51
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Lurie DJ, Kessler D, Bassett DS, Betzel RF, Breakspear M, Kheilholz S, Kucyi A, Liégeois R, Lindquist MA, McIntosh AR, Poldrack RA, Shine JM, Thompson WH, Bielczyk NZ, Douw L, Kraft D, Miller RL, Muthuraman M, Pasquini L, Razi A, Vidaurre D, Xie H, Calhoun VD. Questions and controversies in the study of time-varying functional connectivity in resting fMRI. Netw Neurosci 2020; 4:30-69. [PMID: 32043043 PMCID: PMC7006871 DOI: 10.1162/netn_a_00116] [Citation(s) in RCA: 268] [Impact Index Per Article: 67.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Accepted: 11/22/2019] [Indexed: 12/12/2022] Open
Abstract
The brain is a complex, multiscale dynamical system composed of many interacting regions. Knowledge of the spatiotemporal organization of these interactions is critical for establishing a solid understanding of the brain's functional architecture and the relationship between neural dynamics and cognition in health and disease. The possibility of studying these dynamics through careful analysis of neuroimaging data has catalyzed substantial interest in methods that estimate time-resolved fluctuations in functional connectivity (often referred to as "dynamic" or time-varying functional connectivity; TVFC). At the same time, debates have emerged regarding the application of TVFC analyses to resting fMRI data, and about the statistical validity, physiological origins, and cognitive and behavioral relevance of resting TVFC. These and other unresolved issues complicate interpretation of resting TVFC findings and limit the insights that can be gained from this promising new research area. This article brings together scientists with a variety of perspectives on resting TVFC to review the current literature in light of these issues. We introduce core concepts, define key terms, summarize controversies and open questions, and present a forward-looking perspective on how resting TVFC analyses can be rigorously and productively applied to investigate a wide range of questions in cognitive and systems neuroscience.
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Affiliation(s)
- Daniel J. Lurie
- Department of Psychology, University of California, Berkeley, Berkeley, CA, USA
| | - Daniel Kessler
- Departments of Statistics and Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Danielle S. Bassett
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics & Astronomy, College of Arts & Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical & Systems Engineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Richard F. Betzel
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Breakspear
- University of Newcastle, Callaghan, NSW, 2308, Australia
- QIMR Berghofer, Brisbane, Australia
| | - Shella Kheilholz
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Aaron Kucyi
- Department of Neurology and Neurological Sciences, Stanford University, Stanford CA, USA
| | - Raphaël Liégeois
- Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Switzerland
| | | | - Anthony Randal McIntosh
- Rotman Research Institute - Baycrest Centre, Toronto, Canada
- Department of Psychology, University of Toronto, Toronto, Canada
| | | | - James M. Shine
- Brain and Mind Centre, University of Sydney, NSW, Australia
| | - William Hedley Thompson
- Department of Psychology, Stanford University, Stanford, CA, USA
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | | | - Linda Douw
- Department of Anatomy and Neurosciences, VU University Medical Center, Amsterdam, The Netherlands
| | - Dominik Kraft
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | | | - Muthuraman Muthuraman
- Biomedical Statistics and Multimodal Signal Processing Unit, Movement Disorders and Neurostimulation, Department of Neurology, Focus Program Translational Neuroscience, Johannes-Gutenberg-University Hospital, Mainz, Germany
| | - Lorenzo Pasquini
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Adeel Razi
- Monash Institute of Cognitive and Clinical Neurosciences and Monash Biomedical Imaging, Monash University, Clayton, Australia
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, United Kingdom
- Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan
| | - Diego Vidaurre
- Wellcome Trust Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, University of Oxford, United Kingdom
| | - Hua Xie
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, Georgia, USA
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52
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Mennigen E, Jolles DD, Hegarty CE, Gupta M, Jalbrzikowski M, Olde Loohuis LM, Ophoff RA, Karlsgodt KH, Bearden CE. State-Dependent Functional Dysconnectivity in Youth With Psychosis Spectrum Symptoms. Schizophr Bull 2020; 46:408-421. [PMID: 31219595 PMCID: PMC7442416 DOI: 10.1093/schbul/sbz052] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Psychosis spectrum disorders are conceptualized as neurodevelopmental disorders accompanied by disruption of large-scale functional brain networks. Dynamic functional dysconnectivity has been described in patients with schizophrenia and in help-seeking individuals at clinical high risk for psychosis. Less is known, about developmental aspects of dynamic functional network connectivity (dFNC) associated with psychotic symptoms (PS) in the general population. Here, we investigate resting state functional magnetic resonance imaging data using established dFNC methods in the Philadelphia Neurodevelopmental Cohort (ages 8-22 years), including 129 participants experiencing PS and 452 participants without PS (non-PS). Functional networks were identified using group spatial independent component analysis. A sliding window approach and k-means clustering were applied to covariance matrices of all functional networks to identify recurring whole-brain connectivity states. PS-associated dysconnectivity of default mode, salience, and executive networks occurred only in a few states, whereas dysconnectivity in the sensorimotor and visual systems in PS youth was more pervasive, observed across multiple states. This study provides new evidence that disruptions of dFNC are present even at the less severe end of the psychosis continuum in youth, complementing previous work on help-seeking and clinically diagnosed cohorts that represent the more severe end of this spectrum.
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Affiliation(s)
- Eva Mennigen
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA
| | - Dietsje D Jolles
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA
| | - Catherine E Hegarty
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA
| | - Mohan Gupta
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA
| | | | - Loes M Olde Loohuis
- Center for Neurobehavioral Genetics, University of California, Los Angeles, Los Angeles, CA
| | - Roel A Ophoff
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA,Center for Neurobehavioral Genetics, University of California, Los Angeles, Los Angeles, CA
| | - Katherine H Karlsgodt
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA,Department of Psychology, University of California, Los Angeles, Los Angeles, CA
| | - Carrie E Bearden
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA,Department of Psychology, University of California, Los Angeles, Los Angeles, CA,To whom correspondence should be addressed; tel: +1 310 825 3458, fax: +1 310 825 6766, e-mail:
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53
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Gupta S, Rajapakse JC, Welsch RE. Ambivert degree identifies crucial brain functional hubs and improves detection of Alzheimer's Disease and Autism Spectrum Disorder. Neuroimage Clin 2020; 25:102186. [PMID: 32000101 PMCID: PMC7042673 DOI: 10.1016/j.nicl.2020.102186] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 01/08/2020] [Accepted: 01/13/2020] [Indexed: 11/30/2022]
Abstract
Functional modules in the human brain support its drive for specialization whereas brain hubs act as focal points for information integration. Brain hubs are brain regions that have a large number of both within and between module connections. We argue that weak connections in brain functional networks lead to misclassification of brain regions as hubs. In order to resolve this, we propose a new measure called ambivert degree that considers the node's degree as well as connection weights in order to identify nodes with both high degree and high connection weights as hubs. Using resting-state functional MRI scans from the Human Connectome Project, we show that ambivert degree identifies brain hubs that are not only crucial but also invariable across subjects. We hypothesize that nodal measures based on ambivert degree can be effectively used to classify patients from healthy controls for diseases that are known to have widespread hub disruption. Using patient data for Alzheimer's Disease and Autism Spectrum Disorder, we show that the hubs in the patient and healthy groups are very different for both the diseases and deep feedforward neural networks trained on nodal hub features lead to a significantly higher classification accuracy with significantly fewer trainable weights compared to using functional connectivity features. Thus, the ambivert degree improves identification of crucial brain hubs in healthy subjects and can be used as a diagnostic feature to detect neurological diseases characterized by hub disruption.
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Affiliation(s)
- Sukrit Gupta
- School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore
| | - Jagath C Rajapakse
- School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore.
| | - Roy E Welsch
- MIT Center for Statistics and Data Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
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54
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Savva AD, Kassinopoulos M, Smyrnis N, Matsopoulos GK, Mitsis GD. Effects of motion related outliers in dynamic functional connectivity using the sliding window method. J Neurosci Methods 2019; 330:108519. [PMID: 31730872 DOI: 10.1016/j.jneumeth.2019.108519] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 11/01/2019] [Accepted: 11/11/2019] [Indexed: 10/25/2022]
Abstract
BACKGROUND It has been suggested that the use of window functions, other than the rectangular, in the sliding window method, may be beneficial for reducing the effects of motion-related outliers in the time-series, when assessing dynamic functional connectivity (dFC) in resting-state fMRI (rs-fMRI). METHODOLOGY Ten window functions for a wide range of window lengths (20-150 s) combined with Pearson and Kendall correlation metrics, were investigated. One hundred high quality rs-fMRI datasets from healthy controls, were used to systematically assess the effect of varying the window function and length on dFC assessment. To this end, two approaches were implemented: a) simulated outliers were added to the experimental data and b) the experimental data were divided into low and high motion subgroups. RESULTS The presence of experimental motion-noise tended to inflate the number of dynamic connections for longer (≥100 s) wide-shaped windows, while shorter (20-30 s) narrow-shaped windows exhibited increased sensitivity in the presence of simulated outliers. Moreover, window sizes from 60 s to 90 s were mildly affected by motion-related effects. In most cases, the number of dynamic connections increased, and gradually lower frequencies were captured, with an increasing window size. CONCLUSIONS Subject motion considerably affects the obtained dFC patterns; thus, it is preferable to perform motion artefact removal in the pre-processing stage rather than using alternative window functions to mitigate their effects. Provided that motion-noise is not excessive, the choice of a rectangular window is adequate. Finally, low frequency oscillations in functional connectivity seem to play an important role in the context of dFC assessment.
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Affiliation(s)
- Antonis D Savva
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
| | - Michalis Kassinopoulos
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Nikolaos Smyrnis
- Laboratory of Cognitive Neuroscience, University Mental Health Research Institute, Athens, Greece; Psychiatry Department, Medical School, Eginition Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - George K Matsopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | - Georgios D Mitsis
- Department of Bioengineering, McGill University, Montreal, QC, Canada
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55
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de Lacy N, McCauley E, Kutz JN, Calhoun VD. Sex-related differences in intrinsic brain dynamism and their neurocognitive correlates. Neuroimage 2019; 202:116116. [DOI: 10.1016/j.neuroimage.2019.116116] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 08/20/2019] [Indexed: 01/13/2023] Open
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56
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Kunert-Graf JM, Eschenburg KM, Galas DJ, Kutz JN, Rane SD, Brunton BW. Extracting Reproducible Time-Resolved Resting State Networks Using Dynamic Mode Decomposition. Front Comput Neurosci 2019; 13:75. [PMID: 31736734 PMCID: PMC6834549 DOI: 10.3389/fncom.2019.00075] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 10/11/2019] [Indexed: 12/19/2022] Open
Abstract
Resting state networks (RSNs) extracted from functional magnetic resonance imaging (fMRI) scans are believed to reflect the intrinsic organization and network structure of brain regions. Most traditional methods for computing RSNs typically assume these functional networks are static throughout the duration of a scan lasting 5-15 min. However, they are known to vary on timescales ranging from seconds to years; in addition, the dynamic properties of RSNs are affected in a wide variety of neurological disorders. Recently, there has been a proliferation of methods for characterizing RSN dynamics, yet it remains a challenge to extract reproducible time-resolved networks. In this paper, we develop a novel method based on dynamic mode decomposition (DMD) to extract networks from short windows of noisy, high-dimensional fMRI data, allowing RSNs from single scans to be resolved robustly at a temporal resolution of seconds. After validating the method on a synthetic dataset, we analyze data from 120 individuals from the Human Connectome Project and show that unsupervised clustering of DMD modes discovers RSNs at both the group (gDMD) and the single subject (sDMD) levels. The gDMD modes closely resemble canonical RSNs. Compared to established methods, sDMD modes capture individualized RSN structure that both better resembles the population RSN and better captures subject-level variation. We further leverage this time-resolved sDMD analysis to infer occupancy and transitions among RSNs with high reproducibility. This automated DMD-based method is a powerful tool to characterize spatial and temporal structures of RSNs in individual subjects.
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Affiliation(s)
| | | | - David J. Galas
- Pacific Northwest Research Institute, Seattle, WA, United States
| | - J. Nathan Kutz
- Department of Applied Math, University of Washington, Seattle, WA, United States
| | - Swati D. Rane
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Bingni W. Brunton
- Department of Biology, University of Washington, Seattle, WA, United States
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57
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Li Y, Zhu Y, Nguchu BA, Wang Y, Wang H, Qiu B, Wang X. Dynamic Functional Connectivity Reveals Abnormal Variability and Hyper‐connected Pattern in Autism Spectrum Disorder. Autism Res 2019; 13:230-243. [DOI: 10.1002/aur.2212] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 09/08/2019] [Accepted: 09/10/2019] [Indexed: 12/19/2022]
Affiliation(s)
- Yu Li
- Center for Biomedical Engineering, University of Science and Technology of China Hefei China
| | - Yuying Zhu
- Center for Biomedical Engineering, University of Science and Technology of China Hefei China
- School of Information Engineering, Southwest University of Science and Technology Mianyang China
| | | | - Yanming Wang
- Center for Biomedical Engineering, University of Science and Technology of China Hefei China
| | - Huijuan Wang
- Center for Biomedical Engineering, University of Science and Technology of China Hefei China
| | - Bensheng Qiu
- Center for Biomedical Engineering, University of Science and Technology of China Hefei China
| | - Xiaoxiao Wang
- Center for Biomedical Engineering, University of Science and Technology of China Hefei China
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58
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Altered dynamic functional connectivity in weakly-connected state in major depressive disorder. Clin Neurophysiol 2019; 130:2096-2104. [PMID: 31541987 DOI: 10.1016/j.clinph.2019.08.009] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Revised: 05/27/2019] [Accepted: 08/14/2019] [Indexed: 12/11/2022]
Abstract
OBJECTIVE Major depressive disorder (MDD) is accompanied by abnormal changes in dynamic functional connectivity (FC) among brain regions. The aim of this study is to investigate whether the abnormalities of dynamic FC in MDD are state-dependent (related to a specific connectivity state). METHODS We performed time-varying connectivity analysis on resting-state functional magnetic resonance imaging (rs-fMRI) of 49 MDD patients and 54 matched healthy controls (HCs). FC differences between groups in each connectivity state were analyzed and associations between disease severity and dynamics of aberrant FC were explored. RESULTS Two distinct connectivity states (i.e., weakly-connected and strongly-connected state) were identified. Compared to HCs, MDD patients were associated with increased mean dwell time and decreased FC between and within subnetworks in the weakly-connected state. Dynamics of reduced FC between cognitive control network and default mode network as well as within cognitive control network predicted individual differences in depression symptom severity. CONCLUSIONS Our findings suggested that the MDD-caused FC alterations mostly appeared in the weakly-connected state, which might contribute to clinical diagnosis of MDD. SIGNIFICANCE These findings provide new perspectives for understanding the state-dependent neurophysiological mechanisms in MDD.
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59
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Rabany L, Brocke S, Calhoun VD, Pittman B, Corbera S, Wexler BE, Bell MD, Pelphrey K, Pearlson GD, Assaf M. Dynamic functional connectivity in schizophrenia and autism spectrum disorder: Convergence, divergence and classification. Neuroimage Clin 2019; 24:101966. [PMID: 31401405 PMCID: PMC6700449 DOI: 10.1016/j.nicl.2019.101966] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 05/15/2019] [Accepted: 07/31/2019] [Indexed: 01/16/2023]
Abstract
BACKGROUND Over the recent years there has been a growing debate regarding the extent and nature of the overlap in neuropathology between schizophrenia (SZ) and autism spectrum disorder (ASD). Dynamic functional network connectivity (dFNC) is a recent analysis method that explores temporal patterns of functional connectivity (FC). We compared resting-state dFNC in SZ, ASD and healthy controls (HC), characterized the associations between temporal patterns and symptoms, and performed a three-way classification analysis based on dFNC indices. METHODS Resting-state fMRI was collected from 100 young adults: 33 SZ, 33 ASD, 34 HC. Independent component analysis (ICA) was performed, followed by dFNC analysis (window = 33 s, step = 1TR, k-means clustering). Temporal patterns were compared between groups, correlated with symptoms, and classified via cross-validated three-way discriminant analysis. RESULTS Both clinical groups displayed an increased fraction of time (FT) spent in a state of weak, intra-network connectivity [p < .001] and decreased FT in a highly-connected state [p < .001]. SZ further showed decreased number of transitions between states [p < .001], decreased FT in a widely-connected state [p < .001], increased dwell time (DT) in the weakly-connected state [p < .001], and decreased DT in the highly-connected state [p = .001]. Social behavior scores correlated with DT in the widely-connected state in SZ [r = 0.416, p = .043], but not ASD. Classification correctly identified SZ at high rates (81.8%), while ASD and HC at lower rates. CONCLUSIONS Results indicate a severe and pervasive pattern of temporal aberrations in SZ (specifically, being "stuck" in a state of weak connectivity), that distinguishes SZ participants from both ASD and HC, and is associated with clinical symptoms.
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Affiliation(s)
- Liron Rabany
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA.
| | - Sophy Brocke
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Vince D Calhoun
- Mind Research Network, Albuquerque, NM, USA; University of New Mexico, Department of ECE, Albuquerque, NM, USA; Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA
| | - Brian Pittman
- Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA
| | - Silvia Corbera
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA; Central Connecticut State University, Department of Psychological Science, New Britain, CT, USA
| | - Bruce E Wexler
- Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA
| | - Morris D Bell
- Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA; VA Connecticut Healthcare System West Haven, CT, USA
| | - Kevin Pelphrey
- Autism and Neurodevelopment Disorders Institute, George Washington University and Children's National Medical Center, DC, USA
| | - Godfrey D Pearlson
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA; Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA; Yale University School of Medicine, Department of Neuroscience, New Haven, CT, USA
| | - Michal Assaf
- Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA; Yale University, School of Medicine, Department of Psychiatry, New Haven, CT, USA
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60
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de Lacy N, McCauley E, Kutz JN, Calhoun VD. Multilevel Mapping of Sexual Dimorphism in Intrinsic Functional Brain Networks. Front Neurosci 2019; 13:332. [PMID: 31024243 PMCID: PMC6460937 DOI: 10.3389/fnins.2019.00332] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2018] [Accepted: 03/21/2019] [Indexed: 12/17/2022] Open
Abstract
Differences in cognitive performance between males and females are well-described, most commonly in certain spatial and language tasks. Sex-related differences in cognition are relevant to the study of the neurotypical brain and to neuropsychiatric disorders, which exhibit prominent disparities in the incidence, prevalence and severity of symptoms between men and women. While structural dimorphism in the human brain is well-described, controversy exists regarding the existence and degree of sex-related differences in brain function. We analyzed resting-state functional MRI from 650 neurotypical young adults matched for age and sex to determine the degree of sexual dimorphism present in intrinsic functional networks. Multilevel modeling was pursued to create 8-, 24-, and 51-network models of whole-brain data to quantify sex-related effects in network activity with increasing resolution. We determined that sexual dimorphism is present in the majority of intrinsic brain networks and affects ∼0.5-2% of brain locations surveyed in the three whole-brain network models. It is particularly common in task-positive control networks and is pervasive among default mode networks. The size of sex-related effects varied by network but can be moderate or even large in size. Female > male effects were on average larger, but male > female effects spread across greater network territory. Using a novel methodology, we mapped dimorphic locations to meta-analytic association test maps derived from task fMRI, demonstrating that the neurocognitive footprint of intrinsic neural correlates is much larger in males. All results were replicated in a motion-matched sub-sample. Our findings argue that sex is an important biological variable in human brain function and suggest that observed differences in neurocognitive performance have identifiable intrinsic neural correlates.
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Affiliation(s)
- Nina de Lacy
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, United States
| | - Elizabeth McCauley
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, United States
| | - J. Nathan Kutz
- Department of Applied Mathematics, University of Washington, Seattle, WA, United States
| | - Vince D. Calhoun
- Mind Research Network, Albuquerque, NM, United States
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States
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Fu Z, Caprihan A, Chen J, Du Y, Adair JC, Sui J, Rosenberg GA, Calhoun VD. Altered static and dynamic functional network connectivity in Alzheimer's disease and subcortical ischemic vascular disease: shared and specific brain connectivity abnormalities. Hum Brain Mapp 2019; 40:3203-3221. [PMID: 30950567 DOI: 10.1002/hbm.24591] [Citation(s) in RCA: 87] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 03/19/2019] [Accepted: 03/23/2019] [Indexed: 12/16/2022] Open
Abstract
Subcortical ischemic vascular disease (SIVD) is a major subtype of vascular dementia with features that overlap clinically with Alzheimer's disease (AD), confounding diagnosis. Neuroimaging is a more specific and biologically based approach for detecting brain changes and thus may help to distinguish these diseases. There is still a lack of knowledge regarding the shared and specific functional brain abnormalities, especially functional connectivity changes in relation to AD and SIVD. In this study, we investigated both static functional network connectivity (sFNC) and dynamic FNC (dFNC) between 54 intrinsic connectivity networks in 19 AD patients, 19 SIVD patients, and 38 age-matched healthy controls. The results show that both patient groups have increased sFNC between the visual and cerebellar (CB) domains but decreased sFNC between the cognitive-control and CB domains. SIVD has specifically decreased sFNC within the sensorimotor domain while AD has specifically altered sFNC between the default-mode and CB domains. In addition, SIVD has more occurrences and a longer dwell time in the weakly connected dFNC states, but with fewer occurrences and a shorter dwell time in the strongly connected dFNC states. AD has both similar and opposite changes in certain dynamic features. More importantly, the dynamic features are found to be associated with cognitive performance. Our findings highlight similar and distinct functional connectivity alterations in AD and SIVD from both static and dynamic perspectives and indicate dFNC to be a more important biomarker for dementia since its progressively altered patterns can better track cognitive impairment in AD and SIVD.
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Affiliation(s)
- Zening Fu
- The Mind Research Network, Albuquerque, New Mexico
| | | | - Jiayu Chen
- The Mind Research Network, Albuquerque, New Mexico
| | - Yuhui Du
- The Mind Research Network, Albuquerque, New Mexico.,School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - John C Adair
- Department of Neurology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico
| | - Jing Sui
- The Mind Research Network, Albuquerque, New Mexico.,Chinese Academy of Sciences (CAS), Centre for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Gary A Rosenberg
- Department of Neurology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, New Mexico.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
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Wu X, He H, Shi L, Xia Y, Zuang K, Feng Q, Zhang Y, Ren Z, Wei D, Qiu J. Personality traits are related with dynamic functional connectivity in major depression disorder: A resting-state analysis. J Affect Disord 2019; 245:1032-1042. [PMID: 30699845 DOI: 10.1016/j.jad.2018.11.002] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Revised: 09/14/2018] [Accepted: 11/01/2018] [Indexed: 12/11/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is one of the most well-known psychiatric disorders, which can be destructive for its damage to people's normal cognitive, emotional and social functions. Personality refers to the unique and stable character of thinking and behavior style of an individual, which has long been thought as a key influence factor for MDD. Although some knowledge about the common neural basic between MDD and personality traits has been acquired, there are few studies exploring dynamic neural mechanism behind them, which changes brain connectivity pattern rapidly to adapt to the environment over time. METHODS In this study, the emerging dynamic functional network connectivity (DFNC) method was used in resting-state fMRI data to find the differences between healthy group (N = 107) and MDD group (N = 109) in state-based dynamic measures, and the correlations between these measures and personality traits (extraversion and neuroticism in Eysenck Personality Questionnaire, EPQ) were explored. RESULTS The results showed that MDD was significantly less than the health control group in dwell time and fraction time of state 4, which was positively correlated with extraversion score and negatively correlated with neuroticism score. Further exploration on state 4 showed that it had low modularity, hyper-connectedness of sensory-related regions and DMN, and weak connections between cortex and subcortical areas, which suggested that the absence of this state in MDD might represent a decrease in activity and positive emotions. CONCLUSION We found the dynamic functional connectivity mechanism underlying MDD, confirmed our hypothesis that there existed the interacted relationship between trait, disease and the brain's dynamic characteristic, and suggested some reference for treatment of depression.
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Affiliation(s)
- Xinran Wu
- Key Laboratory of Cognition and Personality, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Hong He
- Key Laboratory of Cognition and Personality, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Liang Shi
- Key Laboratory of Cognition and Personality, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Yunman Xia
- Key Laboratory of Cognition and Personality, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Kaixiang Zuang
- Key Laboratory of Cognition and Personality, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Qiuyang Feng
- Key Laboratory of Cognition and Personality, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Yao Zhang
- Key Laboratory of Cognition and Personality, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Zhiting Ren
- Key Laboratory of Cognition and Personality, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China.
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China.
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63
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Harlalka V, Bapi RS, Vinod PK, Roy D. Atypical Flexibility in Dynamic Functional Connectivity Quantifies the Severity in Autism Spectrum Disorder. Front Hum Neurosci 2019; 13:6. [PMID: 30774589 PMCID: PMC6367662 DOI: 10.3389/fnhum.2019.00006] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 01/08/2019] [Indexed: 01/10/2023] Open
Abstract
Resting-state functional connectivity (FC) analyses have shown atypical connectivity in autism spectrum disorder (ASD) as compared to typically developing (TD). However, this view emerges from investigating static FC overlooking the whole brain transient connectivity patterns. In our study, we investigated how age and disease influence the dynamic changes in functional connectivity of TD and ASD. We used resting-state functional magnetic resonance imaging (rs-fMRI) data stratified into three cohorts: children (7-11 years), adolescents (12-17 years), and adults (18+ years) for the analysis. The dynamic variability in the connection strength and the modular organization in terms of measures such as flexiblity, cohesion strength, and disjointness were explored for each subject to characterize the differences between ASD and TD. In ASD, we observed significantly higher inter-subject dynamic variability in connection strength as compared to TD. This hyper-variability relates to the symptom severity in ASD. We also found that whole-brain flexibility correlates with static modularity only in TD. Further, we observed a core-periphery organization in the resting-state, with Sensorimotor and Visual regions in the rigid core; and DMN and attention areas in the flexible periphery. TD also develops a more cohesive organization of sensorimotor areas. However, in ASD we found a strong positive correlation of symptom severity with flexibility of rigid areas and with disjointness of sensorimotor areas. The regions of the brain showing high predictive power of symptom severity were distributed across the cortex, with stronger bearings in the frontal, motor, and occipital cortices. Our study demonstrates that the dynamic framework best characterizes the variability in ASD.
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Affiliation(s)
- Vatika Harlalka
- Center for Computational Natural Sciences and Bioinformatics, IIIT Hyderabad, Hyderabad, India
| | - Raju S. Bapi
- Cognitive Science Lab, IIIT Hyderabad, Hyderabad, India
- School of Computer and Information Sciences, University of Hyderabad, Hyderabad, India
| | - P. K. Vinod
- Center for Computational Natural Sciences and Bioinformatics, IIIT Hyderabad, Hyderabad, India
| | - Dipanjan Roy
- Cognitive Brain Dynamics Lab, National Brain Research Centre, Manesar, India
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64
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Mash LE, Linke AC, Olson LA, Fishman I, Liu TT, Müller RA. Transient states of network connectivity are atypical in autism: A dynamic functional connectivity study. Hum Brain Mapp 2019; 40:2377-2389. [PMID: 30681228 DOI: 10.1002/hbm.24529] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 01/09/2019] [Indexed: 01/17/2023] Open
Abstract
There is ample evidence of atypical functional connectivity (FC) in autism spectrum disorders (ASDs). However, transient relationships between neural networks cannot be captured by conventional static FC analyses. Dynamic FC (dFC) approaches have been used to identify repeating, transient connectivity patterns ("states"), revealing spatiotemporal network properties not observable in static FC. Recent studies have found atypical dFC in ASDs, but questions remain about the nature of group differences in transient connectivity, and the degree to which states persist or change over time. This study aimed to: (a) describe and relate static and dynamic FC in typical development and ASDs, (b) describe group differences in transient states and compare them with static FC patterns, and (c) examine temporal stability and flexibility between identified states. Resting-state functional magnetic resonance imaging (fMRI) data were collected from 62 ASD and 57 typically developing (TD) children and adolescents. Whole-brain, data-driven regions of interest were derived from group independent component analysis. Sliding window analysis and k-means clustering were used to explore dFC and identify transient states. Across all regions, static overconnnectivity and increased variability over time in ASDs predominated. Furthermore, significant patterns of group differences emerged in two transient states that were not observed in the static FC matrix, with group differences in one state primarily involving sensory and motor networks, and in the other involving higher-order cognition networks. Default mode network segregation was significantly reduced in ASDs in both states. Results highlight that dynamic approaches may reveal more nuanced transient patterns of atypical FC in ASDs.
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Affiliation(s)
- Lisa E Mash
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, California
| | - Annika C Linke
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Lindsay A Olson
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, California
| | - Inna Fishman
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California
| | - Thomas T Liu
- Center for Functional MRI, Department of Radiology, University of California San Diego, San Diego, California
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, California.,Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, California
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65
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de Lacy N, Calhoun VD. Dynamic connectivity and the effects of maturation in youth with attention deficit hyperactivity disorder. Netw Neurosci 2018; 3:195-216. [PMID: 30793080 PMCID: PMC6372020 DOI: 10.1162/netn_a_00063] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Accepted: 06/05/2018] [Indexed: 11/04/2022] Open
Abstract
The analysis of time-varying connectivity by using functional MRI has gained momentum given its ability to complement traditional static methods by capturing additional patterns of variation in human brain function. Attention deficit hyperactivity disorder (ADHD) is a complex, common developmental neuropsychiatric disorder associated with heterogeneous connectivity differences that are challenging to disambiguate. However, dynamic connectivity has not been examined in ADHD, and surprisingly few whole-brain analyses of static functional network connectivity (FNC) using independent component analysis (ICA) exist. We present the first analyses of time-varying connectivity and whole-brain FNC using ICA in ADHD, introducing a novel framework for comparing local and global dynamic connectivity in a 44-network model. We demonstrate that dynamic connectivity analysis captures robust motifs associated with group effects consequent on the diagnosis of ADHD, implicating increased global dynamic range, but reduced fluidity and range localized to the default mode network system. These differentiate ADHD from other major neuropsychiatric disorders of development. In contrast, static FNC based on a whole-brain ICA decomposition revealed solely age effects, without evidence of group differences. Our analysis advances current methods in time-varying connectivity analysis, providing a structured example of integrating static and dynamic connectivity analysis to further investigation into functional brain differences during development.
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Affiliation(s)
- Nina de Lacy
- Department of Psychiatry and Behavioral Science, University of Washington, Seattle, WA, USA
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM, USA
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
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66
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Engels G, Vlaar A, McCoy B, Scherder E, Douw L. Dynamic Functional Connectivity and Symptoms of Parkinson's Disease: A Resting-State fMRI Study. Front Aging Neurosci 2018; 10:388. [PMID: 30532703 PMCID: PMC6266764 DOI: 10.3389/fnagi.2018.00388] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 11/05/2018] [Indexed: 12/18/2022] Open
Abstract
Research has shown that dynamic functional connectivity (dFC) in Parkinson’s disease (PD) is associated with better attention performance and with motor symptom severity. In the current study, we aimed to investigate dFC of both the default mode network (DMN) and the frontoparietal network (FPN) as neural correlates of cognitive functioning in patients with PD. Additionally, we investigated pain and motor problems as symptoms of PD in relation to dFC. Twenty-four PD patients and 27 healthy controls participated in this study. Memory and executive functioning were assessed with neuropsychological tests. Pain was assessed with the Numeric Rating Scale (NRS); motor symptom severity was assessed with the Unified Parkinson’s Disease Rating Scale (UPDRS). All subjects underwent resting-state functional magnetic resonance imaging (fMRI), from which dFC was defined by calculating the variability of functional connectivity over a number of sliding windows within each scan. dFC of both the DMN and FPN with the rest of the brain was calculated. Patients performed worse on tests of visuospatial memory, verbal memory and working memory. No difference existed between groups regarding dFC of the DMN nor the FPN with the rest of the brain. A positive correlation existed between dFC of the DMN and visuospatial memory. Our results suggest that dynamics during the resting state are a neural correlate of visuospatial memory in PD patients. Furthermore, we suggest that brain dynamics of the DMN, as measured with dFC, could be a phenomenon specifically linked to cognitive functioning in PD, but not to other symptoms.
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Affiliation(s)
- Gwenda Engels
- Department of Clinical, Neuro and Developmental Psychology, Faculty of Behavior and Movement Sciences, VU University, Amsterdam, Netherlands
| | - Annemarie Vlaar
- Department of Neurology, Onze Lieve Vrouwe Gasthuis (OLVG), Amsterdam, Netherlands
| | - Brónagh McCoy
- Department of Experimental and Applied Psychology & Institute of Brain and Behavior, Faculty of Behavior and Movement Sciences, VU University, Amsterdam, Netherlands
| | - Erik Scherder
- Department of Clinical, Neuro and Developmental Psychology, Faculty of Behavior and Movement Sciences, VU University, Amsterdam, Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, VU University Medical Center, Amsterdam, Netherlands.,Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
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67
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Delbruck E, Yang M, Yassine A, Grossman ED. Functional connectivity in ASD: Atypical pathways in brain networks supporting action observation and joint attention. Brain Res 2018; 1706:157-165. [PMID: 30392771 DOI: 10.1016/j.brainres.2018.10.029] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Revised: 10/18/2018] [Accepted: 10/26/2018] [Indexed: 10/28/2022]
Abstract
Autism Spectrum Disorder (ASD) is a developmental disorder characterized by impaired social communication, including attending to and interpreting social cues, initiating and responding to joint attention, and engaging in abstract social cognitive reasoning. Current studies emphasize a underconnectivity in ASD, particularly for brain systems that support abstract social reasoning and introspective thought. Here, we evaluate intrinsic connectivity in children with ASD, targeting brain systems that support the developmental precursors to social reasoning, namely perception of social cues and joint attention. Using resting state fMRI made available through the Autism Brain Imaging Data Exchange (ABIDE), we compute functional connectivity within and between nodes in the action observation, attention and social cognitive networks in children and adolescents with ASD. We also compare connectivity strength to observational assessments that explicitly evaluate severity of ASD on two distinct subdomains using the ADOS-Revised schedule: social affective (SA) and restricted, repetitive behaviors (RRB). Compared to age-matched controls, children with ASD have decreased functional connectivity in a number of connections in the action observation network, particularly in the lateral occipital cortex (LOTC) and fusiform gyrus (FG). Distinct patterns of connections were also correlated with symptom severity on the two subdomains of the ADOS. ADOS-SA severity most strongly correlated with connectivity to the left TPJ, while ADOS-RRB severity correlated with connectivity to the dMPFC. We conclude that atypical connectivity in the action observation system may underlie some of the more complex deficits in social cognitive systems in ASD.
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Affiliation(s)
- Elita Delbruck
- Department of Cognitive Sciences, University of California, Irvine, United States
| | - Melody Yang
- Department of Cognitive Sciences, University of California, Irvine, United States
| | - Ahmed Yassine
- Department of Cognitive Sciences, University of California, Irvine, United States
| | - Emily D Grossman
- Department of Cognitive Sciences, University of California, Irvine, United States.
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68
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He C, Chen Y, Jian T, Chen H, Guo X, Wang J, Wu L, Chen H, Duan X. Dynamic functional connectivity analysis reveals decreased variability of the default-mode network in developing autistic brain. Autism Res 2018; 11:1479-1493. [PMID: 30270547 DOI: 10.1002/aur.2020] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Accepted: 08/28/2018] [Indexed: 11/11/2022]
Abstract
Accumulating neuroimaging evidence suggests that abnormal functional connectivity of the default mode network (DMN) contributes to the social-cognitive deficits of autism spectrum disorder (ASD). Although most previous studies relied on conventional functional connectivity methods, which assume that connectivity patterns remain constant over time, understanding the temporal dynamics of functional connectivity during rest may provide new insights into the dysfunction of the DMN in ASD. In this work, dynamic functional connectivity analysis based on sliding time window correlation was applied to the resting-state functional magnetic resonance imaging data of 28 young children with ASD (age range: 3-7 years) and 29 matched typically developing controls (TD group). In addition, k-means cluster analysis was performed to identify distinct temporal states based on the spatial similarity of each functional connectivity pattern. Compared with the TD group, young children with ASD showed decreased dynamic functional connectivity variance between the posterior cingulate cortex (PCC) and the right precentral gyrus, which is negatively correlated with social motivation and social relating. Cluster analysis revealed significant differences in functional connectivity patterns between the ASD and TD groups in discrete temporal states. Our findings reveal that atypical dynamic interactions between the PCC and sensorimotor cortex are associated with social deficits in ASD. Results also highlight the critical role of PCC in the social-cognitive deficits of ASD and support the concept that understanding the dynamic neural interactions among brain regions can provide insights into functional abnormalities in ASD. Autism Research 2018, 11: 1479-1493. © 2018 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Social cognitive dysfunction in autism spectrum disorder (ASD) is associated with dysfunction of the default mode network (DMN), a set of brain areas involved in various domains of social processing. We found that decreases in the dynamic functional connectivity variance between the posterior cingulate cortex and the sensorimotor cortex are associated with deficits in social motivation and social relating in young children with ASD. This result suggests that aberrations in the DMN and its dynamic interactions with other networks contribute to atypical integration of information with respect to self and others.
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Affiliation(s)
- Changchun He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformaiton, University of Electronic Science and Technology of China, Chengdu, 611731, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Yanchi Chen
- Chengdu Shishi High School, Chengdu, 610041, China
| | - Taorong Jian
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformaiton, University of Electronic Science and Technology of China, Chengdu, 611731, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Heng Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformaiton, University of Electronic Science and Technology of China, Chengdu, 611731, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Xiaonan Guo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformaiton, University of Electronic Science and Technology of China, Chengdu, 611731, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Jia Wang
- Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin, 150081, China
| | - Lijie Wu
- Department of Children's and Adolescent Health, Public Health College of Harbin Medical University, Harbin, 150081, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformaiton, University of Electronic Science and Technology of China, Chengdu, 611731, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Xujun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformaiton, University of Electronic Science and Technology of China, Chengdu, 611731, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
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69
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Fu Z, Tu Y, Di X, Du Y, Sui J, Biswal BB, Zhang Z, de Lacy N, Calhoun VD. Transient increased thalamic-sensory connectivity and decreased whole-brain dynamism in autism. Neuroimage 2018; 190:191-204. [PMID: 29883735 DOI: 10.1016/j.neuroimage.2018.06.003] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2017] [Revised: 05/31/2018] [Accepted: 06/03/2018] [Indexed: 11/19/2022] Open
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with social communication deficits and restricted/repetitive behaviors and is characterized by large-scale atypical subcortical-cortical connectivity, including impaired resting-state functional connectivity between thalamic and sensory regions. Previous studies have typically focused on the abnormal static connectivity in ASD and overlooked potential valuable dynamic patterns in brain connectivity. However, resting-state brain connectivity is indeed highly dynamic, and abnormalities in dynamic brain connectivity have been widely identified in psychiatric disorders. In this study, we investigated the dynamic functional network connectivity (dFNC) between 51 intrinsic connectivity networks in 170 individuals with ASD and 195 age-matched typically developing (TD) controls using independent component analysis and a sliding window approach. A hard clustering state analysis and a fuzzy meta-state analysis were conducted respectively, for the exploration of local and global aberrant dynamic connectivity patterns in ASD. We examined the group difference in dFNC between thalamic and sensory networks in each functional state and group differences in four high-dimensional dynamic measures. The results showed that compared with TD controls, individuals with ASD show an increase in transient connectivity between hypothalamus/subthalamus and some sensory networks (right postcentral gyrus, bi paracentral lobule, and lingual gyrus) in certain functional states, and diminished global meta-state dynamics of the whole-brain functional network. In addition, these atypical dynamic patterns are significantly associated with autistic symptoms indexed by the Autism Diagnostic Observation Schedule. These converging results support and extend previous observations regarding hyperconnectivity between thalamic and sensory regions and stable whole-brain functional configuration in ASD. Dynamic brain connectivity may serve as a potential biomarker of ASD and further investigation of these dynamic patterns might help to advance our understanding of behavioral differences in this complex neurodevelopmental disorder.
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Affiliation(s)
- Zening Fu
- The Mind Research Network, Albuquerque, NM, USA; School of Biomedical Engineering, Shenzhen University, Shenzhen, China.
| | - Yiheng Tu
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; School of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Yuhui Du
- The Mind Research Network, Albuquerque, NM, USA; School of Computer & Information Technology, Shanxi University, Taiyuan, China
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Zhiguo Zhang
- School of Biomedical Engineering, Shenzhen University, Shenzhen, China
| | - N de Lacy
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
| | - V D Calhoun
- The Mind Research Network, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
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70
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Keilholz S, Caballero-Gaudes C, Bandettini P, Deco G, Calhoun V. Time-Resolved Resting-State Functional Magnetic Resonance Imaging Analysis: Current Status, Challenges, and New Directions. Brain Connect 2018; 7:465-481. [PMID: 28874061 DOI: 10.1089/brain.2017.0543] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Time-resolved analysis of resting-state functional magnetic resonance imaging (rs-fMRI) data allows researchers to extract more information about brain function than traditional functional connectivity analysis, yet a number of challenges in data analysis and interpretation remain. This article briefly summarizes common methods for time-resolved analysis and presents some of the pressing issues and opportunities in the field. From there, the discussion moves to interpretation of the network dynamics observed with rs-fMRI and the role that rs-fMRI can play in elucidating the large-scale organization of brain activity.
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Affiliation(s)
- Shella Keilholz
- 1 Department of Biomedical Engineering, Emory University/Georgia Institute of Technology , Atlanta, Georgia
| | | | - Peter Bandettini
- 3 Section on Functional Imaging Methods, NIMH, NIH, Bethesda, Maryland.,4 Functional MRI Core Facility, NIMH, NIH, Bethesda, Maryland
| | - Gustavo Deco
- 5 Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra , Barcelona, Spain .,6 Institució Catalana de la Recerca i Estudis Avançats (ICREA) , Barcelona, Spain.,7 Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences , Leipzig, Germany .,8 School of Psychological Sciences, Monash University , Melbourne, Australia
| | - Vince Calhoun
- 9 The Mind Research Network, Albuquerque, New Mexico.,10 Department of Electrical and Computer Engineering, The University of New Mexico , Albuquerque, New Mexico
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71
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Mash LE, Reiter MA, Linke AC, Townsend J, Müller RA. Multimodal approaches to functional connectivity in autism spectrum disorders: An integrative perspective. Dev Neurobiol 2018; 78:456-473. [PMID: 29266810 PMCID: PMC5897150 DOI: 10.1002/dneu.22570] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Revised: 12/18/2017] [Accepted: 12/18/2017] [Indexed: 12/22/2022]
Abstract
Atypical functional connectivity has been implicated in autism spectrum disorders (ASDs). However, the literature to date has been largely inconsistent, with mixed and conflicting reports of hypo- and hyper-connectivity. These discrepancies are partly due to differences between various neuroimaging modalities. Functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and magnetoencephalography (MEG) measure distinct indices of functional connectivity (e.g., blood-oxygenation level-dependent [BOLD] signal vs. electrical activity). Furthermore, each method has unique benefits and disadvantages with respect to spatial and temporal resolution, vulnerability to specific artifacts, and practical implementation. Thus far, functional connectivity research on ASDs has remained almost exclusively unimodal; therefore, interpreting findings across modalities remains a challenge. Multimodal integration of fMRI, EEG, and MEG data is critical in resolving discrepancies in the literature, and working toward a unifying framework for interpreting past and future findings. This review aims to provide a theoretical foundation for future multimodal research on ASDs. First, we will discuss the merits and shortcomings of several popular theories in ASD functional connectivity research, using examples from the literature to date. Next, the neurophysiological relationships between imaging modalities, including their relationship with invasive neural recordings, will be reviewed. Finally, methodological approaches to multimodal data integration will be presented, and their future application to ASDs will be discussed. Analyses relating transient patterns of neural activity ("states") are particularly promising. This strategy provides a comparable measure across modalities, captures complex spatiotemporal patterns, and is a natural extension of recent dynamic fMRI research in ASDs. © 2017 Wiley Periodicals, Inc. Develop Neurobiol 78: 456-473, 2018.
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Affiliation(s)
- Lisa E. Mash
- SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University
| | - Maya A. Reiter
- SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University
| | - Annika C. Linke
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University
| | - Jeanne Townsend
- University of California, San Diego, Department of Neurosciences
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University
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72
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Rashid B, Blanken LME, Muetzel RL, Miller R, Damaraju E, Arbabshirani MR, Erhardt EB, Verhulst FC, van der Lugt A, Jaddoe VWV, Tiemeier H, White T, Calhoun V. Connectivity dynamics in typical development and its relationship to autistic traits and autism spectrum disorder. Hum Brain Mapp 2018; 39:3127-3142. [PMID: 29602272 DOI: 10.1002/hbm.24064] [Citation(s) in RCA: 71] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 03/09/2018] [Accepted: 03/20/2018] [Indexed: 12/19/2022] Open
Abstract
Recent advances in neuroimaging techniques have provided significant insights into developmental trajectories of human brain function. Characterizations of typical neurodevelopment provide a framework for understanding altered neurodevelopment, including differences in brain function related to developmental disorders and psychopathology. Historically, most functional connectivity studies of typical and atypical development operate under the assumption that connectivity remains static over time. We hypothesized that relaxing stationarity assumptions would reveal novel features of both typical brain development related to children on the autism spectrum. We employed a "chronnectomic" (recurring, time-varying patterns of connectivity) approach to evaluate transient states of connectivity using resting-state functional MRI in a population-based sample of 774 6- to 10-year-old children. Dynamic connectivity was evaluated using a sliding-window approach, and revealed four transient states. Internetwork connectivity increased with age in modularized dynamic states, illustrating an important pattern of connectivity in the developing brain. Furthermore, we demonstrated that higher levels of autistic traits and ASD diagnosis were associated with longer dwell times in a globally disconnected state. These results provide a roadmap to the chronnectomic organization of the developing brain and suggest that characteristics of functional brain connectivity are related to children on the autism spectrum.
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Affiliation(s)
- Barnaly Rashid
- Harvard Medical School, Harvard University, Boston, Massachusetts
| | - Laura M E Blanken
- The Generation R Study Group, Erasmus MC, Rotterdam, The Netherlands.,Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC-Sophia, Rotterdam, The Netherlands
| | - Ryan L Muetzel
- The Generation R Study Group, Erasmus MC, Rotterdam, The Netherlands.,Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC-Sophia, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Robyn Miller
- The Mind Research Network & LBERI, Albuquerque, New Mexico, 87106
| | - Eswar Damaraju
- The Mind Research Network & LBERI, Albuquerque, New Mexico, 87106.,Department of ECE, University of New Mexico, Albuquerque, New Mexico, 87131
| | | | - Erik B Erhardt
- Department of Mathematics and Statistics, University of New Mexico, Albuquerque, New Mexico, 87131
| | - Frank C Verhulst
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC-Sophia, Rotterdam, The Netherlands
| | | | - Vincent W V Jaddoe
- The Generation R Study Group, Erasmus MC, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands.,Department of Pediatrics, Erasmus MC, Rotterdam, The Netherlands
| | - Henning Tiemeier
- Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC-Sophia, Rotterdam, The Netherlands.,Department of Epidemiology, Erasmus MC, Rotterdam, The Netherlands
| | - Tonya White
- The Generation R Study Group, Erasmus MC, Rotterdam, The Netherlands.,Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC-Sophia, Rotterdam, The Netherlands.,Department of Radiology, Erasmus MC, Rotterdam, The Netherlands
| | - Vince Calhoun
- The Mind Research Network & LBERI, Albuquerque, New Mexico, 87106.,Department of ECE, University of New Mexico, Albuquerque, New Mexico, 87131
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73
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Zhang Y, Zhang S, Ide JS, Hu S, Zhornitsky S, Wang W, Dong G, Tang X, Li CSR. Dynamic network dysfunction in cocaine dependence: Graph theoretical metrics and stop signal reaction time. NEUROIMAGE-CLINICAL 2018; 18:793-801. [PMID: 29876265 PMCID: PMC5988015 DOI: 10.1016/j.nicl.2018.03.016] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Revised: 01/09/2018] [Accepted: 03/14/2018] [Indexed: 01/04/2023]
Abstract
Graphic theoretical metrics have become increasingly popular in characterizing functional connectivity of neural networks and how network connectivity is compromised in neuropsychiatric illnesses. Here, we add to this literature by describing dynamic network connectivities of 78 cocaine dependent (CD) and 85 non-drug using healthy control (HC) participants who underwent fMRI during performance of a stop signal task (SST). Compared to HC, CD showed prolonged stop signal reaction time (SSRT), consistent with deficits in response inhibition. In graph theoretical analysis of dynamic functional connectivity, we examined temporal flexibility and spatiotemporal diversity of 14 networks covering the whole brain. Temporal flexibility quantifies how frequently a brain region interacts with regions of other communities across time, with high temporal flexibility indicating that a region interacts predominantly with regions outside its own community. Spatiotemporal diversity quantifies how uniformly a brain region interacts with regions in other communities over time, with high spatiotemporal diversity indicating that the interactions are more evenly distributed across communities. Compared to HC, CD exhibited decreased temporal flexibility and increased spatiotemporal diversity in the great majority of neural networks. The graph metric measures of the default mode network negatively correlated with SSRT in CD but not HC. The findings are consistent with diminished temporal flexibility and a compensatory increase in spatiotemporal diversity, in association with impairment of a critical executive function, in cocaine addiction. More broadly, the findings suggest that graph theoretical metrics provide new insights for connectivity analyses to elucidate network dysfunction that may elude conventional measures. Cocaine addiction (CA) is associated with prolonged stop signal reaction time (SSRT). CA is associated with decreased temporal flexibility (TF) of neural networks. CA is associated with increased spatial temporal diversity (STD) of neural networks. The TF and STD of default mode network correlated negatively with SSRT in CA. Dynamic connectivity captures network dysfunction in link with inhibition deficits in CA.
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Affiliation(s)
- Yihe Zhang
- Department of Biomedical engineering, School of Life Sciences, Beijing Institute of technology, Beijing, China; Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Sheng Zhang
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Jaime S Ide
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Sien Hu
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Department of Psychology, State University of New York, Oswego, NY, USA
| | - Simon Zhornitsky
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Wuyi Wang
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Guozhao Dong
- Department of Biomedical engineering, School of Life Sciences, Beijing Institute of technology, Beijing, China
| | - Xiaoying Tang
- Department of Biomedical engineering, School of Life Sciences, Beijing Institute of technology, Beijing, China.
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA; Department of Neuroscience, Yale University School of Medicine, New Haven, CT, USA; Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT, USA; Beijing Huilongguan Hospital, Beijing, China.
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74
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Faghiri A, Stephen JM, Wang YP, Wilson TW, Calhoun VD. Changing brain connectivity dynamics: From early childhood to adulthood. Hum Brain Mapp 2017; 39:1108-1117. [PMID: 29205692 DOI: 10.1002/hbm.23896] [Citation(s) in RCA: 63] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2017] [Revised: 10/06/2017] [Accepted: 11/13/2017] [Indexed: 12/19/2022] Open
Abstract
Brain maturation through adolescence has been the topic of recent studies. Previous works have evaluated changes in morphometry and also changes in functional connectivity. However, most resting-state fMRI studies have focused on static connectivity. Here we examine the relationship between age/maturity and the dynamics of brain functional connectivity. Utilizing a resting fMRI dataset comprised 421 subjects ages 3-22 from the PING study, we first performed group ICA to extract independent components and their time courses. Next, dynamic functional network connectivity (dFNC) was calculated via a sliding window followed by clustering of connectivity patterns into 5 states. Finally, we evaluated the relationship between age and the amount of time each participant spent in each state as well as the transitions among different states. Results showed that older participants tend to spend more time in states which reflect overall stronger connectivity patterns throughout the brain. In addition, the relationship between age and state transition is symmetric. This can mean individuals change functional connectivity through time within a specific set of states. On the whole, results indicated that dynamic functional connectivity is an important factor to consider when examining brain development across childhood.
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Affiliation(s)
- Ashkan Faghiri
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, New Mexico.,Electrical and Computer Engineering Department, University of New Mexico, Albuquerque, New Mexico
| | - Julia M Stephen
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, New Mexico
| | - Yu-Ping Wang
- Biomedical Engineering Department, Tulane University, New Orleans, Louisiana.,Center of Genomics and Bioinformatics, Tulane University, New Orleans, Louisiana
| | - Tony W Wilson
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, Nebraska.,Center for Magnetoencephalography, University of Nebraska Medical Center, Omaha, Nebraska
| | - Vince D Calhoun
- The Mind Research Network, 1101 Yale Blvd NE, Albuquerque, New Mexico.,Electrical and Computer Engineering Department, University of New Mexico, Albuquerque, New Mexico
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75
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Fu Z, Tu Y, Di X, Du Y, Pearlson GD, Turner JA, Biswal BB, Zhang Z, Calhoun VD. Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity: An application to schizophrenia. Neuroimage 2017; 180:619-631. [PMID: 28939432 DOI: 10.1016/j.neuroimage.2017.09.035] [Citation(s) in RCA: 146] [Impact Index Per Article: 20.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Revised: 09/05/2017] [Accepted: 09/18/2017] [Indexed: 12/23/2022] Open
Abstract
The human brain is a highly dynamic system with non-stationary neural activity and rapidly-changing neural interaction. Resting-state dynamic functional connectivity (dFC) has been widely studied during recent years, and the emerging aberrant dFC patterns have been identified as important features of many mental disorders such as schizophrenia (SZ). However, only focusing on the time-varying patterns in FC is not enough, since the local neural activity itself (in contrast to the inter-connectivity) is also found to be highly fluctuating from research using high-temporal-resolution imaging techniques. Exploring the time-varying patterns in brain activity and their relationships with time-varying brain connectivity is important for advancing our understanding of the co-evolutionary property of brain network and the underlying mechanism of brain dynamics. In this study, we introduced a framework for characterizing time-varying brain activity and exploring its associations with time-varying brain connectivity, and applied this framework to a resting-state fMRI dataset including 151 SZ patients and 163 age- and gender matched healthy controls (HCs). In this framework, 48 brain regions were first identified as intrinsic connectivity networks (ICNs) using group independent component analysis (GICA). A sliding window approach was then adopted for the estimation of dynamic amplitude of low-frequency fluctuation (dALFF) and dFC, which were used to measure time-varying brain activity and time-varying brain connectivity respectively. The dALFF was further clustered into six reoccurring states by the k-means clustering method and the group difference in occurrences of dALFF states was explored. Lastly, correlation coefficients between dALFF and dFC were calculated and the group difference in these dALFF-dFC correlations was explored. Our results suggested that 1) ALFF of brain regions was highly fluctuating during the resting-state and such dynamic patterns are altered in SZ, 2) dALFF and dFC were correlated in time and their correlations are altered in SZ. The overall results support and expand prior work on abnormalities of brain activity, static FC (sFC) and dFC in SZ, and provide new evidence on aberrant time-varying brain activity and its associations with brain connectivity in SZ, which might underscore the disrupted brain cognitive functions in this mental disorder.
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Affiliation(s)
- Zening Fu
- The Mind Research Network, Albuquerque, NM, USA; School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
| | - Yiheng Tu
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Yuhui Du
- The Mind Research Network, Albuquerque, NM, USA
| | - G D Pearlson
- Olin Neuropsychiatry Research Center, The Institute of Living, Hartford, CT, USA; Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - J A Turner
- Department of Psychology, Georgia State University, GA, USA
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Zhiguo Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - V D Calhoun
- The Mind Research Network, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
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76
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Dynamics of large-scale fMRI networks: Deconstruct brain activity to build better models of brain function. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2017. [DOI: 10.1016/j.cobme.2017.09.008] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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