychology and Psychiatry, Rotman Research Institute, University of Toronto, Toronto, ON, M6A 2E1, Canada
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, 37075, Göttingen, Germany.,Clinic for Child and Adolescent Psychiatry, Medical University of Vienna, Vienna, Austria
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Department of Psychiatry and Psychotherapy, Charité, Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany
| | - Gunter Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS) and MRC-SGDP Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Robert R Althoff
- Department of Psychological Science, University of Vermont, Burlington, VT, 05401, USA.,Department of Psychiatry, University of Vermont, Burlington, VT, USA
| | - Hugh Garavan
- Vermont Center on Behavior and Health, University of Vermont, Burlington, VT, USA.,Department of Psychological Science, University of Vermont, Burlington, VT, 05401, USA.,Department of Psychiatry, University of Vermont, Burlington, VT, USA
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496
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Ryyppö E, Glerean E, Brattico E, Saramäki J, Korhonen O. Regions of Interest as nodes of dynamic functional brain networks. Netw Neurosci 2018; 2:513-535. [PMID: 30294707 PMCID: PMC6147715 DOI: 10.1162/netn_a_00047] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2017] [Accepted: 02/06/2018] [Indexed: 11/04/2022] Open
Abstract
The properties of functional brain networks strongly depend on how their nodes are chosen. Commonly, nodes are defined by Regions of Interest (ROIs), predetermined groupings of fMRI measurement voxels. Earlier, we demonstrated that the functional homogeneity of ROIs, captured by their spatial consistency, varies widely across ROIs in commonly used brain atlases. Here, we ask how ROIs behave as nodes of dynamic brain networks. To this end, we use two measures: spatiotemporal consistency measures changes in spatial consistency across time and network turnover quantifies the changes in the local network structure around an ROI. We find that spatial consistency varies non-uniformly in space and time, which is reflected in the variation of spatiotemporal consistency across ROIs. Furthermore, we see time-dependent changes in the network neighborhoods of the ROIs, reflected in high network turnover. Network turnover is nonuniformly distributed across ROIs: ROIs with high spatiotemporal consistency have low network turnover. Finally, we reveal that there is rich voxel-level correlation structure inside ROIs. Because the internal structure and the connectivity of ROIs vary in time, the common approach of using static node definitions may be surprisingly inaccurate. Therefore, network neuroscience would greatly benefit from node definition strategies tailored for dynamical networks.
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Affiliation(s)
- Elisa Ryyppö
- Department of Computer Science, School of Science, Aalto University, Espoo, Finland
| | - Enrico Glerean
- Turku PET Centre, University of Turku, Turku, Finland
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
| | - Elvira Brattico
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, and The Royal Academy of Music Aarhus/Aalborg, Denmark
| | - Jari Saramäki
- Department of Computer Science, School of Science, Aalto University, Espoo, Finland
| | - Onerva Korhonen
- Department of Computer Science, School of Science, Aalto University, Espoo, Finland
- Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland
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497
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Xie H, Gonzalez-Castillo J, Handwerker DA, Bandettini PA, Calhoun VD, Chen G, Damaraju E, Liu X, Mitra S. Time-varying whole-brain functional network connectivity coupled to task engagement. Netw Neurosci 2018; 3:49-66. [PMID: 30793073 PMCID: PMC6326730 DOI: 10.1162/netn_a_00051] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2017] [Accepted: 03/16/2018] [Indexed: 11/30/2022] Open
Abstract
Brain functional connectivity (FC), as measured by blood oxygenation level-dependent (BOLD) signal, fluctuates at the scale of 10s of seconds. It has recently been found that whole-brain dynamic FC (dFC) patterns contain sufficient information to permit identification of ongoing tasks. Here, we hypothesize that dFC patterns carry fine-grained information that allows for tracking short-term task engagement levels (i.e., 10s of seconds long). To test this hypothesis, 25 subjects were scanned continuously for 25 min while they performed and transitioned between four different tasks: working memory, visual attention, math, and rest. First, we estimated dFC patterns by using a sliding window approach. Next, we extracted two engagement-specific FC patterns representing active engagement and passive engagement by using k-means clustering. Then, we derived three metrics from whole-brain dFC patterns to track engagement level, that is, dissimilarity between dFC patterns and engagement-specific FC patterns, and the level of brainwide integration level. Finally, those engagement markers were evaluated against windowed task performance by using a linear mixed effects model. Significant relationships were observed between abovementioned metrics and windowed task performance for the working memory task only. These findings partially confirm our hypothesis and underscore the potential of whole-brain dFC to track short-term task engagement levels.
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Affiliation(s)
- Hua Xie
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Daniel A. Handwerker
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Peter A. Bandettini
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
- Functional MRI Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
| | - Gang Chen
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Eswar Damaraju
- The Mind Research Network, Albuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
| | - Xiangyu Liu
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA
| | - Sunanda Mitra
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA
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498
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Cheng W, Rolls ET, Ruan H, Feng J. Functional Connectivities in the Brain That Mediate the Association Between Depressive Problems and Sleep Quality. JAMA Psychiatry 2018; 75:1052-1061. [PMID: 30046833 PMCID: PMC6233808 DOI: 10.1001/jamapsychiatry.2018.1941] [Citation(s) in RCA: 154] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
IMPORTANCE Depression is associated with poor sleep quality. Understanding the neural connectivity that underlies both conditions and mediates the association between them is likely to lead to better-directed treatments for depression and associated sleep problems. OBJECTIVE To identify the brain areas that mediate the association of depressive symptoms with poor sleep quality and advance understanding of the differences in brain connectivity in depression. DESIGN, SETTING, AND PARTICIPANTS This study collected data from participants in the Human Connectome Project using the Adult Self-report of Depressive Problems portion of the Achenbach Adult Self-Report for Ages 18-59, a survey of self-reported sleep quality, and resting-state functional magnetic resonance imaging. Cross-validation of the sleep findings was conducted in 8718 participants from the UK Biobank. MAIN OUTCOMES AND MEASURES Correlations between functional connectivity, scores on the Adult Self-Report of Depressive Problems, and sleep quality. RESULTS A total of 1017 participants from the Human Connectome Project (of whom 546 [53.7%] were female; age range, 22 to 35 years) drawn from a general population in the United States were included. The Depressive Problems score was positively correlated with poor sleep quality (r = 0.371; P < .001). A total of 162 functional connectivity links involving areas associated with sleep, such as the precuneus, anterior cingulate cortex, and the lateral orbitofrontal cortex, were identified. Of these links, 39 were also associated with the Depressive Problems scores. The brain areas with increased functional connectivity associated with both sleep and Depressive Problems scores included the lateral orbitofrontal cortex, dorsolateral prefrontal cortex, anterior and posterior cingulate cortices, insula, parahippocampal gyrus, hippocampus, amygdala, temporal cortex, and precuneus. A mediation analysis showed that these functional connectivities underlie the association of the Depressive Problems score with poor sleep quality (β = 0.0139; P < .001). CONCLUSIONS AND RELEVANCE The implication of these findings is that the increased functional connectivity between these brain regions provides a neural basis for the association between depression and poor sleep quality. An important finding was that the Depressive Problems scores in this general population were correlated with functional connectivities between areas, including the lateral orbitofrontal cortex, cingulate cortex, precuneus, angular gyrus, and temporal cortex. The findings have implications for the treatment of depression and poor sleep quality.
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Affiliation(s)
- Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Edmund T. Rolls
- Department of Computer Science, University of Warwick, Coventry, United Kingdom,Oxford Centre for Computational Neuroscience, Oxford, United Kingdom
| | - Hongtao Ruan
- School of Mathematical Sciences, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China,Department of Computer Science, University of Warwick, Coventry, United Kingdom,School of Mathematical Sciences, Fudan University, Shanghai, China,School of Life Science, Fudan University, Shanghai, China,The Collaborative Innovation Center for Brain Science, Fudan University, Shanghai, China
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499
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Hjelm RD, Damaraju E, Cho K, Laufs H, Plis SM, Calhoun VD. Spatio-Temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks. Front Neurosci 2018; 12:600. [PMID: 30294250 PMCID: PMC6158311 DOI: 10.3389/fnins.2018.00600] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Accepted: 08/09/2018] [Indexed: 11/18/2022] Open
Abstract
We introduce a novel recurrent neural network (RNN) approach to account for temporal dynamics and dependencies in brain networks observed via functional magnetic resonance imaging (fMRI). Our approach directly parameterizes temporal dynamics through recurrent connections, which can be used to formulate blind source separation with a conditional (rather than marginal) independence assumption, which we call RNN-ICA. This formulation enables us to visualize the temporal dynamics of both first order (activity) and second order (directed connectivity) information in brain networks that are widely studied in a static sense, but not well-characterized dynamically. RNN-ICA predicts dynamics directly from the recurrent states of the RNN in both task and resting state fMRI. Our results show both task-related and group-differentiating directed connectivity.
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Affiliation(s)
- R Devon Hjelm
- Montréal Institute for Learning Algorithms, Montreal, QC, Canada.,Microsoft Research, Montreal, QC, Canada
| | - Eswar Damaraju
- The Mind Research Network, Albuquerque, NM, United States.,The University of New Mexico, Albuquerque, NM, United States
| | | | | | - Sergey M Plis
- The Mind Research Network, Albuquerque, NM, United States
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, United States.,The University of New Mexico, Albuquerque, NM, United States
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500
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Zhuang J, Dvornek NC, Li X, Ventola P, Duncan JS. Prediction of severity and treatment outcome for ASD from fMRI. PREDICTIVE INTELLIGENCE IN MEDICINE. PRIME (WORKSHOP) 2018; 11121:9-17. [PMID: 32984867 PMCID: PMC7513883 DOI: 10.1007/978-3-030-00320-3_2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelop-mental syndrome. Early diagnosis and precise treatment are essential for ASD patients. Although researchers have built many analytical models, there has been limited progress in accurate predictive models for early diagnosis. In this project, we aim to build an accurate model to predict treatment outcome and ASD severity from early stage functional magnetic resonance imaging (fMRI) scans. The difficulty in building large databases of patients who have received specific treatments and the high dimensionality of medical image analysis problems are challenges in this work. We propose a generic and accurate two-level approach for high-dimensional regression problems in medical image analysis. First, we perform region-level feature selection using a predefined brain parcellation. Based on the assumption that voxels within one region in the brain have similar values, for each region we use the bootstrapped mean of voxels within it as a feature. In this way, the dimension of data is reduced from number of voxels to number of regions. Then we detect predictive regions by various feature selection methods. Second, we extract voxels within selected regions, and perform voxel-level feature selection. To use this model in both linear and non-linear cases with limited training examples, we apply two-level elastic net regression and random forest (RF) models respectively. To validate accuracy and robustness of this approach, we perform experiments on both task-fMRI and resting state fMRI datasets. Furthermore, we visualize the influence of each region, and show that the results match well with other findings.
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Affiliation(s)
- Juntang Zhuang
- Biomedical Engineering, Yale University, New Haven, CT USA
| | - Nicha C Dvornek
- Child Study Center, Yale University, New Haven, CT USA
- Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT USA
| | - Xiaoxiao Li
- Biomedical Engineering, Yale University, New Haven, CT USA
| | | | - James S Duncan
- Biomedical Engineering, Yale University, New Haven, CT USA
- Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT USA
- Electrical Engineering, Yale University, New Haven, CT USA
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