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Yang Y, Tang D, Wang Z, Liu Y, Chen F, Jie B, Ni T, Xu C, Li J, Wang C. Identification of high-functioning autism spectrum disorders based on gray-white matter functional network connectivity. J Psychiatr Res 2024; 178:107-113. [PMID: 39128219 DOI: 10.1016/j.jpsychires.2024.08.006] [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: 11/28/2023] [Revised: 08/04/2024] [Accepted: 08/05/2024] [Indexed: 08/13/2024]
Abstract
In the field of autism spectrum disorder (ASD), research on functional connectivity between gray matter and white matter remains under-researched. To address this gap, this study innovatively introduced a nested cross-validation method that integrates gray-white matter functional connectivity with an F-Score algorithm. This method calculates the correlation based on signals extracted from functional magnetic resonance imaging data using gray matter and white matter brain region templates. After applying the method to a New York University Langone Medical Center dataset consisting of 55 individuals with high-functioning ASD and 52 healthy subjects, we achieved a classification accuracy of 72.94%. This study found abnormal functional connectivity, primarily involving the left anterior prefrontal cortex and right superior corona radiata, left retrosplenial cortex and left superior corona radiata, as well as the left ventral anterior cingulate cortex and body of corpus callosum. Besides, we discovered that these abnormal connections are closely related to social impairment and restrictive and repetitive behaviors in ASD. In conclusion, this study provides a gray-white matter functional connectivity perspective for the diagnosis and understanding of ASD.
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Affiliation(s)
- Yang Yang
- School of Computer and Information, Anhui Normal University, WuHu, 241002, Anhui, China; Anhui Engineering Research Center of Medical Big Data Intelligent System, WuHu, 241002, Anhui, China
| | - Detao Tang
- School of Computer and Information, Anhui Normal University, WuHu, 241002, Anhui, China; Anhui Engineering Research Center of Medical Big Data Intelligent System, WuHu, 241002, Anhui, China
| | - Zhiwei Wang
- School of Computer and Information, Anhui Normal University, WuHu, 241002, Anhui, China; Anhui Engineering Research Center of Medical Big Data Intelligent System, WuHu, 241002, Anhui, China
| | - Yifei Liu
- School of Computer and Information, Anhui Normal University, WuHu, 241002, Anhui, China; Anhui Engineering Research Center of Medical Big Data Intelligent System, WuHu, 241002, Anhui, China
| | - Fulong Chen
- School of Computer and Information, Anhui Normal University, WuHu, 241002, Anhui, China; Anhui Engineering Research Center of Medical Big Data Intelligent System, WuHu, 241002, Anhui, China.
| | - Biao Jie
- School of Computer and Information, Anhui Normal University, WuHu, 241002, Anhui, China; Anhui Engineering Research Center of Medical Big Data Intelligent System, WuHu, 241002, Anhui, China
| | - Tianjiao Ni
- School of Computer and Information, Anhui Normal University, WuHu, 241002, Anhui, China; Anhui Engineering Research Center of Medical Big Data Intelligent System, WuHu, 241002, Anhui, China
| | - Chenglong Xu
- School of Computer and Information, Anhui Normal University, WuHu, 241002, Anhui, China; Anhui Engineering Research Center of Medical Big Data Intelligent System, WuHu, 241002, Anhui, China
| | - Jintao Li
- School of Computer and Information, Anhui Normal University, WuHu, 241002, Anhui, China; Anhui Engineering Research Center of Medical Big Data Intelligent System, WuHu, 241002, Anhui, China
| | - Chao Wang
- School of Computer and Information, Anhui Normal University, WuHu, 241002, Anhui, China; Anhui Engineering Research Center of Medical Big Data Intelligent System, WuHu, 241002, Anhui, China
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Lou W, Li X, Jin R, Peng W. Time-varying phase synchronization of resting-state functional magnetic resonance imaging reveals a shift toward self-referential processes during sustained pain. Pain 2024; 165:1493-1504. [PMID: 38193830 DOI: 10.1097/j.pain.0000000000003152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 11/20/2023] [Indexed: 01/10/2024]
Abstract
ABSTRACT Growing evidence has suggested that time-varying functional connectivity between different brain regions might underlie the dynamic experience of pain. This study used a novel, data-driven framework to characterize the dynamic interactions of large-scale brain networks during sustained pain by estimating recurrent patterns of phase-synchronization. Resting-state functional magnetic resonance imaging signals were collected from 50 healthy participants before (once) and after (twice) the onset of sustained pain that was induced by topical application of capsaicin cream. We first decoded the instantaneous phase of neural activity and then applied leading eigenvector dynamic analysis on the time-varying phase-synchronization. We identified 3 recurrent brain states that show distinctive phase-synchronization. The presence of state 1, characterized by phase-synchronization between the default mode network and auditory, visual, and sensorimotor networks, together with transitions towards this brain state, increased during sustained pain. These changes can account for the perceived pain intensity and reported unpleasantness induced by capsaicin application. In contrast, state 3, characterized by phase-synchronization between the cognitive control network and sensory networks, decreased after the onset of sustained pain. These results are indicative of a shift toward internally directed self-referential processes (state 1) and away from externally directed cognitive control processes (state 3) during sustained pain.
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Affiliation(s)
- Wutao Lou
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Xiaoyun Li
- School of Psychology, Shenzhen University, Shenzhen, Guangdong, China
| | - Richu Jin
- Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Weiwei Peng
- School of Psychology, Shenzhen University, Shenzhen, Guangdong, China
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Sengupta A, Mishra A, Wang F, Chen LM, Gore JC. Characteristic BOLD signals are detectable in white matter of the spinal cord at rest and after a stimulus. Proc Natl Acad Sci U S A 2024; 121:e2316117121. [PMID: 38776372 PMCID: PMC11145258 DOI: 10.1073/pnas.2316117121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 02/16/2024] [Indexed: 05/25/2024] Open
Abstract
We report the reliable detection of reproducible patterns of blood-oxygenation-level-dependent (BOLD) MRI signals within the white matter (WM) of the spinal cord during a task and in a resting state. Previous functional MRI studies have shown that BOLD signals are robustly detectable not only in gray matter (GM) in the brain but also in cerebral WM as well as the GM within the spinal cord, but similar signals in WM of the spinal cord have been overlooked. In this study, we detected BOLD signals in the WM of the spinal cord in squirrel monkeys and studied their relationships with the locations and functions of ascending and descending WM tracts. Tactile sensory stimulus -evoked BOLD signal changes were detected in the ascending tracts of the spinal cord using a general-linear model. Power spectral analysis confirmed that the amplitude at the fundamental frequency of the response to a periodic stimulus was significantly higher in the ascending tracts than the descending ones. Independent component analysis of resting-state signals identified coherent fluctuations from eight WM hubs which correspond closely to the known anatomical locations of the major WM tracts. Resting-state analyses showed that the WM hubs exhibited correlated signal fluctuations across spinal cord segments in reproducible patterns that correspond well with the known neurobiological functions of WM tracts in the spinal cord. Overall, these findings provide evidence of a functional organization of intraspinal WM tracts and confirm that they produce hemodynamic responses similar to GM both at baseline and under stimulus conditions.
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Affiliation(s)
- Anirban Sengupta
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37235
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN37235
| | - Arabinda Mishra
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37235
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN37235
| | - Feng Wang
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37235
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN37235
| | - Li Min Chen
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37235
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN37235
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN37235
| | - John C. Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37235
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN37235
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN37235
- Department of Physics and Astronomy, Vanderbilt University, Nashville, TN37235
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Gong ZQ, Zuo XN. Connectivity gradients in spontaneous brain activity at multiple frequency bands. Cereb Cortex 2023; 33:9718-9728. [PMID: 37381580 PMCID: PMC10656950 DOI: 10.1093/cercor/bhad238] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 06/11/2023] [Accepted: 06/12/2023] [Indexed: 06/30/2023] Open
Abstract
The intrinsic organizational structure of the brain is reflected in spontaneous brain oscillations. Its functional integration and segregation hierarchy have been discovered in space by leveraging gradient approaches to low-frequency functional connectivity. This hierarchy of brain oscillations has not yet been fully understood, since previous studies have mainly concentrated on the brain oscillations from a single limited frequency range (~ 0.01-0.1 Hz). In this work, we extended the frequency range and performed gradient analysis across multiple frequency bands of fast resting-state fMRI signals from the Human Connectome Project and condensed a frequency-rank cortical map of the highest gradient. We found that the coarse skeletons of the functional organization hierarchy are generalizable across the multiple frequency bands. Beyond that, the highest integration levels of connectivity vary in the frequency domain across different large-scale brain networks. These findings are replicated in another independent dataset and demonstrated that different brain networks can integrate information at varying rates, indicating the significance of examining the intrinsic architecture of spontaneous brain activity from the perspective of multiple frequency bands.
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Affiliation(s)
- Zhu-Qing Gong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Developmental Population Neuroscience Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Developmental Population Neuroscience Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- National Basic Science Data Center, Beijing 100190, China
- Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
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Li M, Gao Y, Lawless RD, Xu L, Zhao Y, Schilling KG, Ding Z, Anderson AW, Landman BA, Gore JC. Changes in white matter functional networks across late adulthood. Front Aging Neurosci 2023; 15:1204301. [PMID: 37455933 PMCID: PMC10347529 DOI: 10.3389/fnagi.2023.1204301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 06/14/2023] [Indexed: 07/18/2023] Open
Abstract
Introduction The aging brain is characterized by decreases in not only neuronal density but also reductions in myelinated white matter (WM) fibers that provide the essential foundation for communication between cortical regions. Age-related degeneration of WM has been previously characterized by histopathology as well as T2 FLAIR and diffusion MRI. Recent studies have consistently shown that BOLD (blood oxygenation level dependent) effects in WM are robustly detectable, are modulated by neural activities, and thus represent a complementary window into the functional organization of the brain. However, there have been no previous systematic studies of whether or how WM BOLD signals vary with normal aging. We therefore performed a comprehensive quantification of WM BOLD signals across scales to evaluate their potential as indicators of functional changes that arise with aging. Methods By using spatial independent component analysis (ICA) of BOLD signals acquired in a resting state, WM voxels were grouped into spatially distinct functional units. The functional connectivities (FCs) within and among those units were measured and their relationships with aging were assessed. On a larger spatial scale, a graph was reconstructed based on the pair-wise connectivities among units, modeling the WM as a complex network and producing a set of graph-theoretical metrics. Results The spectral powers that reflect the intensities of BOLD signals were found to be significantly affected by aging across more than half of the WM units. The functional connectivities (FCs) within and among those units were found to decrease significantly with aging. We observed a widespread reduction of graph-theoretical metrics, suggesting a decrease in the ability to exchange information between remote WM regions with aging. Discussion Our findings converge to support the notion that WM BOLD signals in specific regions, and their interactions with other regions, have the potential to serve as imaging markers of aging.
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Affiliation(s)
- Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Richard D. Lawless
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Lyuan Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Kurt G. Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
- Department of Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Adam W. Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Bennett A. Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - John C. Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
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Huang Y, Wei PH, Xu L, Chen D, Yang Y, Song W, Yi Y, Jia X, Wu G, Fan Q, Cui Z, Zhao G. Intracranial electrophysiological and structural basis of BOLD functional connectivity in human brain white matter. Nat Commun 2023; 14:3414. [PMID: 37296147 PMCID: PMC10256794 DOI: 10.1038/s41467-023-39067-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 05/25/2023] [Indexed: 06/12/2023] Open
Abstract
While functional MRI (fMRI) studies have mainly focused on gray matter, recent studies have consistently found that blood-oxygenation-level-dependent (BOLD) signals can be reliably detected in white matter, and functional connectivity (FC) has been organized into distributed networks in white matter. Nevertheless, it remains unclear whether this white matter FC reflects underlying electrophysiological synchronization. To address this question, we employ intracranial stereotactic-electroencephalography (SEEG) and resting-state fMRI data from a group of 16 patients with drug-resistant epilepsy. We find that BOLD FC is correlated with SEEG FC in white matter, and this result is consistent across a wide range of frequency bands for each participant. By including diffusion spectrum imaging data, we also find that white matter FC from both SEEG and fMRI are correlated with white matter structural connectivity, suggesting that anatomical fiber tracts underlie the functional synchronization in white matter. These results provide evidence for the electrophysiological and structural basis of white matter BOLD FC, which could be a potential biomarker for psychiatric and neurological disorders.
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Affiliation(s)
- Yali Huang
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Peng-Hu Wei
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Longzhou Xu
- Chinese Institute for Brain Research, Beijing, 102206, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China
| | - Desheng Chen
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Yanfeng Yang
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Wenkai Song
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Yangyang Yi
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Xiaoli Jia
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Guowei Wu
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Qingchen Fan
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Zaixu Cui
- Chinese Institute for Brain Research, Beijing, 102206, China.
| | - Guoguang Zhao
- Department of Neurosurgery, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
- National Medical Center for Neurological Diseases, Beijing, 100053, China.
- Beijing Municipal Geriatric Medical Research Center, Beijing, 100053, China.
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7
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Jiang C, Cai S, Zhang L. Functional Connectivity of White Matter and Its Association with Sleep Quality. Nat Sci Sleep 2023; 15:287-300. [PMID: 37123094 PMCID: PMC10132294 DOI: 10.2147/nss.s406120] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 04/04/2023] [Indexed: 05/02/2023] Open
Abstract
Purpose Functional magnetic resonance imaging (fMRI) has been widely adopted to investigate the neural activity in gray matter (GM) in the field of sleep research, but the neural activity in white matter (WM) has received much less attention. The current study set out to test our hypothesis that WM functional abnormality is associated with poor sleep quality. Participants and Methods K-means clustering analysis was performed on 78 healthy adults drawn from the Human Connectome Project dataset to extract stable WM functional networks (WM-FNs) and GM-FNs. The differences in functional connectivity within WM-FNs and between WM- and GM-FNs, as well as the power spectrum between good sleep quality group (Pittsburgh Sleep Quality Index (PSQI) <6, daytime dysfunction = 0) and poor sleep quality group (PSQI >6, daytime dysfunction >0) were examined between groups with good and poor sleep quality. Additionally, linear relationships between sleep quality and altered functional characteristics of WM-FNs were evaluated. Results Functional connectivity between middle and superficial WM-FNs, short- and long-range functional connectivity between WM- and GM-FNs were decreased in poor sleepers and negatively correlated with PSQI score. The mean amplitudes of right sensorimotor WM networks at whole, high and low frequency bands were higher in poor sleepers and were positively correlated with PSQI score. Conclusion WM functional abnormality is associated with poor sleep quality. The neurobiological mechanisms that underlie the functional alterations of WM-FNs in poor sleepers need to be investigated in future studies.
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Affiliation(s)
- Chunxiang Jiang
- Paul. C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing, People’s Republic of China
| | - Siqi Cai
- Paul. C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing, People’s Republic of China
| | - Lijuan Zhang
- Paul. C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, People’s Republic of China
- University of Chinese Academy of Sciences, Beijing, People’s Republic of China
- Correspondence: Lijuan Zhang, Paul. C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, Shenzhen University Town, Shenzhen, 518055, People’s Republic of China, Tel +86 0755 86392247, Fax +86 0755 86392299, Email
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Ma J, Liu F, Wang Y, Ma L, Niu Y, Wang J, Ye Z, Zhang J. Frequency-dependent white-matter functional network changes associated with cognitive deficits in subcortical vascular cognitive impairment. Neuroimage Clin 2022; 36:103245. [PMID: 36451351 PMCID: PMC9668649 DOI: 10.1016/j.nicl.2022.103245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 10/07/2022] [Accepted: 10/21/2022] [Indexed: 11/11/2022]
Abstract
Vascular cognitive impairment (VCI) refers to all forms of cognitive decline associated with cerebrovascular diseases, in which white matter (WM) is highly vulnerable. Although previous studies have shown that blood oxygen level-dependent (BOLD) signals inside WM can effectively reflect neural activities, whether WM BOLD signal alterations are present and their roles underlying cognitive impairment in VCI remain largely unknown. In this study, 36 subcortical VCI (SVCI) patients and 36 healthy controls were enrolled to evaluate WM dysfunction. Specifically, fourteen distinct WM networks were identified from resting-state functional MRI using K-means clustering analysis. Subsequently, between-network functional connectivity (FC) and within-network BOLD signal amplitude of WM networks were calculated in three frequency bands (band A: 0.01-0.15 Hz, band B: 0.08-0.15 Hz, and band C: 0.01-0.08 Hz). Patients with SVCI manifested decreased FC mainly in bilateral parietal WM regions, forceps major, superior and inferior longitudinal fasciculi. These connections extensively linked with distinct WM networks and with gray-matter networks such as frontoparietal control, dorsal and ventral attention networks, which exhibited frequency-specific alterations in SVCI. Additionally, extensive amplitude reductions were found in SVCI, showing frequency-dependent properties in parietal, anterior corona radiate, pre/post central, superior and inferior longitudinal fasciculus networks. Furthermore, these decreased FC and amplitudes showed significant positive correlations with cognitive performances in SVCI, and high diagnostic performances for SVCI especially combining all bands. Our study indicated that VCI-related cognitive deficits were characterized by frequency-dependent WM functional abnormalities, which offered novel applicable neuromarkers for VCI.
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Affiliation(s)
- Juanwei Ma
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China; National Clinical Research Center for Cancer, Tianjin, China; Key Laboratory of Cancer Prevention and Therapy, Tianjin, China; Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yang Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Lin Ma
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yali Niu
- Department of Rehabilitation, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Jing Wang
- Department of Rehabilitation, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China; National Clinical Research Center for Cancer, Tianjin, China; Key Laboratory of Cancer Prevention and Therapy, Tianjin, China; Tianjin's Clinical Research Center for Cancer, Tianjin, China.
| | - Jing Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
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9
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Zhao Y, Gao Y, Zu Z, Li M, Schilling KG, Anderson AW, Ding Z, Gore JC. Detection of functional activity in brain white matter using fiber architecture informed synchrony mapping. Neuroimage 2022; 258:119399. [PMID: 35724855 PMCID: PMC9388229 DOI: 10.1016/j.neuroimage.2022.119399] [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: 02/22/2022] [Revised: 06/13/2022] [Accepted: 06/17/2022] [Indexed: 01/12/2023] Open
Abstract
A general linear model is widely used for analyzing fMRI data, in which the blood oxygenation-level dependent (BOLD) signals in gray matter (GM) evoked in response to neural stimulation are modeled by convolving the time course of the expected neural activity with a canonical hemodynamic response function (HRF) obtained a priori. The maps of brain activity produced reflect the magnitude of local BOLD responses. However, detecting BOLD signals in white matter (WM) is more challenging as the BOLD signals are weaker and the HRF is different, and may vary more across the brain. Here we propose a model-free approach to detect changes in BOLD signals in WM by measuring task-evoked increases of BOLD signal synchrony in WM fibers. The proposed approach relies on a simple assumption that, in response to a functional task, BOLD signals in relevant fibers are modulated by stimulus-evoked neural activity and thereby show greater synchrony than when measured in a resting state, even if their magnitudes do not change substantially. This approach is implemented in two technical stages. First, for each voxel a fiber-architecture-informed spatial window is created with orientation distribution functions constructed from diffusion imaging data. This provides the basis for defining neighborhoods in WM that share similar local fiber architectures. Second, a modified principal component analysis (PCA) is used to estimate the synchrony of BOLD signals in each spatial window. The proposed approach is validated using a 3T fMRI dataset from the Human Connectome Project (HCP) at a group level. The results demonstrate that neural activity can be reliably detected as increases in fMRI signal synchrony within WM fibers that are engaged in a task with high sensitivities and reproducibility.
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Affiliation(s)
- Yu Zhao
- Vanderbilt University Institute of Imaging Science, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, United States.
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, United States,Department of Biomedical Engineering, Vanderbilt University, United States
| | - Zhongliang Zu
- Vanderbilt University Institute of Imaging Science, United States,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, United States
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, United States,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, United States
| | - Kurt G. Schilling
- Vanderbilt University Institute of Imaging Science, United States,Department of Biomedical Engineering, Vanderbilt University, United States
| | - Adam W. Anderson
- Vanderbilt University Institute of Imaging Science, United States,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, United States,Department of Biomedical Engineering, Vanderbilt University, United States
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, United States; Department of Biomedical Engineering, Vanderbilt University, United States; Department of Electrical and Computer Engineering, Vanderbilt University, United States.
| | - John C. Gore
- Vanderbilt University Institute of Imaging Science, United States,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, United States,Department of Biomedical Engineering, Vanderbilt University, United States,Department of Molecular Physiology and Biophysics, Vanderbilt University, United States,Department of Physics and Astronomy, Vanderbilt University, United States
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10
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Schilling KG, Li M, Rheault F, Ding Z, Anderson AW, Kang H, Landman BA, Gore JC. Anomalous and heterogeneous characteristics of the BOLD hemodynamic response function in white matter. Cereb Cortex Commun 2022; 3:tgac035. [PMID: 36196360 PMCID: PMC9519945 DOI: 10.1093/texcom/tgac035] [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: 06/09/2022] [Revised: 08/09/2022] [Accepted: 08/12/2022] [Indexed: 01/12/2023] Open
Abstract
Detailed knowledge of the BOLD hemodynamic response function (HRF) is crucial for accurate analyses and interpretation of functional MRI data. Considerable efforts have been made to characterize the HRF in gray matter (GM), but much less attention has been paid to BOLD effects in white matter (WM). However, several recent reports have demonstrated reliable detection and analyses of WM BOLD signals both after stimulation and in a resting state. WM and GM differ in composition, energy requirements, and blood flow, so their neurovascular couplings also may well be different. We aimed to derive a comprehensive characterization of the HRF in WM across a population, including accurate measurements of its shape and its variation along and between WM pathways, using resting-state fMRI acquisitions. Our results show that the HRF is significantly different between WM and GM. Features of the HRF, such as a prominent initial dip, show strong relationships with features of the tissue microstructure derived from diffusion imaging, and these relationships differ between WM and GM, consistent with BOLD signal fluctuations reflecting different energy demands and neurovascular couplings in tissues of different composition and function. We also show that the HRF varies in shape significantly along WM pathways and is different between different WM pathways, suggesting the temporal evolution of BOLD signals after an event vary in different parts of the WM. These features of the HRF in WM are especially relevant for interpretation of the biophysical basis of BOLD effects in WM.
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Affiliation(s)
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Francois Rheault
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37232, USA
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37232, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University, Nashville, TN 37232, USA
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37232, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37232, USA
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11
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Yang Y, Wang S, Liu J, Zou G, Jiang J, Jiang B, Cao W, Zou Q. Changes in white matter functional networks during wakefulness and sleep. Hum Brain Mapp 2022; 43:4383-4396. [PMID: 35615855 PMCID: PMC9435017 DOI: 10.1002/hbm.25961] [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: 12/05/2021] [Revised: 05/10/2022] [Accepted: 05/10/2022] [Indexed: 11/23/2022] Open
Abstract
Blood oxygenation level‐dependent (BOLD) signals in the white matter (WM) have been demonstrated to encode neural activities by showing structure‐specific temporal correlations during resting‐state and task‐specific imaging of fiber pathways with various degrees of correlations in strength and time delay. Previous neuroimaging studies have shown state‐dependent functional connectivity and regional amplitude of signal fluctuations in brain gray matter across wakefulness and nonrapid eye movement (NREM) sleep cycles. However, the functional characteristics of WM during sleep remain unknown. Using simultaneous electroencephalography and functional magnetic resonance imaging data during wakefulness and NREM sleep collected from 66 healthy participants, we constructed 10 stable WM functional networks using clustering analysis. Functional connectivity between these WM functional networks and regional amplitude of WM signal fluctuations across multiple low‐frequency bands were evaluated. In general, decreased WM functional connectivity between superficial and middle layer WM functional networks was observed from wakefulness to sleep. In addition, functional connectivity between the deep and cerebellar networks was higher during light sleep and lower during both wakefulness and deep sleep. The regional fluctuation amplitude was always higher during light sleep and lower during deep sleep. Importantly, slow‐wave activity during deep sleep negatively correlated with functional connectivity between WM functional networks but positively correlated with fluctuation strength in the WM. These observations provide direct physiological evidence that neural activities in the WM are modulated by the sleep–wake cycle. This study provided the initial mapping of functional changes in WM during sleep.
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Affiliation(s)
- Yang Yang
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Shilei Wang
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Jiayi Liu
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Guangyuan Zou
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Jun Jiang
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Binghu Jiang
- Department of Radiology, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College, Nanchong, China
| | - Wentian Cao
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
| | - Qihong Zou
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,National Clinical Research Center for Mental Health, Peking University Sixth Hospital, China
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12
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Li M, Gao Y, Anderson AW, Ding Z, Gore JC. Dynamic variations of resting-state BOLD signal spectra in white matter. Neuroimage 2022; 250:118972. [PMID: 35131432 PMCID: PMC8915948 DOI: 10.1016/j.neuroimage.2022.118972] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 01/27/2022] [Accepted: 02/03/2022] [Indexed: 12/03/2022] Open
Abstract
Recent studies have demonstrated that the mathematical model used for analyzing and interpreting fMRI data in gray matter (GM) is inappropriate for detecting or describing blood-oxygenation-level-dependent (BOLD) signals in white matter (WM). In particular the hemodynamic response function (HRF) which serves as the regressor in general linear models is different in WM compared to GM. We recently reported measurements of the frequency contents of resting-state signal time courses in WM that showed distinct power spectra which depended on local structural-vascular-functional associations. In addition, multiple studies of GM have revealed how functional connectivity between regions, as measured by the correlation between BOLD time series, varies dynamically over time. We therefore investigated whether and how BOLD signals from WM in a resting state varied over time. We measured voxel-wise spectrograms, which reflect the time-varying spectral patterns of WM time courses. The results suggest that the spectral patterns are non-stationary but could be categorized into five modes that recurred over time. These modes showed distinct spatial distributions of their occurrences and durations, and the distributions were highly consistent across individuals. In addition, one of the modes exhibited a strong coupling of its occurrence between GM and WM across individuals, and two communities of WM voxels were identified according to the hierarchical structures of transitions among modes. Moreover, these modes are coupled to the shape of instantaneous HRFs. Our findings extend previous studies and reveal the non-stationary nature of spectral patterns of BOLD signals over time, providing a spatial-temporal-frequency characterization of resting-state signals in WM.
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Affiliation(s)
- Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States; Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232, United States.
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, United States
| | - Adam W. Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States,Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232, United States,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, United States
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, United States,Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN 37235, United States
| | - John C. Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, 1161 21st Ave. S, Medical Center North, AA-1105, Nashville, TN 37232-2310, United States,Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232, United States,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, United States,Department of Physics and Astronomy, Vanderbilt University, Nashville, TN 37232, United State
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13
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Wu Z, Cao M, Di X, Wu K, Gao Y, Li X. Regional Topological Aberrances of White Matter- and Gray Matter-Based Functional Networks for Attention Processing May Foster Traumatic Brain Injury-Related Attention Deficits in Adults. Brain Sci 2021; 12:brainsci12010016. [PMID: 35053760 PMCID: PMC8774280 DOI: 10.3390/brainsci12010016] [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: 11/30/2021] [Revised: 12/21/2021] [Accepted: 12/22/2021] [Indexed: 12/31/2022] Open
Abstract
Traumatic brain injury (TBI) is highly prevalent in adults. TBI-related functional brain alterations have been linked with common post-TBI neurobehavioral sequelae, with unknown neural substrates. This study examined the systems-level functional brain alterations in white matter (WM) and gray matter (GM) for visual sustained-attention processing, and their interactions and contributions to post-TBI attention deficits. Task-based functional MRI data were collected from 42 adults with TBI and 43 group-matched normal controls (NCs), and analyzed using the graph theoretic technique. Global and nodal topological properties were calculated and compared between the two groups. Correlation analyses were conducted between the neuroimaging measures that showed significant between-group differences and the behavioral symptom measures in attention domain in the groups of TBI and NCs, respectively. Significantly altered nodal efficiencies and/or degrees in several WM and GM nodes were reported in the TBI group, including the posterior corona radiata (PCR), posterior thalamic radiation (PTR), postcentral gyrus (PoG), and superior temporal sulcus (STS). Subjects with TBI also demonstrated abnormal systems-level functional synchronization between the PTR and STS in the right hemisphere, hypo-interaction between the PCR and PoG in the left hemisphere, as well as the involvement of systems-level functional aberrances in the PCR in TBI-related behavioral impairments in the attention domain. The findings of the current study suggest that TBI-related systems-level functional alterations associated with these two major-association WM tracts, and their anatomically connected GM regions may play critical role in TBI-related behavioral deficits in attention domains.
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Affiliation(s)
- Ziyan Wu
- Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA;
| | - Meng Cao
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA; (M.C.); (X.D.)
| | - Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA; (M.C.); (X.D.)
| | - Kai Wu
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou 510630, China;
| | - Yu Gao
- Department of Psychology, Brooklyn College, The City University of New York, New York, NY 11210, USA;
- The Graduate Center, The City University of New York, New York, NY 10016, USA
| | - Xiaobo Li
- Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA;
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA; (M.C.); (X.D.)
- Correspondence: or ; Tel.: +1-973-596-5880
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