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Foster M, Scheinost D. Brain states as wave-like motifs. Trends Cogn Sci 2024; 28:492-503. [PMID: 38582654 DOI: 10.1016/j.tics.2024.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 02/29/2024] [Accepted: 03/11/2024] [Indexed: 04/08/2024]
Abstract
There is ample evidence of wave-like activity in the brain at multiple scales and levels. This emerging literature supports the broader adoption of a wave perspective of brain activity. Specifically, a brain state can be described as a set of recurring, sequential patterns of propagating brain activity, namely a wave. We examine a collective body of experimental work investigating wave-like properties. Based on these works, we consider brain states as waves using a scale-agnostic framework across time and space. Emphasis is placed on the sequentiality and periodicity associated with brain activity. We conclude by discussing the implications, prospects, and experimental opportunities of this framework.
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Affiliation(s)
- Maya Foster
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - Dustin Scheinost
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA; Department of Radiology and Biomedical Engineering, Yale School of Medicine, New Haven, CT, USA
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2
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van der Wijk G, Enkhbold Y, Cnudde K, Szostakiwskyj MW, Blier P, Knott V, Jaworska N, Protzner AB. One size does not fit all: notable individual variation in brain activity correlates of antidepressant treatment response. Front Psychiatry 2024; 15:1358018. [PMID: 38628260 PMCID: PMC11018891 DOI: 10.3389/fpsyt.2024.1358018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 03/18/2024] [Indexed: 04/19/2024] Open
Abstract
Introduction To date, no robust electroencephalography (EEG) markers of antidepressant treatment response have been identified. Variable findings may arise from the use of group analyses, which neglect individual variation. Using a combination of group and single-participant analyses, we explored individual variability in EEG characteristics of treatment response. Methods Resting-state EEG data and Montgomery-Åsberg Depression Rating Scale (MADRS) symptom scores were collected from 43 patients with depression before, at 1 and 12 weeks of pharmacotherapy. Partial least squares (PLS) was used to: 1) identify group differences in EEG connectivity (weighted phase lag index) and complexity (multiscale entropy) between eventual medication responders and non-responders, and 2) determine whether group patterns could be identified in individual patients. Results Responders showed decreased alpha and increased beta connectivity, and early, widespread decreases in complexity over treatment. Non-responders showed an opposite connectivity pattern, and later, spatially confined decreases in complexity. Thus, as in previous studies, our group analyses identified significant differences between groups of patients with different treatment outcomes. These group-level EEG characteristics were only identified in ~40-60% of individual patients, as assessed quantitatively by correlating the spatiotemporal brain patterns between groups and individual results, and by independent raters through visualization. Discussion Our single-participant analyses suggest that substantial individual variation exists, and needs to be considered when investigating characteristics of antidepressant treatment response for potential clinical applicability. Clinical trial registration https://clinicaltrials.gov, identifier NCT00519428.
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Affiliation(s)
- Gwen van der Wijk
- Department of Psychology, University of Calgary, Calgary, AB, Canada
| | - Yaruuna Enkhbold
- Department of Psychology, University of Calgary, Calgary, AB, Canada
| | - Kelsey Cnudde
- Department of Psychology, University of Calgary, Calgary, AB, Canada
| | | | - Pierre Blier
- Institute of Mental Health Research, Affiliated with the University of Ottawa, Ottawa, ON, Canada
- Department of Cellular & Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Verner Knott
- Institute of Mental Health Research, Affiliated with the University of Ottawa, Ottawa, ON, Canada
- Department of Cellular & Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Natalia Jaworska
- Institute of Mental Health Research, Affiliated with the University of Ottawa, Ottawa, ON, Canada
- Department of Cellular & Molecular Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Andrea B. Protzner
- Department of Psychology, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Mathison Centre, University of Calgary, Calgary, AB, Canada
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3
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Acharya K, Olivares F, Zanin M. How representative are air transport functional complex networks? A quantitative validation. CHAOS (WOODBURY, N.Y.) 2024; 34:043133. [PMID: 38598674 DOI: 10.1063/5.0189642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 03/21/2024] [Indexed: 04/12/2024]
Abstract
Functional networks have emerged as powerful instruments to characterize the propagation of information in complex systems, with applications ranging from neuroscience to climate and air transport. In spite of their success, reliable methods for validating the resulting structures are still missing, forcing the community to resort to expert knowledge or simplified models of the system's dynamics. We here propose the use of a real-world problem, involving the reconstruction of the structure of flights in the US air transport system from the activity of individual airports, as a way to explore the limits of such an approach. While the true connectivity is known and is, therefore, possible to provide a quantitative benchmark, this problem presents challenges commonly found in other fields, including the presence of non-stationarities and observational noise, and the limitedness of available time series. We explore the impact of elements like the specific functional metric employed, the way of detrending the time series, or the size of the reconstructed system and discuss how the conclusions here drawn could have implications for similar analyses in neuroscience.
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Affiliation(s)
- Kishor Acharya
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122 Palma de Mallorca, Spain
| | - Felipe Olivares
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122 Palma de Mallorca, Spain
| | - Massimiliano Zanin
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, 07122 Palma de Mallorca, Spain
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Liu Y, Zhang Y, Zhong Y, Liu J, Zhang C, Meng Y, Pang N, Cheng X, Wang H. Favoritism or bias? Cooperation and competition under different intergroup relationships: evidence from EEG hyperscanning. Cereb Cortex 2024; 34:bhae131. [PMID: 38566514 DOI: 10.1093/cercor/bhae131] [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: 12/29/2023] [Revised: 03/08/2024] [Accepted: 03/09/2024] [Indexed: 04/04/2024] Open
Abstract
Cooperation and competition are the most common forms of social interaction in various social relationships. Intergroup relationships have been posited to influence individuals' interpersonal interactions significantly. Using electroencephalography hyperscanning, this study aimed to establish whether intergroup relationships influence interpersonal cooperation and competition and the underlying neural mechanisms. According to the results, the in-group Coop-index is better than the out-group, whereas the out-group Comp-index is stronger than the in-group. The in-group functional connectivity between the frontal-central region and the right temporoparietal junction in the β band was stronger in competition than cooperation. The out-group functional connectivity between the frontal-central region and the left temporoparietal junction in the α band was stronger in cooperation than competition. In both cooperation and competition, the in-group exhibited higher interbrain synchronization between the prefrontal cortex and parietal region in the θ band, as well as between the frontal-central region and frontal-central region in the α band, compared to the out-group. The intrabrain phase-locking value in both the α and β bands can effectively predict performance in competition tasks. Interbrain phase-locking value in both the α and θ bands can be effectively predicted in a performance cooperation task. This study offers neuroscientific evidence for in-group favoritism and out-group bias at an interpersonal level.
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Affiliation(s)
- Yingjie Liu
- School of Psychology and Mental Health, North China University of Science and Technology, 21 Bohai avenue, Caofeidian district, Tangshan, Hebei province 063210, China
- School of Public Health, North China University of Science and Technology, 21 Bohai avenue, Caofeidian district, Tangshan, Hebei province 063210, China
| | - Ye Zhang
- School of Psychology and Mental Health, North China University of Science and Technology, 21 Bohai avenue, Caofeidian district, Tangshan, Hebei province 063210, China
| | - Yifei Zhong
- School of Psychology and Mental Health, North China University of Science and Technology, 21 Bohai avenue, Caofeidian district, Tangshan, Hebei province 063210, China
| | - Jingyue Liu
- School of Psychology and Mental Health, North China University of Science and Technology, 21 Bohai avenue, Caofeidian district, Tangshan, Hebei province 063210, China
| | - Chenyu Zhang
- School of Psychology and Mental Health, North China University of Science and Technology, 21 Bohai avenue, Caofeidian district, Tangshan, Hebei province 063210, China
| | - Yujia Meng
- School of Psychology and Mental Health, North China University of Science and Technology, 21 Bohai avenue, Caofeidian district, Tangshan, Hebei province 063210, China
| | - Nan Pang
- School of Psychology and Mental Health, North China University of Science and Technology, 21 Bohai avenue, Caofeidian district, Tangshan, Hebei province 063210, China
| | - Xuemei Cheng
- Department of Mechanical and Electrical Engineering, Beijing Polytechnic, 100081
| | - He Wang
- School of Psychology and Mental Health, North China University of Science and Technology, 21 Bohai avenue, Caofeidian district, Tangshan, Hebei province 063210, China
- School of Public Health, North China University of Science and Technology, 21 Bohai avenue, Caofeidian district, Tangshan, Hebei province 063210, China
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5
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Kumar VA, Lee J, Liu HL, Allen JW, Filippi CG, Holodny AI, Hsu K, Jain R, McAndrews MP, Peck KK, Shah G, Shimony JS, Singh S, Zeineh M, Tanabe J, Vachha B, Vossough A, Welker K, Whitlow C, Wintermark M, Zaharchuk G, Sair HI. Recommended Resting-State fMRI Acquisition and Preprocessing Steps for Preoperative Mapping of Language and Motor and Visual Areas in Adult and Pediatric Patients with Brain Tumors and Epilepsy. AJNR Am J Neuroradiol 2024; 45:139-148. [PMID: 38164572 DOI: 10.3174/ajnr.a8067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Accepted: 10/12/2023] [Indexed: 01/03/2024]
Abstract
Resting-state (rs) fMRI has been shown to be useful for preoperative mapping of functional areas in patients with brain tumors and epilepsy. However, its lack of standardization limits its widespread use and hinders multicenter collaboration. The American Society of Functional Neuroradiology, American Society of Pediatric Neuroradiology, and the American Society of Neuroradiology Functional and Diffusion MR Imaging Study Group recommend specific rs-fMRI acquisition approaches and preprocessing steps that will further support rs-fMRI for future clinical use. A task force with expertise in fMRI from multiple institutions provided recommendations on the rs-fMRI steps needed for mapping of language, motor, and visual areas in adult and pediatric patients with brain tumor and epilepsy. These were based on an extensive literature review and expert consensus.Following rs-fMRI acquisition parameters are recommended: minimum 6-minute acquisition time; scan with eyes open with fixation; obtain rs-fMRI before both task-based fMRI and contrast administration; temporal resolution of ≤2 seconds; scanner field strength of 3T or higher. The following rs-fMRI preprocessing steps and parameters are recommended: motion correction (seed-based correlation analysis [SBC], independent component analysis [ICA]); despiking (SBC); volume censoring (SBC, ICA); nuisance regression of CSF and white matter signals (SBC); head motion regression (SBC, ICA); bandpass filtering (SBC, ICA); and spatial smoothing with a kernel size that is twice the effective voxel size (SBC, ICA).The consensus recommendations put forth for rs-fMRI acquisition and preprocessing steps will aid in standardization of practice and guide rs-fMRI program development across institutions. Standardized rs-fMRI protocols and processing pipelines are essential for multicenter trials and to implement rs-fMRI as part of standard clinical practice.
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Affiliation(s)
- V A Kumar
- From the The University of Texas MD Anderson Cancer Center (V.A.K., J.L., H.-L.L., M.W.), Houston, Texas
| | - J Lee
- From the The University of Texas MD Anderson Cancer Center (V.A.K., J.L., H.-L.L., M.W.), Houston, Texas
| | - H-L Liu
- From the The University of Texas MD Anderson Cancer Center (V.A.K., J.L., H.-L.L., M.W.), Houston, Texas
| | - J W Allen
- Emory University (J.W.A.), Atlanta, Georgia
| | - C G Filippi
- Tufts University (C.G.F.), Boston, Massachusetts
| | - A I Holodny
- Memorial Sloan Kettering Cancer Center (A.I.H., K.K.P.), New York, New York
| | - K Hsu
- New York University (K.H., R.J.), New York, New York
| | - R Jain
- New York University (K.H., R.J.), New York, New York
| | - M P McAndrews
- University of Toronto (M.P.M.), Toronto, Ontario, Canada
| | - K K Peck
- Memorial Sloan Kettering Cancer Center (A.I.H., K.K.P.), New York, New York
| | - G Shah
- University of Michigan (G.S.), Ann Arbor, Michigan
| | - J S Shimony
- Washington University School of Medicine (J.S.S.), St. Louis, Missouri
| | - S Singh
- University of Texas Southwestern Medical Center (S.S.), Dallas, Texas
| | - M Zeineh
- Stanford University (M.Z., G.Z.), Palo Alto, California
| | - J Tanabe
- University of Colorado (J.T.), Aurora, Colorado
| | - B Vachha
- University of Massachusetts (B.V.), Worcester, Massachusetts
| | - A Vossough
- Children's Hospital of Philadelphia, University of Pennsylvania (A.V.), Philadelphia, Pennsylvania
| | - K Welker
- Mayo Clinic (K.W.), Rochester, Minnesota
| | - C Whitlow
- Wake Forest University (C.W.), Winston-Salem, North Carolina
| | - M Wintermark
- From the The University of Texas MD Anderson Cancer Center (V.A.K., J.L., H.-L.L., M.W.), Houston, Texas
| | - G Zaharchuk
- Stanford University (M.Z., G.Z.), Palo Alto, California
| | - H I Sair
- Johns Hopkins University (H.I.S.), Baltimore, Maryland
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6
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Fu Z, Sui J, Iraji A, Liu J, Calhoun V. Cognitive and Psychiatric Relevance of Dynamic Functional Connectivity States in a Large (N>10,000) Children Population. RESEARCH SQUARE 2024:rs.3.rs-3586731. [PMID: 38260417 PMCID: PMC10802706 DOI: 10.21203/rs.3.rs-3586731/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Children's brains dynamically adapt to the stimuli from the internal state and the external environment, allowing for changes in cognitive and mental behavior. In this work, we performed a large-scale analysis of dynamic functional connectivity (DFC) in children aged 9 ~ 11 years, investigating how brain dynamics relate to cognitive performance and mental health at an early age. A hybrid independent component analysis framework was applied to the Adolescent Brain Cognitive Development (ABCD) data containing 10,988 children. We combined a sliding-window approach with k-means clustering to identify five brain states with distinct DFC patterns. Interestingly, the occurrence of a strongly connected state was negatively correlated with cognitive performance and positively correlated with dimensional psychopathology in children. Meanwhile, opposite relationships were observed for a sparsely connected state. The composite cognitive score and the ADHD score were the most significantly correlated with the DFC states. The mediation analysis further showed that attention problems mediated the effect of DFC states on cognitive performance. This investigation unveils the neurological underpinnings of DFC states, which suggests that tracking the transient dynamic connectivity may help to characterize cognitive and mental problems in children and guide people to provide early intervention to buffer adverse influences.
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Affiliation(s)
- Zening Fu
- Georgia Institute of Technology, Emory University and Georgia State University
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7
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Nobukawa S, Ikeda T, Kikuchi M, Takahashi T. Atypical instantaneous spatio-temporal patterns of neural dynamics in Alzheimer's disease. Sci Rep 2024; 14:88. [PMID: 38167950 PMCID: PMC10761722 DOI: 10.1038/s41598-023-50265-3] [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: 06/16/2023] [Accepted: 12/18/2023] [Indexed: 01/05/2024] Open
Abstract
Cognitive functions produced by large-scale neural integrations are the most representative 'emergence phenomena' in complex systems. A novel approach focusing on the instantaneous phase difference of brain oscillations across brain regions has succeeded in detecting moment-to-moment dynamic functional connectivity. However, it is restricted to pairwise observations of two brain regions, contrary to large-scale spatial neural integration in the whole-brain. In this study, we introduce a microstate analysis to capture whole-brain instantaneous phase distributions instead of pairwise differences. Upon applying this method to electroencephalography signals of Alzheimer's disease (AD), which is characterised by progressive cognitive decline, the AD-specific state transition among the four states defined as the leading phase location due to the loss of brain regional interactions could be promptly characterised. In conclusion, our synthetic analysis approach, focusing on the microstate and instantaneous phase, enables the capture of the instantaneous spatiotemporal neural dynamics of brain activity and characterises its pathological conditions.
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Affiliation(s)
- Sou Nobukawa
- Department of Computer Science, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, 275-0016, Chiba, Japan.
- Research Center for Mathematical Engineering, Chiba Institute of Technology, 2-17-1 Tsudanuma, Narashino, 275-0016, Chiba, Japan.
- Department of Preventive Intervention for Psychiatric Disorders, National Institute of Mental Health, National Center of Neurology and Psychiatry, 4-1-1 Ogawa-Higashi, Kodaira, 187-8661, Tokyo, Japan.
| | - Takashi Ikeda
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takaramachi, Kanazawa, 920-8640, Ishikawa, Japan
- United Graduate School of Child Development, Osaka University, Kanazawa University, Hamamatsu University School of Medicine, Chiba University, and University of Fukui, 2-2 Yamadaoka, Suita, 565-0871, Osaka, Japan
| | - Mitsuru Kikuchi
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takaramachi, Kanazawa, 920-8640, Ishikawa, Japan
- Department of Psychiatry and Behavioral Science, Kanazawa University, 13-1 Takaramachi, Kanazawa, 920-8640, Ishikawa, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, 13-1 Takaramachi, Kanazawa, 920-8640, Ishikawa, Japan
- Department of Neuropsychiatry, University of Fukui, 23-3 Matsuoka, Yoshida, 910-1193, Fukui, Japan
- Uozu Shinkei Sanatorium, 1784-1 Eguchi, Uozu, 937-0017, Toyama, Japan
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8
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Lyu W, Wu Y, Huang H, Chen Y, Tan X, Liang Y, Ma X, Feng Y, Wu J, Kang S, Qiu S, Yap PT. Aberrant dynamic functional network connectivity in type 2 diabetes mellitus individuals. Cogn Neurodyn 2023; 17:1525-1539. [PMID: 37969945 PMCID: PMC10640562 DOI: 10.1007/s11571-022-09899-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 09/11/2022] [Accepted: 10/09/2022] [Indexed: 11/24/2022] Open
Abstract
An increasing number of recent brain imaging studies are dedicated to understanding the neuro mechanism of cognitive impairment in type 2 diabetes mellitus (T2DM) individuals. In contrast to efforts to date that are limited to static functional connectivity, here we investigate abnormal connectivity in T2DM individuals by characterizing the time-varying properties of brain functional networks. Using group independent component analysis (GICA), sliding-window analysis, and k-means clustering, we extracted thirty-one intrinsic connectivity networks (ICNs) and estimated four recurring brain states. We observed significant group differences in fraction time (FT) and mean dwell time (MDT), and significant negative correlation between the Montreal Cognitive Assessment (MoCA) scores and FT/MDT. We found that in the T2DM group the inter- and intra-network connectivity decreases and increases respectively for the default mode network (DMN) and task-positive network (TPN). We also found alteration in the precuneus network (PCUN) and enhanced connectivity between the salience network (SN) and the TPN. Our study provides evidence of alterations of large-scale resting networks in T2DM individuals and shed light on the fundamental mechanisms of neurocognitive deficits in T2DM.
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Affiliation(s)
- Wenjiao Lyu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Ye Wu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC USA
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu China
| | - Haoming Huang
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Yuna Chen
- Department of Endocrinology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Xin Tan
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Yi Liang
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Xiaomeng Ma
- Department of Radiology, Jingzhou First People’s Hospital of Hubei Province, Jingzhou, Hubei China
| | - Yue Feng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Jinjian Wu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Shangyu Kang
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC USA
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Zhu J, Jiao Y, Chen R, Wang XH, Han Y. Aberrant dynamic and static functional connectivity of the striatum across specific low-frequency bands in patients with autism spectrum disorder. Psychiatry Res Neuroimaging 2023; 336:111749. [PMID: 37977097 DOI: 10.1016/j.pscychresns.2023.111749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 10/06/2023] [Accepted: 11/03/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND Dysfunctions of the striatum have been repeatedly observed in autism spectrum disorder (ASD). However, previous studies have explored the static functional connectivity (sFC) of the striatum in a single frequency band, ignoring the dynamics and frequency specificity of brain FC. Therefore, we investigated the dynamic FC (dFC) and sFC of the striatum in the slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) frequency bands. METHODS Data of 47 ASD patients and 47 typically developing (TD) controls were obtained from the Autism Brain Imaging Data Exchange (ABIDE) database. A seed-based approach was used to compute the dFC and sFC. Then, a two-sample t-test was performed. For regions showing abnormal sFC and dFC, we performed clinical correlation analysis and constructed support vector machine (SVM) models. RESULTS The middle frontal gyrus (MFG), precuneus, and medial superior frontal gyrus (mPFC) showed both dynamic and static alterations. The reduced striatal dFC in the right MFG was associated with autism symptoms. The dynamic‒static FC model had a great performance in ASD classification, with 95.83 % accuracy. CONCLUSIONS The striatal dFC and sFC were altered in ASD, which were frequency specific. Examining brain activity using dynamic and static FC provides a comprehensive view of brain activity.
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Affiliation(s)
- Junsa Zhu
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing 210009, China
| | - Yun Jiao
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing 210009, China; Network Information Center, Zhongda Hospital, Medical School of Southeast University, Nanjing 210009, China.
| | - Ran Chen
- Jiangsu Key Laboratory of Molecular and Functional Imaging, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, Nanjing 210009, China
| | - Xun-Heng Wang
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Yunyan Han
- Public Health School of Dalian Medical University, Dalian 116000, China
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10
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Zapała D, Augustynowicz P, Tokovarov M, Iwanowicz P, Droździel P. Brief Visual Deprivation Effects on Brain Oscillations During Kinesthetic and Visual-motor Imagery. Neuroscience 2023; 532:37-49. [PMID: 37625688 DOI: 10.1016/j.neuroscience.2023.08.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 08/10/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023]
Abstract
It is widely recognized that opening and closing the eyes can direct attention to external or internal stimuli processing. This has been confirmed by studies showing the effects of changes in visual stimulation changes on cerebral activity during different tasks, e.g., motor imagery and execution. However, an essential aspect of creating a mental representation of motion, such as imagery perspective, has not yet been investigated in the present context. Our study aimed to verify the effect of brief visual deprivation (under eyes open [EO] and eyes closed [EC] conditions) on brain wave oscillations and behavioral performance during kinesthetic imagery (KMI) and visual-motor imagery (VMI) tasks. We focused on the alpha and beta rhythms from visual- and motor-related EEG activity sources. Additionally, we used machine learning algorithms to establish whether the registered differences in brain oscillations might affect motor imagery brain-computer interface (MI-BCI) performance. The results showed that the occipital areas in the EC condition presented significantly stronger desynchronization during VMI tasks, which is typical for enhanced visual stimuli processing. Furthermore, the stronger desynchronization of alpha rhythms from motor areas in the EO, than EC condition confirmed previous effects obtained during real movements. It was also found that simulating movement under EC/EO conditions affected signal classification accuracy, which has practical implications for MI-BCI effectiveness. These findings suggest that shifting processing toward external or internal stimuli modulates brain rhythm oscillations associated with different perspectives on the mental representation of movement.
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Affiliation(s)
- Dariusz Zapała
- Institute of Psychology, Department of Experimental Psychology, The John Paul II Catholic University of Lublin, 20950 Lublin, Poland.
| | - Paweł Augustynowicz
- Institute of Psychology, Department of Experimental Psychology, The John Paul II Catholic University of Lublin, 20950 Lublin, Poland.
| | | | - Paulina Iwanowicz
- Institute of Psychology, Department of Experimental Psychology, The John Paul II Catholic University of Lublin, 20950 Lublin, Poland.
| | - Paulina Droździel
- Institute of Psychology, Department of Experimental Psychology, The John Paul II Catholic University of Lublin, 20950 Lublin, Poland.
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11
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Ma M, Li Y, Shao Y, Weng X. Effect of total sleep deprivation on effective EEG connectivity for young male in resting-state networks in different eye states. Front Neurosci 2023; 17:1204457. [PMID: 37928738 PMCID: PMC10620317 DOI: 10.3389/fnins.2023.1204457] [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: 04/12/2023] [Accepted: 10/09/2023] [Indexed: 11/07/2023] Open
Abstract
Background Many studies have investigated the effect of total sleep deprivation (TSD) on resting-state functional networks, especially the default mode network (DMN) and sensorimotor network (SMN), using functional connectivity. While it is known that the activities of these networks differ based on eye state, it remains unclear how TSD affects them in different eye states. Therefore, we aimed to examine the effect of TSD on DMN and SMN in different eye states using effective functional connectivity via isolated effective coherence (iCoh) in exact low-resolution brain electromagnetic tomography (eLORETA). Methods Resting-state electroencephalogram (EEG) signals were collected from 24 male college students, and each participant completed a psychomotor vigilance task (PVT) while behavioral data were acquired. Each participant underwent 36-h TSD, and the data were acquired in two sleep-deprivation times (rested wakefulness, RW: 0 h; and TSD: 36 h) and two eye states (eyes closed, EC; and eyes open, EO). Changes in neural oscillations and effective connectivity were compared based on paired t-test. Results The behavioral results showed that PVT reaction time was significantly longer in TSD compared with that of RW. The EEG results showed that in the EO state, the activity of high-frequency bands in the DMN and SMN were enhanced compared to those of the EC state. Furthermore, when compared with the DMN and SMN of RW, in TSD, the activity of DMN was decreased, and SMN was increased. Moreover, the changed effective connectivity in the DMN and SMN after TSD was positively correlated with an increased PVT reaction time. In addition, the effective connectivity in the different network (EO-EC) of the SMN was reduced in the β band after TSD compared with that of RW. Conclusion These findings indicate that TSD impairs alertness and sensory information input in the SMN to a greater extent in an EO than in an EC state.
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Affiliation(s)
- Mengke Ma
- School of Psychology, Beijing Sport University, Beijing, China
| | - Yutong Li
- School of Psychology, Beijing Sport University, Beijing, China
| | - Yongcong Shao
- School of Psychology, Beijing Sport University, Beijing, China
- Key Laboratory for Biomechanics and Mechanobiology of the Ministry of Education, School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Xiechuan Weng
- Department of Neuroscience, Beijing Institute of Basic Medical Sciences, Beijing, China
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12
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Yang M, Liu L, Cui H, Deng C, Xiong W, Zhao G, Du S, Kosten TR, Chen H, Li Z, Zhang X. Dynamic functional thalamocortical dysconnectivity in schizophrenia correlates to antipsychotics response. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:40. [PMID: 37402747 DOI: 10.1038/s41537-023-00371-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Accepted: 06/26/2023] [Indexed: 07/06/2023]
Abstract
Although many studies have showed abnormal thalamocortical networks in patients with schizophrenia (SCZ), the dynamic functional thalamocortical connectivity of individuals with SCZ and the effect of antipsychotics on this connectivity have not been investigated. Drug-naïve first-episode individuals with SCZ and healthy controls were recruited. Patients were treated with risperidone for 12 weeks. Resting-state functional magnetic resonance imaging was acquired at baseline and week 12. We identified six functional thalamic subdivisions. The sliding window strategy was used to determine the dynamic functional connectivity (dFC) of each functional thalamic subdivision. Individuals with SCZ displayed decreased or increased dFC variance in different thalamic subdivisions. The baseline dFC between ventral posterior-lateral (VPL) portions and right dorsolateral superior frontal gyrus (rdSFG) correlated with psychotic symptoms. The dFC variance between VPL and right medial orbital superior frontal gyrus (rmoSFG) or rdSFG decreased after 12-week risperidone treatment. The decreased dFC variance between VPL and rmoSFG correlated with the reduction of PANSS scores. Interestingly, the dFC between VPL and rmoSFG or rdSFG decreased in responders. The dFC variance change of VPL and the averaged whole brain signal correlated with the risperidone efficacy. Our study demonstrates abnormal variability in thalamocortical dFC may be implicated in psychopathological symptoms and risperidone response in individuals with schizophrenia, suggesting that thalamocortical dFC variance may be correlated to the efficacy of antipsychotic treatment.Registration: ClinicalTrials.gov Identifier: NCT00435370. https://www.clinicaltrials.gov/ct2/show/NCT00435370?term=NCT00435370&draw=2&rank=1.
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Affiliation(s)
- Mi Yang
- The fourth people's hospital of Chengdu, Chengdu, China
| | - Liju Liu
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Hongmei Cui
- Qingdao Mental Health Center, Qingdao University, Qingdao, China
| | - Chijun Deng
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Weisen Xiong
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China
| | - Guocheng Zhao
- The fourth people's hospital of Chengdu, Chengdu, China
| | - Shulin Du
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Thomas R Kosten
- Department of Psychiatry, Baylor College of Medicine, Houston, TX, USA.
- Epidemiology and Behavioral Science, MD Anderson Cancer Center, Houston, TX, USA.
| | - Huafu Chen
- MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, China.
- Sichuan Provincial Center for Mental Health, The Center of Psychosomatic Medicine of Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
| | - Zezhi Li
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China.
| | - Xiangyang Zhang
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China.
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China.
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13
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Wang S, Li X. A revisit of the amygdala theory of autism: Twenty years after. Neuropsychologia 2023; 183:108519. [PMID: 36803966 PMCID: PMC10824605 DOI: 10.1016/j.neuropsychologia.2023.108519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 01/23/2023] [Accepted: 02/16/2023] [Indexed: 02/19/2023]
Abstract
The human amygdala has long been implicated to play a key role in autism spectrum disorder (ASD). Yet it remains unclear to what extent the amygdala accounts for the social dysfunctions in ASD. Here, we review studies that investigate the relationship between amygdala function and ASD. We focus on studies that employ the same task and stimuli to directly compare people with ASD and patients with focal amygdala lesions, and we also discuss functional data associated with these studies. We show that the amygdala can only account for a limited number of deficits in ASD (primarily face perception tasks but not social attention tasks), a network view is, therefore, more appropriate. We next discuss atypical brain connectivity in ASD, factors that can explain such atypical brain connectivity, and novel tools to analyze brain connectivity. Lastly, we discuss new opportunities from multimodal neuroimaging with data fusion and human single-neuron recordings that can enable us to better understand the neural underpinnings of social dysfunctions in ASD. Together, the influential amygdala theory of autism should be extended with emerging data-driven scientific discoveries such as machine learning-based surrogate models to a broader framework that considers brain connectivity at the global scale.
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Affiliation(s)
- Shuo Wang
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, USA; Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA.
| | - Xin Li
- Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA.
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14
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Shoaib Z, Akbar A, Kim ES, Kamran MA, Kim JH, Jeong MY. Utilizing EEG and fNIRS for the detection of sleep-deprivation-induced fatigue and its inhibition using colored light stimulation. Sci Rep 2023; 13:6465. [PMID: 37081056 PMCID: PMC10119294 DOI: 10.1038/s41598-023-33426-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 04/12/2023] [Indexed: 04/22/2023] Open
Abstract
Drowsy driving is a common, but underestimated phenomenon in terms of associated risks as it often results in crashes causing fatalities and serious injuries. It is a challenging task to alert or reduce the driver's drowsy state using non-invasive techniques. In this study, a drowsiness reduction strategy has been developed and analyzed using exposure to different light colors and recording the corresponding electrical and biological brain activities. 31 subjects were examined by dividing them into 2 classes, a control group, and a healthy group. Fourteen EEG and 42 fNIRS channels were used to gather neurological data from two brain regions (prefrontal and visual cortices). Experiments shining 3 different colored lights have been carried out on them at certain times when there is a high probability to get drowsy. The results of this study show that there is a significant increase in HbO of a sleep-deprived participant when he is exposed to blue light. Similarly, the beta band of EEG also showed an increased response. However, the study found that there is no considerable increase in HbO and beta band power in the case of red and green light exposures. In addition to that, values of other physiological signals acquired such as heart rate, eye blinking, and self-reported Karolinska Sleepiness Scale scores validated the findings predicted by the electrical and biological signals. The statistical significance of the signals achieved has been tested using repeated measures ANOVA and t-tests. Correlation scores were also calculated to find the association between the changes in the data signals with the corresponding changes in the alertness level.
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Affiliation(s)
- Zeshan Shoaib
- Department of Cogno-Mechatronics Engineering, Pusan National University, Busandaehak-ro 63 beon-gil 2, Geumjeong-gu, Busan, 46241, Korea
| | - Arbab Akbar
- Department of Cogno-Mechatronics Engineering, Pusan National University, Busandaehak-ro 63 beon-gil 2, Geumjeong-gu, Busan, 46241, Korea
| | - Eung Soo Kim
- Department of Electronic and Robot Engineering, Busan University of Foreign Studies, 65, KeumSaem-Ro 485 beongil, KeumJeong-Gu, Busan, 46234, Korea
| | - Muhammad Ahmad Kamran
- Department of Cogno-Mechatronics Engineering, Pusan National University, Busandaehak-ro 63 beon-gil 2, Geumjeong-gu, Busan, 46241, Korea
| | - Jun Hyun Kim
- Department of Cogno-Mechatronics Engineering, Pusan National University, Busandaehak-ro 63 beon-gil 2, Geumjeong-gu, Busan, 46241, Korea
| | - Myung Yung Jeong
- Department of Cogno-Mechatronics Engineering, Pusan National University, Busandaehak-ro 63 beon-gil 2, Geumjeong-gu, Busan, 46241, Korea.
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15
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Yang D, Li J, Ke Z, Qin R, Mao C, Huang L, Mo Y, Hu Z, Lv W, Huang Y, Zhang B, Xu Y. Subsystem mechanisms of default mode network underlying white matter hyperintensity-related cognitive impairment. Hum Brain Mapp 2023; 44:2365-2379. [PMID: 36722495 PMCID: PMC10028636 DOI: 10.1002/hbm.26215] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/09/2023] [Accepted: 01/10/2023] [Indexed: 02/02/2023] Open
Abstract
Functional changes of default mode network (DMN) have been proven to be closely associated with white matter hyperintensity (WMH) related cognitive impairment (CI). However, subsystem mechanisms of DMN underlying WMH-related CI remain unclear. The present study recruited WMH patients (n = 206) with mild CI and normal cognition, as well as healthy controls (HC, n = 102). Static/dynamic functional connectivity (FC) of the DMN's three subsystems were calculated using resting-state functional MRI. K-means clustering analyses were performed to extract distinct dynamic connectivity states. Compared with the WMH-NC group, the WMH-MCI group displayed lower static FC within medial temporal lobe (MTL) and core subsystem, between core-MTL subsystem, as well as between core and dorsal medial prefrontal cortex subsystem. All these static alterations were positively associated with information processing speed (IPS). Regarding dynamic FC, the WMH-MCI group exhibited higher dynamic FC within MTL subsystem than the HC and WMH-NC groups. Altered dynamic FC within MTL subsystem mediated the relationship between WMH and memory span (indirect effect: -0.2251, 95% confidence interval [-0.6295, -0.0267]). Additionally, dynamic FCs of DMN subsystems could be clustered into two recurring states. For dynamic FCs within MTL subsystem, WMH-MCI subjects exhibited longer mean dwell time (MDT) and higher reoccurrence fraction (RF) in a sparsely connected state (State 2). Altered MDT and RF in State 2 were negatively associated with IPS. Taken together, these findings indicated static/dynamic FC of DMN subsystems can provide relevant information on cognitive decline from different aspects, which provides a comprehensive view of subsystem mechanisms of DMN underlying WMH-related CI.
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Affiliation(s)
- Dan Yang
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Jiangnan Li
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Zhihong Ke
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
| | - Ruomeng Qin
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - ChengLu Mao
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Lili Huang
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Yuting Mo
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Zheqi Hu
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Weiping Lv
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Yanan Huang
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
| | - Bing Zhang
- Department of Radiology, Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China
- Department of Neurology, Drum Tower Hospital, Medical School and The State Key Laboratory of Pharmaceutical Biotechnology, Institute of Brain Science, Nanjing University, Nanjing, China
- Jiangsu Key Laboratory for Molecular Medicine, Medical School of Nanjing University, Nanjing, China
- Jiangsu Province Stroke Center for Diagnosis and Therapy, Nanjing, China
- Nanjing Neurology Clinic Medical Center, Nanjing, China
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16
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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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17
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Allouch S, Kabbara A, Duprez J, Khalil M, Modolo J, Hassan M. Effect of channel density, inverse solutions and connectivity measures on EEG resting-state networks reconstruction: A simulation study. Neuroimage 2023; 271:120006. [PMID: 36914106 DOI: 10.1016/j.neuroimage.2023.120006] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 02/06/2023] [Accepted: 03/07/2023] [Indexed: 03/13/2023] Open
Abstract
Along with the study of brain activity evoked by external stimuli, the past two decades witnessed an increased interest in characterizing the spontaneous brain activity occurring during resting conditions. The identification of connectivity patterns in this so-called "resting-state" has been the subject of a great number of electrophysiology-based studies, using the Electro/Magneto-Encephalography (EEG/MEG) source connectivity method. However, no consensus has been reached yet regarding a unified (if possible) analysis pipeline, and several involved parameters and methods require cautious tuning. This is particularly challenging when different analytical choices induce significant discrepancies in results and drawn conclusions, thereby hindering the reproducibility of neuroimaging research. Hence, our objective in this study was to shed light on the effect of analytical variability on outcome consistency by evaluating the implications of parameters involved in the EEG source connectivity analysis on the accuracy of resting-state networks (RSNs) reconstruction. We simulated, using neural mass models, EEG data corresponding to two RSNs, namely the default mode network (DMN) and dorsal attentional network (DAN). We investigated the impact of five channel densities (19, 32, 64, 128, 256), three inverse solutions (weighted minimum norm estimate (wMNE), exact low-resolution brain electromagnetic tomography (eLORETA), and linearly constrained minimum variance (LCMV) beamforming) and four functional connectivity measures (phase-locking value (PLV), phase-lag index (PLI), and amplitude envelope correlation (AEC) with and without source leakage correction), on the correspondence between reconstructed and reference networks. We showed that, with different analytical choices related to the number of electrodes, source reconstruction algorithm, and functional connectivity measure, high variability is present in the results. More specifically, our results show that a higher number of EEG channels significantly increased the accuracy of the reconstructed networks. Additionally, our results showed significant variability in the performance of the tested inverse solutions and connectivity measures. Such methodological variability and absence of analysis standardization represent a critical issue for neuroimaging studies that should be prioritized. We believe that this work could be useful for the field of electrophysiology connectomics, by increasing awareness regarding the challenge of variability in methodological approaches and its implications on reported results.
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Affiliation(s)
- Sahar Allouch
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France; Azm Center for Research in Biotechnology and Its Applications, EDST, Tripoli, Lebanon.
| | - Aya Kabbara
- MINDIG, Rennes F-35000, France; LASeR - Lebanese Association for Scientific Research, Tripoli, Lebanon
| | - Joan Duprez
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France
| | - Mohamad Khalil
- Azm Center for Research in Biotechnology and Its Applications, EDST, Tripoli, Lebanon; CRSI research center, Faculty of Engineering, Lebanese University, Beirut, Lebanon
| | - Julien Modolo
- Univ Rennes, INSERM, LTSI - UMR 1099, Rennes F-35000, France
| | - Mahmoud Hassan
- MINDIG, Rennes F-35000, France; School of Science and Engineering, Reykjavik University, Reykjavik, Iceland
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18
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Wu L, Calhoun V. Joint connectivity matrix independent component analysis: Auto-linking of structural and functional connectivities. Hum Brain Mapp 2023; 44:1533-1547. [PMID: 36420833 PMCID: PMC9921228 DOI: 10.1002/hbm.26155] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 09/25/2022] [Accepted: 11/04/2022] [Indexed: 11/25/2022] Open
Abstract
The study of human brain connectivity, including structural connectivity (SC) and functional connectivity (FC), provides insights into the neurophysiological mechanism of brain function and its relationship to human behavior and cognition. Both types of connectivity measurements provide crucial yet complementary information. However, integrating these two modalities into a single framework remains a challenge, because of the differences in their quantitative interdependencies as well as their anatomical representations due to distinctive imaging mechanisms. In this study, we introduced a new method, joint connectivity matrix independent component analysis (cmICA), which provides a data-driven parcellation and automated-linking of SC and FC information simultaneously using a joint analysis of functional magnetic resonance imaging (MRI) and diffusion-weighted MRI data. We showed that these two connectivity modalities produce common cortical segregation, though with various degrees of (dis)similarity. Moreover, we show conjoint FC networks and structural white matter tracts that directly link these cortical parcellations/sources, within one analysis. Overall, data-driven joint cmICA provides a new approach for integrating or fusing structural connectivity and FC systematically and conveniently, and provides an effective tool for connectivity-based multimodal data fusion in brain.
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Affiliation(s)
- Lei Wu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Center, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, USA
| | - Vince Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Center, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, USA
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19
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Zhang S, Goodale SE, Gold BP, Morgan VL, Englot DJ, Chang C. Vigilance associates with the low-dimensional structure of fMRI data. Neuroimage 2023; 267:119818. [PMID: 36535323 PMCID: PMC10074161 DOI: 10.1016/j.neuroimage.2022.119818] [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: 08/21/2022] [Revised: 11/24/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
The human brain exhibits rich dynamics that reflect ongoing functional states. Patterns in fMRI data, detected in a data-driven manner, have uncovered recurring configurations that relate to individual and group differences in behavioral, cognitive, and clinical traits. However, resolving the neural and physiological processes that underlie such measurements is challenging, particularly without external measurements of brain state. A growing body of work points to underlying changes in vigilance as one driver of time-windowed fMRI connectivity states, calculated on the order of tens of seconds. Here we examine the degree to which the low-dimensional spatial structure of instantaneous fMRI activity is associated with vigilance levels, by testing whether vigilance-state detection can be carried out in an unsupervised manner based on individual BOLD time frames. To investigate this question, we first reduce the spatial dimensionality of fMRI data, and apply Gaussian Mixture Modeling to cluster the resulting low-dimensional data without any a priori vigilance information. Our analysis includes long-duration task and resting-state scans that are conducive to shifts in vigilance. We observe a close alignment between low-dimensional fMRI states (data-driven clusters) and measurements of vigilance derived from concurrent electroencephalography (EEG) and behavior. Whole-brain coactivation analysis revealed cortical anti-correlation patterns that resided primarily during higher behavioral- and EEG-defined levels of vigilance, while cortical activity was more often spatially uniform in states corresponding to lower vigilance. Overall, these findings indicate that vigilance states may be detected in the low-dimensional structure of fMRI data, even within individual time frames.
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Affiliation(s)
- Shengchao Zhang
- Department of Electrical and Computer Engineering, Vanderbilt University, 400 24th Avenue S., Nashville, TN 37212, USA.
| | - Sarah E Goodale
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Benjamin P Gold
- Department of Electrical and Computer Engineering, Vanderbilt University, 400 24th Avenue S., Nashville, TN 37212, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Victoria L Morgan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Dario J Englot
- Department of Electrical and Computer Engineering, Vanderbilt University, 400 24th Avenue S., Nashville, TN 37212, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Catie Chang
- Department of Electrical and Computer Engineering, Vanderbilt University, 400 24th Avenue S., Nashville, TN 37212, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA.
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20
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Song I, Lee TH. Considering dynamic nature of the brain: the clinical importance of connectivity variability in machine learning classification and prediction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.26.525765. [PMID: 36747828 PMCID: PMC9901018 DOI: 10.1101/2023.01.26.525765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
The brain connectivity of resting-state fMRI (rs-fMRI) represents an intrinsic state of brain architecture, and it has been used as a useful neural marker for detecting psychiatric conditions as well as for predicting psychosocial characteristics. However, most studies using brain connectivity have focused more on the strength of functional connectivity over time (static-FC) but less attention to temporal characteristics of connectivity changes (FC-variability). The primary goal of the current study was to investigate the effectiveness of using the FC-variability in classifying an individual's pathological characteristics from others and predicting psychosocial characteristics. In addition, the current study aimed to prove that benefits of the FC-variability are reliable across various analysis procedures. To this end, three open public large resting-state fMRI datasets including individuals with Autism Spectrum Disorder (ABIDE; N = 1249), Schizophrenia disorder (COBRE; N = 145), and typical development (NKI; N = 672) were utilized for the machine learning (ML) classification and prediction based on their static-FC and the FC-variability metrics. To confirm the robustness of FC-variability utility, we benchmarked the ML classification and prediction with various brain parcellations and sliding window parameters. As a result, we found that the ML performances were significantly improved when the ML included FC-variability features in classifying pathological populations from controls (e.g., individuals with autism spectrum disorder vs. typical development) and predicting psychiatric severity (e.g., score of autism diagnostic observation schedule), regardless of parcellation selection and sliding window size. Additionally, the ML performance deterioration was significantly prevented with FC-variability features when excessive features were inputted into the ML models, yielding more reliable results. In conclusion, the current finding proved the usefulness of the FC-variability and its reliability.
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Affiliation(s)
- Inuk Song
- Department of Psychology, Virginia Tech
| | - Tae-Ho Lee
- Department of Psychology, Virginia Tech
- School of Neuroscience, Virginia Tech
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21
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Fu Z, Abbott CC, Sui J, Calhoun VD. Predictive signature of static and dynamic functional connectivity for ECT clinical outcomes. Front Pharmacol 2023; 14:1102413. [PMID: 36755955 PMCID: PMC9899999 DOI: 10.3389/fphar.2023.1102413] [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: 11/18/2022] [Accepted: 01/11/2023] [Indexed: 01/24/2023] Open
Abstract
Introduction: Electroconvulsive therapy (ECT) remains one of the most effective approaches for treatment-resistant depressive episodes, despite the potential cognitive impairment associated with this treatment. As a potent stimulator of neuroplasticity, ECT might normalize aberrant depression-related brain function via the brain's reconstruction by forming new neural connections. Multiple lines of evidence have demonstrated that functional connectivity (FC) changes are reliable indicators of antidepressant efficacy and cognitive changes from static and dynamic perspectives. However, no previous studies have directly ascertained whether and how different aspects of FC provide complementary information in terms of neuroimaging-based prediction of clinical outcomes. Methods: In this study, we implemented a fully automated independent component analysis framework to an ECT dataset with subjects (n = 50, age = 65.54 ± 8.92) randomized to three treatment amplitudes (600, 700, or 800 milliamperes [mA]). We extracted the static functional network connectivity (sFNC) and dynamic FNC (dFNC) features and employed a partial least square regression to build predictive models for antidepressant outcomes and cognitive changes. Results: We found that both antidepressant outcomes and memory changes can be robustly predicted by the changes in sFNC (permutation test p < 5.0 × 10-3). More interestingly, by adding dFNC information, the model achieved higher accuracy for predicting changes in the Hamilton Depression Rating Scale 24-item (HDRS24, t = 9.6434, p = 1.5 × 10-21). The predictive maps of clinical outcomes show a weakly negative correlation, indicating that the ECT-induced antidepressant outcomes and cognitive changes might be associated with different functional brain neuroplasticity. Discussion: The overall results reveal that dynamic FC is not redundant but reflects mechanisms of ECT that cannot be captured by its static counterpart, especially for the prediction of antidepressant efficacy. Tracking the predictive signatures of static and dynamic FC will help maximize antidepressant outcomes and cognitive safety with individualized ECT dosing.
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Affiliation(s)
- Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States,*Correspondence: Christopher C. Abbott, ; Zening Fu,
| | - Christopher C. Abbott
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, United States,*Correspondence: Christopher C. Abbott, ; Zening Fu,
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia Institute of Technology, Georgia State University, Emory University, Atlanta, GA, United States,Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, United States
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22
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Feng Y, Kang X, Wang H, Cong J, Zhuang W, Xue K, Li F, Yao D, Xu P, Zhang T. The relationships between dynamic resting-state networks and social behavior in autism spectrum disorder revealed by fuzzy entropy-based temporal variability analysis of large-scale network. Cereb Cortex 2023; 33:764-776. [PMID: 35297491 DOI: 10.1093/cercor/bhac100] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 02/13/2022] [Accepted: 02/14/2022] [Indexed: 02/03/2023] Open
Abstract
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by a core deficit in social processes. However, it is still unclear whether the core clinical symptoms of the disorder can be reflected by the temporal variability of resting-state network functional connectivity (FC). In this article, we examined the large-scale network FC temporal variability at the local region, within-network, and between-network levels using the fuzzy entropy technique. Then, we correlated the network FC temporal variability to social-related scores. We found that the social behavior correlated with the FC temporal variability of the precuneus, parietal, occipital, temporal, and precentral. Our results also showed that social behavior was significantly negatively correlated with the temporal variability of FC within the default mode network, between the frontoparietal network and cingulo-opercular task control network, and the dorsal attention network. In contrast, social behavior correlated significantly positively with the temporal variability of FC within the subcortical network. Finally, using temporal variability as a feature, we construct a model to predict the social score of ASD. These findings suggest that the network FC temporal variability has a close relationship with social behavioral inflexibility in ASD and may serve as a potential biomarker for predicting ASD symptom severity.
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Affiliation(s)
- Yu Feng
- Mental Health Education Center and School of Science, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China
| | - Xiaodong Kang
- The Department of Sichuan 81 Rehabilitation Center, Chengdu University of TCM, No.37, Twelfth Bridge Road,Chengdu 610075, China
| | - Hesong Wang
- Department of Gastroenterology, Guangdong Provincial Key Laboratory of Gastroenterology, Institute of Gastroenterology of Guangdong Province, Nanfang Hospital, Southern Medical University, No. 1023-1063, Shatai South Road, Baiyun District, Guangzhou 510515, China
| | - Jing Cong
- Mental Health Education Center and School of Science, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China
| | - Wenwen Zhuang
- Mental Health Education Center and School of Science, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China
| | - Kaiqing Xue
- School of Computer and Software Engineering, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China
| | - Fali Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Dadao, Gaoxin District, Chengdu 611731, China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Dadao, Gaoxin District, Chengdu 611731, China
| | - Peng Xu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Dadao, Gaoxin District, Chengdu 611731, China
| | - Tao Zhang
- Mental Health Education Center and School of Science, Xihua University, No. 999, Jinzhou Road, Jinniu District, Chengdu 610039, China.,The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Dadao, Gaoxin District, Chengdu 611731, China
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23
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Sato Y, Nishimaru H, Matsumoto J, Setogawa T, Nishijo H. Electroencephalographic Effective Connectivity Analysis of the Neural Networks during Gesture and Speech Production Planning in Young Adults. Brain Sci 2023; 13:brainsci13010100. [PMID: 36672081 PMCID: PMC9856316 DOI: 10.3390/brainsci13010100] [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: 11/24/2022] [Revised: 12/19/2022] [Accepted: 12/29/2022] [Indexed: 01/06/2023] Open
Abstract
Gestures and speech, as linked communicative expressions, form an integrated system. Previous functional magnetic resonance imaging studies have suggested that neural networks for gesture and spoken word production share similar brain regions consisting of fronto-temporo-parietal brain regions. However, information flow within the neural network may dynamically change during the planning of two communicative expressions and also differ between them. To investigate dynamic information flow in the neural network during the planning of gesture and spoken word generation in this study, participants were presented with spatial images and were required to plan the generation of gestures or spoken words to represent the same spatial situations. The evoked potentials in response to spatial images were recorded to analyze the effective connectivity within the neural network. An independent component analysis of the evoked potentials indicated 12 clusters of independent components, the dipoles of which were located in the bilateral fronto-temporo-parietal brain regions and on the medial wall of the frontal and parietal lobes. Comparison of effective connectivity indicated that information flow from the right middle cingulate gyrus (MCG) to the left supplementary motor area (SMA) and from the left SMA to the left precentral area increased during gesture planning compared with that of word planning. Furthermore, information flow from the right MCG to the left superior frontal gyrus also increased during gesture planning compared with that of word planning. These results suggest that information flow to the brain regions for hand praxis is more strongly activated during gesture planning than during word planning.
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Affiliation(s)
- Yohei Sato
- Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama 930-0194, Japan
| | - Hiroshi Nishimaru
- Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama 930-0194, Japan
- Research Center for Idling Brain Science (RCIBS), University of Toyama, Toyama 930-0194, Japan
| | - Jumpei Matsumoto
- Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama 930-0194, Japan
- Research Center for Idling Brain Science (RCIBS), University of Toyama, Toyama 930-0194, Japan
| | - Tsuyoshi Setogawa
- Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama 930-0194, Japan
- Research Center for Idling Brain Science (RCIBS), University of Toyama, Toyama 930-0194, Japan
| | - Hisao Nishijo
- Department of System Emotional Science, Faculty of Medicine, University of Toyama, Toyama 930-0194, Japan
- Research Center for Idling Brain Science (RCIBS), University of Toyama, Toyama 930-0194, Japan
- Correspondence:
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24
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Abnormal dynamic functional network connectivity in first-episode, drug-naïve patients with major depressive disorder. J Affect Disord 2022; 319:336-343. [PMID: 36084757 DOI: 10.1016/j.jad.2022.08.072] [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: 04/18/2022] [Revised: 06/25/2022] [Accepted: 08/22/2022] [Indexed: 11/20/2022]
Abstract
Dynamic functional network connectivity (dFNC) could capture temporal features of spontaneous brain activity during MRI scanning, and it might be a powerful tool to examine functional brain network alters in major depressive disorder (MDD). Therefore, this study investigated the changes in temporal properties of dFNC of first-episode, drug-naïve patients with MDD. A total of 48 first-episode, drug-naïve MDD patients and 46 age- and gender-matched healthy controls were recruited in this study. Sliding windows were implied to construct dFNC. We assessed the relationships between altered dFNC temporal properties and depressive symptoms. Receiver operating characteristic (ROC) curve analyses were used to examine the diagnostic performance of these altered temporal properties. The results showed that patients with MDD have more occurrences and spent more time in a weak connection state, but with fewer occurrences and shorter dwell time in a strong connection state. Importantly, the fractional time and mean dwell time of state 2 was negatively correlated with Hamilton Depression Rating Scale (HDRS) scores. ROC curve analysis demonstrated that these temporal properties have great identified power including the fractional time and mean dwell time in state 2, and the AUC is 0.872, 0.837, respectively. The AUC of the combination of fractional time and mean dwell time in state 2 with age, gender is 0.881. Our results indicated the temporal properties of dFNC are altered in first-episode, drug-naïve patients with MDD, and these changes' properties could serve as a potential biomarker in MDD.
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25
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Pupíková M, Šimko P, Lamoš M, Gajdoš M, Rektorová I. Inter-individual differences in baseline dynamic functional connectivity are linked to cognitive aftereffects of tDCS. Sci Rep 2022; 12:20754. [PMID: 36456622 PMCID: PMC9715685 DOI: 10.1038/s41598-022-25016-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 11/23/2022] [Indexed: 12/05/2022] Open
Abstract
Transcranial direct current stimulation (tDCS) has the potential to modulate cognitive training in healthy aging; however, results from various studies have been inconsistent. We hypothesized that inter-individual differences in baseline brain state may contribute to the varied results. We aimed to explore whether baseline resting-state dynamic functional connectivity (rs-dFC) and/or conventional resting-state static functional connectivity (rs-sFC) may be related to the magnitude of cognitive aftereffects of tDCS. To achieve this aim, we used data from our double-blind randomized sham-controlled cross-over tDCS trial in 25 healthy seniors in which bifrontal tDCS combined with cognitive training had induced significant behavioral aftereffects. We performed a backward regression analysis including rs-sFC/rs-dFC measures to explain the variability in the magnitude of tDCS-induced improvements in visual object-matching task (VOMT) accuracy. Rs-dFC analysis revealed four rs-dFC states. The occurrence rate of a rs-dFC state 4, characterized by a high correlation between the left fronto-parietal control network and the language network, was significantly associated with tDCS-induced VOMT accuracy changes. The rs-sFC measure was not significantly associated with the cognitive outcome. We show that flexibility of the brain state representing readiness for top-down control of object identification implicated in the studied task is linked to the tDCS-enhanced task accuracy.
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Affiliation(s)
- Monika Pupíková
- Applied Neuroscience Research Group, Central European Institute of Technology - CEITEC, Masaryk University, Brno, Czech Republic
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Patrik Šimko
- Applied Neuroscience Research Group, Central European Institute of Technology - CEITEC, Masaryk University, Brno, Czech Republic
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Martin Lamoš
- Brain and Mind Research Program, Central European Institute of Technology - CEITEC, Masaryk university, Brno, Czech Republic
| | - Martin Gajdoš
- Multimodal and Functional Neuroimaging Research Group, Central European Institute of Technology - CEITEC, Masaryk University, Brno, Czech Republic
| | - Irena Rektorová
- Applied Neuroscience Research Group, Central European Institute of Technology - CEITEC, Masaryk University, Brno, Czech Republic.
- First Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic.
- International Clinical Research Center, ICRC, St Anne's University Hospital and Faculty of Medicine, Brno, Czech Republic.
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26
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Adamovich T, Zakharov I, Tabueva A, Malykh S. The thresholding problem and variability in the EEG graph network parameters. Sci Rep 2022; 12:18659. [PMID: 36333413 PMCID: PMC9636266 DOI: 10.1038/s41598-022-22079-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
Abstract
Graph thresholding is a frequently used practice of eliminating the weak connections in brain functional connectivity graphs. The main aim of the procedure is to delete the spurious connections in the data. However, the choice of the threshold is arbitrary, and the effect of the threshold choice is not fully understood. Here we present the description of the changes in the global measures of a functional connectivity graph depending on the different proportional thresholds based on the 146 resting-state EEG recordings. The dynamics is presented in five different synchronization measures (wPLI, ImCoh, Coherence, ciPLV, PPC) in sensors and source spaces. The analysis shows significant changes in the graph's global connectivity measures as a function of the chosen threshold which may influence the outcome of the study. The choice of the threshold could lead to different study conclusions; thus it is necessary to improve the reasoning behind the choice of the different analytic options and consider the adoption of different analytic approaches. We also proposed some ways of improving the procedure of thresholding in functional connectivity research.
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Affiliation(s)
- Timofey Adamovich
- grid.466465.3Psychological Institute of Russian Academy of Education, Moscow, Russia ,grid.412761.70000 0004 0645 736XUral Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
| | - Ilya Zakharov
- grid.466465.3Psychological Institute of Russian Academy of Education, Moscow, Russia ,grid.412761.70000 0004 0645 736XUral Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
| | - Anna Tabueva
- grid.466465.3Psychological Institute of Russian Academy of Education, Moscow, Russia ,grid.412761.70000 0004 0645 736XUral Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
| | - Sergey Malykh
- grid.466465.3Psychological Institute of Russian Academy of Education, Moscow, Russia ,grid.412761.70000 0004 0645 736XUral Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
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27
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Altered spontaneous brain activity in Down syndrome and its relation with cognitive outcome. Sci Rep 2022; 12:15410. [PMID: 36104362 PMCID: PMC9474876 DOI: 10.1038/s41598-022-19627-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 08/31/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractAlthough Down syndrome (DS) is the most common genetic cause of neurodevelopmental delay, few neuroimaging studies have explored this population. This investigation aimed to study whole-brain resting-state spontaneous brain activity using fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) strategies to find differences in spontaneous brain activity among young people with DS and controls and to correlate these results with cognitive outcomes. The sample comprised 18 persons with DS (age mean = 28.67, standard deviation = 4.18) and 18 controls (age mean = 28.56, standard deviation = 4.26). fALFF and ReHo analyses were performed, and the results were correlated with other cognitive variables also collected (KBIT-2 and verbal fluency test). Increased activity was found in DS using fALFF in areas involving the frontal and temporal lobes and left cerebellum anterior lobe. Decreased activity in DS was found in the left parietal and occipital lobe, the left limbic lobe and the left cerebellum posterior lobe. ReHo analysis showed increased activity in certain DS areas of the left frontal lobe and left rectus, as well as the inferior temporal lobe. The areas with decreased activity in the DS participants were regions of the frontal lobe and the right limbic lobe. Altered fALFF and ReHo were found in the DS population, and this alteration could predict the cognitive abilities of the participants. To our knowledge, this is the first study to explore regional spontaneous brain activity in a population with DS. Moreover, this study suggests the possibility of using fALFF and ReHo as biomarkers of cognitive function, which is highly important given the difficulties in cognitively evaluating this population to assess dementia. More research is needed, however, to demonstrate its utility.
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28
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Lee K, Horien C, O’Connor D, Garand-Sheridan B, Tokoglu F, Scheinost D, Lake EM, Constable RT. Arousal impacts distributed hubs modulating the integration of brain functional connectivity. Neuroimage 2022; 258:119364. [PMID: 35690257 PMCID: PMC9341222 DOI: 10.1016/j.neuroimage.2022.119364] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 06/02/2022] [Accepted: 06/07/2022] [Indexed: 12/12/2022] Open
Abstract
Even when subjects are at rest, it is thought that brain activity is organized into distinct brain states during which reproducible patterns are observable. Yet, it is unclear how to define or distinguish different brain states. A potential source of brain state variation is arousal, which may play a role in modulating functional interactions between brain regions. Here, we use simultaneous resting state functional magnetic resonance imaging (fMRI) and pupillometry to study the impact of arousal levels indexed by pupil area on the integration of large-scale brain networks. We employ a novel sparse dictionary learning-based method to identify hub regions participating in between-network integration stratified by arousal, by measuring k-hubness, the number (k) of functionally overlapping networks in each brain region. We show evidence of a brain-wide decrease in between-network integration and inter-subject variability at low relative to high arousal, with differences emerging across regions of the frontoparietal, default mode, motor, limbic, and cerebellum networks. State-dependent changes in k-hubness relate to the actual patterns of network integration within these hubs, suggesting a brain state transition from high to low arousal characterized by global synchronization and reduced network overlaps. We demonstrate that arousal is not limited to specific brain areas known to be directly associated with arousal regulation, but instead has a brain-wide impact that involves high-level between-network communications. Lastly, we show a systematic change in pairwise fMRI signal correlation structures in the arousal state-stratified data, and demonstrate that the choice of global signal regression could result in different conclusions in conventional graph theoretical analysis and in the analysis of k-hubness when studying arousal modulations. Together, our results suggest the presence of global and local effects of pupil-linked arousal modulations on resting state brain functional connectivity.
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Affiliation(s)
- Kangjoo Lee
- Department of Radiology and Bioimaging Sciences, Yale University School of Medicine, New Haven, CT 06520, United States.
| | - Corey Horien
- Interdepartmental Neuroscience Program, Yale University
School of Medicine, New Haven, CT 06520, United States
| | - David O’Connor
- Department of Biomedical Engineering, Yale University, New
Haven, CT 06520, United States
| | | | - Fuyuze Tokoglu
- Department of Radiology and Bioimaging Sciences, Yale
University School of Medicine, New Haven, CT 06520, United States
| | - Dustin Scheinost
- Department of Radiology and Bioimaging Sciences, Yale
University School of Medicine, New Haven, CT 06520, United States,Department of Biomedical Engineering, Yale University, New
Haven, CT 06520, United States,The Child Study Center, Yale University School of Medicine,
New Haven, CT 06520, United States,Department of Statistics and Data Science, Yale University,
New Haven, CT 06511, United States
| | - Evelyn M.R. Lake
- Department of Radiology and Bioimaging Sciences, Yale
University School of Medicine, New Haven, CT 06520, United States
| | - R. Todd Constable
- Department of Radiology and Bioimaging Sciences, Yale
University School of Medicine, New Haven, CT 06520, United States,Department of Biomedical Engineering, Yale University, New
Haven, CT 06520, United States,Department of Neurosurgery, Yale University School of
Medicine, New Haven, CT 06520, United States
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29
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Peng L, Luo Z, Zeng LL, Hou C, Shen H, Zhou Z, Hu D. Parcellating the human brain using resting-state dynamic functional connectivity. Cereb Cortex 2022; 33:3575-3590. [PMID: 35965076 DOI: 10.1093/cercor/bhac293] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 07/01/2022] [Accepted: 07/02/2022] [Indexed: 11/14/2022] Open
Abstract
Brain cartography has expanded substantially over the past decade. In this regard, resting-state functional connectivity (FC) plays a key role in identifying the locations of putative functional borders. However, scant attention has been paid to the dynamic nature of functional interactions in the human brain. Indeed, FC is typically assumed to be stationary across time, which may obscure potential or subtle functional boundaries, particularly in regions with high flexibility and adaptability. In this study, we developed a dynamic FC (dFC)-based parcellation framework, established a new functional human brain atlas termed D-BFA (DFC-based Brain Functional Atlas), and verified its neurophysiological plausibility by stereo-EEG data. As the first dFC-based whole-brain atlas, the proposed D-BFA delineates finer functional boundaries that cannot be captured by static FC, and is further supported by good correspondence with cytoarchitectonic areas and task activation maps. Moreover, the D-BFA reveals the spatial distribution of dynamic variability across the brain and generates more homogenous parcels compared with most alternative parcellations. Our results demonstrate the superiority and practicability of dFC in brain parcellation, providing a new template to exploit brain topographic organization from a dynamic perspective. The D-BFA will be publicly available for download at https://github.com/sliderplm/D-BFA-618.
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Affiliation(s)
- Limin Peng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Zhiguo Luo
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Ling-Li Zeng
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Chenping Hou
- College of Science, National University of Defense Technology, Changsha 410073, China
| | - Hui Shen
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Zongtan Zhou
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
| | - Dewen Hu
- College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
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30
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Cao J, Zhao Y, Shan X, Blackburn D, Wei J, Erkoyuncu JA, Chen L, Sarrigiannis PG. Ultra-high-resolution time-frequency analysis of EEG to characterise brain functional connectivity with the application in Alzheimer's disease. J Neural Eng 2022; 19. [PMID: 35896105 DOI: 10.1088/1741-2552/ac84ac] [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: 12/28/2021] [Accepted: 07/27/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE This study aims to explore the potential of high-resolution brain functional connectivity based on electroencephalogram (EEG), a non-invasive low-cost technique, to be translated into a long-overdue biomarker and a diagnostic method for Alzheimer's disease (AD). APPROACH The paper proposes a novel ultra-high-resolution time-frequency nonlinear cross-spectrum method to construct a promising biomarker of AD pathophysiology. Specifically, using the peak frequency estimated from a Revised Hilbert-Huang Transformation cross-spectrum as a biomarker, the Support Vector Machine classifier is used to distinguish AD from healthy controls (HC). MAIN RESULTS With the combinations of the proposed biomarker and machine learning, we achieved a promising accuracy of 89%. The proposed method performs better than the wavelet cross-spectrum and other functional connectivity measures in the temporal or frequency domain, particularly in the Full, Delta and Alpha bands. Besides, a novel visualisation approach developed from topography is introduced to represent the brain functional connectivity, with which the difference between AD and HCs can be clearly displayed. The interconnections between posterior and other brain regions are obviously affected in AD. SIGNIFICANCE Those findings imply that the proposed RHHT approach could better track dynamic and nonlinear functional connectivity information, paving the way for the development of a novel diagnostic approach.
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Affiliation(s)
- Jun Cao
- Cranfield University, Building 30, Cranfield, Bedford, Cranfield, Bedfordshire, MK43 0AL, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Yifan Zhao
- Cranfield University, Building 30, Cranfield, Bedford, Cranfield, Bedfordshire, MK43 0AL, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Xiaocai Shan
- Cranfield University, Building 30, Cranfield, Bedford, Bedfordshire, MK43 0AL, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Daniel Blackburn
- Department of Neuroscience, University of Sheffield, 385a Glossop Road, Sheffield, S10 7HQ, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Jize Wei
- Hong Kong Polytechnic University University Learning Hub, Department of Applied Mathematics, Kowloon, HONG KONG
| | - John Ahmet Erkoyuncu
- Cranfield University, Building 30, Cranfield, Bedford, Cranfield, Bedfordshire, MK43 0AL, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
| | - Liangyu Chen
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Sanhao street, Shenyang, 110004, CHINA
| | - Ptolemaios G Sarrigiannis
- Royal Devon and Exeter NHS Foundation Trust, 1, Exeter, EX2 5DW, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
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31
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Cai LM, Shi JY, Dong QY, Wei J, Chen HJ. Aberrant stability of brain functional architecture in cirrhotic patients with minimal hepatic encephalopathy. Brain Imaging Behav 2022; 16:2258-2267. [PMID: 35729463 DOI: 10.1007/s11682-022-00696-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/03/2022] [Indexed: 01/22/2024]
Abstract
To investigate the stability changes of brain functional architecture and the relationship between stability change and cognitive impairment in cirrhotic patients. Fifty-one cirrhotic patients (21 with minimal hepatic encephalopathy (MHE) and 30 without MHE (NHE)) and 29 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging and neurocognitive assessment using the Psychometric Hepatic Encephalopathy Score (PHES). Voxel-wise functional connectivity density (FCD) was calculated as the sum of connectivity strength between one voxel and others within the entire brain. The sliding window correlation approach was subsequently utilized to calculate the FCD dynamics over time. Functional stability (FS) is measured as the concordance of dynamic FCD. From HCs to the NHE and MHE groups, a stepwise reduction of FS was found in the right supramarginal gyrus (RSMG), right middle cingulate cortex, left superior frontal gyrus, and bilateral posterior cingulate cortex (BPCC), whereas a progressive increment of FS was observed in the left middle occipital gyrus (LMOG) and right temporal pole (RTP). The mean FS values in RSMG/LMOG/RTP (r = 0.470 and P = 0.001; r = -0.458 and P = 0.001; and r = -0.384 and P = 0.005, respectively) showed a correlation with PHES in cirrhotic patients. The FS index in RSMG/LMOG/BPCC/RTP showed moderate discrimination potential between the NHE and MHE groups. Changes in FS may be linked to neuropathological bias of cognitive impairment in cirrhotic patients and could serve as potential biomarkers for MHE diagnosis and monitoring the progression of hepatic encephalopathy.
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Affiliation(s)
- Li-Min Cai
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Jia-Yan Shi
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Qiu-Yi Dong
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Jin Wei
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Hua-Jun Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
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32
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Duprez J, Tabbal J, Hassan M, Modolo J, Kabbara A, Mheich A, Drapier S, Vérin M, Sauleau P, Wendling F, Benquet P, Houvenaghel JF. Spatio-temporal dynamics of large-scale electrophysiological networks during cognitive action control in healthy controls and Parkinson's disease patients. Neuroimage 2022; 258:119331. [PMID: 35660459 DOI: 10.1016/j.neuroimage.2022.119331] [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: 12/13/2021] [Revised: 05/16/2022] [Accepted: 05/23/2022] [Indexed: 10/18/2022] Open
Abstract
Among the cognitive symptoms that are associated with Parkinson's disease (PD), alterations in cognitive action control (CAC) are commonly reported in patients. CAC enables the suppression of an automatic action, in favor of a goal-directed one. The implementation of CAC is time-resolved and arguably associated with dynamic changes in functional brain networks. However, the electrophysiological functional networks involved, their dynamic changes, and how these changes are affected by PD, still remain unknown. In this study, to address this gap of knowledge, 10 PD patients and 10 healthy controls (HC) underwent a Simon task while high-density electroencephalography (HD-EEG) was recorded. Source-level dynamic connectivity matrices were estimated using the phase-locking value in the beta (12-25 Hz) and gamma (30-45 Hz) frequency bands. Temporal independent component analyses were used as a dimension reduction tool to isolate the task-related brain network states. Typical microstate metrics were quantified to investigate the presence of these states at the subject-level. Our results first confirmed that PD patients experienced difficulties in inhibiting automatic responses during the task. At the group-level, we found three functional network states in the beta band that involved fronto-temporal, temporo-cingulate and fronto-frontal connections with typical CAC-related prefrontal and cingulate nodes (e.g., inferior frontal cortex). The presence of these networks did not differ between PD patients and HC when analyzing microstates metrics, and no robust correlations with behavior were found. In the gamma band, five networks were found, including one fronto-temporal network that was identical to the one found in the beta band. These networks also included CAC-related nodes previously identified in different neuroimaging modalities. Similarly to the beta networks, no subject-level differences were found between PD patients and HC. Interestingly, in both frequency bands, the dominant network at the subject-level was never the one that was the most durably modulated by the task. Altogether, this study identified the dynamic functional brain networks observed during CAC, but did not highlight PD-related changes in these networks that might explain behavioral changes. Although other new methods might be needed to investigate the presence of task-related networks at the subject-level, this study still highlights that task-based dynamic functional connectivity is a promising approach in understanding the cognitive dysfunctions observed in PD and beyond.
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Key Words
- Cognitive control
- DIFFIT, Difference in data fitting
- DLPFC, Dorso-lateral prefrontal cortex
- EEG, Electroencephalography
- FC, Functional connectivity
- Functional connectivity
- HC, Healthy controls
- HD-EEG, High-density EEG
- ICA, Independent component analysis
- IFC, Inferior frontal cortex
- MEG, Magnetoencephalography
- Networks, Dynamics
- PD, Parkinson's disease
- PLV, Phase locking value
- Parkinson's disease Abbreviations CAC, Cognitive action control
- ROIS, Regions of interest
- RT, Reaction time
- Simon task
- dBNS, Dynamic brain network state
- dFC, Dynamic functional connectivity
- fMRI, Functional magnetic resonance imaging
- high density EEG
- pre-SMA, Pre-supplementary motor area
- tICA, Temporal ICA
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Affiliation(s)
- Joan Duprez
- Univ Rennes, LTSI - U1099, F-35000 Rennes, France
| | - Judie Tabbal
- Univ Rennes, LTSI - U1099, F-35000 Rennes, France; Azm Center for Research in Biotechnology and Its Applications, EDST, Lebanese University, Beirut, Lebanon
| | - Mahmoud Hassan
- MINDig, F-35000 Rennes, France; School of Engineering, Reykjavik University, Iceland
| | | | | | | | - Sophie Drapier
- CIC INSERM 1414, Rennes, France; Neurology Department, Pontchaillou Hospital, Rennes University Hospital, France
| | - Marc Vérin
- Neurology Department, Pontchaillou Hospital, Rennes University Hospital, France; Behavioral and Basal Ganglia' Research Unit, University of Rennes 1-Rennes University Hospital, France
| | - Paul Sauleau
- Behavioral and Basal Ganglia' Research Unit, University of Rennes 1-Rennes University Hospital, France; Neurophysiology department, Rennes University Hospital, France
| | | | | | - Jean-François Houvenaghel
- Neurology Department, Pontchaillou Hospital, Rennes University Hospital, France; Behavioral and Basal Ganglia' Research Unit, University of Rennes 1-Rennes University Hospital, France
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33
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Petro NM, Ott L, Penhale S, Rempe M, Embury C, Picci G, Wang YP, Stephen JM, Calhoun VD, Wilson TW. Eyes-closed versus eyes-open differences in spontaneous neural dynamics during development. Neuroimage 2022; 258:119337. [PMID: 35636737 PMCID: PMC9385211 DOI: 10.1016/j.neuroimage.2022.119337] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 05/24/2022] [Accepted: 05/26/2022] [Indexed: 01/23/2023] Open
Abstract
Background: Assessing brain activity during rest has become a widely used approach in developmental neuroscience. Extant literature has measured resting brain activity both during eyes-open and eyes-closed conditions, but the difference between these conditions has not yet been well characterized. Studies, limited to fMRI and EEG, have suggested that eyes-open versus -closed conditions may differentially impact neural activity, especially in visual cortices. Methods: Spontaneous cortical activity was recorded using MEG from 108 typically developing youth (9-15 years-old; 55 female) during separate sessions of eyes-open and eyes-closed rest. MEG source images were computed, and the strength of spontaneous neural activity was estimated in the canonical delta, theta, alpha, beta, and gamma bands, respectively. Power spectral density maps for eyes-open were subtracted from eyes-closed rest, and then submitted to vertex-wise regression models to identify spatially specific differences between conditions and as a function of age and sex. Results: Relative alpha power was weaker in the eyes-open compared to -closed condition, but otherwise eyes-open was stronger in all frequency bands, with differences concentrated in the occipital cortex. Relative theta power became stronger in the eyes-open compared to the eyes-closed condition with increasing age in frontal cortex. No differences were observed between males and females. Conclusions: The differences in relative power from eyes-closed to -open conditions are consistent with changes observed in task-based visual sensory responses. Age differences occurred in relatively late developing frontal regions, consistent with canonical attention regions, suggesting that these differences could be reflective of developmental changes in attention processes during puberty. Taken together, resting-state paradigms using eyes-open versus -closed produce distinct results and, in fact, can help pinpoint sensory related brain activity.
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Affiliation(s)
- Nathan M Petro
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Lauren Ott
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Samantha Penhale
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Maggie Rempe
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA; College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
| | - Christine Embury
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA; Department of Psychology, University of Nebraska Omaha, Omaha, NE, USA
| | - Giorgia Picci
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, USA
| | | | - Vince D Calhoun
- Mind Research Network, Albuquerque, NM, USA; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA; Department of Pharmacology & Neuroscience, Creighton University, Omaha, NE, USA.
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Wang Y, Li J, Zeng L, Wang H, Yang T, Shao Y, Weng X. Open Eyes Increase Neural Oscillation and Enhance Effective Brain Connectivity of the Default Mode Network: Resting-State Electroencephalogram Research. Front Neurosci 2022; 16:861247. [PMID: 35573310 PMCID: PMC9092973 DOI: 10.3389/fnins.2022.861247] [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: 01/24/2022] [Accepted: 04/05/2022] [Indexed: 11/13/2022] Open
Abstract
The default mode network (DMN) has a unique activity pattern in the resting brain. Studies on resting-state brain activity are helpful to identify various brain dynamic characteristics of patients with mental diseases and those of healthy people. The brain produces a series of changes in different eye states. However, the relationship between eye states and the DMN, which is closely related to the resting state, has not been widely examined. This study recruited 42 healthy students aged 17–22. Participants completed the Profile of Mood States questionnaire. Thereafter, the electroencephalogram data was collected with the patients’ eyes open and closed. Changes in neural oscillation and the DMN’s information transmission during different eye openness states were compared. The results showed that the neural oscillation activities of the parietal-occipital network such as the superior parietal lobule and precuneus were significantly enhanced in the eyes open state. In addition, the effective connectivity within the DMN was enhanced during opened eyes, especially from the left precuneus to the left posterior cingulate cortex, and this connectivity was negatively correlated with the Vigor-Activity mood state in the eyes open state. The activity of the DMN in the resting-state is regulated by eye states, which may relate to mood and emotional perception.
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Affiliation(s)
- Yi Wang
- Department of Physical Education, Renmin University of China, Beijing, China.,School of Life Sciences and Technology, Harbin Institute of Technology, Harbin, China
| | - Jialu Li
- School of Psychology, University of Leeds, Leeds, United Kingdom
| | - Lingjing Zeng
- School of Psychology, University of Leeds, Leeds, United Kingdom
| | - Haiteng Wang
- School of Psychology, Beijing Sport University, Beijing, China
| | - Tianyi Yang
- School of Psychology, Beijing Sport University, Beijing, China
| | - Yongcong Shao
- School of Psychology, Beijing Sport University, Beijing, China
| | - Xiechuan Weng
- Beijing Institute of Basic Medical Sciences, Beijing, China
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35
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Iraji A, Faghiri A, Fu Z, Kochunov P, Adhikari BM, Belger A, Ford JM, McEwen S, Mathalon DH, Pearlson GD, Potkin SG, Preda A, Turner JA, Van Erp TGM, Chang C, Calhoun VD. Moving beyond the 'CAP' of the Iceberg: Intrinsic connectivity networks in fMRI are continuously engaging and overlapping. Neuroimage 2022; 251:119013. [PMID: 35189361 PMCID: PMC9107614 DOI: 10.1016/j.neuroimage.2022.119013] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 02/11/2022] [Accepted: 02/17/2022] [Indexed: 11/05/2022] Open
Abstract
Resting-state functional magnetic resonance imaging is currently the mainstay of functional neuroimaging and has allowed researchers to identify intrinsic connectivity networks (aka functional networks) at different spatial scales. However, little is known about the temporal profiles of these networks and whether it is best to model them as continuous phenomena in both space and time or, rather, as a set of temporally discrete events. Both categories have been supported by series of studies with promising findings. However, a critical question is whether focusing only on time points presumed to contain isolated neural events and disregarding the rest of the data is missing important information, potentially leading to misleading conclusions. In this work, we argue that brain networks identified within the spontaneous blood oxygenation level-dependent (BOLD) signal are not limited to temporally sparse burst moments and that these event present time points (EPTs) contain valuable but incomplete information about the underlying functional patterns. We focus on the default mode and show evidence that is consistent with its continuous presence in the BOLD signal, including during the event absent time points (EATs), i.e., time points that exhibit minimum activity and are the least likely to contain an event. Moreover, our findings suggest that EPTs may not contain all the available information about their corresponding networks. We observe distinct default mode connectivity patterns obtained from all time points (AllTPs), EPTs, and EATs. We show evidence of robust relationships with schizophrenia symptoms that are both common and unique to each of the sets of time points (AllTPs, EPTs, EATs), likely related to transient patterns of connectivity. Together, these findings indicate the importance of leveraging the full temporal data in functional studies, including those using event-detection approaches.
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Affiliation(s)
- A 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, United States of America.
| | - A 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, United States of America
| | - Z 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, United States of America
| | - P Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States of America
| | - B M Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States of America
| | - A Belger
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, United States of America
| | - J M Ford
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, United States of America; San Francisco VA Medical Center, San Francisco, CA, United States of America
| | - S McEwen
- Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, United States of America
| | - D H Mathalon
- Department of Psychiatry, University of California San Francisco, San Francisco, CA, United States of America; San Francisco VA Medical Center, San Francisco, CA, United States of America
| | - G D Pearlson
- Departments of Psychiatry and Neuroscience, Yale University, School of Medicine, New Haven, CT, United States of America
| | - S G Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, United States of America
| | - A Preda
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, United States of America
| | - J A Turner
- Department of Psychology, Georgia State University, Atlanta, GA, United States of America
| | - T G M Van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, United States of America
| | - C Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States of America
| | - V 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, United States of America.
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36
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Zheng R, Chen Y, Jiang Y, Zhou B, Li S, Wei Y, Wang C, Han S, Zhang Y, Cheng J. Abnormal dynamic functional connectivity in first-episode, drug-naïve adolescents with major depressive disorder. J Neurosci Res 2022; 100:1463-1475. [PMID: 35393711 DOI: 10.1002/jnr.25047] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 03/14/2022] [Accepted: 03/15/2022] [Indexed: 01/18/2023]
Abstract
Previous neuroimaging studies have identified disrupted large-scale functional brain networks in major depressive disorder (MDD); however, most of them focused on adult patients and were based on static functional connectivity (FC). Thus, we aimed to investigate the patterns of change in dynamic FC in depressed adolescents. Resting-state functional magnetic resonance imaging data were acquired from 60 first-episode, drug-naïve adolescents with MDD and 60 matched healthy controls (HCs). Then, the dynamic FC properties were analyzed using a sliding windows approach, k-means clustering, and graph theory methods. The intrinsic brain FC were clustered into two configuration states-a more frequent and relatively sparsely connected State 1 and a less frequent and more strongly interconnected State 2. Compared with HCs, depressed adolescents had higher reoccurrence fraction and dwell time in State 1, and lower reoccurrence fraction and dwell time in State 2, and higher total number of transitions between the two states. Depressed adolescents showed decreased FC within the default mode network (DMN) and between the DMN and other networks in State 1. Additionally, the MDD group showed higher variances in the global and local efficiency. Furthermore, the duration of illness was positively correlated with the number of state transitions, and the 17-item Hamilton Depression Rating Scale score was positively correlated with the mean dwell time in State 1. This study demonstrated abnormal dynamic FC in depressed adolescents, which provided new insights into the pathophysiological mechanisms of adolescent-onset depression.
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Affiliation(s)
- Ruiping Zheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China
| | - Yuan Chen
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China
| | - Yu Jiang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China
| | - Bingqian Zhou
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China
| | - Shuying Li
- Department of Psychiatry, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, P.R. China
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37
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Maltbie E, Yousefi B, Zhang X, Kashyap A, Keilholz S. Comparison of Resting-State Functional MRI Methods for Characterizing Brain Dynamics. Front Neural Circuits 2022; 16:681544. [PMID: 35444518 PMCID: PMC9013751 DOI: 10.3389/fncir.2022.681544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 03/11/2022] [Indexed: 11/13/2022] Open
Abstract
Resting-state functional MRI (fMRI) exhibits time-varying patterns of functional connectivity. Several different analysis approaches have been developed for examining these resting-state dynamics including sliding window connectivity (SWC), phase synchrony (PS), co-activation pattern (CAP), and quasi-periodic patterns (QPP). Each of these approaches can be used to generate patterns of activity or inter-areal coordination which vary across time. The individual frames can then be clustered to produce temporal groupings commonly referred to as “brain states.” Several recent publications have investigated brain state alterations in clinical populations, typically using a single method for quantifying frame-wise functional connectivity. This study directly compares the results of k-means clustering in conjunction with three of these resting-state dynamics methods (SWC, CAP, and PS) and quantifies the brain state dynamics across several metrics using high resolution data from the human connectome project. Additionally, these three dynamics methods are compared by examining how the brain state characterizations vary during the repeated sequences of brain states identified by a fourth dynamic analysis method, QPP. The results indicate that the SWC, PS, and CAP methods differ in the clusters and trajectories they produce. A clear illustration of these differences is given by how each one results in a very different clustering profile for the 24s sequences explicitly identified by the QPP algorithm. PS clustering is sensitive to QPPs with the mid-point of most QPP sequences grouped into the same single cluster. CAPs are also highly sensitive to QPPs, separating each phase of the QPP sequences into different sets of clusters. SWC (60s window) is less sensitive to QPPs. While the QPPs are slightly more likely to occur during specific SWC clusters, the SWC clustering does not vary during the 24s QPP sequences, the goal of this work is to improve both the practical and theoretical understanding of different resting-state dynamics methods, thereby enabling investigators to better conceptualize and implement these tools for characterizing functional brain networks.
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38
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Xu N, LaGrow TJ, Anumba N, Lee A, Zhang X, Yousefi B, Bassil Y, Clavijo GP, Khalilzad Sharghi V, Maltbie E, Meyer-Baese L, Nezafati M, Pan WJ, Keilholz S. Functional Connectivity of the Brain Across Rodents and Humans. Front Neurosci 2022; 16:816331. [PMID: 35350561 PMCID: PMC8957796 DOI: 10.3389/fnins.2022.816331] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 02/14/2022] [Indexed: 12/15/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI), which measures the spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, is increasingly utilized for the investigation of the brain's physiological and pathological functional activity. Rodents, as a typical animal model in neuroscience, play an important role in the studies that examine the neuronal processes that underpin the spontaneous fluctuations in the BOLD signal and the functional connectivity that results. Translating this knowledge from rodents to humans requires a basic knowledge of the similarities and differences across species in terms of both the BOLD signal fluctuations and the resulting functional connectivity. This review begins by examining similarities and differences in anatomical features, acquisition parameters, and preprocessing techniques, as factors that contribute to functional connectivity. Homologous functional networks are compared across species, and aspects of the BOLD fluctuations such as the topography of the global signal and the relationship between structural and functional connectivity are examined. Time-varying features of functional connectivity, obtained by sliding windowed approaches, quasi-periodic patterns, and coactivation patterns, are compared across species. Applications demonstrating the use of rs-fMRI as a translational tool for cross-species analysis are discussed, with an emphasis on neurological and psychiatric disorders. Finally, open questions are presented to encapsulate the future direction of the field.
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Affiliation(s)
- Nan Xu
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Theodore J. LaGrow
- Electrical and Computer Engineering, Georgia Tech, Atlanta, GA, United States
| | - Nmachi Anumba
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Azalea Lee
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
- Emory University School of Medicine, Atlanta, GA, United States
| | - Xiaodi Zhang
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Behnaz Yousefi
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Yasmine Bassil
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
| | - Gloria P. Clavijo
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | | | - Eric Maltbie
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Lisa Meyer-Baese
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Maysam Nezafati
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Wen-Ju Pan
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Shella Keilholz
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
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39
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Fu Z, Sui J, Espinoza R, Narr K, Qi S, Sendi MSE, Abbot CC, Calhoun VD. Whole-Brain Functional Connectivity Dynamics Associated With Electroconvulsive Therapy Treatment Response. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:312-322. [PMID: 34303848 PMCID: PMC8783932 DOI: 10.1016/j.bpsc.2021.07.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Revised: 07/13/2021] [Accepted: 07/14/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Depressive episodes (DEPs), characterized by abnormalities in cognitive functions and mood, are a leading cause of disability. Electroconvulsive therapy (ECT), which involves a brief electrical stimulation of the anesthetized brain, is one of the most effective treatments used in patients with DEP due to its rapid efficacy. METHODS In this work, we investigated how dynamic brain functional connectivity responds to ECT and whether the dynamic responses are associated with treatment outcomes and side effects in patients. We applied a fully automated independent component analysis-based pipeline to 110 patients with DEP (including diagnosis of unipolar depression or bipolar depression) and 60 healthy control subjects. The dynamic functional connectivity was analyzed by a combination of the sliding window approach and clustering analysis. RESULTS Five recurring connectivity states were identified, and patients with DEPs had fewer occurrences in one brain state (state 1) with strong positive and negative connectivity. Patients with DEP changed the occupancy of two states (states 3 and 4) after ECT, resulting in significantly different occurrences of one additional state (state 3) compared with healthy control subjects. We further found that patients with DEP had diminished global metastate dynamism, two of which recovered to normal after ECT. The changes in dynamic connectivity characteristics were associated with the changes in memory recall and Hamilton Depression Rating Scale of DEP after ECT. CONCLUSIONS These converging results extend current findings on subcortical-cortical dysfunction and dysrhythmia in DEP and demonstrate that ECT might cause remodeling of brain functional dynamics that enhance the neuroplasticity of the diseased brain.
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Affiliation(s)
- Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Jing Sui
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China,University of Chinese Academy of Sciences, Beijing, China
| | - Randall Espinoza
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, United States
| | - Katherine Narr
- Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California, United States
| | - Shile Qi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States
| | - Mohammad S. E. Sendi
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States,Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States
| | - Christopher C. Abbot
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico, United States,Corresponding author: Dr. Christopher C. Abbott, Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico, United States, , Phone: 505-272-0406
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States,Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States,Department of Psychiatry, Yale University, School of Medicine, New Haven, Connecticut, United States,Department of Psychology, Computer Science, Neuroscience Institute, and Physics, Georgia State University, Atlanta, Georgia, United States
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Optimizing EEG Source Reconstruction with Concurrent fMRI-Derived Spatial Priors. Brain Topogr 2022; 35:282-301. [PMID: 35142957 PMCID: PMC9098592 DOI: 10.1007/s10548-022-00891-3] [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: 06/30/2021] [Accepted: 01/31/2022] [Indexed: 02/01/2023]
Abstract
Reconstructing EEG sources involves a complex pipeline, with the inverse problem being the most challenging. Multiple inversion algorithms are being continuously developed, aiming to tackle the non-uniqueness of this problem, which has been shown to be partially circumvented by including prior information in the inverse models. Despite a few efforts, there are still current and persistent controversies regarding the inversion algorithm of choice and the optimal set of spatial priors to be included in the inversion models. The use of simultaneous EEG-fMRI data is one approach to tackle this problem. The spatial resolution of fMRI makes fMRI derived spatial priors very convenient for EEG reconstruction, however, only task activation maps and resting-state networks (RSNs) have been explored so far, overlooking the recent, but already accepted, notion that brain networks exhibit dynamic functional connectivity fluctuations. The lack of a systematic comparison between different source reconstruction algorithms, considering potentially more brain-informative priors such as fMRI, motivates the search for better reconstruction models. Using simultaneous EEG-fMRI data, here we compared four different inversion algorithms (minimum norm, MN; low resolution electromagnetic tomography, LORETA; empirical Bayes beamformer, EBB; and multiple sparse priors, MSP) under a Bayesian framework (as implemented in SPM), each with three different sets of priors consisting of: (1) those specific to the algorithm; (2) those specific to the algorithm plus fMRI task activation maps and RSNs; and (3) those specific to the algorithm plus fMRI task activation maps and RSNs and network modules of task-related dFC states estimated from the dFC fluctuations. The quality of the reconstructed EEG sources was quantified in terms of model-based metrics, namely the expectation of the posterior probability P(model|data) and variance explained of the inversion models, and the overlap/proportion of brain regions known to be involved in the visual perception tasks that the participants were submitted to, and RSN templates, with/within EEG source components. Model-based metrics suggested that model parsimony is preferred, with the combination MSP and priors specific to this algorithm exhibiting the best performance. However, optimal overlap/proportion values were found using EBB and priors specific to this algorithm and fMRI task activation maps and RSNs or MSP and considering all the priors (algorithm priors, fMRI task activation maps and RSNs and dFC state modules), respectively, indicating that fMRI spatial priors, including dFC state modules, might contain useful information to recover EEG source components reflecting neuronal activity of interest. Our main results show that providing fMRI spatial derived priors that reflect the dynamics of the brain might be useful to map neuronal activity more accurately from EEG-fMRI. Furthermore, this work paves the way towards a more informative selection of the optimal EEG source reconstruction approach, which may be critical in future studies.
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Huang Y, Yu Z. Representation Learning for Dynamic Functional Connectivities via Variational Dynamic Graph Latent Variable Models. ENTROPY 2022; 24:e24020152. [PMID: 35205448 PMCID: PMC8871213 DOI: 10.3390/e24020152] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/14/2022] [Accepted: 01/14/2022] [Indexed: 02/04/2023]
Abstract
Latent variable models (LVMs) for neural population spikes have revealed informative low-dimensional dynamics about the neural data and have become powerful tools for analyzing and interpreting neural activity. However, these approaches are unable to determine the neurophysiological meaning of the inferred latent dynamics. On the other hand, emerging evidence suggests that dynamic functional connectivities (DFC) may be responsible for neural activity patterns underlying cognition or behavior. We are interested in studying how DFC are associated with the low-dimensional structure of neural activities. Most existing LVMs are based on a point process and fail to model evolving relationships. In this work, we introduce a dynamic graph as the latent variable and develop a Variational Dynamic Graph Latent Variable Model (VDGLVM), a representation learning model based on the variational information bottleneck framework. VDGLVM utilizes a graph generative model and a graph neural network to capture dynamic communication between nodes that one has no access to from the observed data. The proposed computational model provides guaranteed behavior-decoding performance and improves LVMs by associating the inferred latent dynamics with probable DFC.
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Wang K, Zhang Y, Zhu Y, Luo Y. Associations between cortical activation and network interaction during sleep. Behav Brain Res 2022; 422:113751. [PMID: 35038462 DOI: 10.1016/j.bbr.2022.113751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 01/03/2022] [Accepted: 01/12/2022] [Indexed: 11/02/2022]
Abstract
Cortical activation and network interaction, two characterizations of the cortical states, are separately studied in most previous studies. To further clarify the underlying mechanism, the association between these two indicators during sleep was investigated in this study. Twenty healthy individuals were enrolled and all of them underwent overnight polysomnography (PSG) recording. The relative spectral powers and the phase transfer entropy (PTE) of various frequency components were extracted from 6 electroencephalographic (EEG) channels, to assess the cortical activation and network interaction, respectively. Pearson correlation coefficient was employed to estimate their associations. The results suggested that there was a negative correlation between spectral power and phase transfer entropy in δ and α frequency bands during sleep. As the sleep deepened, an increased negative correlation in the δ frequency band was noted, but the negative correlation became less extreme in the α frequency band. The extremum of the correlation coefficient was noted in δ of N3, and α of Wake. Overall, this study provides a connection between these two cortical activity assessments, especially reveals the variable characteristics of different frequency components, which is conducive to better understand sleep state.
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Affiliation(s)
- Kejie Wang
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yangting Zhang
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yongpeng Zhu
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China
| | - Yuxi Luo
- School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China; Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, Sun Yat-Sen University, Guangzhou, China.
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43
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Resting state network connectivity is attenuated by fMRI acoustic noise. Neuroimage 2021; 247:118791. [PMID: 34920084 DOI: 10.1016/j.neuroimage.2021.118791] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 10/21/2021] [Accepted: 12/07/2021] [Indexed: 12/11/2022] Open
Abstract
INTRODUCTION During the past decades there has been an increasing interest in tracking brain network fluctuations in health and disease by means of resting state functional magnetic resonance imaging (rs-fMRI). Rs-fMRI however does not provide the ideal environmental setting, as participants are continuously exposed to noise generated by MRI coils during acquisition of Echo Planar Imaging (EPI). We investigated the effect of EPI noise on resting state activity and connectivity using magnetoencephalography (MEG), by reproducing the acoustic characteristics of rs-fMRI environment during the recordings. As compared to fMRI, MEG has little sensitivity to brain activity generated in deep brain structures, but has the advantage to capture both the dynamic of cortical magnetic oscillations with high temporal resolution and the slow magnetic fluctuations highly correlated with BOLD signal. METHODS Thirty healthy subjects were enrolled in a counterbalanced design study including three conditions: a) silent resting state (Silence), b) resting state upon EPI noise (fMRI), and c) resting state upon white noise (White). White noise was employed to test the specificity of fMRI noise effect. The amplitude envelope correlation (AEC) in alpha band measured the connectivity of seven Resting State Networks (RSN) of interest (default mode network, dorsal attention network, language, left and right auditory and left and right sensory-motor). Vigilance dynamic was estimated from power spectral activity. RESULTS fMRI and White acoustic noise consistently reduced connectivity of cortical networks. The effects were widespread, but noise and network specificities were also present. For fMRI noise, decreased connectivity was found in the right auditory and sensory-motor networks. Progressive increase of slow theta-delta activity related to drowsiness was found in all conditions, but was significantly higher for fMRI . Theta-delta significantly and positively correlated with variations of cortical connectivity. DISCUSSION rs-fMRI connectivity is biased by unavoidable environmental factors during scanning, which warrant more careful control and improved experimental designs. MEG is free from acoustic noise and allows a sensitive estimation of resting state connectivity in cortical areas. Although underutilized, MEG could overcome issues related to noise during fMRI, in particular when investigation of motor and auditory networks is needed.
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44
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Zhang P, Jiang Y, Liu G, Han J, Wang J, Ma L, Hu W, Zhang J. Altered brain functional network dynamics in classic trigeminal neuralgia: a resting-state functional magnetic resonance imaging study. J Headache Pain 2021; 22:147. [PMID: 34895135 PMCID: PMC8903588 DOI: 10.1186/s10194-021-01354-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 11/06/2021] [Indexed: 12/20/2022] Open
Abstract
Background Accumulating studies have indicated a wide range of brain alterations with respect to the structure and function of classic trigeminal neuralgia (CTN). Given the dynamic nature of pain experience, the exploration of temporal fluctuations in interregional activity covariance may enhance the understanding of pain processes in the brain. The present study aimed to characterize the temporal features of functional connectivity (FC) states as well as topological alteration in CTN. Methods Resting-state functional magnetic resonance imaging and three-dimensional T1-weighted images were obtained from 41 CTN patients and 43 matched healthy controls (HCs). After group independent component analysis, sliding window based dynamic functional network connectivity (dFNC) analysis was applied to investigate specific FC states and related temporal properties. Then, the dynamics of the whole brain topological organization were estimated by calculating the coefficient of variation of graph-theoretical properties. Further correlation analyses were performed between all these measurements and clinical data. Results Two distinct states were identified. Of these, the state 2, characterized by complicated coupling between default mode network (DMN) and cognitive control network (CC) and tight connections within DMN, was expressed more in CTN patients and presented as increased fractional windows and dwell time. Moreover, patients switched less frequently between states than HCs. Regarding the dynamic topological analysis, disruptions in global graph-theoretical properties (including network efficiency and small-worldness) were observed in patients, coupled with decreased variability in nodal efficiency of anterior cingulate cortex (ACC) in the salience network (SN) and the thalamus and caudate nucleus in the subcortical network (SC). The variation of topological properties showed negative correlation with disease duration and attack frequency. Conclusions The present study indicated disrupted flexibility of brain topological organization under persistent noxious stimulation and further highlighted the important role of “dynamic pain connectome” regions (including DMN/CC/SN) in the pathophysiology of CTN from the temporal fluctuation aspect. Additionally, the findings provided supplementary evidence for current knowledge about the aberrant cortical-subcortical interaction in pain development. Supplementary Information The online version contains supplementary material available at 10.1186/s10194-021-01354-z.
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Affiliation(s)
- Pengfei Zhang
- Second Clinical School, Lanzhou University, Lanzhou, 730000, China.,Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China
| | - Yanli Jiang
- Second Clinical School, Lanzhou University, Lanzhou, 730000, China.,Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China
| | - Guangyao Liu
- Second Clinical School, Lanzhou University, Lanzhou, 730000, China.,Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China
| | - Jiao Han
- Second Clinical School, Lanzhou University, Lanzhou, 730000, China
| | - Jun Wang
- Second Clinical School, Lanzhou University, Lanzhou, 730000, China.,Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China
| | - Laiyang Ma
- Second Clinical School, Lanzhou University, Lanzhou, 730000, China.,Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China
| | - Wanjun Hu
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China
| | - Jing Zhang
- Department of Magnetic Resonance, Lanzhou University Second Hospital, Lanzhou, 730000, China. .,Gansu Province Clinical Research Center for Functional and Molecular Imaging, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, P. R. China.
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45
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Martin CG, He BJ, Chang C. State-related neural influences on fMRI connectivity estimation. Neuroimage 2021; 244:118590. [PMID: 34560268 PMCID: PMC8815005 DOI: 10.1016/j.neuroimage.2021.118590] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 09/11/2021] [Accepted: 09/16/2021] [Indexed: 12/01/2022] Open
Abstract
The spatiotemporal structure of functional magnetic resonance imaging (fMRI) signals has provided a valuable window into the network underpinnings of human brain function and dysfunction. Although some cross-regional temporal correlation patterns (functional connectivity; FC) exhibit a high degree of stability across individuals and species, there is growing acknowledgment that measures of FC can exhibit marked changes over a range of temporal scales. Further, FC can covary with experimental task demands and ongoing neural processes linked to arousal, consciousness and perception, cognitive and affective state, and brain-body interactions. The increased recognition that such interrelated neural processes modulate FC measurements has raised both challenges and new opportunities in using FC to investigate brain function. Here, we review recent advances in the quantification of neural effects that shape fMRI FC and discuss the broad implications of these findings in the design and analysis of fMRI studies. We also discuss how a more complete understanding of the neural factors that shape FC measurements can resolve apparent inconsistencies in the literature and lead to more interpretable conclusions from fMRI studies.
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Affiliation(s)
- Caroline G Martin
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Biyu J He
- Neuroscience Institute, New York University School of Medicine, New York, NY 10016, USA; Departments of Neurology, Neuroscience & Physiology, and Radiology, New York University School of Medicine, New York, NY 10016, USA
| | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
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Duda M, Koutra D, Sripada C. Validating dynamicity in resting state fMRI with activation-informed temporal segmentation. Hum Brain Mapp 2021; 42:5718-5735. [PMID: 34510647 PMCID: PMC8559473 DOI: 10.1002/hbm.25649] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 08/11/2021] [Accepted: 08/24/2021] [Indexed: 12/18/2022] Open
Abstract
Confirming the presence (or absence) of dynamic functional connectivity (dFC) states during rest is an important open question in the field of cognitive neuroscience. The prevailing dFC framework aims to identify dynamics directly from connectivity estimates with a sliding window approach, however this method suffers from several drawbacks including sensitivity to window size and poor test-retest reliability. We hypothesize that time-varying changes in functional connectivity are mirrored by significant temporal changes in functional activation, and that this coupling can be leveraged to study dFC without the need for a predefined sliding window. Here, we introduce a data-driven dFC framework, which involves informed segmentation of fMRI time series at candidate FC state transition points estimated from changes in whole-brain functional activation, rather than a fixed-length sliding window. We show our approach reliably identifies true cognitive state change points when applied on block-design working memory task data and outperforms the standard sliding window approach in both accuracy and computational efficiency in this context. When applied to data from four resting state fMRI scanning sessions, our method consistently recovers five reliable FC states, and subject-specific features derived from these states show significant correlation with behavioral phenotypes of interest (cognitive ability, personality). Overall, these results suggest abrupt whole-brain changes in activation can be used as a marker for changes in connectivity states and provides new evidence for the existence of time-varying FC in rest.
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Affiliation(s)
- Marlena Duda
- Department of Computational Medicine and BioinformaticsUniversity of MichiganAnn ArborMichiganUSA
| | - Danai Koutra
- Department of Computer Science and EngineeringUniversity of MichiganAnn ArborMichiganUSA
| | - Chandra Sripada
- Department of PsychiatryUniversity of MichiganAnn ArborMichiganUSA
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47
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Network-based forecasting of climate phenomena. Proc Natl Acad Sci U S A 2021; 118:1922872118. [PMID: 34782455 DOI: 10.1073/pnas.1922872118] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/30/2021] [Indexed: 12/26/2022] Open
Abstract
Network theory, as emerging from complex systems science, can provide critical predictive power for mitigating the global warming crisis and other societal challenges. Here we discuss the main differences of this approach to classical numerical modeling and highlight several cases where the network approach substantially improved the prediction of high-impact phenomena: 1) El Niño events, 2) droughts in the central Amazon, 3) extreme rainfall in the eastern Central Andes, 4) the Indian summer monsoon, and 5) extreme stratospheric polar vortex states that influence the occurrence of wintertime cold spells in northern Eurasia. In this perspective, we argue that network-based approaches can gainfully complement numerical modeling.
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48
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Wei J, Lin JH, Cai LM, Shi JY, Zhang XH, Zou ZY, Chen HJ. Abnormal Stability of Dynamic Functional Architecture in Amyotrophic Lateral Sclerosis: A Preliminary Resting-State fMRI Study. Front Neurol 2021; 12:744688. [PMID: 34721270 PMCID: PMC8548741 DOI: 10.3389/fneur.2021.744688] [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: 07/20/2021] [Accepted: 09/08/2021] [Indexed: 12/28/2022] Open
Abstract
Purpose: Static and dynamic analyses for identifying functional connectivity (FC) have demonstrated brain dysfunctions in amyotrophic lateral sclerosis (ALS). However, few studies on the stability of dynamic FC have been conducted among ALS patients. This study explored the change of functional stability in ALS and how it correlates with disease severity. Methods: We gathered resting-state functional magnetic resonance data from 20 patients with ALS and 22 healthy controls (HCs). The disease severity was assessed with the Revised ALS Functional Rating Scale (ALSFRS-R). We used a sliding window correlation approach to identify dynamic FC and measured the concordance of dynamic FC over time to obtain the functional stability of each voxel. We assessed the between-group difference in functional stability by voxel-wise two-sample t-test. The correlation between the functional stability index and ALSFRS-R in ALS patients was evaluated using Spearman's correlation analysis. Results: Compared with the HC group, the ALS group had significantly increased functional stability in the left pre-central and post-central gyrus and right temporal pole while decreased functional stability in the right middle and inferior frontal gyrus. The results revealed a significant correlation between ALSFRS-R and the mean functional stability in the right temporal pole (r = −0.452 and P = 0.046) in the ALS patients. Conclusions: ALS patients have abnormal stability of brain functional architecture, which is associated with the severity of the disease.
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Affiliation(s)
- Jin Wei
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jia-Hui Lin
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Li-Min Cai
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jia-Yan Shi
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Xiao-Hong Zhang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Zhang-Yu Zou
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Hua-Jun Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
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Dissemination in time and space in presymptomatic granulin mutation carriers: a GENFI spatial chronnectome study. Neurobiol Aging 2021; 108:155-167. [PMID: 34607248 DOI: 10.1016/j.neurobiolaging.2021.09.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 08/28/2021] [Accepted: 09/01/2021] [Indexed: 11/24/2022]
Abstract
The presymptomatic brain changes of granulin (GRN) disease, preceding by years frontotemporal dementia, has not been fully characterized. New approaches focus on the spatial chronnectome can capture both spatial network configurations and their dynamic changes over time. To investigate the spatial dynamics in 141 presymptomatic GRN mutation carriers and 282 noncarriers from the Genetic Frontotemporal dementia research Initiative cohort. We considered time-varying patterns of the default mode network, the language network, and the salience network, each summarized into 4 distinct recurring spatial configurations. Dwell time (DT) (the time each individual spends in each spatial state of each network), fractional occupacy (FO) (the total percentage of time spent by each individual in a state of a specific network) and total transition number (the total number of transitions performed by each individual in a specifict state) were considered. Correlations between DT, FO, and transition number and estimated years from expected symptom onset (EYO) and clinical performances were assessed. Presymptomatic GRN mutation carriers spent significantly more time in those spatial states characterised by greater activation of the insula and the parietal cortices, as compared to noncarriers (p < 0.05, FDR-corrected). A significant correlation between DT and FO of these spatial states and EYO was found, the longer the time spent in the spatial states, the closer the EYO. DT and FO significantly correlated with performances at tests tapping processing speed, with worse scores associated with increased spatial states' DT. Our results demonstrated that presymptomatic GRN disease presents a complex dynamic reorganization of brain connectivity. Change in both the spatial and temporal aspects of brain network connectivity could provide a unique glimpse into brain function and potentially allowing a more sophisticated evaluation of the earliest disease changes and the understanding of possible mechanisms in GRN disease.
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Mitterová K, Lamoš M, Mareček R, Pupíková M, Šimko P, Grmela R, Skotáková A, Vaculíková P, Rektorová I. Dynamic Functional Connectivity Signifies the Joint Impact of Dance Intervention and Cognitive Reserve. Front Aging Neurosci 2021; 13:724094. [PMID: 34566626 PMCID: PMC8462054 DOI: 10.3389/fnagi.2021.724094] [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: 06/11/2021] [Accepted: 08/18/2021] [Indexed: 11/13/2022] Open
Abstract
Research on dance interventions (DIs) in the elderly has shown promising benefits to physical and cognitive outcomes. The effect of DIs on resting-state functional connectivity (rs-FC) varies, which is possibly due to individual variability. In this study, we assessed the moderation effects of residual cognitive reserve (CR) on DI-induced changes in dynamic rs-FC and their association on cognitive outcomes. Dynamic rs-FC (rs-dFC) and cognitive functions were evaluated in non-demented elderly subjects before and after a 6-month DI (n = 36) and a control group, referred to as the life-as-usual (LAU) group (n = 32). Using linear mixed models and moderation, we examined the interaction effect of DIs and CR on changes in the dwell time and coverage of rs-dFC. Cognitive reserve was calculated as the residual difference between the observed memory performance and the performance predicted by brain state. Partial correlations accounting for CR evaluated the unique association between changes in rs-dFC and cognition in the DI group. In subjects with lower residual CR, we observed DI-induced increases in dwell time [t(58) = -2.14, p = 0.036] and coverage [t(58) = -2.22, p = 0.030] of a rs-dFC state, which was implicated in bottom-up information processing. Increased dwell time was also correlated with a DI-induced improvement in Symbol Search (r = 0.42, p = 0.02). In subjects with higher residual CR, we observed a DI-induced increase in coverage [t(58) = 2.11, p = 0.039] of another rs-dFC state, which was implicated in top-down information processing. The study showed that DIs have a differential and behaviorally relevant effect on dynamic rs-dFC, but these benefits depend on the current CR level.
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Affiliation(s)
- Kristína Mitterová
- Applied Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Brno, Czechia.,Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Martin Lamoš
- Brain and Mind Research Program, Central European Institute of Technology, Masaryk University, Brno, Czechia
| | - Radek Mareček
- Brain and Mind Research Program, Central European Institute of Technology, Masaryk University, Brno, Czechia
| | - Monika Pupíková
- Applied Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Brno, Czechia.,Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Patrik Šimko
- Applied Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Brno, Czechia.,Faculty of Medicine, Masaryk University, Brno, Czechia
| | - Roman Grmela
- Department of Health Promotion, Faculty of Sports Studies, Masaryk University, Brno, Czechia
| | - Alena Skotáková
- Department of Gymnastics and Combatives, Faculty of Sports Studies, Masaryk University, Brno, Czechia
| | - Pavlína Vaculíková
- Department of Gymnastics and Combatives, Faculty of Sports Studies, Masaryk University, Brno, Czechia
| | - Irena Rektorová
- Applied Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Brno, Czechia.,First Department of Neurology, Faculty of Medicine, Masaryk University and St. Anne's University Hospital, Brno, Czechia
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