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Fu Y, Xue L, Niu M, Gao Y, Huang Y, Zhang H, Tian M, Zhuo C. Sex-dependent nonlinear Granger connectivity patterns of brain aging in healthy population. Prog Neuropsychopharmacol Biol Psychiatry 2024; 135:111088. [PMID: 39033955 DOI: 10.1016/j.pnpbp.2024.111088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Revised: 06/27/2024] [Accepted: 07/10/2024] [Indexed: 07/23/2024]
Abstract
BACKGROUND Brain aging is a complex process that involves functional alterations in multiple subnetworks and brain regions. However, most previous studies investigating aging-related functional connectivity (FC) changes using resting-state functional magnetic resonance images (rs-fMRIs) have primarily focused on the linear correlation between brain subnetworks, ignoring the nonlinear casual properties of fMRI signals. METHODS We introduced the neural Granger causality technique to investigate the sex-dependent nonlinear Granger connectivity (NGC) during aging on a publicly available dataset of 227 healthy participants acquired cross-sectionally in Leipzig, Germany. RESULTS Our findings indicate that brain aging may cause widespread declines in NGC at both regional and subnetwork scales. These findings exhibit high reproducibility across different network sparsities, demonstrating the efficacy of static and dynamic analysis strategies. Females exhibit greater heterogeneity and reduced stability in NGC compared to males during aging, especially the NGC between the visual network and other subnetworks. Besides, NGC strengths can well reflect the individual cognitive function, which may therefore work as a sensitive metric in cognition-related experiments for individual-scale or group-scale mechanism understanding. CONCLUSION These findings indicate that NGC analysis is a potent tool for identifying sex-dependent brain aging patterns. Our results offer valuable perspectives that could substantially enhance the understanding of sex differences in neurological diseases in the future, especially in degenerative disorders.
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Affiliation(s)
- Yu Fu
- Lanzhou University, Lanzhou, China; Zhejiang University, Hangzhou, China
| | - Le Xue
- Huashan Hospital and Human Phenome Institute, Fudan University, Shanghai, China
| | - Meng Niu
- Lanzhou University, Lanzhou, China; Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, China
| | | | | | - Hong Zhang
- Department of Nuclear Medicine and Medical PET Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, China.
| | - Mei Tian
- Huashan Hospital and Human Phenome Institute, Fudan University, Shanghai, China.
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Wehrheim MH, Faskowitz J, Schubert A, Fiebach CJ. Reliability of variability and complexity measures for task and task-free BOLD fMRI. Hum Brain Mapp 2024; 45:e26778. [PMID: 38980175 PMCID: PMC11232465 DOI: 10.1002/hbm.26778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 05/06/2024] [Accepted: 06/24/2024] [Indexed: 07/10/2024] Open
Abstract
Brain activity continuously fluctuates over time, even if the brain is in controlled (e.g., experimentally induced) states. Recent years have seen an increasing interest in understanding the complexity of these temporal variations, for example with respect to developmental changes in brain function or between-person differences in healthy and clinical populations. However, the psychometric reliability of brain signal variability and complexity measures-which is an important precondition for robust individual differences as well as longitudinal research-is not yet sufficiently studied. We examined reliability (split-half correlations) and test-retest correlations for task-free (resting-state) BOLD fMRI as well as split-half correlations for seven functional task data sets from the Human Connectome Project to evaluate their reliability. We observed good to excellent split-half reliability for temporal variability measures derived from rest and task fMRI activation time series (standard deviation, mean absolute successive difference, mean squared successive difference), and moderate test-retest correlations for the same variability measures under rest conditions. Brain signal complexity estimates (several entropy and dimensionality measures) showed moderate to good reliabilities under both, rest and task activation conditions. We calculated the same measures also for time-resolved (dynamic) functional connectivity time series and observed moderate to good reliabilities for variability measures, but poor reliabilities for complexity measures derived from functional connectivity time series. Global (i.e., mean across cortical regions) measures tended to show higher reliability than region-specific variability or complexity estimates. Larger subcortical regions showed similar reliability as cortical regions, but small regions showed lower reliability, especially for complexity measures. Lastly, we also show that reliability scores are only minorly dependent on differences in scan length and replicate our results across different parcellation and denoising strategies. These results suggest that the variability and complexity of BOLD activation time series are robust measures well-suited for individual differences research. Temporal variability of global functional connectivity over time provides an important novel approach to robustly quantifying the dynamics of brain function. PRACTITIONER POINTS: Variability and complexity measures of BOLD activation show good split-half reliability and moderate test-retest reliability. Measures of variability of global functional connectivity over time can robustly quantify neural dynamics. Length of fMRI data has only a minor effect on reliability.
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Affiliation(s)
- Maren H. Wehrheim
- Department of PsychologyGoethe University FrankfurtFrankfurtGermany
- Department of Computer Science and MathematicsGoethe University FrankfurtFrankfurtGermany
- Frankfurt Institute for Advanced Studies (FIAS)FrankfurtGermany
| | - Joshua Faskowitz
- Department of Psychological and Brain SciencesIndiana UniversityBloomingtonUSA
| | - Anna‐Lena Schubert
- Department of PsychologyJohannes Gutenberg‐Universität MainzMainzGermany
| | - Christian J. Fiebach
- Department of PsychologyGoethe University FrankfurtFrankfurtGermany
- Brain Imaging CenterGoethe University FrankfurtFrankfurtGermany
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Zheng J, Cheng Y, Wu X, Li X, Fu Y, Yang Z. Rich-club organization of whole-brain spatio-temporal multilayer functional connectivity networks. Front Neurosci 2024; 18:1405734. [PMID: 38855440 PMCID: PMC11157044 DOI: 10.3389/fnins.2024.1405734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Accepted: 05/07/2024] [Indexed: 06/11/2024] Open
Abstract
Objective In this work, we propose a novel method for constructing whole-brain spatio-temporal multilayer functional connectivity networks (FCNs) and four innovative rich-club metrics. Methods Spatio-temporal multilayer FCNs achieve a high-order representation of the spatio-temporal dynamic characteristics of brain networks by combining the sliding time window method with graph theory and hypergraph theory. The four proposed rich-club scales are based on the dynamic changes in rich-club node identity, providing a parameterized description of the topological dynamic characteristics of brain networks from both temporal and spatial perspectives. The proposed method was validated in three independent differential analysis experiments: male-female gender difference analysis, analysis of abnormality in patients with autism spectrum disorders (ASD), and individual difference analysis. Results The proposed method yielded results consistent with previous relevant studies and revealed some innovative findings. For instance, the dynamic topological characteristics of specific white matter regions effectively reflected individual differences. The increased abnormality in internal functional connectivity within the basal ganglia may be a contributing factor to the occurrence of repetitive or restrictive behaviors in ASD patients. Conclusion The proposed methodology provides an efficacious approach for constructing whole-brain spatio-temporal multilayer FCNs and conducting analysis of their dynamic topological structures. The dynamic topological characteristics of spatio-temporal multilayer FCNs may offer new insights into physiological variations and pathological abnormalities in neuroscience.
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Affiliation(s)
- Jianhui Zheng
- College of Electronic Engineering, Chengdu University of Information Technology, Chengdu, China
| | - Yuhao Cheng
- Huaxi Molecular Imaging Research Laboratory, Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Xi Wu
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, China
| | - Xiaojie Li
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, China
| | - Ying Fu
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, China
| | - Zhipeng Yang
- College of Electronic Engineering, Chengdu University of Information Technology, Chengdu, China
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Xie A, Sun Y, Chen H, Li L, Liu P, Liao Y, Li Y. Altered dynamic functional connectivity of insular subdivisions among male cigarette smokers. Front Psychiatry 2024; 15:1353103. [PMID: 38827448 PMCID: PMC11140567 DOI: 10.3389/fpsyt.2024.1353103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 05/06/2024] [Indexed: 06/04/2024] Open
Abstract
Background Insular subdivisions show distinct patterns of resting state functional connectivity with specific brain regions, each with different functional significance in chronic cigarette smokers. This study aimed to explore the altered dynamic functional connectivity (dFC) of distinct insular subdivisions in smokers. Methods Resting-state BOLD data of 31 smokers with nicotine dependence and 27 age-matched non-smokers were collected. Three bilateral insular regions of interest (dorsal, ventral, and posterior) were set as seeds for analyses. Sliding windows method was used to acquire the dFC metrics of different insular seeds. Support vector machine based on abnormal insular dFC was applied to classify smokers from non-smokers. Results We found that smokers showed lower dFC variance between the left ventral anterior insula and both the right superior parietal cortex and the left inferior parietal cortex, as well as greater dFC variance the right ventral anterior insula with the right middle cingulum cortex relative to non-smokers. Moreover, compared to non-smokers, it is found that smokers demonstrated altered dFC variance of the right dorsal insula and the right middle temporal gyrus. Correlation analysis showed the higher dFC between the right dorsal insula and the right middle temporal gyrus was associated with longer years of smoking. The altered insular subdivision dFC can classify smokers from non-smokers with an accuracy of 89.66%, a sensitivity of 96.30% and a specify of 83.87%. Conclusions Our findings highlighted the abnormal patterns of fluctuating connectivity of insular subdivision circuits in smokers and suggested that these abnormalities may play a significant role in the mechanisms underlying nicotine addiction and could potentially serve as a neural biomarker for addiction treatment.
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Affiliation(s)
- An Xie
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
- Department of Radiology, The People’s Hospital of Hunan Province (The First Affiliated Hospital of Hunan Normal University), Changsha, Hunan, China
- Center for Mind & Brain Sciences, Hunan Normal University, Changsha, Hunan, China
| | - Yunkai Sun
- Department of Psychiatry, Sir Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Haobo Chen
- Department of Radiology, The People’s Hospital of Hunan Province (The First Affiliated Hospital of Hunan Normal University), Changsha, Hunan, China
- Center for Mind & Brain Sciences, Hunan Normal University, Changsha, Hunan, China
| | - Ling Li
- Department of Psychiatry, Sir Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Peng Liu
- Department of Radiology, The People’s Hospital of Hunan Province (The First Affiliated Hospital of Hunan Normal University), Changsha, Hunan, China
- Center for Mind & Brain Sciences, Hunan Normal University, Changsha, Hunan, China
| | - Yanhui Liao
- Department of Radiology, The People’s Hospital of Hunan Province (The First Affiliated Hospital of Hunan Normal University), Changsha, Hunan, China
- Center for Mind & Brain Sciences, Hunan Normal University, Changsha, Hunan, China
- Department of Psychiatry, Sir Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yonggang Li
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
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Kuai C, Pu J, Wang D, Tan Z, Wang Y, Xue SW. The association between gray matter volume in the hippocampal subfield and antidepressant efficacy mediated by abnormal dynamic functional connectivity. Sci Rep 2024; 14:8940. [PMID: 38637536 PMCID: PMC11026377 DOI: 10.1038/s41598-024-56866-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 03/12/2024] [Indexed: 04/20/2024] Open
Abstract
An abnormality of structures and functions in the hippocampus may have a key role in the pathophysiology of major depressive disorder (MDD). However, it is unclear whether structure factors of the hippocampus effectively impact antidepressant responses by hippocampal functional activity in MDD patients. We collected longitudinal data from 36 MDD patients before and after a 3-month course of antidepressant pharmacotherapy. Additionally, we obtained baseline data from 43 healthy controls matched for sex and age. Using resting-state functional magnetic resonance imaging (rs-fMRI), we estimated the dynamic functional connectivity (dFC) of the hippocampal subregions using a sliding-window method. The gray matter volume was calculated using voxel-based morphometry (VBM). The results indicated that patients with MDD exhibited significantly lower dFC of the left rostral hippocampus (rHipp.L) with the right precentral gyrus, left superior temporal gyrus and left postcentral gyrus compared to healthy controls at baseline. In MDD patients, the dFC of the rHipp.L with right precentral gyrus at baseline was correlated with both the rHipp.L volume and HAMD remission rate, and also mediated the effects of the rHipp.L volume on antidepressant performance. Our findings suggested that the interaction between hippocampal structure and functional activity might affect antidepressant performance, which provided a novel insight into the hippocampus-related neurobiological mechanism of MDD.
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Affiliation(s)
- Changxiao Kuai
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, No. 2318, Yuhangtang Rd, Hangzhou, 311121, Zhejiang Province, People's Republic of China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, People's Republic of China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, People's Republic of China
| | - Jiayong Pu
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, No. 2318, Yuhangtang Rd, Hangzhou, 311121, Zhejiang Province, People's Republic of China
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, People's Republic of China
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, People's Republic of China
| | - Donglin Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, No. 2318, Yuhangtang Rd, Hangzhou, 311121, Zhejiang Province, People's Republic of China.
| | - Zhonglin Tan
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, People's Republic of China
| | - Yan Wang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, No. 2318, Yuhangtang Rd, Hangzhou, 311121, Zhejiang Province, People's Republic of China
| | - Shao-Wei Xue
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, No. 2318, Yuhangtang Rd, Hangzhou, 311121, Zhejiang Province, People's Republic of China.
- Institute of Psychological Science, Hangzhou Normal University, Hangzhou, Zhejiang Province, People's Republic of China.
- Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, Zhejiang Province, People's Republic of China.
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Islam S, Khanra P, Nakuci J, Muldoon SF, Watanabe T, Masuda N. State-transition dynamics of resting-state functional magnetic resonance imaging data: model comparison and test-to-retest analysis. BMC Neurosci 2024; 25:14. [PMID: 38438838 PMCID: PMC10913599 DOI: 10.1186/s12868-024-00854-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 02/07/2024] [Indexed: 03/06/2024] Open
Abstract
Electroencephalogram (EEG) microstate analysis entails finding dynamics of quasi-stable and generally recurrent discrete states in multichannel EEG time series data and relating properties of the estimated state-transition dynamics to observables such as cognition and behavior. While microstate analysis has been widely employed to analyze EEG data, its use remains less prevalent in functional magnetic resonance imaging (fMRI) data, largely due to the slower timescale of such data. In the present study, we extend various data clustering methods used in EEG microstate analysis to resting-state fMRI data from healthy humans to extract their state-transition dynamics. We show that the quality of clustering is on par with that for various microstate analyses of EEG data. We then develop a method for examining test-retest reliability of the discrete-state transition dynamics between fMRI sessions and show that the within-participant test-retest reliability is higher than between-participant test-retest reliability for different indices of state-transition dynamics, different networks, and different data sets. This result suggests that state-transition dynamics analysis of fMRI data could discriminate between different individuals and is a promising tool for performing fingerprinting analysis of individuals.
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Affiliation(s)
- Saiful Islam
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA
| | - Pitambar Khanra
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA
| | - Johan Nakuci
- School of Psychology, Georgia Institute of Technology, North Avenue, Atlanta, 30332, GA, USA
| | - Sarah F Muldoon
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA
- Neuroscience Program, University at Buffalo, State University of New York at Buffalo, 955 Main Street, Buffalo, 14203, NY, USA
| | - Takamitsu Watanabe
- International Research Centre for Neurointelligence, The University of Tokyo Institutes for Advanced Study, 731 Hongo Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Naoki Masuda
- Department of Mathematics , University at Buffalo, State University of New York at Buffalo, 244 Mathematics Building , Buffalo, 14260, NY, USA.
- Institute for Artificial Intelligence and Data Science, University at Buffalo, State University of New York at Buffalo, 215 Lockwood Hall, Buffalo, 14260, NY, USA.
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Zhan L, Gao Y, Huang L, Zhang H, Huang G, Wang Y, Sun J, Xie Z, Li M, Jia X, Cheng L, Yu Y. Brain functional connectivity alterations of Wernicke's area in individuals with autism spectrum conditions in multi-frequency bands: A mega-analysis. Heliyon 2024; 10:e26198. [PMID: 38404781 PMCID: PMC10884452 DOI: 10.1016/j.heliyon.2024.e26198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 02/05/2024] [Accepted: 02/08/2024] [Indexed: 02/27/2024] Open
Abstract
Characterized by severe deficits in communication, most individuals with autism spectrum conditions (ASC) experience significant language dysfunctions, thereby impacting their overall quality of life. Wernicke's area, a classical and traditional brain region associated with language processing, plays a substantial role in the manifestation of language impairments. The current study carried out a mega-analysis to attain a comprehensive understanding of the neural mechanisms underpinning ASC, particularly in the context of language processing. The study employed the Autism Brain Image Data Exchange (ABIDE) dataset, which encompasses data from 443 typically developing (TD) individuals and 362 individuals with ASC. The objective was to detect abnormal functional connectivity (FC) between Wernicke's area and other language-related functional regions, and identify frequency-specific altered FC using Wernicke's area as the seed region in ASC. The findings revealed that increased FC in individuals with ASC has frequency-specific characteristics. Further, in the conventional frequency band (0.01-0.08 Hz), individuals with ASC exhibited increased FC between Wernicke's area and the right thalamus compared with TD individuals. In the slow-5 frequency band (0.01-0.027 Hz), increased FC values were observed in the left cerebellum Crus II and the right lenticular nucleus, pallidum. These results provide novel insights into the potential neural mechanisms underlying communication deficits in ASC from the perspective of language impairments.
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Affiliation(s)
- Linlin Zhan
- School of Western Studies, Heilongjiang University, Harbin, China
| | - Yanyan Gao
- College of Teacher Education, Zhejiang Normal University, Jinhua, China
| | - Lina Huang
- Department of Radiology, Changshu No. 2 People's Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, Jiangsu, China
| | - Hongqiang Zhang
- Department of Radiology, Changshu No. 2 People's Hospital, The Affiliated Changshu Hospital of Xuzhou Medical University, Changshu, Jiangsu, China
| | - Guofeng Huang
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Yadan Wang
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Jiawei Sun
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Zhou Xie
- School of Information and Electronics Technology, Jiamusi University, Jiamusi, China
| | - Mengting Li
- College of Teacher Education, Zhejiang Normal University, Jinhua, China
| | - Xize Jia
- College of Teacher Education, Zhejiang Normal University, Jinhua, China
| | - Lulu Cheng
- School of Foreign Studies, China University of Petroleum (East China), Qingdao, China
- Shanghai Center for Research in English Language Education, Shanghai International Studies University, Shanghai, China
| | - Yang Yu
- Psychiatry Department, The Second Affiliated Hospital Zhejiang University School of Medicine, Zhejiang, Hangzhou, China
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Matkovič A, Anticevic A, Murray JD, Repovš G. Static and dynamic fMRI-derived functional connectomes represent largely similar information. Netw Neurosci 2023; 7:1266-1301. [PMID: 38144686 PMCID: PMC10631791 DOI: 10.1162/netn_a_00325] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 06/06/2023] [Indexed: 12/26/2023] Open
Abstract
Functional connectivity (FC) of blood oxygen level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson's/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate), and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods by (i) assessing similarities between FC matrices, (ii) by comparing node centrality measures, and (iii) by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.
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Affiliation(s)
- Andraž Matkovič
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - John D. Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Grega Repovš
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
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Guan S, Jiang R, Chen DY, Michael A, Meng C, Biswal B. Multifractal long-range dependence pattern of functional magnetic resonance imaging in the human brain at rest. Cereb Cortex 2023; 33:11594-11608. [PMID: 37851793 DOI: 10.1093/cercor/bhad393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/01/2023] [Accepted: 10/02/2023] [Indexed: 10/20/2023] Open
Abstract
Long-range dependence is a prevalent phenomenon in various biological systems that characterizes the long-memory effect of temporal fluctuations. While recent research suggests that functional magnetic resonance imaging signal has fractal property, it remains unknown about the multifractal long-range dependence pattern of resting-state functional magnetic resonance imaging signals. The current study adopted the multifractal detrended fluctuation analysis on highly sampled resting-state functional magnetic resonance imaging scans to investigate long-range dependence profile associated with the whole-brain voxels as specific functional networks. Our findings revealed the long-range dependence's multifractal properties. Moreover, long-term persistent fluctuations are found for all stations with stronger persistency in whole-brain regions. Subsets with large fluctuations contribute more to the multifractal spectrum in the whole brain. Additionally, we found that the preprocessing with band-pass filtering provided significantly higher reliability for estimating long-range dependence. Our validation analysis confirmed that the optimal pipeline of long-range dependence analysis should include band-pass filtering and removal of daily temporal dependence. Furthermore, multifractal long-range dependence characteristics in healthy control and schizophrenia are different significantly. This work has provided an analytical pipeline for the multifractal long-range dependence in the resting-state functional magnetic resonance imaging signal. The findings suggest differential long-memory effects in the intrinsic functional networks, which may offer a neural marker finding for understanding brain function and pathology.
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Affiliation(s)
- Sihai Guan
- College of Electronic and Information, Southwest Minzu University, Chengdu 610041, China
- Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu 610041, China
| | - Runzhou Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
- Medical Equipment Department, Xiangyang No.1 People's Hospital, Xiangyang 441000, China
| | - Donna Y Chen
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, United States
| | - Andrew Michael
- Duke Institute for Brain Sciences, Duke University, Durham, NC 27708, United States
| | - Chun Meng
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bharat Biswal
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, United States
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10
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Hu P, Wang P, Zhao R, Yang H, Biswal BB. Characterizing the spatiotemporal features of functional connectivity across the white matter and gray matter during the naturalistic condition. Front Neurosci 2023; 17:1248610. [PMID: 38027509 PMCID: PMC10665512 DOI: 10.3389/fnins.2023.1248610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Introduction The naturalistic stimuli due to its ease of operability has attracted many researchers in recent years. However, the influence of the naturalistic stimuli for whole-brain functions compared with the resting state is still unclear. Methods In this study, we clustered gray matter (GM) and white matter (WM) masks both at the ROI- and network-levels. Functional connectivity (FC) and inter-subject functional connectivity (ISFC) were calculated in GM, WM, and between GM and WM under the movie-watching and the resting-state conditions. Furthermore, intra-class correlation coefficients (ICC) of FC and ISFC were estimated on different runs of fMRI data to denote the reliability of them during the two conditions. In addition, static and dynamic connectivity indices were calculated with Pearson correlation coefficient to demonstrate the associations between the movie-watching and the resting-state. Results As the results, we found that the movie-watching significantly affected FC in whole-brain compared with the resting-state, but ISFC did not show significant connectivity induced by the naturalistic condition. ICC of FC and ISFC was generally higher during movie-watching compared with the resting-state, demonstrating that naturalistic stimuli could promote the reliability of connectivity. The associations between static and dynamic ISFC were weakly negative correlations in the naturalistic stimuli while there is no correlation between them under resting-state condition. Discussion Our findings confirmed that compared to resting-state condition, the connectivity indices under the naturalistic stimuli were more reliable and stable to investigate the normal functional activities of the human brain, and might promote the applications of FC in the cerebral dysfunction in various mental disorders.
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Affiliation(s)
- Peng Hu
- MOE Key Laboratory for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Pan Wang
- MOE Key Laboratory for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Rong Zhao
- MOE Key Laboratory for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Hang Yang
- MOE Key Laboratory for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Chinese Institute for Brain Research, Beijing, China
| | - Bharat B. Biswal
- MOE Key Laboratory for Neuroinformation, The Clinical Hospital of Chengdu Brain Science Institute, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States
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Zhang H, Meng C, Di X, Wu X, Biswal B. Static and dynamic functional connectome reveals reconfiguration profiles of whole-brain network across cognitive states. Netw Neurosci 2023; 7:1034-1050. [PMID: 37781145 PMCID: PMC10473282 DOI: 10.1162/netn_a_00314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 03/21/2023] [Indexed: 10/03/2023] Open
Abstract
Assessment of functional connectivity (FC) has revealed a great deal of knowledge about the macroscale spatiotemporal organization of the brain network. Recent studies found task-versus-rest network reconfigurations were crucial for cognitive functioning. However, brain network reconfiguration remains unclear among different cognitive states, considering both aggregate and time-resolved FC profiles. The current study utilized static FC (sFC, i.e., long timescale aggregate FC) and sliding window-based dynamic FC (dFC, i.e., short timescale time-varying FC) approaches to investigate the similarity and alterations of edge weights and network topology at different cognitive loads, particularly their relationships with specific cognitive process. Both dFC/sFC networks showed subtle but significant reconfigurations that correlated with task performance. At higher cognitive load, brain network reconfiguration displayed increased functional integration in the sFC-based aggregate network, but faster and larger variability of modular reorganization in the dFC-based time-varying network, suggesting difficult tasks require more integrated and flexible network reconfigurations. Moreover, sFC-based network reconfigurations mainly linked with the sensorimotor and low-order cognitive processes, but dFC-based network reconfigurations mainly linked with the high-order cognitive process. Our findings suggest that reconfiguration profiles of sFC/dFC networks provide specific information about cognitive functioning, which could potentially be used to study brain function and disorders.
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Affiliation(s)
- Heming Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
| | - Xiao Wu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, USA
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12
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Yang H, Yao X, Zhang H, Meng C, Biswal B. Estimating dynamic individual coactivation patterns based on densely sampled resting-state fMRI data and utilizing it for better subject identification. Brain Struct Funct 2023; 228:1755-1769. [PMID: 37572108 DOI: 10.1007/s00429-023-02689-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/16/2023] [Indexed: 08/14/2023]
Abstract
As a complex dynamic system, the brain exhibits spatially organized recurring patterns of activity over time. Coactivation patterns (CAPs), which analyzes data from each single frame, have been utilized to detect transient brain activity states recently. However, previous CAP analyses have been conducted at the group level, which might neglect meaningful individual differences. Here, we estimated individual CAP states at both subject- and scan-level based on a densely sampled dataset: Midnight Scan Club. We used differential identifiability, which measures the gap between intra- and inter-subject similarity, to evaluate individual differences. We found individual CAPs at the subject-level achieved the best fingerprinting ability by maintaining high intra-subject similarity and enlarging inter-subject differences, and brain regions of association networks mainly contributed to the identifiability. On the other hand, scan-level CAP states were unstable across scans for the same participant. Expectedly, we found subject-specific CAPs became more reliable and discriminative with more data (i.e., longer duration). As the acquisition time of each participant is limited in practice, our results recommend a data collection strategy that collects more scans with appropriate duration (e.g., 12 ~ 15 min/scan) to obtain more reliable subject-specific CAPs, when total acquisition time is fixed (e.g., 150 min). In summary, this work has constructed reliable subject-specific CAP states with meaningful individual differences, and recommended an appropriate data collection strategy, which can guide subsequent investigations into individualized brain dynamics.
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Affiliation(s)
- Hang Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Chinese Institute for Brain Research, Beijing, 102206, China.
| | - Xing Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Hong Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Bharat Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China.
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights, 607 Fenster Hall, Newark, NJ, 07102, USA.
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13
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Li MT, Sun JW, Zhan LL, Antwi CO, Lv YT, Jia XZ, Ren J. The effect of seed location on functional connectivity: evidence from an image-based meta-analysis. Front Neurosci 2023; 17:1120741. [PMID: 37325032 PMCID: PMC10264592 DOI: 10.3389/fnins.2023.1120741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 05/15/2023] [Indexed: 06/17/2023] Open
Abstract
Introduction Default mode network (DMN) is the most involved network in the study of brain development and brain diseases. Resting-state functional connectivity (rsFC) is the most used method to study DMN, but different studies are inconsistent in the selection of seed. To evaluate the effect of different seed selection on rsFC, we conducted an image-based meta-analysis (IBMA). Methods We identified 59 coordinates of seed regions of interest (ROIs) within the default mode network (DMN) from 11 studies (retrieved from Web of Science and Pubmed) to calculate the functional connectivity; then, the uncorrected t maps were obtained from the statistical analyses. The IBMA was performed with the t maps. Results We demonstrate that the overlap of meta-analytic maps across different seeds' ROIs within DMN is relatively low, which cautions us to be cautious with seeds' selection. Discussion Future studies using the seed-based functional connectivity method should take the reproducibility of different seeds into account. The choice of seed may significantly affect the connectivity results.
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Affiliation(s)
- Meng-Ting Li
- School of Psychology, Zhejiang Normal University, Jinhua, China
| | - Jia-Wei Sun
- Department of Clinical Neuroscience, Division of Neuro, Karolinska Institutet, Stockholm, Sweden
| | - Lin-Lin Zhan
- School of Western Studies, Heilongjiang University, Harbin, China
| | | | - Ya-Ting Lv
- Center for Cognition and Brain Disorders, The Affiliated Hospital, Hangzhou Normal University, Hangzhou, China
| | - Xi-Ze Jia
- School of Psychology, Zhejiang Normal University, Jinhua, China
| | - Jun Ren
- School of Psychology, Zhejiang Normal University, Jinhua, China
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14
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Long Y, Ouyang X, Yan C, Wu Z, Huang X, Pu W, Cao H, Liu Z, Palaniyappan L. Evaluating test-retest reliability and sex-/age-related effects on temporal clustering coefficient of dynamic functional brain networks. Hum Brain Mapp 2023; 44:2191-2208. [PMID: 36637216 PMCID: PMC10028647 DOI: 10.1002/hbm.26202] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 11/25/2022] [Accepted: 01/01/2023] [Indexed: 01/14/2023] Open
Abstract
The multilayer dynamic network model has been proposed as an effective method to understand the brain function. In particular, derived from the definition of clustering coefficient in static networks, the temporal clustering coefficient provides a direct measure of the topological stability of dynamic brain networks and shows potential in predicting altered brain functions. However, test-retest reliability and demographic-related effects on this measure remain to be evaluated. Using a data set from the Human Connectome Project (157 male and 180 female healthy adults; 22-37 years old), the present study investigated: (1) the test-retest reliability of temporal clustering coefficient across four repeated resting-state functional magnetic resonance imaging scans as measured by intraclass correlation coefficient (ICC); and (2) sex- and age-related effects on temporal clustering coefficient. The results showed that (1) the temporal clustering coefficient had overall moderate test-retest reliability (ICC > 0.40 over a wide range of densities) at both global and subnetwork levels, (2) female subjects showed significantly higher temporal clustering coefficient than males at both global and subnetwork levels, particularly within the default-mode and subcortical regions, and (3) temporal clustering coefficient of the subcortical subnetwork was positively correlated with age in young adults. The results of sex effects were robustly replicated in an independent REST-meta-MDD data set, while the results of age effects were not. Our findings suggest that the temporal clustering coefficient is a relatively reliable and reproducible approach for identifying individual differences in brain function, and provide evidence for demographically related effects on the human brain dynamic connectomes.
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Affiliation(s)
- Yicheng Long
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Xuan Ouyang
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Chaogan Yan
- CAS Key Laboratory of Behavioral Science, Institute of PsychologyChinese Academy of SciencesBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
- International Big‐Data Center for Depression Research, Institute of PsychologyChinese Academy of SciencesBeijingChina
| | - Zhipeng Wu
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Xiaojun Huang
- Department of PsychiatryJiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical CollegeNanchangChina
| | - Weidan Pu
- Medical Psychological InstituteThe Second Xiangya Hospital, Central South UniversityChangshaChina
| | - Hengyi Cao
- Center for Psychiatric NeuroscienceFeinstein Institute for Medical ResearchManhassetNew YorkUSA
- Division of Psychiatry ResearchZucker Hillside HospitalGlen OaksNew YorkUSA
| | - Zhening Liu
- Department of Psychiatry, and National Clinical Research Center for Mental DisordersThe Second Xiangya Hospital of Central South UniversityChangshaHunanChina
| | - Lena Palaniyappan
- Department of PsychiatryUniversity of Western OntarioLondonOntarioCanada
- Robarts Research InstituteUniversity of Western OntarioLondonOntarioCanada
- Lawson Health Research InstituteLondonOntarioCanada
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15
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Candelaria-Cook FT, Schendel ME, Flynn L, Cerros C, Hill DE, Stephen JM. Disrupted dynamic functional network connectivity in fetal alcohol spectrum disorders. ALCOHOL, CLINICAL & EXPERIMENTAL RESEARCH 2023; 47:687-703. [PMID: 36880528 PMCID: PMC10281251 DOI: 10.1111/acer.15046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/30/2023] [Accepted: 02/23/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND Prenatal alcohol exposure (PAE) can result in harmful and long-lasting neurodevelopmental changes. Children with PAE or a fetal alcohol spectrum disorder (FASD) have decreased white matter volume and resting-state spectral power compared to typically developing controls (TDC) and impaired resting-state static functional connectivity. The impact of PAE on resting-state dynamic functional network connectivity (dFNC) is unknown. METHODS Using eyes-closed and eyes-open magnetoencephalography (MEG) resting-state data, global dFNC statistics and meta-states were examined in 89 children aged 6-16 years (51 TDC, 38 with FASD). Source analyzed MEG data were used as input to group spatial independent component analysis to derive functional networks from which the dFNC was calculated. RESULTS During eyes-closed, relative to TDC, participants with FASD spent a significantly longer time in state 2, typified by anticorrelation (i.e., decreased connectivity) within and between default mode network (DMN) and visual network (VN), and state 4, typified by stronger internetwork correlation. The FASD group exhibited greater dynamic fluidity and dynamic range (i.e., entered more states, changed from one meta-state to another more often, and traveled greater distances) than TDC. During eyes-open, TDC spent significantly more time in state 1, typified by positive intra- and interdomain connectivity with modest correlation within the frontal network (FN), while participants with FASD spent a larger fraction of time in state 2, typified by anticorrelation within and between DMN and VN and strong correlation within and between FN, attention network, and sensorimotor network. CONCLUSIONS There are important resting-state dFNC differences between children with FASD and TDC. Participants with FASD exhibited greater dynamic fluidity and dynamic range and spent more time in states typified by anticorrelation within and between DMN and VN, and more time in a state typified by high internetwork connectivity. Taken together, these network aberrations indicate that prenatal alcohol exposure has a global effect on resting-state connectivity.
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Affiliation(s)
| | - Megan E. Schendel
- The Mind Research Network and Lovelace Biomedical Research Institute, Albuquerque, New Mexico, USA
| | - Lucinda Flynn
- The Mind Research Network and Lovelace Biomedical Research Institute, Albuquerque, New Mexico, USA
| | - Cassandra Cerros
- Department of Pediatrics, University of New Mexico Health Sciences Center, Albuquerque, NM
| | - Dina E. Hill
- Department of Psychiatry and Behavioral Sciences, University of New Mexico Health Sciences Center, Albuquerque, NM
| | - Julia M. Stephen
- The Mind Research Network and Lovelace Biomedical Research Institute, Albuquerque, New Mexico, USA
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16
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Long Y, Liu X, Liu Z. Temporal Stability of the Dynamic Resting-State Functional Brain Network: Current Measures, Clinical Research Progress, and Future Perspectives. Brain Sci 2023; 13:brainsci13030429. [PMID: 36979239 PMCID: PMC10046056 DOI: 10.3390/brainsci13030429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/20/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023] Open
Abstract
Based on functional magnetic resonance imaging and multilayer dynamic network model, the brain network’s quantified temporal stability has shown potential in predicting altered brain functions. This manuscript aims to summarize current knowledge, clinical research progress, and future perspectives on brain network’s temporal stability. There are a variety of widely used measures of temporal stability such as the variance/standard deviation of dynamic functional connectivity strengths, the temporal variability, the flexibility (switching rate), and the temporal clustering coefficient, while there is no consensus to date which measure is the best. The temporal stability of brain networks may be associated with several factors such as sex, age, cognitive functions, head motion, circadian rhythm, and data preprocessing/analyzing strategies, which should be considered in clinical studies. Multiple common psychiatric disorders such as schizophrenia, major depressive disorder, and bipolar disorder have been found to be related to altered temporal stability, especially during the resting state; generally, both excessively decreased and increased temporal stabilities were thought to reflect disorder-related brain dysfunctions. However, the measures of temporal stability are still far from applications in clinical diagnoses for neuropsychiatric disorders partly because of the divergent results. Further studies with larger samples and in transdiagnostic (including schizoaffective disorder) subjects are warranted.
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17
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Feng P, Jiang R, Wei L, Calhoun VD, Jing B, Li H, Sui J. Determining four confounding factors in individual cognitive traits prediction with functional connectivity: an exploratory study. Cereb Cortex 2023; 33:2011-2020. [PMID: 35567795 PMCID: PMC9977351 DOI: 10.1093/cercor/bhac189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/24/2022] [Accepted: 04/25/2022] [Indexed: 11/14/2022] Open
Abstract
Resting-state functional connectivity (RSFC) has been widely adopted for individualized trait prediction. However, multiple confounding factors may impact the predicted brain-behavior relationships. In this study, we investigated the impact of 4 confounding factors including time series length, functional connectivity (FC) type, brain parcellation choice, and variance of the predicted target. The data from Human Connectome Project including 1,206 healthy subjects were employed, with 3 cognitive traits including fluid intelligence, working memory, and picture vocabulary ability as the prediction targets. We compared the prediction performance under different settings of these 4 factors using partial least square regression. Results demonstrated appropriate time series length (300 time points) and brain parcellation (independent component analysis, ICA100/200) can achieve better prediction performance without too much time consumption. FC calculated by Pearson, Spearman, and Partial correlation achieves higher accuracy and lower time cost than mutual information and coherence. Cognitive traits with larger variance among subjects can be better predicted due to the well elaboration of individual variability. In addition, the beneficial effects of increasing scan duration to prediction partially arise from the improved test-retest reliability of RSFC. Taken together, the study highlights the importance of determining these factors in RSFC-based prediction, which can facilitate standardization of RSFC-based prediction pipelines going forward.
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Affiliation(s)
- Pujie Feng
- School of Biomedical Engineering, Capital Medical University, Xitoutiao No. 10, Youanmenwai Street, Fengtai District, 100069 Beijing, China
| | - Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, 300 Cedar Street, New Haven, 06510 CT, United States
| | - Lijiang Wei
- School of Biomedical Engineering, Capital Medical University, Xitoutiao No. 10, Youanmenwai Street, Fengtai District, 100069 Beijing, China.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekou Outer Street, Haidian District, 100875 Beijing, China
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, 55 Park Pl NE, Atlanta, 30303, GA, United States
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical University, Xitoutiao No. 10, Youanmenwai Street, Fengtai District, 100069 Beijing, China
| | - Haiyun Li
- School of Biomedical Engineering, Capital Medical University, Xitoutiao No. 10, Youanmenwai Street, Fengtai District, 100069 Beijing, China
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19, Xinjiekou Outer Street, Haidian District, 100875 Beijing, China.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, 55 Park Pl NE, Atlanta, 30303, GA, United States
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18
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Tan W, Ouyang X, Huang D, Wu Z, Liu Z, He Z, Long Y. Disrupted intrinsic functional brain network in patients with late-life depression: Evidence from a multi-site dataset. J Affect Disord 2023; 323:631-639. [PMID: 36521664 DOI: 10.1016/j.jad.2022.12.019] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 12/04/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022]
Abstract
BACKGROUND Late-life depression (LLD) is a common and serious mental disorder, whose neural mechanisms are not yet fully understood. In this study, we aimed to characterize LLD-related changes in intrinsic functional brain networks using a large, multi-site sample. METHODS Using resting-state functional magnetic resonance imaging, the edge-based functional connectivity (FC) as well as multiple topological brain network metrics at both global and nodal levels were compared between 206 LLD patients and 210 normal controls (NCs). RESULTS Compared with NCs, the LLD patients had extensive alterations in the intrinsic brain FCs, especially significant decreases in FCs within the default mode network (DMN) and within the somatomotor network (SMN). The LLD patients also showed alterations in several global brain network metrics compared with NCs, including significant decreases in global efficiency, local efficiency, clustering coefficient, and small-worldness, as well as a significantly increased characteristic path length. Moreover, significant alterations in nodal network metrics (increased nodal betweenness and decreased nodal efficiency) were found in patients with LLD, which mainly involved the DMN and SMN. Post-hoc subgroup analyses indicated that the above changes in FC strengths were present in both first-episode, drug-naïve (FEDN) and non-FEDN patients, and were correlated with depression severity in the FEDN patients. Moreover, changes in FC strengths were found in both the early/late-onset (depression starts before/after the age of 50) patients, while altered topological metrics were found in only the late-onset patients. CONCLUSIONS These results may help to strengthen our understanding of the underlying neural mechanisms and biological heterogeneity in LLD.
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Affiliation(s)
- Wenjian Tan
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xuan Ouyang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Danqing Huang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zhipeng Wu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zhening Liu
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zhong He
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China; Clinical Research Center For Medical Imaging in Hunan Province, Changsha, Hunan, China.
| | - Yicheng Long
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
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19
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Fu Y, Niu M, Gao Y, Dong S, Huang Y, Zhang Z, Zhuo C. Altered nonlinear Granger causality interactions in the large-scale brain networks of patients with schizophrenia. J Neural Eng 2022; 19. [PMID: 36579785 DOI: 10.1088/1741-2552/acabe7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
Objective.It has been demonstrated that schizophrenia (SZ) is characterized by functional dysconnectivity involving extensive brain networks. However, the majority of previous studies utilizing resting-state functional magnetic resonance imaging (fMRI) to infer abnormal functional connectivity (FC) in patients with SZ have focused on the linear correlation that one brain region may influence another, ignoring the inherently nonlinear properties of fMRI signals.Approach. In this paper, we present a neural Granger causality (NGC) technique for examining the changes in SZ's nonlinear causal couplings. We develop static and dynamic NGC-based analyses of large-scale brain networks at several network levels, estimating complicated temporal and causal relationships in SZ patients.Main results. We find that the NGC-based FC matrices can detect large and significant differences between the SZ and healthy control groups at both the regional and subnetwork scales. These differences are persistent and significantly overlapped at various network sparsities regardless of whether the brain networks were built using static or dynamic techniques. In addition, compared to controls, patients with SZ exhibited extensive NGC confusion patterns throughout the entire brain.Significance. These findings imply that the NGC-based FCs may be a useful method for quantifying the abnormalities in the causal influences of patients with SZ, hence shedding fresh light on the pathophysiology of this disorder.
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Affiliation(s)
- Yu Fu
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, People's Republic of China
| | - Meng Niu
- Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, People's Republic of China
| | - Yuanhang Gao
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, People's Republic of China
| | - Shunjie Dong
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, People's Republic of China
| | - Yanyan Huang
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, People's Republic of China
| | - Zhe Zhang
- School of Physics, Hangzhou Normal University, Hangzhou, People's Republic of China.,Institute of Brain Science, Hangzhou Normal University, Hangzhou, People's Republic of China
| | - Cheng Zhuo
- College of Information Science & Electronic Engineering, Zhejiang University, Hangzhou, People's Republic of China.,Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province, Hangzhou, People's Republic of China
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20
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Liu D, Liu X, Long Y, Xiang Z, Wu Z, Liu Z, Bian D, Tang S. Problematic smartphone use is associated with differences in static and dynamic brain functional connectivity in young adults. Front Neurosci 2022; 16:1010488. [PMID: 36340758 PMCID: PMC9635624 DOI: 10.3389/fnins.2022.1010488] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 10/07/2022] [Indexed: 11/17/2022] Open
Abstract
Introduction This study aimed to investigate the possible associations between problematic smartphone use and brain functions in terms of both static and dynamic functional connectivity patterns. Materials and methods Resting-state functional magnetic resonance imaging data were scanned from 53 young healthy adults, all of whom completed the Short Version of the Smartphone Addiction Scale (SAS-SV) to assess their problematic smartphone use severity. Both static and dynamic functional brain network measures were evaluated for each participant. The brain network measures were correlated the SAS-SV scores, and compared between participants with and without a problematic smartphone use after adjusting for sex, age, education, and head motion. Results Two participants were excluded because of excessive head motion, and 56.9% (29/51) of the final analyzed participants were found to have a problematic smartphone use (SAS-SV scores ≥ 31 for males and ≥ 33 for females, as proposed in prior research). At the global network level, the SAS-SV score was found to be significantly positively correlated with the global efficiency and local efficiency of static brain networks, and negatively correlated with the temporal variability using the dynamic brain network model. Large-scale subnetwork analyses indicated that a higher SAS-SV score was significantly associated with higher strengths of static functional connectivity within the frontoparietal and cinguloopercular subnetworks, as well as a lower temporal variability of dynamic functional connectivity patterns within the attention subnetwork. However, no significant differences were found when directly comparing between the groups of participants with and without a problematic smartphone use. Conclusion Our results suggested that problematic smartphone use is associated with differences in both the static and dynamic brain network organizations in young adults. These findings may help to identify at-risk population for smartphone addiction and guide targeted interventions for further research. Nevertheless, it might be necessary to confirm our findings in a larger sample, and to investigate if a more applicable SAS-SV cutoff point is required for defining problematic smartphone use in young Chinese adults nowadays.
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Affiliation(s)
- Dayi Liu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Xiaoxuan Liu
- Department of Neurology, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yicheng Long
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zhibiao Xiang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zhipeng Wu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zhening Liu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Dujun Bian
- Department of Radiology, Clinical Research Center for Medical Imaging in Hunan Province, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Shixiong Tang
- Department of Radiology, Clinical Research Center for Medical Imaging in Hunan Province, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China
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21
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Ouyang X, Long Y, Wu Z, Liu D, Liu Z, Huang X. Temporal Stability of Dynamic Default Mode Network Connectivity Negatively Correlates with Suicidality in Major Depressive Disorder. Brain Sci 2022; 12:brainsci12091263. [PMID: 36138998 PMCID: PMC9496878 DOI: 10.3390/brainsci12091263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 11/26/2022] Open
Abstract
Previous studies have demonstrated that the suicidality in patients with major depressive disorder (MDD) is related to abnormal brain functional connectivity (FC) patterns. However, little is known about its relationship with dynamic functional connectivity (dFC) based on the assumption that brain FCs fluctuate over time. Temporal stabilities of dFCs within the whole brain and nine key networks were compared between 52 MDD patients and 21 age, sex-matched healthy controls (HCs) using resting-state functional magnetic resonance imaging and temporal correlation coefficients. The alterations in MDD were further correlated with the scores of suicidality item in the Hamilton Rating Scale for Depression (HAMD). Compared with HCs, the MDD patients showed a decreased temporal stability of dFC as indicated by a significantly decreased temporal correlation coefficient at the global level, as well as within the default mode network (DMN) and subcortical network. In addition, temporal correlation coefficients of the DMN were found to be significantly negatively correlated with the HAMD suicidality item scores in MDD patients. These results suggest that MDD may be characterized by excessive temporal fluctuations of dFCs within the DMN and subcortical network, and that decreased stability of DMN connectivity may be particularly associated with the suicidality in MDD.
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Affiliation(s)
- Xuan Ouyang
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Yicheng Long
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Zhipeng Wu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Dayi Liu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Zhening Liu
- Department of Psychiatry, National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital, Central South University, Changsha 410011, China
| | - Xiaojun Huang
- Department of Psychiatry, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang 330006, China
- Correspondence:
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22
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Srivastava P, Fotiadis P, Parkes L, Bassett DS. The expanding horizons of network neuroscience: From description to prediction and control. Neuroimage 2022; 258:119250. [PMID: 35659996 PMCID: PMC11164099 DOI: 10.1016/j.neuroimage.2022.119250] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 04/15/2022] [Accepted: 04/25/2022] [Indexed: 01/11/2023] Open
Abstract
The field of network neuroscience has emerged as a natural framework for the study of the brain and has been increasingly applied across divergent problems in neuroscience. From a disciplinary perspective, network neuroscience originally emerged as a formal integration of graph theory (from mathematics) and neuroscience (from biology). This early integration afforded marked utility in describing the interconnected nature of neural units, both structurally and functionally, and underscored the relevance of that interconnection for cognition and behavior. But since its inception, the field has not remained static in its methodological composition. Instead, it has grown to use increasingly advanced graph-theoretic tools and to bring in several other disciplinary perspectives-including machine learning and systems engineering-that have proven complementary. In doing so, the problem space amenable to the discipline has expanded markedly. In this review, we discuss three distinct flavors of investigation in state-of-the-art network neuroscience: (i) descriptive network neuroscience, (ii) predictive network neuroscience, and (iii) a perturbative network neuroscience that draws on recent advances in network control theory. In considering each area, we provide a brief summary of the approaches, discuss the nature of the insights obtained, and highlight future directions.
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Affiliation(s)
- Pragya Srivastava
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Panagiotis Fotiadis
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neuroscience, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Linden Parkes
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia PA 19104, USA; Santa Fe Institute, Santa Fe NM 87501, USA.
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23
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Di X, Zhang Z, Xu T, Biswal BB. Dynamic and stationary brain connectivity during movie watching as revealed by functional MRI. Brain Struct Funct 2022; 227:2299-2312. [PMID: 35767066 PMCID: PMC9420792 DOI: 10.1007/s00429-022-02522-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 06/04/2022] [Indexed: 11/25/2022]
Abstract
Spatially remote brain regions show synchronized activity as typically revealed by correlated functional MRI (fMRI) signals. An emerging line of research has focused on the temporal fluctuations of connectivity; however, its relationships with stationary connectivity have not been clearly illustrated. We examined dynamic and stationary connectivity when the participants watched four different movie clips. We calculated point-by-point multiplication between two regional time series to estimate the time-resolved dynamic connectivity, and estimated the inter-individual consistency of the dynamic connectivity time series. Widespread consistent dynamic connectivity was observed for each movie clip, which also showed differences between the clips. For example, a cartoon movie clip, Wall-E, showed more consistent of dynamic connectivity with the posterior cingulate cortex and supramarginal gyrus, while a court drama clip, A Few Good Men, showed more consistent of dynamic connectivity with the auditory cortex and temporoparietal junction, which might suggest the involvement of specific brain processing for different movie contents. In contrast, the stationary connectivity as measured by the correlations between regional time series was highly similar among the movie clips, and showed fewer statistically significant differences. The patterns of consistent dynamic connectivity could be used to classify different movie clips with higher accuracy than the stationary connectivity and regional activity. These results support the functional significance of dynamic connectivity in reflecting functional brain changes, which could provide more functionally relevant information than stationary connectivity.
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Affiliation(s)
- Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Height, Newark, NJ, 07102, USA.
| | - Zhiguo Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, People's Republic of China
- Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, People's Republic of China
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY, 10022, USA
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Height, Newark, NJ, 07102, USA.
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24
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Vedaei F, Alizadeh M, Romo V, Mohamed FB, Wu C. The effect of general anesthesia on the test–retest reliability of resting-state fMRI metrics and optimization of scan length. Front Neurosci 2022; 16:937172. [PMID: 36051647 PMCID: PMC9425911 DOI: 10.3389/fnins.2022.937172] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 07/27/2022] [Indexed: 01/01/2023] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) has been known as a powerful tool in neuroscience. However, exploring the test–retest reliability of the metrics derived from the rs-fMRI BOLD signal is essential, particularly in the studies of patients with neurological disorders. Here, two factors, namely, the effect of anesthesia and scan length, have been estimated on the reliability of rs-fMRI measurements. A total of nine patients with drug-resistant epilepsy (DRE) requiring interstitial thermal therapy (LITT) were scanned in two states. The first scan was performed in an awake state before surgery on the same patient. The second scan was performed 2 weeks later under general anesthesia necessary for LITT surgery. At each state, two rs-fMRI sessions were obtained that each one lasted 15 min, and the effect of scan length was evaluated. Voxel-wise rs-fMRI metrics, including the amplitude of low-frequency fluctuation (ALFF), the fractional amplitude of low-frequency fluctuation (fALFF), functional connectivity (FC), and regional homogeneity (ReHo), were measured. Intraclass correlation coefficient (ICC) was calculated to estimate the reliability of the measurements in two states of awake and under anesthesia. Overall, it appeared that the reliability of rs-fMRI metrics improved under anesthesia. From the 15-min data, we found mean ICC values in awake state including 0.81, 0.51, 0.65, and 0.84 for ALFF, fALFF, FC, and ReHo, respectively, as well as 0.80, 0.59, 0.83, and 0.88 for ALFF, fALFF, FC, and ReHo, respectively, under anesthesia. Additionally, our findings revealed that reliability increases as the function of scan length. We showed that the optimized scan length to achieve less variability of rs-fMRI measurements was 3.1–7.5 min shorter in an anesthetized, compared to a wakeful state.
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Affiliation(s)
- Faezeh Vedaei
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
- *Correspondence: Faezeh Vedaei
| | - Mahdi Alizadeh
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, PA, United States
| | - Victor Romo
- Department of Anesthesiology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Feroze B. Mohamed
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
| | - Chengyuan Wu
- Department of Radiology, Jefferson Integrated Magnetic Resonance Imaging Center, Thomas Jefferson University, Philadelphia, PA, United States
- Department of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, PA, United States
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25
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Honnorat N, Habes M. Covariance shrinkage can assess and improve functional connectomes. Neuroimage 2022; 256:119229. [PMID: 35460918 PMCID: PMC9189899 DOI: 10.1016/j.neuroimage.2022.119229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 03/24/2022] [Accepted: 04/19/2022] [Indexed: 11/21/2022] Open
Abstract
Connectomes derived from resting-state functional MRI scans have significantly benefited from the development of dedicated fMRI motion correction and denoising algorithms. But they are based on empirical correlations that can produce unreliable results in high dimension low sample size settings. A family of statistical estimators, the covariance shrinkage methods, could mitigate this issue. Unfortunately, these methods have rarely been used to correct functional connectomes and no extensive experiment has been conducted so far to compare the shrinkage methods available for this task. In this work, we propose to fix this issue by processing a benchmark dataset made of a thousand high-resolution resting-state fMRI scans provided by the Human Connectome Project to compare the ability of five prominent covariance shrinkage methods to produce reliable functional connectomes at different spatial resolutions and scans duration: the pioneer linear covariance shrinkage method introduced by Ledoit and Wolf, the Oracle Approximating Shrinkage, the QuEST method, the NERCOME method, and a recent analytical approximation of the QuEST approach. Our experiments establish that all covariance shrinkage methods significantly improve functional connectomes derived from short fMRI scans. The Oracle Approximating Shrinkage and the QuEST method produced the best results. Lastly, we present shrinkage intensity charts that can be used for designing and analyzing fMRI studies. These charts indicate that sparse connectomes are difficult to estimate from short fMRI scans, and they describe a range of settings where dynamic functional connectivity should not be computed.
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Affiliation(s)
- Nicolas Honnorat
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA.
| | - Mohamad Habes
- Neuroimage Analytics Laboratory and Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
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26
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Liu X, He Y, Gao Y, Booth JR, Zhang L, Zhang S, Lu C, Liu L. Developmental differences of large-scale functional brain networks for spoken word processing. BRAIN AND LANGUAGE 2022; 231:105149. [PMID: 35777141 DOI: 10.1016/j.bandl.2022.105149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 06/03/2022] [Accepted: 06/13/2022] [Indexed: 06/15/2023]
Abstract
A dual-stream dissociation for separate phonological and semantic processing has been implicated in adults' language processing, but it is unclear how this dissociation emerges with development. By employing a graph-theory based brain network analysis, we compared functional interaction architecture during a rhyming and meaning judgment task of children (aged 8-12) with adults (aged 19-26). We found adults had stronger functional connectivity strength than children between bilateral inferior frontal gyri and left inferior parietal lobule in the rhyming task, between middle frontal gyrus and angular gyrus, and within occipital areas in the meaning task. Meanwhile, adults but not children manifested between-task differences in these properties. In contrast, children had stronger functional connectivity strength or nodal degree in Heschl's gyrus, superior temporal gyrus, and subcortical areas. Our findings indicated spoken word processing development is characterized by increased functional specialization, relying on the dorsal and ventral pathways for phonological and semantic processing respectively.
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Affiliation(s)
- Xin Liu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/ McGovern, Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
| | - Yin He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/ McGovern, Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yue Gao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/ McGovern, Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - James R Booth
- Department of Psychology and Human Development, Vanderbilt University, Nashville, TN 37203, USA
| | - Lihuan Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/ McGovern, Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Shudong Zhang
- Faculty of Education, Beijing Normal University, Beijing 100875, China
| | - Chunming Lu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/ McGovern, Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Li Liu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/ McGovern, Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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27
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Pirondini E, Kinany N, Sueur CL, Griffis JC, Shulman GL, Corbetta M, Ville DVD. Post-stroke reorganization of transient brain activity characterizes deficits and recovery of cognitive functions. Neuroimage 2022; 255:119201. [PMID: 35405342 DOI: 10.1016/j.neuroimage.2022.119201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 03/24/2022] [Accepted: 04/07/2022] [Indexed: 02/06/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) has been widely employed to study stroke pathophysiology. In particular, analyses of fMRI signals at rest were directed at quantifying the impact of stroke on spatial features of brain networks. However, brain networks have intrinsic time features that were, so far, disregarded in these analyses. In consequence, standard fMRI analysis failed to capture temporal imbalance resulting from stroke lesions, hence restricting their ability to reveal the interdependent pathological changes in structural and temporal network features following stroke. Here, we longitudinally analyzed hemodynamic-informed transient activity in a large cohort of stroke patients (n = 103) to assess spatial and temporal changes of brain networks after stroke. Metrics extracted from the hemodynamic-informed transient activity were replicable within- and between-individuals in healthy participants, hence supporting their robustness and their clinical applicability. While large-scale spatial patterns of brain networks were preserved after stroke, their durations were altered, with stroke subjects exhibiting a varied pattern of longer and shorter network activations compared to healthy individuals. Specifically, patients showed a longer duration in the lateral precentral gyrus and anterior cingulum, and a shorter duration in the occipital lobe and in the cerebellum. These temporal alterations were associated with white matter damage in projection and association pathways. Furthermore, they were tied to deficits in specific behavioral domains as restoration of healthy brain dynamics paralleled recovery of cognitive functions (attention, language and spatial memory), but was not significantly correlated to motor recovery. These findings underscore the critical importance of network temporal properties in dissecting the pathophysiology of brain changes after stroke, thus shedding new light on the clinical potential of time-resolved methods for fMRI analysis.
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Affiliation(s)
- Elvira Pirondini
- Department of Radiology and Medical Informatics, University of Geneva; 1211 Geneva, Switzerland; Medical Image Processing Laboratory, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland; Department of Physical Medicine and Rehabilitation, University of Pittsburgh; Pittsburgh, PA, USA; Rehabilitation Neural Engineering Laboratories, University of Pittsburgh; Pittsburgh, PA, USA; Department of BioEngineering, University of Pittsburgh; Pittsburgh, PA, USA.
| | - Nawal Kinany
- Department of Radiology and Medical Informatics, University of Geneva; 1211 Geneva, Switzerland; Medical Image Processing Laboratory, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland; Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, Institute of Bioengineerin, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland
| | - Cécile Le Sueur
- Medical Image Processing Laboratory, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland
| | - Joseph C Griffis
- Department of Neurology, Washington University School of Medicine, St. Louis; MO, 63110, USA
| | - Gordon L Shulman
- Department of Neurology, Washington University School of Medicine, St. Louis; MO, 63110, USA
| | - Maurizio Corbetta
- Department of Neurology, Washington University School of Medicine, St. Louis; MO, 63110, USA; Department of Radiology, Washington University School of Medicine, St. Louis; MO, 63110, USA; Department of Anatomy and Neurobiology, Washington University School of Medicine, St. Louis; MO, 63110, USA; Department of Bioengineering, Washington University School of Medicine, St. Louis; MO, 63110, USA; Department of Neuroscience and Padua Neuroscience Center, University of Padua; Padua, Italy; Venetian Institute of Molecular Medicine (VIMM); Padua, Italy
| | - Dimitri Van De Ville
- Department of Radiology and Medical Informatics, University of Geneva; 1211 Geneva, Switzerland; Medical Image Processing Laboratory, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland.
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28
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Zhao L, Bo Q, Zhang Z, Chen Z, Wang Y, Zhang D, Li T, Yang N, Zhou Y, Wang C. Altered Dynamic Functional Connectivity in Early Psychosis Between the Salience Network and Visual Network. Neuroscience 2022; 491:166-175. [DOI: 10.1016/j.neuroscience.2022.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 04/01/2022] [Accepted: 04/04/2022] [Indexed: 11/29/2022]
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29
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Divergent time-varying connectivity of thalamic sub-regions characterizes clinical phenotypes and cognitive status in multiple sclerosis. Mol Psychiatry 2022; 27:1765-1773. [PMID: 34992237 DOI: 10.1038/s41380-021-01401-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 11/17/2021] [Accepted: 11/23/2021] [Indexed: 12/17/2022]
Abstract
We aimed to investigate abnormal time-varying functional connectivity (FC) for thalamic sub-regions in multiple sclerosis (MS) and their clinical, cognitive and MRI correlates. Eighty-nine MS patients (49 relapsing-remitting [RR] MS; 40 progressive [P] MS) and 53 matched healthy controls underwent neurological, neuropsychological and resting state fMRI assessment. Time-varying connectivity (TVC) was quantified using sliding-window seed-voxel correlation analysis. Standard deviation of FC across windows was taken as measure of TVC, while mean connectivity across windows expressed static FC. MS patients showed reduced TVC vs controls between most of thalamic sub-regions and fronto-temporo-occipital regions. At the same time, they showed increased static FC between all thalamic sub-regions and structurally connected cortico-subcortical regions. TVC reduction was mainly driven by RRMS; while PMS exhibited a variable pattern of TVC abnormalities, characterized by reduced TVC between frontal/motor thalamic seeds and default-mode network areas and increased TVC vs controls/RRMS between posterior thalamic sub-regions and occipito-temporo-insular cortices, associated with severity of clinical disability. Compared with controls, both cognitively preserved and impaired patients showed reduced TVC between anterior thalamic sub-regions and frontal cortex. Cognitively impaired patients also showed increased TVC of the right postcentral thalamic sub-region with the cingulate cortex and postcentral gyrus vs both controls and cognitively preserved patients. Divergent patterns of TVC thalamic abnormalities were found between RRMS and PMS patients. TVC reduction in RRMS may represent the attempt of thalamic network to keep with stable connections. Conversely, increased TVC of posterior thalamic sub-regions characterized PMS and cognitively impaired MS, possibly reflecting maladaptive mechanisms.
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30
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Tang S, Wu Z, Cao H, Chen X, Wu G, Tan W, Liu D, Yang J, Long Y, Liu Z. Age-Related Decrease in Default-Mode Network Functional Connectivity Is Accelerated in Patients With Major Depressive Disorder. Front Aging Neurosci 2022; 13:809853. [PMID: 35082661 PMCID: PMC8785895 DOI: 10.3389/fnagi.2021.809853] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 12/20/2021] [Indexed: 12/14/2022] Open
Abstract
Major depressive disorder (MDD) is a common psychiatric disorder which is associated with an accelerated biological aging. However, little is known whether such process would be reflected by a more rapid aging of the brain function. In this study, we tested the hypothesis that MDD would be characterized by accelerated aging of the brain's default-mode network (DMN) functions. Resting-state functional magnetic resonance imaging data of 971 MDD patients and 902 healthy controls (HCs) was analyzed, which was drawn from a publicly accessible, multicenter dataset in China. Strength of functional connectivity (FC) and temporal variability of dynamic functional connectivity (dFC) within the DMN were calculated. Age-related effects on FC/dFC were estimated by linear regression models with age, diagnosis, and diagnosis-by-age interaction as variables of interest, controlling for sex, education, site, and head motion effects. The regression models revealed (1) a significant main effect of age in the predictions of both FC strength and dFC variability; and (2) a significant main effect of diagnosis and a significant diagnosis-by-age interaction in the prediction of FC strength, which was driven by stronger negative correlation between age and FC strength in MDD patients. Our results suggest that (1) both healthy participants and MDD patients experience decrease in DMN FC strength and increase in DMN dFC variability along age; and (2) age-related decrease in DMN FC strength may occur at a faster rate in MDD patients than in HCs. However, further longitudinal studies are still needed to understand the causation between MDD and accelerated aging of brain.
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Affiliation(s)
- Shixiong Tang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, China
| | - Zhipeng Wu
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Hengyi Cao
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, Manhasset, NY, United States
- Division of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, United States
| | - Xudong Chen
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Guowei Wu
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Wenjian Tan
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Dayi Liu
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Jie Yang
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Yicheng Long
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Zhening Liu
- National Clinical Research Center for Mental Disorders, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
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31
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Zhang X, Liu J, Yang Y, Zhao S, Guo L, Han J, Hu X. Test-retest reliability of dynamic functional connectivity in naturalistic paradigm functional magnetic resonance imaging. Hum Brain Mapp 2021; 43:1463-1476. [PMID: 34870361 PMCID: PMC8837589 DOI: 10.1002/hbm.25736] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 11/19/2021] [Accepted: 11/24/2021] [Indexed: 01/30/2023] Open
Abstract
Dynamic functional connectivity (dFC) has been increasingly used to characterize the brain transient temporal functional patterns and their alterations in diseased brains. Meanwhile, naturalistic neuroimaging paradigms have been an emerging approach for cognitive neuroscience with high ecological validity. However, the test–retest reliability of dFC in naturalistic paradigm neuroimaging is largely unknown. To address this issue, we examined the test–retest reliability of dFC in functional magnetic resonance imaging (fMRI) under natural viewing condition. The intraclass correlation coefficients (ICC) of four dFC statistics including standard deviation (Std), coefficient of variation (COV), amplitude of low frequency fluctuation (ALFF), and excursion (Excursion) were used to measure the test–retest reliability. The test–retest reliability of dFC in naturalistic viewing condition was then compared with that under resting state. Our experimental results showed that: (a) Global test–retest reliability of dFC was much lower than that of static functional connectivity (sFC) in both resting‐state and naturalistic viewing conditions; (b) Both global and local (including visual, limbic and default mode networks) test–retest reliability of dFC could be significantly improved in naturalistic viewing condition compared to that in resting state; (c) There existed strong negative correlation between sFC and dFC, weak negative correlation between dFC and dFC‐ICC (i.e., ICC of dFC), as well as weak positive correlation between dFC‐ICC and sFC‐ICC (i.e., ICC of sFC). The present study provides novel evidence for the promotion of naturalistic paradigm fMRI in functional brain network studies.
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Affiliation(s)
- Xin Zhang
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Jiayue Liu
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Yang Yang
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Shijie Zhao
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Xintao Hu
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China
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32
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Wang P, Wang J, Michael A, Wang Z, Klugah-Brown B, Meng C, Biswal BB. White Matter Functional Connectivity in Resting-State fMRI: Robustness, Reliability, and Relationships to Gray Matter. Cereb Cortex 2021; 32:1547-1559. [PMID: 34753176 DOI: 10.1093/cercor/bhab181] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 04/05/2021] [Accepted: 04/09/2021] [Indexed: 01/21/2023] Open
Abstract
A comprehensive characterization of the spatiotemporal organization in the whole brain is critical to understand both the function and dysfunction of the human brain. Resting-state functional connectivity (FC) of gray matter (GM) has helped in uncovering the inherent baseline networks of brain. However, the white matter (WM), which composes almost half of brain, has been largely ignored in this characterization despite studies indicating that FC in WM does change during task and rest functional magnetic resonance imaging (fMRI). In this study, we identify 9 white matter functional networks (WM-FNs) and 9 gray matter functional networks (GM-FNs) of resting fMRI. Intraclass correlation coefficient (ICC) was calculated on multirun fMRI data to estimate the reliability of static functional connectivity (SFC) and dynamic functional connectivity (DFC). Associations between SFC, DFC, and their respective ICCs are estimated for GM-FNs, WM-FNs, and GM-WM-FNs. SFC of GM-FNs were stronger than that of WM-FNs, but the corresponding DFC of GM-FNs was lower, indicating that WM-FNs were more dynamic. Associations between SFC, DFC, and their ICCs were similar in both GM- and WM-FNs. These findings suggest that WM fMRI signal contains rich spatiotemporal information similar to that of GM and may hold important cues to better establish the functional organization of the whole brain.
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Affiliation(s)
- Pan Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jianlin Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Andrew Michael
- Duke Institute for Brain Sciences, Duke University, Durham, NC 27708, USA
| | - Zedong Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Benjamin Klugah-Brown
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Chun Meng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bharat B Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.,Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
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33
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Li Y, Zhang L, Wu Y, Zhang J, Liu K. A Longitudinal Randomized Controlled Trial Protocol to Evaluate the Effects of Wuqinxi on Dynamic Functional Connectivity in Parkinson's Disease Patients. Front Hum Neurosci 2021; 15:711703. [PMID: 34566601 PMCID: PMC8461094 DOI: 10.3389/fnhum.2021.711703] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 08/20/2021] [Indexed: 12/18/2022] Open
Abstract
Background: Parkinson’s disease (PD) is a neurodegenerative movement disease that includes non-motor symptoms such as cognitive impairment. Long-term mind-body exercise has been shown to improve cognitive ability in PD patients, but the methods of assessment and intervention were inconsistent across studies. Wuqinxi is a mind-body exercise that is easy to learn, has few physical and cognitive demands, and is recommended for PD patients. Dynamic functional connectivity (DFC) has been associated with cognitive alterations in PD patients, but no studies have yet explored the effects of Wuqinxi on this association. The current protocol is designed to measure the effects of long-term Wuqinxi intervention on cognition in PD patients, and explore the underlying neural mechanisms through DFC. Methods: A long-term single-blind, randomized trial will be conducted. PD patients and age- and gender-matched HC will be recruited; PD patients will be randomly assigned to either Wuqinxi or balance groups, and HC will all receive health education. The Wuqinxi group will receive a 90-min session of Wuqinxi intervention three times a week for 24 weeks, while the balance group will receive balance exercise instruction on the same schedule. Primary outcomes will include assessment of cognitive domains and dynamic temporal characteristics of functional connectivity. Secondary outcomes will include severity of motor symptoms, mobility, balance, and emotional state. Assessments will be conducted at baseline, at the end of 24 weeks of intervention, and 12 weeks after interventions have ended. Discussion: This study will provide evidence to the effects of Wuqinxi exercise on cognitive improvements in PD patients from the perspective of DFC, and will contribute to the understanding of neural mechanisms underlying cognitive enhancement through Wuqinxi practice. Clinical Trial Registration:www.chictr.org.cn, identifier ChiCTR2000038517.
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Affiliation(s)
- Yuting Li
- School of Nursing, Anhui University of Chinese Medicine, Hefei, China.,School of Psychology, Shanghai University of Sport, Shanghai, China
| | - Lanlan Zhang
- School of Leisure Sport and Management, Guangzhou Sport University, Guangzhou, China
| | - Yin Wu
- School of Economics and Management, Shanghai University of Sport, Shanghai, China
| | - Jian Zhang
- School of Psychology, Shanghai University of Sport, Shanghai, China
| | - Ke Liu
- Shanghai Punan Hospital of Pudong New District, Shanghai, China
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34
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Takao H, Amemiya S, Abe O. Longitudinal stability of resting-state networks in normal aging, mild cognitive impairment, and Alzheimer's disease. Magn Reson Imaging 2021; 82:55-73. [PMID: 34153437 DOI: 10.1016/j.mri.2021.06.020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 06/16/2021] [Accepted: 06/16/2021] [Indexed: 11/18/2022]
Abstract
Test-retest reliability is essential for using resting-state functional magnetic resonance imaging (rs-fMRI) as a potential biomarker for Alzheimer's disease (AD), especially when monitoring longitudinal changes and treatment effects. In addition, test-retest variability itself might represent a feature of AD. Using 3.0 T rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we examined the long-term (1-year) test-retest reliability of resting-state networks (RSNs) in 31 healthy elderly subjects, 63 patients with mild cognitive impairment (MCI), and 17 patients with AD by applying temporal concatenation group independent component analysis and dual regression. The intraclass correlation coefficient estimates of RSN amplitudes ranged from 0.44 to 0.77 in healthy elderly subjects, from 0.31 to 0.62 in patients with MCI, and from -0.06 to 0.44 in patients with AD. The overall test-retest reliability of RSNs was lower in patients with MCI than in healthy elderly subjects, and was lower in patients with AD than in patients with MCI. The differences in the test-retest reliabilities were due to the RSN amplitudes rather than the RSN shapes. Head motion was not significantly different among the three groups of subjects. The results indicate that the test-retest stability of RSNs generally declines with progression to MCI and AD, mainly due to the RSN amplitudes rather than the RSN shapes. The test-retest instability in MCI and AD may reflect progressive neurofunctional alterations related to the pathology of AD.
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Affiliation(s)
- Hidemasa Takao
- Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan.
| | - Shiori Amemiya
- Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan
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35
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Bonkhoff AK, Schirmer MD, Bretzner M, Etherton M, Donahue K, Tuozzo C, Nardin M, Giese A, Wu O, D. Calhoun V, Grefkes C, Rost NS. Abnormal dynamic functional connectivity is linked to recovery after acute ischemic stroke. Hum Brain Mapp 2021; 42:2278-2291. [PMID: 33650754 PMCID: PMC8046120 DOI: 10.1002/hbm.25366] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 01/26/2021] [Accepted: 01/29/2021] [Indexed: 12/30/2022] Open
Abstract
The aim of the current study was to explore the whole-brain dynamic functional connectivity patterns in acute ischemic stroke (AIS) patients and their relation to short and long-term stroke severity. We investigated resting-state functional MRI-based dynamic functional connectivity of 41 AIS patients two to five days after symptom onset. Re-occurring dynamic connectivity configurations were obtained using a sliding window approach and k-means clustering. We evaluated differences in dynamic patterns between three NIHSS-stroke severity defined groups (mildly, moderately, and severely affected patients). Furthermore, we built Bayesian hierarchical models to evaluate the predictive capacity of dynamic connectivity and examine the interrelation with clinical measures, such as white matter hyperintensity lesions. Finally, we established correlation analyses between dynamic connectivity and AIS severity as well as 90-day neurological recovery (ΔNIHSS). We identified three distinct dynamic connectivity configurations acutely post-stroke. More severely affected patients spent significantly more time in a configuration that was characterized by particularly strong connectivity and isolated processing of functional brain domains (three-level ANOVA: p < .05, post hoc t tests: p < .05, FDR-corrected). Configuration-specific time estimates possessed predictive capacity of stroke severity in addition to the one of clinical measures. Recovery, as indexed by the realized change of the NIHSS over time, was significantly linked to the dynamic connectivity between bilateral intraparietal lobule and left angular gyrus (Pearson's r = -.68, p = .003, FDR-corrected). Our findings demonstrate transiently increased isolated information processing in multiple functional domains in case of severe AIS. Dynamic connectivity involving default mode network components significantly correlated with recovery in the first 3 months poststroke.
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Affiliation(s)
- Anna K. Bonkhoff
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
- Cognitive NeuroscienceInstitute of Neuroscience and Medicine (INM‐3), Research Centre JuelichJuelichGermany
| | - Markus D. Schirmer
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
- Department of Population Health SciencesGerman Centre for Neurodegenerative Diseases (DZNE)Germany
| | - Martin Bretzner
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
- Neurosciences and CognitionUniversity of LilleLilleFrance
| | - Mark Etherton
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
| | - Kathleen Donahue
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
| | - Carissa Tuozzo
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
| | - Marco Nardin
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
| | - Anne‐Katrin Giese
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of TechnologyEmory UniversityAtlantaGeorgiaUSA
| | - Christian Grefkes
- Cognitive NeuroscienceInstitute of Neuroscience and Medicine (INM‐3), Research Centre JuelichJuelichGermany
- Department of NeurologyUniversity Hospital CologneCologneGermany
| | - Natalia S. Rost
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
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36
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Ma Y, MacDonald A. "Impact of ICA Dimensionality on the Test-Retest Reliability of Resting-State Functional Connectivity. Brain Connect 2021; 11:875-886. [PMID: 33926215 DOI: 10.1089/brain.2020.0970] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
As resting-state functional connectivity (rsFC) research moves toward the study of individual differences, test-retest reliability is increasingly important to understand. Previous literature supports the test-retest reliability of rsFC derived with independent component analysis (ICA) and dual regression, yet the impact of dimensionality (i.e., the number of components to extract from group-ICA) remained obscure in the current context of large-scale datasets. To provide principled guidelines on this issue, ICA at dimensionalities varying from 25 to 350 was applied to the cortical surface with resting-state functional magnetic resonance imaging data from 1003 participants in the Human Connectome Project. The reliability of two rsFC measures: (within-component) coherence and (between-component) connectivity was estimated. Reliability and its change with dimensionality varied by network: the cognitive (frontoparietal, cingulo-opercular, dorsal attention, and default) networks were measured with the highest reliability which improved with increased dimensionality until at least 150; the visual and somatomotor networks were measured with lower reliability which benefited mildly from increased dimensionality; the temporal pole/orbitofrontal cortex (TP/OFC) network was measured with the lowest reliability. Overall, ICA reliability was optimized at dimensionalities of 150 or above. Compared with two popular binary, non-overlapping cortical atlases, ICA and dual regression resulted in higher reliability for the cognitive networks, lower reliability for the somatomotor network, and similar reliability for the visual and TP/OFC networks. These findings highlight analytical decisions that maximize the reliability of rsFC measures and how they depend on one's networks of interest.
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Affiliation(s)
- Yizhou Ma
- University of Minnesota Twin Cities, 5635, Psychology, Minneapolis, Minnesota, United States;
| | - Angus MacDonald
- University of Minnesota Twin Cities, 5635, Psychology, N219 Elliot Hall 75 E. River Rd., Minneapolis, Minnesota, United States, 55455.,N219 Elliot Hall 75 E. River Rd.Minneapolis, Minnesota, United States, 55455;
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37
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Singh MF, Braver TS, Cole MW, Ching S. Estimation and validation of individualized dynamic brain models with resting state fMRI. Neuroimage 2020; 221:117046. [PMID: 32603858 PMCID: PMC7875185 DOI: 10.1016/j.neuroimage.2020.117046] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 05/08/2020] [Accepted: 06/08/2020] [Indexed: 11/25/2022] Open
Abstract
A key challenge for neuroscience is to develop generative, causal models of the human nervous system in an individualized, data-driven manner. Previous initiatives have either constructed biologically-plausible models that are not constrained by individual-level human brain activity or used data-driven statistical characterizations of individuals that are not mechanistic. We aim to bridge this gap through the development of a new modeling approach termed Mesoscale Individualized Neurodynamic (MINDy) modeling, wherein we fit nonlinear dynamical systems models directly to human brain imaging data. The MINDy framework is able to produce these data-driven network models for hundreds to thousands of interacting brain regions in just 1-3 min per subject. We demonstrate that the models are valid, reliable, and robust. We show that MINDy models are predictive of individualized patterns of resting-state brain dynamical activity. Furthermore, MINDy is better able to uncover the mechanisms underlying individual differences in resting state activity than functional connectivity methods.
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Affiliation(s)
- Matthew F Singh
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, USA; Department of Psychology, Washington University in St. Louis, St. Louis, MO, USA; Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA.
| | - Todd S Braver
- Department of Psychology, Washington University in St. Louis, St. Louis, MO, USA
| | - Michael W Cole
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, 07102, USA
| | - ShiNung Ching
- Department of Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
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38
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Iraji A, Faghiri A, Lewis N, Fu Z, Rachakonda S, Calhoun VD. Tools of the trade: estimating time-varying connectivity patterns from fMRI data. Soc Cogn Affect Neurosci 2020; 16:849-874. [PMID: 32785604 PMCID: PMC8343585 DOI: 10.1093/scan/nsaa114] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 06/24/2020] [Accepted: 08/05/2020] [Indexed: 01/04/2023] Open
Abstract
Given the dynamic nature of the brain, there has always been a motivation to move beyond 'static' functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain's dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.
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Affiliation(s)
- Armin Iraji
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Ashkan Faghiri
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Noah Lewis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Srinivas Rachakonda
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA
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39
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Long Y, Liu Z, Chan CKY, Wu G, Xue Z, Pan Y, Chen X, Huang X, Li D, Pu W. Altered Temporal Variability of Local and Large-Scale Resting-State Brain Functional Connectivity Patterns in Schizophrenia and Bipolar Disorder. Front Psychiatry 2020; 11:422. [PMID: 32477194 PMCID: PMC7235354 DOI: 10.3389/fpsyt.2020.00422] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2020] [Accepted: 04/24/2020] [Indexed: 11/13/2022] Open
Abstract
Schizophrenia and bipolar disorder share some common clinical features and are both characterized by aberrant resting-state functional connectivity (FC). However, little is known about the common and specific aberrant features of the dynamic FC patterns in these two disorders. In this study, we explored the differences in dynamic FC among schizophrenia patients (n = 66), type I bipolar disorder patients (n = 53), and healthy controls (n = 66), by comparing temporal variabilities of FC patterns involved in specific brain regions and large-scale brain networks. Compared with healthy controls, both patient groups showed significantly increased regional FC variabilities in subcortical areas including the thalamus and basal ganglia, as well as increased inter-network FC variability between the thalamus and sensorimotor areas. Specifically, more widespread changes were found in the schizophrenia group, involving increased FC variabilities in sensorimotor, visual, attention, limbic and subcortical areas at both regional and network levels, as well as decreased regional FC variabilities in the default-mode areas. The observed alterations shared by schizophrenia and bipolar disorder may help to explain their overlapped clinical features; meanwhile, the schizophrenia-specific abnormalities in a wider range may support that schizophrenia is associated with more severe functional brain deficits than bipolar disorder. Together, these findings highlight the potentials of using dynamic FC as an objective biomarker for the monitoring and diagnosis of either schizophrenia or bipolar disorder.
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Affiliation(s)
- Yicheng Long
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
| | - Zhening Liu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
| | | | - Guowei Wu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
| | - Zhimin Xue
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
| | - Yunzhi Pan
- Mental Health Institute of Central South University, Changsha, China
| | - Xudong Chen
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
| | - Xiaojun Huang
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Mental Health Institute of Central South University, Changsha, China
| | - Dan Li
- Department of Geriatrics, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Weidan Pu
- Medical Psychological Center, The Second Xiangya Hospital, Central South University, Changsha, China
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40
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Specht K. Current Challenges in Translational and Clinical fMRI and Future Directions. Front Psychiatry 2020; 10:924. [PMID: 31969840 PMCID: PMC6960120 DOI: 10.3389/fpsyt.2019.00924] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 11/20/2019] [Indexed: 12/15/2022] Open
Abstract
Translational neuroscience is an important field that brings together clinical praxis with neuroscience methods. In this review article, the focus will be on functional neuroimaging (fMRI) and its applicability in clinical fMRI studies. In the light of the "replication crisis," three aspects will be critically discussed: First, the fMRI signal itself, second, current fMRI praxis, and, third, the next generation of analysis strategies. Current attempts such as resting-state fMRI, meta-analyses, and machine learning will be discussed with their advantages and potential pitfalls and disadvantages. One major concern is that the fMRI signal shows substantial within- and between-subject variability, which affects the reliability of both task-related, but in particularly resting-state fMRI studies. Furthermore, the lack of standardized acquisition and analysis methods hinders the further development of clinical relevant approaches. However, meta-analyses and machine-learning approaches may help to overcome current shortcomings in the methods by identifying new, and yet hidden relationships, and may help to build new models on disorder mechanisms. Furthermore, better control of parameters that may have an influence on the fMRI signal and that can easily be controlled for, like blood pressure, heart rate, diet, time of day, might improve reliability substantially.
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Affiliation(s)
- Karsten Specht
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Haukeland University Hospital, Bergen, Norway
- Department of Education, UiT/The Arctic University of Norway, Tromsø, Norway
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41
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Li C, Fronczek-Poncelet J, Lange D, Hennecke E, Kroll T, Matusch A, Aeschbach D, Bauer A, Elmenhorst EM, Elmenhorst D. Impact of acute sleep deprivation on dynamic functional connectivity states. Hum Brain Mapp 2019; 41:994-1005. [PMID: 31680379 PMCID: PMC7268022 DOI: 10.1002/hbm.24855] [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: 06/30/2019] [Revised: 09/10/2019] [Accepted: 10/21/2019] [Indexed: 12/20/2022] Open
Abstract
Sleep deprivation (SD) could amplify the temporal fluctuation of spontaneous brain activities that reflect different arousal levels using a dynamic functional connectivity (dFC) approach. Therefore, we intended to evaluate the test–retest reliability of dFC characteristics during rested wakefulness (RW), and to explore how the properties of these dynamic connectivity states were affected by extended durations of acute sleep loss (28/52 hr). We acquired resting‐state fMRI and neuropsychological datasets in two independent studies: (a) twice during RW and once after 28 hr of SD (n = 15) and (b) after 52 hr of SD and after 14 hr of recovery sleep (RS; n = 14). Sliding‐window correlations approach was applied to estimate their covariance matrices and corresponding three connectivity states were generated. The test–retest reliability of dFC properties demonstrated mean dwell time and fraction of connectivity states were reliable. After SD, the mean dwell time of a specific state, featured by strong subcortical–cortical anticorrelations, was significantly increased. Conversely, another globally hypoconnected state was significantly decreased. Subjective sleepiness and objective performances were separately positive and negative correlated with the increased and decreased state. Two brain connectivity states and their alterations might be sufficiently sensitive to reflect changes in the dynamics of brain mental activities after sleep loss.
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Affiliation(s)
- Changhong Li
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, Jülich, Germany
| | | | - Denise Lange
- Department of Sleep and Human Factors Research, Institute of Aerospace Medicine, German Aerospace Center, Cologne, Germany
| | - Eva Hennecke
- Department of Sleep and Human Factors Research, Institute of Aerospace Medicine, German Aerospace Center, Cologne, Germany
| | - Tina Kroll
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, Jülich, Germany
| | - Andreas Matusch
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, Jülich, Germany
| | - Daniel Aeschbach
- Department of Sleep and Human Factors Research, Institute of Aerospace Medicine, German Aerospace Center, Cologne, Germany.,Division of Sleep Medicine, Harvard Medical School, Sleep Division, Boston, Massachusetts
| | - Andreas Bauer
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, Jülich, Germany.,Department of Neurology, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
| | - Eva-Maria Elmenhorst
- Department of Sleep and Human Factors Research, Institute of Aerospace Medicine, German Aerospace Center, Cologne, Germany
| | - David Elmenhorst
- Institute of Neuroscience and Medicine (INM-2), Forschungszentrum Jülich, Jülich, Germany.,Division of Medical Psychology, Rheinische Friedrich-Wilhelms-University Bonn, Bonn, Germany
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42
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Zhao X, Wu Q, Chen Y, Song X, Ni H, Ming D. Hub Patterns-Based Detection of Dynamic Functional Network Metastates in Resting State: A Test-Retest Analysis. Front Neurosci 2019; 13:856. [PMID: 31572105 PMCID: PMC6749078 DOI: 10.3389/fnins.2019.00856] [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: 12/14/2018] [Accepted: 07/30/2019] [Indexed: 11/13/2022] Open
Abstract
The spontaneous dynamic characteristics of resting-state functional networks contain much internal brain physiological or pathological information. The metastate analysis of brain functional networks is an effective technique to quantify the essence of brain functional connectome dynamics. However, the widely used functional connectivity-based metastate analysis ignored the topological structure, which could be locally reflected by node centrality. In this study, 23 healthy young volunteers (21-26 years) were recruited and scanned twice with a 1-week interval. Based on the time sequences of node centrality, we promoted a node centrality-based clustering method to find metastates of functional connectome and conducted a test-retest experiment to assess the stability of those identified metastates using the described method. The hub regions of metastates were further compared with the structural networks' organization to depict its potential relationship with brain structure. Results of extracted metastates showed repeatable dynamic features between repeated scans and high overlapping rate of hub regions with brain intrinsic sub-networks. These identified hub patterns from metastates further highly overlapped with the structural hub regions. These findings indicated that the proposed node centrality-based metastates detection method could reveal reliable and meaningful metastates of spontaneous dynamics and indicate the underlying nature of brain dynamics as well as the potential relationship between these dynamics and the organization of the brain connectome.
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Affiliation(s)
- Xin Zhao
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Qiong Wu
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
| | - Yuanyuan Chen
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Xizi Song
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
| | - Hongyan Ni
- Department of Radiology, Tianjin First Center Hospital, Tianjin, China
| | - Dong Ming
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China
- Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China
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Valsasina P, Hidalgo de la Cruz M, Filippi M, Rocca MA. Characterizing Rapid Fluctuations of Resting State Functional Connectivity in Demyelinating, Neurodegenerative, and Psychiatric Conditions: From Static to Time-Varying Analysis. Front Neurosci 2019; 13:618. [PMID: 31354402 PMCID: PMC6636554 DOI: 10.3389/fnins.2019.00618] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 05/29/2019] [Indexed: 01/27/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) at resting state (RS) has been widely used to characterize the main brain networks. Functional connectivity (FC) has been mostly assessed assuming that FC is static across the whole fMRI examination. However, FC is highly variable at a very fast time-scale, as demonstrated by neurophysiological techniques. Time-varying functional connectivity (TVC) is a novel approach that allows capturing reoccurring patterns of interaction among functional brain networks. Aim of this review is to provide a description of the methods currently used to assess TVC on RS fMRI data, and to summarize the main results of studies applying TVC in healthy controls and patients with multiple sclerosis (MS). An overview of the main results obtained in neurodegenerative and psychiatric conditions is also provided. The most popular TVC approach is based on the so-called “sliding windows,” in which the RS fMRI acquisition is divided in small temporal segments (windows). A window of fixed length is shifted over RS fMRI time courses, and data within each window are used to calculate FC and its variability over time. Sliding windows can be combined with clustering techniques to identify recurring FC states or used to assess global TVC properties of large-scale functional networks or specific brain regions. TVC studies have used heterogeneous methodologies so far. Despite this, similar results have been obtained across investigations. In healthy subjects, the default-mode network (DMN) exhibited the highest degree of connectivity dynamism. In MS patients, abnormal global TVC properties and TVC strengths were found mainly in sensorimotor, DMN and salience networks, and were associated with more severe structural MRI damage and with more severe physical and cognitive disability. Conversely, abnormal TVC measures of the temporal network were correlated with better cognitive performances and less severe fatigue. In patients with neurodegenerative and psychiatric conditions, TVC abnormalities of the DMN, attention and executive networks were associated to more severe clinical manifestations. TVC helps to provide novel insights into fundamental properties of functional networks, and improves the understanding of brain reorganization mechanisms. Future technical advances might help to clarify TVC association with disease prognosis and response to treatment.
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Affiliation(s)
- Paola Valsasina
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Milagros Hidalgo de la Cruz
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
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44
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Lan MJ, Rizk MM, Pantazatos SP, Rubin-Falcone H, Miller JM, Sublette ME, Oquendo MA, Keilp JG, Mann JJ. Resting-state amplitude of low-frequency fluctuation is associated with suicidal ideation. Depress Anxiety 2019; 36:433-441. [PMID: 30900329 PMCID: PMC6488362 DOI: 10.1002/da.22888] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Revised: 01/08/2019] [Accepted: 02/25/2019] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Identifying brain activity patterns that are associated with suicidal ideation (SI) may help to elucidate its pathogenesis and etiology. Suicide poses a significant public health problem, and SI is a risk factor for suicidal behavior. METHODS Forty-one unmedicated adult participants in a major depressive episode (MDE), 26 with SI on the Beck Scale for Suicidal Ideation and 15 without SI, underwent resting-state functional magnetic resonance imaging scanning. Twenty-one healthy volunteers (HVs) were scanned for secondary analyses. Whole brain analysis of both amplitude of low-frequency fluctuations (ALFFs) and fractional ALFF was performed in MDE subjects to identify regions where activity was associated with SI. RESULTS Subjects with SI had greater ALFF than those without SI in two clusters: one in the right hippocampus and one in the thalamus and caudate, bilaterally. Multi-voxel pattern analysis distinguished between those with and without SI. Post hoc analysis of the mean ALFF in the hippocampus cluster found it to be associated with a delayed recall on the Buschke memory task. Mean ALFF from the significant clusters was not associated with depression severity and did not differ between MDE and HV groups. DISCUSSION These results indicate that SI is associated with altered resting-state brain activity. The pattern of elevated activity in the hippocampus may be related to how memories are processed.
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Affiliation(s)
- Martin J. Lan
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY, USA,Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA
| | - Mina M. Rizk
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY, USA,Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA,Department of Psychiatry, Faculty of Medicine, Minia University, Egypt
| | - Spiro P. Pantazatos
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY, USA,Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA
| | - Harry Rubin-Falcone
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY, USA,Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA
| | - Jeffrey M. Miller
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY, USA,Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA
| | - M. Elizabeth Sublette
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY, USA,Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA
| | - Maria A. Oquendo
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - John G. Keilp
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY, USA,Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA
| | - J. John Mann
- Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY, USA,Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA,Department of Radiology, Columbia University College of Physicians and Surgeons, New York, NY, USA
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