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Damiani S, La-Torraca-Vittori P, Tarchi L, Tosi E, Ricca V, Scalabrini A, Politi P, Fusar-Poli P. On the interplay between state-dependent reconfigurations of global signal correlation and BOLD fluctuations: An fMRI study. Neuroimage 2024; 291:120585. [PMID: 38527658 DOI: 10.1016/j.neuroimage.2024.120585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 03/10/2024] [Accepted: 03/22/2024] [Indexed: 03/27/2024] Open
Abstract
BACKGROUND The dynamics of global, state-dependent reconfigurations in brain connectivity are yet unclear. We aimed at assessing reconfigurations of the global signal correlation coefficient (GSCORR), a measure of the connectivity between each voxel timeseries and the global signal, from resting-state to a stop-signal task. The secondary aim was to assess the relationship between GSCORR and blood-oxygen-level-dependent (BOLD) activations or deactivation across three different trial-conditions (GO, STOP-correct, and STOP-incorrect). METHODS As primary analysis we computed whole-brain, voxel-wise GSCORR during resting-state (GSCORR-rest) and stop-signal task (GSCORR-task) in 107 healthy subjects aged 21-50, deriving GSCORR-shift as GSCORR-task minus GSCORR-rest. GSCORR-tr and trGSCORR-shift were also computed on the task residual time series to quantify the impact of the task-related activity during the trials. To test the secondary aim, brain regions were firstly divided in one cluster showing significant task-related activation and one showing significant deactivation across the three trial conditions. Then, correlations between GSCORR-rest/task/shift and activation/deactivation in the two clusters were computed. As sensitivity analysis, GSCORR-shift was computed on the same sample after performing a global signal regression and GSCORR-rest/task/shift were correlated with the task performance. RESULTS Sensory and temporo-parietal regions exhibited a negative GSCORR-shift. Conversely, associative regions (ie. left lingual gyrus, bilateral dorsal posterior cingulate gyrus, cerebellum areas, thalamus, posterolateral parietal cortex) displayed a positive GSCORR-shift (FDR-corrected p < 0.05). GSCORR-shift showed similar patterns to trGSCORR-shift (magnitude increased) and after global signal regression (magnitude decreased). Concerning BOLD changes, Brodmann area 6 and inferior parietal lobule showed activation, while posterior parietal lobule, cuneus, precuneus, middle frontal gyrus showed deactivation (FDR-corrected p < 0.05). No correlations were found between GSCORR-rest/task/shift and beta-coefficients in the activation cluster, although negative correlations were observed between GSCORR-task and GO/STOP-correct deactivation (Pearson rho=-0.299/-0.273; Bonferroni-p < 0.05). Weak associations between GSCORR and task performance were observed (uncorrected p < 0.05). CONCLUSION GSCORR state-dependent reconfiguration indicates a reallocation of functional resources to associative areas during stop-signal task. GSCORR, activation and deactivation may represent distinct proxies of brain states with specific neurofunctional relevance.
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Affiliation(s)
- Stefano Damiani
- Department of Brain and Behavioral Sciences, University of Pavia, Via Bassi 21, Pavia, Italy
| | | | - Livio Tarchi
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
| | - Eleonora Tosi
- Department of Brain and Behavioral Sciences, University of Pavia, Via Bassi 21, Pavia, Italy
| | - Valdo Ricca
- Psychiatry Unit, Department of Health Sciences, University of Florence, Florence, Italy
| | - Andrea Scalabrini
- Department of Human and Social Sciences, University of Bergamo, Bergamo, Italy
| | - Pierluigi Politi
- Department of Brain and Behavioral Sciences, University of Pavia, Via Bassi 21, Pavia, Italy
| | - Paolo Fusar-Poli
- Department of Brain and Behavioral Sciences, University of Pavia, Via Bassi 21, Pavia, Italy; Department of Psychosis Studies, King's College London, London, UK; Outreach and Support in South-London (OASIS) service, South London and Maudlsey (SLaM) NHS Foundation Trust, UK; Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
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Wang Y, Yang C, Li G, Ao Y, Jiang M, Cui Q, Pang Y, Jing X. Frequency-dependent effective connections between local signals and the global brain signal during resting-state. Cogn Neurodyn 2023; 17:555-560. [PMID: 37007197 PMCID: PMC10050607 DOI: 10.1007/s11571-022-09831-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 03/07/2022] [Accepted: 06/04/2022] [Indexed: 11/03/2022] Open
Abstract
The psychological and physiological meanings of resting-state global brain signal (GS) and GS topography have been well confirmed. However, the causal relationship between GS and local signals was largely unknown. Based on the Human Connectome Project dataset, we investigated the effective GS topography using the Granger causality (GC) method. In consistent with GS topography, both effective GS topographies from GS to local signals and from local signals to GS showed greater GC values in sensory and motor regions in most frequency bands, suggesting that the unimodal superiority is an intrinsic architecture of GS topography. However, the significant frequency effect for GC values from GS to local signals was primarily located in unimodal regions and dominated at slow 4 frequency band whereas that from local signals to GS was mainly located in transmodal regions and dominated at slow 6 frequency band, consisting with the opinion that the more integrated the function, the lower the frequency. These findings provided valuable insight for the frequency-dependent effective GS topography, improving the understanding of the underlying mechanism of GS topography. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09831-0.
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Affiliation(s)
- Yifeng Wang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, No.5, Jing’an Road, Chengdu, 610066 China
| | - Chengxiao Yang
- Institute of Brain and Psychological Sciences, Sichuan Normal University, No.5, Jing’an Road, Chengdu, 610066 China
| | - Gen Li
- Institute of Brain and Psychological Sciences, Sichuan Normal University, No.5, Jing’an Road, Chengdu, 610066 China
| | - Yujia Ao
- Institute of Brain and Psychological Sciences, Sichuan Normal University, No.5, Jing’an Road, Chengdu, 610066 China
| | - Muliang Jiang
- First Affiliated Hospital, Guangxi Medical University, Nanning, 530021 China
| | - Qian Cui
- School of Public Affairs and Administration, University of Electronic Science and Technology of China, Chengdu, China
| | - Yajing Pang
- School of Electrical Engineering, Zhengzhou University, Zhengzhou, China
| | - Xiujuan Jing
- Tianfu College of Southwestern University of Finance and Economics, Chengdu, China
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Chen P, Zhao K, Zhang H, Wei Y, Wang P, Wang D, Song C, Yang H, Zhang Z, Yao H, Qu Y, Kang X, Du K, Fan L, Han T, Yu C, Zhou B, Jiang T, Zhou Y, Lu J, Han Y, Zhang X, Liu B, Liu Y. Altered global signal topography in Alzheimer's disease. EBioMedicine 2023; 89:104455. [PMID: 36758481 PMCID: PMC9941064 DOI: 10.1016/j.ebiom.2023.104455] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 12/31/2022] [Accepted: 01/17/2023] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurodegenerative disease associated with widespread disruptions in intrinsic local specialization and global integration in the functional system of the brain. These changes in integration may further disrupt the global signal (GS) distribution, which might represent the local relative contribution to global activity in functional magnetic resonance imaging (fMRI). METHODS fMRI scans from a discovery dataset (n = 809) and a validated dataset (n = 542) were used in the analysis. We investigated the alteration of GS topography using the GS correlation (GSCORR) in patients with mild cognitive impairment (MCI) and AD. The association between GS alterations and functional network properties was also investigated based on network theory. The underlying mechanism of GSCORR alterations was elucidated using imaging-transcriptomics. FINDINGS Significantly increased GS topography in the frontal lobe and decreased GS topography in the hippocampus, cingulate gyrus, caudate, and middle temporal gyrus were observed in patients with AD (Padj < 0.05). Notably, topographical GS changes in these regions correlated with cognitive ability (P < 0.05). The changes in GS topography also correlated with the changes in functional network segregation (ρ = 0.5). Moreover, the genes identified based on GS topographical changes were enriched in pathways associated with AD and neurodegenerative diseases. INTERPRETATION Our findings revealed significant changes in GS topography and its molecular basis, confirming the informative role of GS in AD and further contributing to the understanding of the relationship between global and local neuronal activities in patients with AD. FUNDING Beijing Natural Science Funds for Distinguished Young Scholars, China; Fundamental Research Funds for the Central Universities, China; National Natural Science Foundation, China.
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Affiliation(s)
- Pindong Chen
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Kun Zhao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China; Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing, China
| | - Han Zhang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Yongbin Wei
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital Tianjin University, Tianjin, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Chengyuan Song
- Department of Neurology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Hongwei Yang
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | | | - Hongxiang Yao
- Department of Radiology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Yida Qu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaopeng Kang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Kai Du
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Lingzhong Fan
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Tong Han
- Department of Radiology, Tianjin Huanhu Hospital, Tianjin, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Bo Zhou
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Yuying Zhou
- Department of Neurology, Tianjin Huanhu Hospital Tianjin University, Tianjin, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China; Beijing Institute of Geriatrics, Beijing, China; National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Bing Liu
- State Key Laboratory of Cognition Neuroscience & Learning, Beijing Normal University, Beijing, China
| | - Yong Liu
- Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
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Brown JA, Lee AJ, Pasquini L, Seeley WW. A dynamic gradient architecture generates brain activity states. Neuroimage 2022; 261:119526. [PMID: 35914669 PMCID: PMC9585924 DOI: 10.1016/j.neuroimage.2022.119526] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 07/26/2022] [Accepted: 07/28/2022] [Indexed: 11/24/2022] Open
Abstract
The human brain exhibits a diverse yet constrained range of activity states. While these states can be faithfully represented in a low-dimensional latent space, our understanding of the constitutive functional anatomy is still evolving. Here we applied dimensionality reduction to task-free and task fMRI data to address whether latent dimensions reflect intrinsic systems and if so, how these systems may interact to generate different activity states. We find that each dimension represents a dynamic activity gradient, including a primary unipolar sensory-association gradient underlying the global signal. The gradients appear stable across individuals and cognitive states, while recapitulating key functional connectivity properties including anticorrelation, modularity, and regional hubness. We then use dynamical systems modeling to show that gradients causally interact via state-specific coupling parameters to create distinct brain activity patterns. Together, these findings indicate that a set of dynamic, intrinsic spatial gradients interact to determine the repertoire of possible brain activity states.
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Affiliation(s)
- Jesse A Brown
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA.
| | - Alex J Lee
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Lorenzo Pasquini
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
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Lu X, Zhang JF, Gu F, Zhang HX, Zhang M, Zhang HS, Song RZ, Shi YC, Li K, Wang B, Zhang ZJ, Northoff G. Altered task modulation of global signal topography in the default-mode network of unmedicated major depressive disorder. J Affect Disord 2022; 297:53-61. [PMID: 34610369 DOI: 10.1016/j.jad.2021.09.093] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 09/07/2021] [Accepted: 09/26/2021] [Indexed: 11/17/2022]
Abstract
BACKGROUND Altered global signal (GS) topography features in the resting-state fMRI of major depressive disorder (MDD), showing abnormally strong global signal representation in the default-mode network (DMN). Whether the abnormal local to global change also shapes activity during task states, and how it relates to psychopathological symptoms, e.g., abnormally slow time speed of motor, cognitive, and affective symptoms, remains unknown. METHODS We investigated fMRI-based GS with its topographical representation during task states in unmedicated 51 MDD subjects and 28 healthy subjects. Task-related global signal correlation (GSCORR) was probed by a novel paradigm testing the processing of negative/neutral emotions during different time speeds, i.e., slow and fast. RESULTS We observed a significant interaction between time speed and emotion of GSCORR in various DMN regions in healthy subjects. Next, we showed that MDD exhibits reduced task-related GSCORR in various DMN regions during specifically the fast processing of negative emotions. Finally, we demonstrated that GSCORR in DMN and other brain regions (motor-related regions, inferior frontal cortex) correlated with the degree of psychomotor retardation especially during the fast emotional stimuli. LIMITATIONS The measurement of interoceptive variables like respiration rate or heart rate were not included in our fMRI acquisition. CONCLUSION Together, we demonstrated the functional relevance of GS topography by showing reduced GSCORR in DMN during specifically the fast processing of negative emotions in MDD, suggesting the abnormal slowness, i.e., reduced time speed, to be a key feature of both brain and symptoms in MDD.
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Affiliation(s)
- Xiang Lu
- Department of Neurology of Affiliated ZhongDa Hospital, Institute of Neuropsychiatry and Medical School of Southeast University, Nanjing 210009, Jiangsu Province, China; Royal Ottawa Mental Health Centre, University of Ottawa(,) Institute of Mental Health Research(,) Ottawa(,) Ontario K1Z 7K4, Canada; Department of Neurology, Northern Jiangsu People's Hospital, Clinical Medical College, Yangzhou University(,) Yangzhou 225001, Jiangsu Province, China
| | - Jian-Feng Zhang
- Center for Brain Disorders and Cognitive Sciences(,) Shenzhen University, Shenzhen 518055, Guangdong Province, China
| | - Feng Gu
- Royal Ottawa Mental Health Centre, University of Ottawa(,) Institute of Mental Health Research(,) Ottawa(,) Ontario K1Z 7K4, Canada
| | - Hong-Xing Zhang
- Department of Psychology of Xinxiang Medical University, Xinxiang 453003, Henan Province, China; Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Xinxiang 453002, Henan Province, China
| | - Meng Zhang
- Department of Psychology of Xinxiang Medical University, Xinxiang 453003, Henan Province, China
| | - Hai-San Zhang
- Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Xinxiang 453002, Henan Province, China
| | - Rui-Ze Song
- Department of Neurology of Affiliated ZhongDa Hospital, Institute of Neuropsychiatry and Medical School of Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Ya-Chen Shi
- Department of Neurology of Affiliated ZhongDa Hospital, Institute of Neuropsychiatry and Medical School of Southeast University, Nanjing 210009, Jiangsu Province, China
| | - Kun Li
- Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Xinxiang 453002, Henan Province, China
| | - Bi Wang
- Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Xinxiang 453002, Henan Province, China
| | - Zhi-Jun Zhang
- Department of Neurology of Affiliated ZhongDa Hospital, Institute of Neuropsychiatry and Medical School of Southeast University, Nanjing 210009, Jiangsu Province, China; Department of Psychology of Xinxiang Medical University, Xinxiang 453003, Henan Province, China; Mental Health Center, Zhejiang University School of Medicine, Hangzhou 310013, Zhejiang Province, China; Shenzhen institute of advanced technology, Chinese academy of sciences, Shenzhen 518055, Guangdong Province, China.
| | - Georg Northoff
- Department of Neurology of Affiliated ZhongDa Hospital, Institute of Neuropsychiatry and Medical School of Southeast University, Nanjing 210009, Jiangsu Province, China; Royal Ottawa Mental Health Centre, University of Ottawa(,) Institute of Mental Health Research(,) Ottawa(,) Ontario K1Z 7K4, Canada; Mental Health Center, Zhejiang University School of Medicine, Hangzhou 310013, Zhejiang Province, China; Brain and Mind Research Institute, Centre for Neural Dynamics, Faculty of Medicine, University of Ottawa(,) Ottawa, Ontario K1Z 7K4(,) Canada; Centre for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou 310013, Zhejiang Province, China.
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Wang X, Liao W, Han S, Li J, Wang Y, Zhang Y, Zhao J, Chen H. Frequency-specific altered global signal topography in drug-naïve first-episode patients with adolescent-onset schizophrenia. Brain Imaging Behav 2021; 15:1876-1885. [PMID: 33188473 DOI: 10.1007/s11682-020-00381-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Adolescent-onset schizophrenia (AOS) is a severe neuropsychiatric disease associated with frequency-specific abnormalities across distributed neural systems in a slow rhythm. Recently, functional magnetic resonance imaging (fMRI) studies have determined that the global signal. (GS) is an important source of the local neuronal activity in 0.01-0.1 Hz frequency band. However, it remains unknown whether the effects follow a specific spatially preferential pattern in different frequency bands in schizophrenia. To address this issue, resting-state fMRI data from 39 drug-naïve AOS patients and 31 healthy controls (HCs) were used to assess the changes in GS topography patterns in the slow-4 (0.027-0.073 Hz) and slow-5 bands (0.01-0.027 Hz). Results revealed that GS mainly affects the default mode network (DMN) in slow-4 and sensory regions in the slow-5 band respectively, and GS has a stronger driving effect in the slow-5 band. Moreover, significant frequency-by-group interaction was observed in the frontoparietal network. Compared with HCs, patients with AOS exhibited altered GS topography mainly located in the DMN. Our findings demonstrated that the influence of the GS on brain networks altered in a frequency-specific way in schizophrenia.
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Affiliation(s)
- Xiao Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Wei Liao
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Shaoqiang Han
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Jiao Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Yifeng Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.,MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Yan Zhang
- Key Laboratory for Mental Health of Hunan Province, Mental Health Institute, the Second Xiangya Hospital of Central South University, Changsha, China
| | - Jingping Zhao
- Mental Health Institute, the Second Xiangya Hospital of Central South University, 139, Middle Renmin Road, Changsha, 410011, Hunan, China
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China. .,Radiology department of the First Affiliated Hospital, the Third Military Medical University, Chongqing, 400038, China.
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Kassinopoulos M, Mitsis GD. Physiological noise modeling in fMRI based on the pulsatile component of photoplethysmograph. Neuroimage 2021; 242:118467. [PMID: 34390877 DOI: 10.1016/j.neuroimage.2021.118467] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/21/2021] [Accepted: 08/10/2021] [Indexed: 02/06/2023] Open
Abstract
The blood oxygenation level-dependent (BOLD) contrast mechanism allows the noninvasive monitoring of changes in deoxyhemoglobin content. As such, it is commonly used in functional magnetic resonance imaging (fMRI) to study brain activity since levels of deoxyhemoglobin are indirectly related to local neuronal activity through neurovascular coupling mechanisms. However, the BOLD signal is severely affected by physiological processes as well as motion. Due to this, several noise correction techniques have been developed to correct for the associated confounds. The present study focuses on cardiac pulsatility fMRI confounds, aiming to refine model-based techniques that utilize the photoplethysmograph (PPG) signal. Specifically, we propose a new technique based on convolution filtering, termed cardiac pulsatility model (CPM) and compare its performance with the cardiac-related RETROICOR (Card-RETROICOR), which is a technique commonly used to model fMRI fluctuations due to cardiac pulsatility. Further, we investigate whether variations in the amplitude of the PPG pulses (PPG-Amp) covary with variations in amplitude of pulse-related fMRI fluctuations, as well as with the systemic low frequency oscillations (SLFOs) component of the fMRI global signal (GS - defined as the mean signal across all gray matter voxels). Capitalizing on 3T fMRI data from the Human Connectome Project, CPM was found to explain a significantly larger fraction of the fMRI signal variance compared to Card-RETROICOR, particularly for subjects with larger heart rate variability during the scan. The amplitude of the fMRI pulse-related fluctuations did not covary with PPG-Amp; however, PPG-Amp explained significant variance in the GS that was not attributed to variations in heart rate or breathing patterns. Our results suggest that the proposed approach can model high-frequency fluctuations due to pulsation as well as low-frequency physiological fluctuations more accurately compared to model-based techniques commonly employed in fMRI studies.
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Affiliation(s)
- Michalis Kassinopoulos
- Graduate Program in Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada.
| | - Georgios D Mitsis
- Department of Bioengineering, McGill University, Montreal, QC, Canada
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Krylova M, Alizadeh S, Izyurov I, Teckentrup V, Chang C, van der Meer J, Erb M, Kroemer N, Koenig T, Walter M, Jamalabadi H. Evidence for modulation of EEG microstate sequence by vigilance level. Neuroimage 2020; 224:117393. [PMID: 32971266 DOI: 10.1016/j.neuroimage.2020.117393] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 09/11/2020] [Accepted: 09/16/2020] [Indexed: 12/25/2022] Open
Abstract
The momentary global functional state of the brain is reflected in its electric field configuration and cluster analytical approaches have consistently shown four configurations, referred to as EEG microstate classes A to D. Changes in microstate parameters are associated with a number of neuropsychiatric disorders, task performance, and mental state establishing their relevance for cognition. However, the common practice to use eye-closed resting state data to assess the temporal dynamics of microstate parameters might induce systematic confounds related to vigilance levels. Here, we studied the dynamics of microstate parameters in two independent data sets and showed that the parameters of microstates are strongly associated with vigilance level assessed both by EEG power analysis and fMRI global signal. We found that the duration and contribution of microstate class C, as well as transition probabilities towards microstate class C were positively associated with vigilance, whereas the sign was reversed for microstate classes A and B. Furthermore, in looking for the origins of the correspondence between microstates and vigilance level, we found Granger-causal effects of vigilance levels on microstate sequence parameters. Collectively, our findings suggest that duration and occurrence of microstates have a different origin and possibly reflect different physiological processes. Finally, our findings indicate the need for taking vigilance levels into consideration in resting-sate EEG investigations.
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Affiliation(s)
- Marina Krylova
- Department of Psychiatry and Psychotherapy, Division for Translational Psychiatry, University of Tübingen, Tübingen, Germany; Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743 Jena, Germany
| | - Sarah Alizadeh
- Department of Psychiatry and Psychotherapy, Division for Translational Psychiatry, University of Tübingen, Tübingen, Germany
| | - Igor Izyurov
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743 Jena, Germany; Department of Psychiatry and Psychotherapy, Division for Translational Psychiatry, University of Tübingen, Tübingen, Germany
| | - Vanessa Teckentrup
- Department of Psychiatry and Psychotherapy, Division for Translational Psychiatry, University of Tübingen, Tübingen, Germany
| | - Catie Chang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, USA
| | | | - Michael Erb
- Division of Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
| | - Nils Kroemer
- Department of Psychiatry and Psychotherapy, Division for Translational Psychiatry, University of Tübingen, Tübingen, Germany
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Martin Walter
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Philosophenweg 3, 07743 Jena, Germany; Department of Psychiatry and Psychotherapy, Division for Translational Psychiatry, University of Tübingen, Tübingen, Germany; Clinical Affective Neuroimaging Laboratory, Magdeburg, Germany; Leibniz Institute for Neurobiology, Magdeburg, Germany; Max Planck Institute for biological cybernetics, Tübingen, Germany.
| | - Hamidreza Jamalabadi
- Department of Psychiatry and Psychotherapy, Division for Translational Psychiatry, University of Tübingen, Tübingen, Germany.
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9
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Umeh A, Kumar J, Francis ST, Liddle PF, Palaniyappan L. Global fMRI signal at rest relates to symptom severity in schizophrenia. Schizophr Res 2020; 220:281-282. [PMID: 32222349 DOI: 10.1016/j.schres.2020.03.035] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 02/09/2020] [Accepted: 03/18/2020] [Indexed: 11/25/2022]
Affiliation(s)
| | - Jyothika Kumar
- Division of Psychiatry and Applied Psychology, University of Nottingham, Nottingham, United Kingdom; Precision Imaging, University of Nottingham, Nottingham, United Kingdom
| | - Susan T Francis
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - Peter F Liddle
- Division of Psychiatry and Applied Psychology, University of Nottingham, Nottingham, United Kingdom
| | - Lena Palaniyappan
- Department of Psychiatry, University of Western Ontario, London, Ontario, Canada; Robarts Research Institute, University of Western Ontario, London, Ontario, Canada; Lawson Health Research Institute, London, Ontario, Canada.
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10
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Gotts SJ, Gilmore AW, Martin A. Brain networks, dimensionality, and global signal averaging in resting-state fMRI: Hierarchical network structure results in low-dimensional spatiotemporal dynamics. Neuroimage 2020; 205:116289. [PMID: 31629827 PMCID: PMC6919311 DOI: 10.1016/j.neuroimage.2019.116289] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 10/12/2019] [Accepted: 10/16/2019] [Indexed: 12/11/2022] Open
Abstract
One of the most controversial practices in resting-state fMRI functional connectivity studies is whether or not to regress out the global average brain signal (GS) during artifact removal. Some groups have argued that it is absolutely essential to regress out the GS in order to fully remove head motion, respiration, and other global imaging artifacts. Others have argued that removing the GS distorts the resulting correlation matrices and inappropriately alters the results of group comparisons and relationships to behavior. At the core of this argument is the assessment of dimensionality in terms of the number of brain networks with uncorrelated time series. If the dimensionality is high, then the distortions due to GS removal could be effectively negligible. In the current paper, we examine the dimensionality of resting-state fMRI data using principal component analyses (PCA) and network clustering analyses. In two independent datasets (Set 1: N = 62, Set 2: N = 32), scree plots of the eigenvalues level off at or prior to 10 principal components, with prominent elbows at 3 and 7 components. While network clustering analyses have previously demonstrated that numerous networks can be distinguished with high thresholding of the voxel-wise correlation matrices, lower thresholding reveals a lower-dimensional hierarchical structure, with the first prominent branch at 2 networks (corresponding to the previously described "task-positive"/"task-negative" distinction) and further stable subdivisions at 4, 7 and 17. Since inter-correlated time series within these larger branches do not cancel to zero when averaged, the hierarchical nature of the correlation structure results in low effective dimensionality. Consistent with this, partial correlation analyses revealed that network-specific variance remains present in the GS at each level of the hierarchy, accounting for at least 14-18% of the overall GS variance in each dataset. These results demonstrate that GS regression is expected to remove substantial portions of network-specific brain signals along with artifacts, not simply whole-brain signals corresponding to arousal levels. We highlight alternative means of controlling for residual global artifacts when not removing the GS.
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Affiliation(s)
- Stephen J Gotts
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
| | - Adrian W Gilmore
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Alex Martin
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
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11
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Abstract
A hallmark of non-rapid eye movement (REM) sleep is the decreased brain activity as measured by global reductions in cerebral blood flow, oxygen metabolism, and glucose metabolism. It is unknown whether the blood oxygen level dependent (BOLD) signal undergoes similar changes. Here we show that, in contrast to the decreases in blood flow and metabolism, the mean global BOLD signal increases with sleep depth in a regionally non-uniform manner throughout gray matter. We relate our findings to the circulatory and metabolic processes influencing the BOLD signal and conclude that because oxygen consumption decreases proportionately more than blood flow in sleep, the resulting decrease in paramagnetic deoxyhemoglobin accounts for the increase in mean global BOLD signal.
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Affiliation(s)
- Mark P McAvoy
- Department of Radiology, Washington University, Saint Louis, MO, USA
| | - Enzo Tagliazucchi
- PICNIC Lab, Institut du Cerveau et de la Moelle épinière, Paris, France
| | - Helmut Laufs
- Department of Neurology, Brain Imaging Center, Goethe-Universität Frankfurt am Main, Frankfurt, Germany
- Department of Neurology, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Marcus E Raichle
- Department of Radiology, Washington University, Saint Louis, MO, USA
- Alan and Edith L. Wolff Distinguished Professor of Medicine, Washington University, Saint Louis, MO, USA
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12
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Nalci A, Luo W, Liu TT. Nuisance effects in inter-scan functional connectivity estimates before and after nuisance regression. Neuroimage 2019; 202:116005. [PMID: 31336189 DOI: 10.1016/j.neuroimage.2019.07.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2019] [Revised: 06/06/2019] [Accepted: 07/08/2019] [Indexed: 12/13/2022] Open
Abstract
In resting-state functional MRI, the correlation between blood-oxygenation-level-dependent (BOLD) signals across brain regions is used to estimate the functional connectivity (FC) of the brain. FC estimates are prone to the influence of nuisance factors including scanner-related artifacts and physiological modulations of the BOLD signal. Nuisance regression is widely performed to reduce the effect of nuisance factors on FC estimates on a per-scan basis. However, a dedicated analysis of nuisance effects on the variability of FC metrics across a collection of scans has been lacking. This work investigates the effects of nuisance factors on the variability of FC estimates across a collection of scans both before and after nuisance regression. Inter-scan variations in FC estimates are shown to be significantly correlated with the geometric norms of various nuisance terms, including head motion measurements, signals derived from white-matter and cerebrospinal regions, and the whole-brain global signal (GS) both before and after nuisance regression. In addition, it is shown that GS regression (GSR) can introduce GS norm-related fluctuations that are negatively correlated with inter-scan FC estimates. The empirical results are shown to be largely consistent with the predictions of a theoretical framework previously developed for the characterization of dynamic FC measures. This work shows that caution must be exercised when interpreting inter-scan FC measures across scans both before and after nuisance regression.
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Affiliation(s)
- Alican Nalci
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA, 92093, USA; Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
| | - Wenjing Luo
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA, 92093, USA
| | - Thomas T Liu
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA, 92093, USA; Departments of Radiology, Psychiatry, and Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
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13
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Zhu J, Cai H, Yuan Y, Yue Y, Jiang D, Chen C, Zhang W, Zhuo C, Yu Y. Variance of the global signal as a pretreatment predictor of antidepressant treatment response in drug-naïve major depressive disorder. Brain Imaging Behav 2018; 12:1768-1774. [PMID: 29473140 PMCID: PMC6302054 DOI: 10.1007/s11682-018-9845-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Several behavioral and neuroimaging markers could be used to predict eventual antidepressant medication (ADM) outcomes in patients with major depressive disorder (MDD). However, these predictors are either subjective or complex, which has limited their clinical use. Thus, we aimed to identify an objective and easy-to-get marker to predict early therapeutic efficacy. Forty-seven drug-naïve patients with MDD and 47 age-, gender- and education-matched healthy controls underwent resting-state functional magnetic resonance imaging (fMRI) scans. We calculated the variable coefficient (VC) of the global signal for each subject. Baseline Hamilton Rating Scale for Depression (HRSD) score and that after 2 weeks of ADM were assessed for patients. Although there was no difference in VC between patients with MDD and healthy controls, we found a significant positive correlation between the VC and the decline rate of HRSD scores in the patients. Compared with the non-responding depression (NRD) group, the treatment-responsive depression (TRD) group had a higher VC. Receiver operator characteristic curve analysis revealed that the VC exhibited a good ability to differentiate TRD from NRD. In addition, the linear and logistic regression analyses showed that the VC was a significant predictor of the decline rate of HRSD scores and the antidepressant treatment response. These findings suggest that variance of the global signal may serve as a useful marker to help clinicians find an appropriate drug for individuals with MDD at the earliest opportunity and then further to facilitate personalized therapy.
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Affiliation(s)
- Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Huanhuan Cai
- Medical Imaging Center, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China
| | - Yonggui Yuan
- Department of Psychosomatics & Psychiatry, Institute of Psychosomatics, Zhongda Hospital, Medical School of Southeast University, Nanjing, China
| | - Yingying Yue
- Department of Psychosomatics & Psychiatry, Institute of Psychosomatics, Zhongda Hospital, Medical School of Southeast University, Nanjing, China
| | - Deguo Jiang
- Department of Psychiatry, Wenzhou Seventh People's Hospital, Wenzhou, China
| | - Ce Chen
- Department of Psychiatry, Wenzhou Seventh People's Hospital, Wenzhou, China
| | - Wei Zhang
- Department of Psychiatry, Wenzhou Seventh People's Hospital, Wenzhou, China
| | - Chuanjun Zhuo
- Department of Psychiatry, Wenzhou Seventh People's Hospital, Wenzhou, China.
- Department of Psychiatry, Tianjin Mental Health Center, Tianjin, China.
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
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14
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Huang X, Long Z, Lei X. Electrophysiological signatures of the resting-state fMRI global signal: A simultaneous EEG-fMRI study. J Neurosci Methods 2018; 311:351-359. [PMID: 30236777 DOI: 10.1016/j.jneumeth.2018.09.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Revised: 09/10/2018] [Accepted: 09/14/2018] [Indexed: 12/24/2022]
Abstract
BACKGROUND The global signal of resting-state functional magnetic resonance imaging (fMRI) constitutes an intrinsic fluctuation and presents an opportunity to characterize and understand the activity of the whole brain. Recently, evidence that the global signal contains neurophysiologic information has been growing, but the global signal of electroencephalography (EEG) has never been determined. NEW METHODS We developed a new method to obtain the EEG global signal. The EEG global signal was reconstructed by the reference electrode standardization technique and represented the outer cortical electrophysiological activity. To investigate its relationship with the global signal of resting-state fMRI, a simultaneous EEG-fMRI signal was recorded, and this was analyzed in 24 subjects. RESULTS We found that the global signal of resting-state fMRI showed a positive correlation with power fluctuations of the EEG global signal in the γ band (30-45 Hz) and a negative correlation in the low-frequency band (4-20 Hz). COMPARISON WITH EXISTING METHOD(S) Compared with the global signal of fMRI, the global signal of EEG provides more temporal information about outer cortical neural activity. CONCLUSIONS These results provide new evidence for the electrophysiology information of the global signal of resting-state fMRI. More importantly, due to its high correlation with the fMRI global signal, the EEG global signal may serve as a new biomarker for neurological disorders.
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Affiliation(s)
- Xiaoli Huang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality of Ministry of Education, Chongqing, 400715, China
| | - Zhiliang Long
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality of Ministry of Education, Chongqing, 400715, China
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, China; Key Laboratory of Cognition and Personality of Ministry of Education, Chongqing, 400715, China; Key Laboratory for NeuroInformation of Ministry of Education, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 610054, China; Chongqing Collaborative Innovation Center for Brain Science, Chongqing, 400715, China.
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15
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Patanaik A, Tandi J, Ong JL, Wang C, Zhou J, Chee MWL. Dynamic functional connectivity and its behavioral correlates beyond vigilance. Neuroimage 2018; 177:1-10. [PMID: 29704612 DOI: 10.1016/j.neuroimage.2018.04.049] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 04/11/2018] [Accepted: 04/21/2018] [Indexed: 11/22/2022] Open
Abstract
Fluctuations in resting-state functional connectivity and global signal have been found to correspond with vigilance fluctuations, but their associations with other behavioral measures are unclear. We evaluated 52 healthy adolescents after a week of adequate sleep followed by five nights of sleep restriction to unmask inter-individual differences in cognition and mood. Resting state scans obtained at baseline only, analyzed using sliding window analysis, consistently yielded two polar dynamic functional connectivity states (DCSs) corresponding to previously reported 'low arousal' and 'high arousal' states. We found that the relative temporal preponderance of two dynamic connectivity states (DCS) in well-rested participants, indexed by a median split of participants, based on the relative time spent in these DCS, revealed highly significant group differences in vigilance at baseline and its decline following multiple nights of sleep restriction. Group differences in processing speed and working memory following manipulation but not at baseline suggest utility of DCS in predicting cognitive vulnerabilities unmasked by a stressor like sleep restriction. DCS temporal predominance was uninformative about mood and sleepiness speaking to specificity in its behavioral predictions. Global signal fluctuation provided information confined to vigilance. This appears to be related to head motion, which increases during periods of low arousal.
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16
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Falahpour M, Chang C, Wong CW, Liu TT. Template-based prediction of vigilance fluctuations in resting-state fMRI. Neuroimage 2018; 174:317-327. [PMID: 29548849 DOI: 10.1016/j.neuroimage.2018.03.012] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Revised: 01/31/2018] [Accepted: 03/05/2018] [Indexed: 01/12/2023] Open
Abstract
Changes in vigilance or alertness during a typical resting state fMRI scan are inevitable and have been found to affect measures of functional brain connectivity. Since it is not often feasible to monitor vigilance with EEG during fMRI scans, it would be of great value to have methods for estimating vigilance levels from fMRI data alone. A recent study, conducted in macaque monkeys, proposed a template-based approach for fMRI-based estimation of vigilance fluctuations. Here, we use simultaneously acquired EEG/fMRI data to investigate whether the same template-based approach can be employed to estimate vigilance fluctuations of awake humans across different resting-state conditions. We first demonstrate that the spatial pattern of correlations between EEG-defined vigilance and fMRI in our data is consistent with the previous literature. Notably, however, we observed a significant difference between the eyes-closed (EC) and eyes-open (EO) conditions, finding stronger negative correlations with vigilance in regions forming the default mode network and higher positive correlations in thalamus and insula in the EC condition when compared to the EO condition. Taking these correlation maps as "templates" for vigilance estimation, we found that the template-based approach produced fMRI-based vigilance estimates that were significantly correlated with EEG-based vigilance measures, indicating its generalizability from macaques to humans. We also demonstrate that the performance of this method was related to the overall amount of variability in a subject's vigilance state, and that the template-based approach outperformed the use of the global signal as a vigilance estimator. In addition, we show that the template-based approach can be used to estimate the variability across scans in the amplitude of the vigilance fluctuations. We discuss the benefits and tradeoffs of using the template-based approach in future fMRI studies.
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Affiliation(s)
- Maryam Falahpour
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA 92093, USA.
| | - Catie Chang
- National Institutes of Health, Bethesda, MD 20892, USA.
| | - Chi Wah Wong
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA 92093, USA.
| | - Thomas T Liu
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA 92093, USA; Department of Radiology, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA; Department of Psychiatry, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA; Department of Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA.
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17
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Byrge L, Kennedy DP. Identifying and characterizing systematic temporally-lagged BOLD artifacts. Neuroimage 2018; 171:376-92. [PMID: 29288128 DOI: 10.1016/j.neuroimage.2017.12.082] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2017] [Revised: 12/20/2017] [Accepted: 12/22/2017] [Indexed: 01/08/2023] Open
Abstract
Residual noise in the BOLD signal remains problematic for fMRI - particularly for techniques such as functional connectivity, where findings can be spuriously influenced by noise sources that can covary with individual differences. Many such potential noise sources - for instance, motion and respiration - can have a temporally lagged effect on the BOLD signal. Thus, here we present a tool for assessing residual lagged structure in the BOLD signal that is associated with nuisance signals, using a construction similar to a peri-event time histogram. Using this method, we find that framewise displacements - both large and very small - were followed by structured, prolonged, and global changes in the BOLD signal that depend on the magnitude of the preceding displacement and extend for tens of seconds. This residual lagged BOLD structure was consistent across datasets, and independently predicted considerable variance in the global cortical signal (as much as 30-40% in some subjects). Mean functional connectivity estimates varied similarly as a function of displacements occurring many seconds in the past, even after strict censoring. Similar patterns of residual lagged BOLD structure were apparent following respiratory fluctuations (which covaried with framewise displacements), implicating respiration as one likely mechanism underlying the displacement-linked structure observed. Global signal regression largely attenuates this artifactual structure. These findings suggest the need for caution in interpreting results of individual difference studies where noise sources might covary with the individual differences of interest, and highlight the need for further development of preprocessing techniques for mitigating such structure in a more nuanced and targeted manner.
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18
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McAvoy M, Mitra A, Tagliazucchi E, Laufs H, Raichle ME. Mapping visual dominance in human sleep. Neuroimage 2017; 150:250-61. [PMID: 28232191 DOI: 10.1016/j.neuroimage.2017.02.053] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2016] [Revised: 02/15/2017] [Accepted: 02/19/2017] [Indexed: 12/19/2022] Open
Abstract
Sleep is a universal behavior, essential for humans and animals alike to survive. Its importance to a person's physical and mental health cannot be overstated. Although lateralization of function is well established in the lesion, split-brain and task based neuroimaging literature, and more recently in functional imaging studies of spontaneous fluctuations of the fMRI BOLD signal during wakeful rest, it is unknown if these asymmetries are present during sleep. We investigated hemispheric asymmetries in the global brain signal during non-REM sleep. Here we show that increasing sleep depth is accompanied by an increasing rightward asymmetry of regions in visual cortex including primary bilaterally and in the right hemisphere along the lingual gyrus and middle temporal cortex. In addition, left hemisphere language regions largely maintained their leftward asymmetry during sleep. Right hemisphere attention related regions expressed a more complicated relation with some regions maintaining a rightward asymmetry while this was lost in others. These results suggest that asymmetries in the human brain are state dependent.
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19
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Abstract
The global signal is widely used as a regressor or normalization factor for removing the effects of global variations in the analysis of functional magnetic resonance imaging (fMRI) studies. However, there is considerable controversy over its use because of the potential bias that can be introduced when it is applied to the analysis of both task-related and resting-state fMRI studies. In this paper we take a closer look at the global signal, examining in detail the various sources that can contribute to the signal. For the most part, the global signal has been treated as a nuisance term, but there is growing evidence that it may also contain valuable information. We also examine the various ways that the global signal has been used in the analysis of fMRI data, including global signal regression, global signal subtraction, and global signal normalization. Furthermore, we describe new ways for understanding the effects of global signal regression and its relation to the other approaches.
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Affiliation(s)
- Thomas T Liu
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA 92093, United States; Departments of Radiology, Psychiatry, and Bioengineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, United States.
| | - Alican Nalci
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA 92093, United States; Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, United States.
| | - Maryam Falahpour
- Center for Functional MRI, University of California San Diego, 9500 Gilman Drive MC 0677, La Jolla, CA 92093, United States.
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20
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Liu P, Li Y, Pinho M, Park DC, Welch BG, Lu H. Cerebrovascular reactivity mapping without gas challenges. Neuroimage 2017; 146:320-6. [PMID: 27888058 DOI: 10.1016/j.neuroimage.2016.11.054] [Citation(s) in RCA: 77] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Revised: 11/16/2016] [Accepted: 11/21/2016] [Indexed: 11/22/2022] Open
Abstract
Cerebrovascular reactivity (CVR), the ability of cerebral vessels to dilate or constrict, has been shown to provide valuable information in the diagnosis and treatment evaluation of patients with various cerebrovascular conditions. CVR mapping is typically performed using hypercapnic gas inhalation as a vasoactive challenge while collecting BOLD images, but the inherent need of gas inhalation and the associated apparatus setup present a practical obstacle in applying it in routine clinical use. Therefore, we aimed to develop a new method to map CVR using resting-state BOLD data without the need of gas inhalation. This approach exploits the natural variation in respiration and measures its influence on BOLD MRI signal. In this work, we first identified a surrogate of the arterial CO2 fluctuation during spontaneous breathing from the global BOLD signal. Second, we tested the feasibility and reproducibility of the proposed approach to use the above-mentioned surrogate as a regressor to estimate voxel-wise CVR. Third, we validated the "resting-state CVR map" with a conventional CVR map obtained with hypercapnic gas inhalation in healthy volunteers. Finally, we tested the utility of this new approach in detecting abnormal CVR in a group of patients with Moyamoya disease, and again validated the results using the conventional gas inhalation method. Our results showed that global BOLD signal fluctuation in the frequency range of 0.02-0.04Hz contains the most prominent contribution from natural variation in arterial CO2. The CVR map calculated using this signal as a regressor is reproducible across runs (ICC=0.91±0.06), and manifests a strong spatial correlation with results measured with a conventional hypercapnia-based method in healthy subjects (r=0.88, p<0.001). We also found that resting-state CVR was able to identify vasodilatory deficit in patients with steno-occlusive disease, the spatial pattern of which matches that obtained using the conventional gas method (r=0.71±0.18). These results suggest that CVR obtained with resting-state BOLD may be a useful alternative in detecting vascular deficits in clinical applications when gas challenge is not feasible.
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21
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Mayhew SD, Mullinger KJ, Ostwald D, Porcaro C, Bowtell R, Bagshaw AP, Francis ST. Global signal modulation of single-trial fMRI response variability: Effect on positive vs negative BOLD response relationship. Neuroimage 2016; 133:62-74. [PMID: 26956909 DOI: 10.1016/j.neuroimage.2016.02.077] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2015] [Revised: 02/22/2016] [Accepted: 02/29/2016] [Indexed: 01/25/2023] Open
Abstract
In functional magnetic resonance imaging (fMRI), the relationship between positive BOLD responses (PBRs) and negative BOLD responses (NBRs) to stimulation is potentially informative about the balance of excitatory and inhibitory brain responses in sensory cortex. In this study, we performed three separate experiments delivering visual, motor or somatosensory stimulation unilaterally, to one side of the sensory field, to induce PBR and NBR in opposite brain hemispheres. We then assessed the relationship between the evoked amplitudes of contralateral PBR and ipsilateral NBR at the level of both single-trial and average responses. We measure single-trial PBR and NBR peak amplitudes from individual time-courses, and show that they were positively correlated in all experiments. In contrast, in the average response across trials the absolute magnitudes of both PBR and NBR increased with increasing stimulus intensity, resulting in a negative correlation between mean response amplitudes. Subsequent analysis showed that the amplitude of single-trial PBR was positively correlated with the BOLD response across all grey-matter voxels and was not specifically related to the ipsilateral sensory cortical response. We demonstrate that the global component of this single-trial response modulation could be fully explained by voxel-wise vascular reactivity, the BOLD signal standard deviation measured in a separate resting-state scan (resting state fluctuation amplitude, RSFA). However, bilateral positive correlation between PBR and NBR regions remained. We further report that modulations in the global brain fMRI signal cannot fully account for this positive PBR-NBR coupling and conclude that the local sensory network response reflects a combination of superimposed vascular and neuronal signals. More detailed quantification of physiological and noise contributions to the BOLD signal is required to fully understand the trial-by-trial PBR and NBR relationship compared with that of average responses.
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Affiliation(s)
- S D Mayhew
- Birmingham University Imaging Centre (BUIC), School of Psychology, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.
| | - K J Mullinger
- Birmingham University Imaging Centre (BUIC), School of Psychology, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK; Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - D Ostwald
- Arbeitsbereich Computational Cognitive Neuroscience, Department of Education and Psychology, Free University Berlin, Berlin, Germany; Center for Adaptive Rationality (ARC), Max-Planck-Institute for Human Development, Berlin, Germany
| | - C Porcaro
- Laboratory of Electrophysiology for Translational Neuroscience (LET'S) - ISTC - CNR, Department of Neuroscience, Fatebenefratelli Hospital Isola Tiberina, Rome, Italy; Institute of Neuroscience, Newcastle University, Newcastle upon Tyne, UK; Department of Information Engineering,Università Politecnica delle Marche, Ancona, Italy
| | - R Bowtell
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
| | - A P Bagshaw
- Birmingham University Imaging Centre (BUIC), School of Psychology, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - S T Francis
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK
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22
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Wong CW, DeYoung PN, Liu TT. Differences in the resting-state fMRI global signal amplitude between the eyes open and eyes closed states are related to changes in EEG vigilance. Neuroimage 2015; 124:24-31. [PMID: 26327245 DOI: 10.1016/j.neuroimage.2015.08.053] [Citation(s) in RCA: 75] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2015] [Revised: 08/21/2015] [Accepted: 08/22/2015] [Indexed: 12/24/2022] Open
Abstract
In resting-state functional connectivity magnetic resonance imaging (fcMRI) studies, measures of functional connectivity are often calculated after the removal of a global mean signal component. While the application of the global signal regression approach has been shown to reduce the influence of physiological artifacts and enhance the detection of functional networks, there is considerable controversy regarding its use as the method can lead to significant bias in the resultant connectivity measures. In addition, evidence from recent studies suggests that the global signal is linked to neural activity and may carry clinically relevant information. For instance, in a prior study we found that the amplitude of the global signal was negatively correlated with EEG measures of vigilance across subjects and experimental runs. Furthermore, caffeine-related decreases in global signal amplitude were associated with increases in EEG vigilance. In this study, we extend the prior work by examining measures of global signal amplitude and EEG vigilance under eyes-closed (EC) and eyes-open (EO) resting-state conditions. We show that changes (EO minus EC) in the global signal amplitude are negatively correlated with the associated changes in EEG vigilance. The slope of this EO-EC relation is comparable with the slope of the previously reported relation between caffeine-related changes in the global signal amplitude and EEG vigilance. Our findings provide further support for a basic relationship between global signal amplitude and EEG vigilance.
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Affiliation(s)
- Chi Wah Wong
- Center for Functional Magnetic Resonance Imaging, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California San Diego, La Jolla, CA, USA.
| | - Pamela N DeYoung
- Division of Pulmonary and Critical Care Medicine, University of California San Diego, La Jolla, CA, USA; Division of Sleep Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Thomas T Liu
- Center for Functional Magnetic Resonance Imaging, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California San Diego, La Jolla, CA, USA; Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
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Wong CW, Olafsson V, Tal O, Liu TT. The amplitude of the resting-state fMRI global signal is related to EEG vigilance measures. Neuroimage 2013; 83:983-90. [PMID: 23899724 DOI: 10.1016/j.neuroimage.2013.07.057] [Citation(s) in RCA: 185] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2013] [Revised: 06/18/2013] [Accepted: 07/20/2013] [Indexed: 10/26/2022] Open
Abstract
In resting-state functional magnetic resonance imaging (fMRI), functional connectivity measures can be influenced by the presence of a strong global component. A widely used pre-processing method for reducing the contribution of this component is global signal regression, in which a global mean time series signal is projected out of the fMRI time series data prior to the computation of connectivity measures. However, the use of global signal regression is controversial because the method can bias the correlation values to have an approximately zero mean and may in some instances create artifactual negative correlations. In addition, while many studies treat the global signal as a non-neural confound that needs to be removed, evidence from electrophysiological and fMRI measures in primates suggests that the global signal may contain significant neural correlates. In this study, we used simultaneously acquired fMRI and electroencephalographic (EEG) measures of resting-state activity to assess the relation between the fMRI global signal and EEG measures of vigilance in humans. We found that the amplitude of the global signal (defined as the standard deviation of the global signal) exhibited a significant negative correlation with EEG vigilance across subjects studied in the eyes-closed condition. In addition, increases in EEG vigilance due to the ingestion of caffeine were significantly associated with both a decrease in global signal amplitude and an increase in the average level of anti-correlation between the default mode network and the task-positive network.
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Affiliation(s)
- Chi Wah Wong
- Center for Functional Magnetic Resonance Imaging, University of California San Diego, 9500 Gilman Drive, MC 0677, La Jolla, CA 92093-0677, USA; Department of Radiology, University of California San Diego, 9500 Gilman Drive, MC 0677, La Jolla, CA 92093-0677, USA.
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Thompson GJ, Merritt MD, Pan WJ, Magnuson ME, Grooms JK, Jaeger D, Keilholz SD. Neural correlates of time-varying functional connectivity in the rat. Neuroimage 2013; 83:826-36. [PMID: 23876248 DOI: 10.1016/j.neuroimage.2013.07.036] [Citation(s) in RCA: 92] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2013] [Revised: 07/10/2013] [Accepted: 07/13/2013] [Indexed: 01/27/2023] Open
Abstract
Functional connectivity between brain regions, measured with resting state functional magnetic resonance imaging, holds great potential for understanding the basis of behavior and neuropsychiatric diseases. Recently it has become clear that correlations between the blood oxygenation level dependent (BOLD) signals from different areas vary over the course of a typical scan (6-10 min in length), though the changes are obscured by standard methods of analysis that assume the relationships are stationary. Unfortunately, because similar variability is observed in signals that share no temporal information, it is unclear which dynamic changes are related to underlying neural events. To examine this question, BOLD data were recorded simultaneously with local field potentials (LFP) from interhemispheric primary somatosensory cortex (SI) in anesthetized rats. LFP signals were converted into band-limited power (BLP) signals including delta, theta, alpha, beta and gamma. Correlation between signals from interhemispheric SI was performed in sliding windows to produce signals of correlation over time for BOLD and each BLP band. Both BOLD and BLP signals showed large changes in correlation over time and the changes in BOLD were significantly correlated to the changes in BLP. The strongest relationship was seen when using the theta, beta and gamma bands. Interestingly, while steady-state BOLD and BLP correlate with the global fMRI signal, dynamic BOLD becomes more like dynamic BLP after the global signal is regressed. As BOLD sliding window connectivity is partially reflecting underlying LFP changes, the present study suggests it may be a valuable method of studying dynamic changes in brain states.
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Affiliation(s)
- Garth John Thompson
- Georgia Institute of Technology and Emory University, Biomedical Engineering, Atlanta, GA, USA.
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