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Liao D, Liang LS, Wang D, Li XH, Liu YC, Guo ZP, Zhang ZQ, Liu XF. Altered static and dynamic functional network connectivity in individuals with subthreshold depression: a large-scale resting-state fMRI study. Eur Arch Psychiatry Clin Neurosci 2024:10.1007/s00406-024-01871-3. [PMID: 39044022 DOI: 10.1007/s00406-024-01871-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Accepted: 07/15/2024] [Indexed: 07/25/2024]
Abstract
Dynamic functional network connectivity (dFNC) is an expansion of static FNC (sFNC) that reflects connectivity variations among brain networks. This study aimed to investigate changes in sFNC and dFNC strength and temporal properties in individuals with subthreshold depression (StD). Forty-two individuals with subthreshold depression and 38 healthy controls (HCs) were included in this study. Group independent component analysis (GICA) was used to determine target resting-state networks, namely, executive control network (ECN), default mode network (DMN), sensorimotor network (SMN) and dorsal attentional network (DAN). Sliding window and k-means clustering analyses were used to identify dFNC patterns and temporal properties in each subject. We compared sFNC and dFNC differences between the StD and HCs groups. Relationships between changes in FNC strength, temporal properties, and neurophysiological score were evaluated by Spearman's correlation analysis. The sFNC analysis revealed decreased FNC strength in StD individuals, including the DMN-CEN, DMN-SMN, SMN-CEN, and SMN-DAN. In the dFNC analysis, 4 reoccurring FNC patterns were identified. Compared to HCs, individuals with StD had increased mean dwell time and fraction time in a weakly connected state (state 4), which is associated with self-focused thinking status. In addition, the StD group demonstrated decreased dFNC strength between the DMN-DAN in state 2. sFNC strength (DMN-ECN) and temporal properties were correlated with HAMD-17 score in StD individuals (all p < 0.01). Our study provides new evidence on aberrant time-varying brain activity and large-scale network interaction disruptions in StD individuals, which may provide novel insight to better understand the underlying neuropathological mechanisms.
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Affiliation(s)
- Dan Liao
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou, China
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, 100010, China
| | - Li-Song Liang
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou, China
| | - Di Wang
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou, China
| | - Xiao-Hai Li
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou, China
| | - Yuan-Cheng Liu
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou, China
| | - Zhi-Peng Guo
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, 100010, China
| | - Zhu-Qing Zhang
- Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, 100010, China
| | - Xin-Feng Liu
- Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou, China.
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2
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Zhang W, He T, Zhou N, Duan L, Chi P, Lin X. Children's oppositional defiant disorder symptoms and neural synchrony in mother-child interactions: An fNIRS study. Neuroimage 2024; 297:120736. [PMID: 39009247 DOI: 10.1016/j.neuroimage.2024.120736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 07/03/2024] [Accepted: 07/11/2024] [Indexed: 07/17/2024] Open
Abstract
Interpersonal neural synchrony (INS) between mothers and children responds to the temporal similarity of brain signals in joint behavior between dyadic partners and is considered an important neural indicator of the formation of adaptive social interaction bonds. Parent-child interactions are particularly important for the development and maintenance of oppositional defiant disorder (ODD) in children, but the underlying neurocognitive mechanisms are unknown. Therefore, in the current study we measured INS between mothers and children in interactions by using simultaneous functional Near-infrared Spectroscopy (fNIRS), and explored its association with ODD symptoms in children. Seventy-two mother-child dyads were recruited to participate in the study, including 35 children with ODD and 37 healthy children to be used as a control. Each mother-child dyad was measured for neural activity in frontal, parietal, and temporal lobe regions while completing free-play as well as positive, and negative topic discussion tasks. We used Phase-locked value to calculate the synchrony strength and then used the K-means algorithm and k-space based alignment tests to confirm the specific patterns of parent-child synchrony in different brain areas. The results showed that, in free-play (right MFG and bilateral SFG), positive (left TPJ and bilateral SFGdor), and negative (bilateral SFGmed, right ANG, and left MFG) topic discussions, the mother-child pairs showed different patterns of INS. These specific INS patterns were significantly lower in the ODD group compared to the control group and were negatively associated with ODD symptoms in children. Network analyses showed that these INS patterns were connected to different nodes in the ODD symptom network. Our findings suggest that ODD mother-child dyads exhibit lower neural synchrony across a wide range of parent-child interactions. Neural synchrony in the context of interpersonal interactions provides new insights into understanding the neural mechanisms of ODD and can be used as an indicator of neural and socio-environmental factors in the network of psychological disorder symptoms.
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Affiliation(s)
- Wenrui Zhang
- Institute of Developmental Psychology, Faculty of Psychology, Beijing Normal University, Beijing 100875, China
| | - Ting He
- Institute of Developmental Psychology, Faculty of Psychology, Beijing Normal University, Beijing 100875, China
| | - Nan Zhou
- Faculty of Education, University of Macau, Macau, China
| | - Lian Duan
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
| | - Peilian Chi
- Department of Psychology, University of Macau, Taipa 999078, Macau
| | - Xiuyun Lin
- Institute of Developmental Psychology, Faculty of Psychology, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing 100875, China.
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3
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Pressl C, Mätlik K, Kus L, Darnell P, Luo JD, Paul MR, Weiss AR, Liguore W, Carroll TS, Davis DA, McBride J, Heintz N. Selective vulnerability of layer 5a corticostriatal neurons in Huntington's disease. Neuron 2024; 112:924-941.e10. [PMID: 38237588 DOI: 10.1016/j.neuron.2023.12.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/18/2023] [Accepted: 12/13/2023] [Indexed: 01/30/2024]
Abstract
The properties of the cell types that are selectively vulnerable in Huntington's disease (HD) cortex, the nature of somatic CAG expansions of mHTT in these cells, and their importance in CNS circuitry have not been delineated. Here, we employed serial fluorescence-activated nuclear sorting (sFANS), deep molecular profiling, and single-nucleus RNA sequencing (snRNA-seq) of motor-cortex samples from thirteen predominantly early stage, clinically diagnosed HD donors and selected samples from cingulate, visual, insular, and prefrontal cortices to demonstrate loss of layer 5a pyramidal neurons in HD. Extensive mHTT CAG expansions occur in vulnerable layer 5a pyramidal cells, and in Betz cells, layers 6a and 6b neurons that are resilient in HD. Retrograde tracing experiments in macaque brains identify layer 5a neurons as corticostriatal pyramidal cells. We propose that enhanced somatic mHTT CAG expansion and altered synaptic function act together to cause corticostriatal disconnection and selective neuronal vulnerability in HD cerebral cortex.
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Affiliation(s)
- Christina Pressl
- Laboratory of Molecular Biology, The Rockefeller University, New York, NY, USA
| | - Kert Mätlik
- Laboratory of Molecular Biology, The Rockefeller University, New York, NY, USA
| | - Laura Kus
- Laboratory of Molecular Biology, The Rockefeller University, New York, NY, USA
| | - Paul Darnell
- Laboratory of Molecular Biology, The Rockefeller University, New York, NY, USA
| | - Ji-Dung Luo
- Bioinformatics Resource Center, The Rockefeller University, New York, NY, USA
| | - Matthew R Paul
- Bioinformatics Resource Center, The Rockefeller University, New York, NY, USA
| | - Alison R Weiss
- Division of Neuroscience, Oregon National Primate Research Center, Beaverton, OR, USA
| | - William Liguore
- Division of Neuroscience, Oregon National Primate Research Center, Beaverton, OR, USA
| | - Thomas S Carroll
- Bioinformatics Resource Center, The Rockefeller University, New York, NY, USA
| | - David A Davis
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Jodi McBride
- Division of Neuroscience, Oregon National Primate Research Center, Beaverton, OR, USA
| | - Nathaniel Heintz
- Laboratory of Molecular Biology, The Rockefeller University, New York, NY, USA.
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Liu Q, Zhu S, Zhou X, Liu F, Becker B, Kendrick KM, Zhao W. Mothers and fathers show different neural synchrony with their children during shared experiences. Neuroimage 2024; 288:120529. [PMID: 38301879 DOI: 10.1016/j.neuroimage.2024.120529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 01/09/2024] [Accepted: 01/29/2024] [Indexed: 02/03/2024] Open
Abstract
Parent-child shared experiences has an important influence on social development in children although contributions of mothers and fathers may differ. Neural synchronicity occurs between mothers and fathers and their children during social interactions but it is unclear whether they differ in this respect. We used data from simultaneous fNIRS hyperscanning in mothers (n = 33) and fathers (n = 29) and their children (3-4 years) to determine different patterns and strengths of neural synchronization in the frontal cortex during co-viewing of videos or free-play. Mothers showed greater synchrony with child than fathers during passive viewing of videos and the synchronization was positively associated with video complexity and negatively associated with parental stress. During play interactions, mothers showed more controlling behaviors over their child and greater evidence for joint gaze and joint imitation play with child whereas fathers spent more time gazing at other things. In addition, different aspects of child communication promoted neural synchrony between mothers and fathers and child during active play interactions. Overall, our findings indicate greater neural and behavioral synchrony between mothers than fathers and young children during passive or active shared experiences, although for both it was weakened by parental distress and child difficulty.
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Affiliation(s)
- Qi Liu
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Siyu Zhu
- School of Sport Training, Chengdu Sport University, Chengdu, 610041, PR China
| | - Xinqi Zhou
- Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, 610066, PR China
| | - Fang Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China
| | - Benjamin Becker
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, PR China; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China; The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, 999077, PR China
| | - Keith M Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
| | - Weihua Zhao
- The Center of Psychosomatic Medicine, Sichuan Provincial Center for Mental Health, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 611731, PR China; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for NeuroInformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, PR China; Institute of Electronic and Information Engineering of UESTC in Guangdong, Dongguan, 523808, PR China.
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5
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Yang B, Su M, Wang Q, Qu X, Wang H, Chen W, Sun Y, Li T, Wang Y, Wang N, Xian J. Altered stability of dynamic brain functional architecture in primary open-angle glaucoma: a surface-based resting-state fMRI study. Brain Imaging Behav 2024; 18:44-56. [PMID: 37857914 PMCID: PMC10844345 DOI: 10.1007/s11682-023-00800-7] [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] [Accepted: 09/07/2023] [Indexed: 10/21/2023]
Abstract
Delineating the neuropathological characteristics of primary open-angle glaucoma (POAG) is critical for understanding its pathophysiology. While temporal stability represents a crucial aspect of the brain's functional architecture, the specific patterns underlying its contribution to POAG remain unclear. This study aims to analyze the brain functional abnormalities in POAG using functional stability, a dynamic functional connectivity (DFC) approach based on resting-state functional magnetic resonance imaging (rs-fMRI). Seventy patients with POAG and forty-five healthy controls underwent rs-fMRI and ophthalmological examinations. The stability of DFC was calculated as the concordance of DFC over time using a sliding-window approach, and the differences in stability between the two groups were compared. Subsequently, Spearman's correlation analyses were conducted to examine the relationship between functional stability and clinical indicators. Compared with healthy controls, patients with POAG exhibited significantly decreased functional stability in the visual network, including the early visual center, ventral and dorsal stream visual cortex in both hemispheres. Conversely, stability values increased in the bilateral inferior parietal gyrus and right inferior frontal cortex. In POAG patients, the dynamic stability of the left early visual cortex and ventral stream visual cortex correlated with the mean deviation of visual field defects (r = 0.251, p = 0.037). The evidence from this study suggests that functional stability may provide a new understanding of brain alterations in the progression of POAG.
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Affiliation(s)
- Bingbing Yang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No.1 of Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China
| | - Mingyue Su
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No.1 of Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China
| | - Qian Wang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No.1 of Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China
| | - Xiaoxia Qu
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No.1 of Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China
| | - Huaizhou Wang
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, No.1 of Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China
| | - Weiwei Chen
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, No.1 of Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China
| | - Yunxiao Sun
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, No.1 of Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China
| | - Ting Li
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No.1 of Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China
| | - Yang Wang
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Ningli Wang
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, No.1 of Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, No.1 of Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, China.
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6
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Lyu W, Wu Y, Huang H, Chen Y, Tan X, Liang Y, Ma X, Feng Y, Wu J, Kang S, Qiu S, Yap PT. Aberrant dynamic functional network connectivity in type 2 diabetes mellitus individuals. Cogn Neurodyn 2023; 17:1525-1539. [PMID: 37969945 PMCID: PMC10640562 DOI: 10.1007/s11571-022-09899-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 09/11/2022] [Accepted: 10/09/2022] [Indexed: 11/24/2022] Open
Abstract
An increasing number of recent brain imaging studies are dedicated to understanding the neuro mechanism of cognitive impairment in type 2 diabetes mellitus (T2DM) individuals. In contrast to efforts to date that are limited to static functional connectivity, here we investigate abnormal connectivity in T2DM individuals by characterizing the time-varying properties of brain functional networks. Using group independent component analysis (GICA), sliding-window analysis, and k-means clustering, we extracted thirty-one intrinsic connectivity networks (ICNs) and estimated four recurring brain states. We observed significant group differences in fraction time (FT) and mean dwell time (MDT), and significant negative correlation between the Montreal Cognitive Assessment (MoCA) scores and FT/MDT. We found that in the T2DM group the inter- and intra-network connectivity decreases and increases respectively for the default mode network (DMN) and task-positive network (TPN). We also found alteration in the precuneus network (PCUN) and enhanced connectivity between the salience network (SN) and the TPN. Our study provides evidence of alterations of large-scale resting networks in T2DM individuals and shed light on the fundamental mechanisms of neurocognitive deficits in T2DM.
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Affiliation(s)
- Wenjiao Lyu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Ye Wu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC USA
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu China
| | - Haoming Huang
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Yuna Chen
- Department of Endocrinology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Xin Tan
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Yi Liang
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Xiaomeng Ma
- Department of Radiology, Jingzhou First People’s Hospital of Hubei Province, Jingzhou, Hubei China
| | - Yue Feng
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Jinjian Wu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Shangyu Kang
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong China
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, NC USA
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Pressl C, Mätlik K, Kus L, Darnell P, Luo JD, Paul MR, Weiss AR, Liguore W, Carroll TS, Davis DA, McBride J, Heintz N. Selective Vulnerability of Layer 5a Corticostriatal Neurons in Huntington's Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.24.538096. [PMID: 37162977 PMCID: PMC10168234 DOI: 10.1101/2023.04.24.538096] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The properties of the cell types that are selectively vulnerable in Huntington's disease (HD) cortex, the nature of somatic CAG expansions of mHTT in these cells, and their importance in CNS circuitry have not been delineated. Here we employed serial fluorescence activated nuclear sorting (sFANS), deep molecular profiling, and single nucleus RNA sequencing (snRNAseq) to demonstrate that layer 5a pyramidal neurons are vulnerable in primary motor cortex and other cortical areas of HD donors. Extensive mHTT -CAG expansions occur in vulnerable layer 5a pyramidal cells, and in Betz cells, layer 6a, layer 6b neurons that are resilient in HD. Retrograde tracing experiments in macaque brains identify the vulnerable layer 5a neurons as corticostriatal pyramidal cells. We propose that enhanced somatic mHTT -CAG expansion and altered synaptic function act together to cause corticostriatal disconnection and selective neuronal vulnerability in the HD cerebral cortex.
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8
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Altered static and dynamic functional network connectivity in post-stroke cognitive impairment. Neurosci Lett 2023; 799:137097. [PMID: 36716911 DOI: 10.1016/j.neulet.2023.137097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 01/20/2023] [Accepted: 01/24/2023] [Indexed: 01/29/2023]
Abstract
Post-stroke cognitive impairment (PSCI) is a common symptom following brain stroke, yet the mechanisms remain unknown. This study aimed to investigate alterations of static and dynamic functional network connectivity (sFNC and dFNC) in PSCI patients. We prospectively recruited 17 PSCI patients and 24 Healthy controls (HC). Restingstate fMRI (rs-fMRI) and Mini-Mental State Examination (MMSE) were performed. Independent component analysis combined with sliding-window and K-means clustering approach were applied to examine the FNC among 11 resting-state networks: auditory network (AUDN), left executive control network (lECN), language network (LN), precuneus network (PCUN), right executive control network (rECN), salience network (SN), visuospatial network (VN), dorsal default mode network (dDMN), higher visual network (hVIS), primary visual network (pVIS), and ventral mode network (vDMN). The FNC and dynamic indices (fraction time, mean dwell time, transition number) were calculated. Static and dynamic measures were compared between two groups and the correlation between clinical and imaging indicators was analyzed. For sFNC, PSCI group showed decreased interactions in dDMN-vDMN, vDMN-SN, dDMN-hVIS, AUDN-rECN, and AUDN-VN. For dFNC, we derived 3 states of FNC that occurred repeatedly. Significant group differences were found, including decreased interactions in the AUDN-VN, AUDN-pVIS in state 2 and dDMN-VN in state 3. The mean dwell time in PSCI group was longer in state 1, and negatively correlated with MMSE score. These results demonstrated that PSCI patients are characterized with altered sFNC and dFNC, which could help us explore the neural mechanisms of the PSCI from a new perspective.
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Liu X, Qiu S, Wang X, Chen H, Tang Y, Qin Y. Aberrant dynamic Functional-Structural connectivity coupling of Large-scale brain networks in poststroke motor dysfunction. Neuroimage Clin 2023; 37:103332. [PMID: 36708666 PMCID: PMC10037213 DOI: 10.1016/j.nicl.2023.103332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 01/11/2023] [Accepted: 01/19/2023] [Indexed: 01/24/2023]
Abstract
BACKGROUND AND PURPOSE Stroke may lead to widespread functional and structural reorganization in the brain. Several studies have reported a potential correlation between functional network changes and structural network changes after stroke. However, it is unclear how functional-structural relationships change dynamically over the course of one resting-state fMRI scan in patients following a stroke; furthermore, we know little about their relationships with the severity of motor dysfunction. Therefore, this study aimed to investigate dynamic functional and structural connectivity (FC-SC) coupling and its relationship with motor function in subcortical stroke from the perspective of network dynamics. METHODS Resting-state functional magnetic resonance imaging and diffusion tensor imaging were obtained from 39 S patients (19 severe and 20 moderate) and 22 healthy controls (HCs). Brain structural networks were constructed by tracking fiber tracts in diffusion tensor imaging, and structural network topology metrics were calculated using a graph-theoretic approach. Independent component analysis, the sliding window method, and k-means clustering were used to calculate dynamic functional connectivity and to estimate different dynamic connectivity states. The temporal patterns and intergroup differences of FC-SC coupling were analyzed within each state. We also calculated dynamic FC-SC coupling and its relationship with functional network efficiency. In addition, the correlation between FC-SC coupling and the Fugl-Meyer assessment scale was analyzed. RESULTS For SC, stroke patients showed lower global efficiency than HCs (all P < 0.05), and severely affected patients had a higher characteristic path length (P = 0.003). For FC and FC-SC coupling, stroke patients predominantly showed lower local efficiency and reduced FC-SC coupling than HCs in state 2 (all P < 0.05). Furthermore, severely affected patients also showed lower local efficiency (P = 0.031) and reduced FC-SC coupling (P = 0.043) in state 3, which was markedly linked to the severity of motor dysfunction after stroke. In addition, FC-SC coupling was correlated with functional network efficiency in state 2 in moderately affected patients (r = 0.631, P = 0.004) but not significantly in severely affected patients. CONCLUSIONS Stroke patients show abnormal dynamic FC-SC coupling characteristics, especially in individuals with severe injuries. These findings may contribute to a better understanding of the anatomical functional interactions underlying motor deficits in stroke patients and provide useful information for personalized rehabilitation strategies.
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Affiliation(s)
- Xiaoying Liu
- Department of Rehabilitation Medicine, The 900th Hospital of People's Liberation Army (Fuzhou General Hospital of Nanjing Military Region), Fuzhou, 350025, China
| | - Shuting Qiu
- Department of Rehabilitation Medicine, The 900th Hospital of People's Liberation Army (Fuzhou General Hospital of Nanjing Military Region), Fuzhou, 350025, China
| | - Xiaoyang Wang
- Department of the Fujian Key Laboratory of Functional Imaging, Department of Radiology, The 900th Hospital of People's Liberation Army (Fuzhou General Hospital of Nanjing Military Region), Fuzhou 350025, China
| | - Hui Chen
- Department of Rehabilitation Medicine, The 900th Hospital of People's Liberation Army (Fuzhou General Hospital of Nanjing Military Region), Fuzhou, 350025, China
| | - Yuting Tang
- Department of Rehabilitation Medicine, The 900th Hospital of People's Liberation Army (Fuzhou General Hospital of Nanjing Military Region), Fuzhou, 350025, China
| | - Yin Qin
- Department of Rehabilitation Medicine, The 900th Hospital of People's Liberation Army (Fuzhou General Hospital of Nanjing Military Region), Fuzhou, 350025, China; Department of Rehabilitation Medicine, Fuzhou General Hospital (Dongfang Hospital), Xiamen University, Fuzhou 350025, China.
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10
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Jiang Y, Yuan TS, Chen YC, Guo P, Lian TH, Liu YY, Liu W, Bai YT, Zhang Q, Zhang W, Zhang JG. Deep brain stimulation of the nucleus basalis of Meynert modulates hippocampal-frontoparietal networks in patients with advanced Alzheimer's disease. Transl Neurodegener 2022; 11:51. [PMID: 36471370 PMCID: PMC9721033 DOI: 10.1186/s40035-022-00327-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 11/17/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Deep brain stimulation (DBS) of the nucleus basalis of Meynert (NBM) has shown potential for the treatment of mild-to-moderate Alzheimer's disease (AD). However, there is little evidence of whether NBM-DBS can improve cognitive functioning in patients with advanced AD. In addition, the mechanisms underlying the modulation of brain networks remain unclear. This study was aimed to assess the cognitive function and the resting-state connectivity following NBM-DBS in patients with advanced AD. METHODS Eight patients with advanced AD underwent bilateral NBM-DBS and were followed up for 12 months. Clinical outcomes were assessed by neuropsychological examinations using the Mini-Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale. Resting-state functional magnetic resonance imaging and positron emission tomography data were also collected. RESULTS The cognitive functioning of AD patients did not change from baseline to the 12-month follow-up. Interestingly, the MMSE score indicated clinical efficacy at 1 month of follow-up. At this time point, the connectivity between the hippocampal network and frontoparietal network tended to increase in the DBS-on state compared to the DBS-off state. Additionally, the increased functional connectivity between the parahippocampal gyrus (PHG) and the parietal cortex was associated with cognitive improvement. Further dynamic functional network analysis showed that NBM-DBS increased the proportion of the PHG-related connections, which was related to improved cognitive performance. CONCLUSION The results indicated that NBM-DBS improves short-term cognitive performance in patients with advanced AD, which may be related to the modulation of multi-network connectivity patterns, and the hippocampus plays an important role within these networks. TRIAL REGISTRATION ChiCTR, ChiCTR1900022324. Registered 5 April 2019-Prospective registration. https://www.chictr.org.cn/showproj.aspx?proj=37712.
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Affiliation(s)
- Yin Jiang
- grid.24696.3f0000 0004 0369 153XDepartment of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, 100070 China
| | - Tian-Shuo Yuan
- grid.24696.3f0000 0004 0369 153XDepartment of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070 China
| | - Ying-Chuan Chen
- grid.24696.3f0000 0004 0369 153XDepartment of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070 China
| | - Peng Guo
- grid.24696.3f0000 0004 0369 153XCenter for Cognitive Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070 China
| | - Teng-Hong Lian
- grid.24696.3f0000 0004 0369 153XCenter for Cognitive Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070 China
| | - Yu-Ye Liu
- grid.24696.3f0000 0004 0369 153XDepartment of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070 China
| | - Wei Liu
- grid.24696.3f0000 0004 0369 153XDepartment of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070 China
| | - Yu-Tong Bai
- grid.24696.3f0000 0004 0369 153XDepartment of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070 China
| | - Quan Zhang
- grid.24696.3f0000 0004 0369 153XDepartment of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070 China
| | - Wei Zhang
- grid.24696.3f0000 0004 0369 153XCenter for Cognitive Neurology, Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070 China
| | - Jian-Guo Zhang
- grid.24696.3f0000 0004 0369 153XDepartment of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, 100070 China ,grid.24696.3f0000 0004 0369 153XDepartment of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070 China ,grid.413259.80000 0004 0632 3337Beijing Key Laboratory of Neurostimulation, Beijing, 100070 China
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11
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Abnormal Dynamic Functional Network Connectivity in Adults with Autism Spectrum Disorder. Clin Neuroradiol 2022; 32:1087-1096. [PMID: 35543744 DOI: 10.1007/s00062-022-01173-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 04/12/2022] [Indexed: 12/15/2022]
Abstract
PURPOSE This study sought to explore changes of brain dynamic functional network connectivity (dFNC) in adults with autism spectrum disorder (ASD) and investigate their relationship with clinical manifestations. METHODS Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from 78 adult ASD patients from autism brain imaging data exchange datasets, and 65 age-matched healthy controls subjects from the local community. Independent component analysis was conducted to evaluate dFNC patterns on the basis of 13 independent components (ICs) within 11 resting-state networks (RSN), namely, auditory network (AUDN), basal ganglia network (BGN), language network (LN), sensorimotor network (SMN), precuneus network (PUCN), salience network (SN), visuospatial network (VSN), dorsal default mode network (dDMN), high visual network (hVIS), primary visual network (pVIS), ventral default mode network (vDMN). Fraction time, mean dwell time, number of transitions, and RSN connectivity were calculated for group comparisons. Correlation analyses were performed between abnormal metrics and autism diagnostic observation schedule (ADOS) scores. RESULTS Compared with controls, ASD patients had higher fraction time and mean dwell time in state 2 (P = 0.017, P = 0.014). Reduced dFNC was found in the SMN with PUCN, SMN with hVIS, and increased dFNC was observed in the dDMN with SN in state 2 in the ASD group. Fraction time and mean dwell time was positively correlated with stereotyped behavior scores of ADOS. CONCLUSION The findings demonstrated the importance of evaluating transient alterations in specific neural networks of adult ASD patients. The abnormal metrics and connectivity may be related to symptoms such as stereotyped behavior.
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Di Nardo F, Manara R, Canna A, Trojsi F, Velletrani G, Sinisi AA, Cirillo M, Tedeschi G, Esposito F. Dynamic spectral signatures of mirror movements in the sensorimotor functional connectivity network of patients with Kallmann syndrome. Front Neurosci 2022; 16:971809. [PMID: 36117618 PMCID: PMC9477102 DOI: 10.3389/fnins.2022.971809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/08/2022] [Indexed: 11/16/2022] Open
Abstract
In Kallmann syndrome (KS), the peculiar phenomenon of bimanual synkinesis or mirror movement (MM) has been associated with a spectral shift, from lower to higher frequencies, of the resting-state fMRI signal of the large-scale sensorimotor brain network (SMN). To possibly determine whether a similar frequency specificity exists across different functional connectivity SMN states, and to capture spontaneous transitions between them, we investigated the dynamic spectral changes of the SMN functional connectivity in KS patients with and without MM symptom. Brain MRI data were acquired at 3 Tesla in 39 KS patients (32 without MM, KSMM-, seven with MM, KSMM+) and 26 age- and sex-matched healthy control (HC) individuals. The imaging protocol included 20-min rs-fMRI scans enabling detailed spectro-temporal analyses of large-scale functional connectivity brain networks. Group independent component analysis was used to extract the SMN. A sliding window approach was used to extract the dynamic spectral power of the SMN functional connectivity within the canonical physiological frequency range of slow rs-fMRI signal fluctuations (0.01–0.25 Hz). K-means clustering was used to determine (and count) the most recurrent dynamic states of the SMN and detect the number of transitions between them. Two most recurrent states were identified, for which the spectral power peaked at a relatively lower (state 1) and higher (state 2) frequency. Compared to KS patients without MM and HC subjects, the SMN of KS patients with MM displayed significantly larger spectral power changes in the slow 3 canonical sub-band (0.073–0.198 Hz) and significantly fewer transitions between state 1 (less recurrent) and state 2 (more recurrent). These findings demonstrate that the presence of MM in KS patients is associated with reduced spontaneous transitions of the SMN between dynamic functional connectivity states and a higher recurrence and an increased spectral power change of the high-frequency state. These results provide novel information about the large-scale brain functional dynamics that could help to understand the pathologic mechanisms of bimanual synkinesis in KS syndrome and, potentially, other neurological disorders where MM may also occur.
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Affiliation(s)
- Federica Di Nardo
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli,”Naples, Italy
| | - Renzo Manara
- Department of Neuroscience, University of Padova, Padova, Italy
| | - Antonietta Canna
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli,”Naples, Italy
| | - Francesca Trojsi
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli,”Naples, Italy
| | - Gianluca Velletrani
- Department of Medicine, Surgery and Dentistry, University of Salerno, Salerno, Italy
| | - Antonio Agostino Sinisi
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli,”Naples, Italy
| | - Mario Cirillo
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli,”Naples, Italy
| | - Gioacchino Tedeschi
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli,”Naples, Italy
| | - Fabrizio Esposito
- Department of Advanced Medical and Surgical Sciences, University of Campania “Luigi Vanvitelli,”Naples, Italy
- *Correspondence: Fabrizio Esposito,
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13
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Li W, Wang C, Lan X, Fu L, Zhang F, Ye Y, Liu H, Wu K, Lao G, Chen J, Li G, Zhou Y, Ning Y. Aberrant Dynamic Functional Connectivity of Posterior Cingulate Cortex Subregions in Major Depressive Disorder With Suicidal Ideation. Front Neurosci 2022; 16:937145. [PMID: 35928017 PMCID: PMC9344055 DOI: 10.3389/fnins.2022.937145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 06/17/2022] [Indexed: 01/08/2023] Open
Abstract
Accumulating evidence indicates the presence of structural and functional abnormalities of the posterior cingulate cortex (PCC) in patients with major depressive disorder (MDD) with suicidal ideation (SI). Nevertheless, the subregional-level dynamic functional connectivity (dFC) of the PCC has not been investigated in MDD with SI. We therefore sought to investigate the presence of aberrant dFC variability in PCC subregions in MDD patients with SI. We analyzed resting-state functional magnetic resonance imaging (fMRI) data from 31 unmedicated MDD patients with SI (SI group), 56 unmedicated MDD patients without SI (NSI group), and 48 matched healthy control (HC) subjects. The sliding-window method was applied to characterize the whole-brain dFC of each PCC subregion [the ventral PCC (vPCC) and dorsal PCC (dPCC)]. In addition, we evaluated associations between clinical variables and the aberrant dFC variability of those brain regions showing significant between-group differences. Compared with HCS, the SI and the NSI groups exhibited higher dFC variability between the left dPCC and left fusiform gyrus and between the right vPCC and left inferior frontal gyrus (IFG). The SI group showed higher dFC variability between the left vPCC and left IFG than the NSI group. Furthermore, the dFC variability between the left vPCC and left IFG was positively correlated with Scale for Suicidal Ideation (SSI) score in patients with MDD (i.e., the SI and NSI groups). Our results indicate that aberrant dFC variability between the vPCC and IFG might provide a neural-network explanation for SI and may provide a potential target for future therapeutic interventions in MDD patients with SI.
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Affiliation(s)
- Weicheng Li
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Chengyu Wang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Xiaofeng Lan
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Ling Fu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Fan Zhang
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Yanxiang Ye
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Haiyan Liu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Kai Wu
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
- School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou, China
| | - Guohui Lao
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Jun Chen
- Guangdong Institute of Medical Instruments, Guangzhou, China
| | - Guixiang Li
- Institute of Biological and Medical Engineering, Guangdong Academy of Sciences, Guangzhou, China
| | - Yanling Zhou
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
| | - Yuping Ning
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
- The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
- Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou, China
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14
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Sahu M, Tripathi R, Jha NK, Jha SK, Ambasta RK, Kumar P. Cross talk mechanism of disturbed sleep patterns in neurological and psychological disorders. Neurosci Biobehav Rev 2022; 140:104767. [PMID: 35811007 DOI: 10.1016/j.neubiorev.2022.104767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/20/2022] [Accepted: 07/01/2022] [Indexed: 11/25/2022]
Abstract
The incidence and prevalence of sleep disorders continue to increase in the elderly populace, particularly those suffering from neurodegenerative and neuropsychiatric disorders. This not only affects the quality of life but also accelerates the progression of the disease. There are many reasons behind sleep disturbances in such patients, for instance, medication use, nocturia, obesity, environmental factors, nocturnal motor disturbances and depressive symptoms. This review focuses on the mechanism and effects of sleep dysfunction in neurodegenerative and neuropsychiatric disorders. Wherein we discuss disturbed circadian rhythm, signaling cascade and regulation of genes during sleep deprivation. Moreover, we explain the perturbation in brainwaves during disturbed sleep and the ocular perspective of neurodegenerative and neuropsychiatric manifestations in sleep disorders. Further, as the pharmacological approach is often futile and carries side effects, therefore, the non-pharmacological approach opens newer possibilities to treat these disorders and widens the landscape of treatment options for patients.
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Affiliation(s)
- Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Delhi, India
| | - Rahul Tripathi
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Delhi, India
| | - Niraj Kumar Jha
- Department of Biotechnology, School of Engineering & Technology (SET) Sharda University, UP, India
| | - Saurabh Kumar Jha
- Department of Biotechnology, School of Engineering & Technology (SET) Sharda University, UP, India.
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Delhi, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Delhi Technological University (Formerly Delhi College of Engineering), Delhi, India.
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15
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Rahaman MA, Damaraju E, Saha DK, Plis SM, Calhoun VD. Statelets: Capturing recurrent transient variations in dynamic functional network connectivity. Hum Brain Mapp 2022; 43:2503-2518. [PMID: 35274791 PMCID: PMC9057100 DOI: 10.1002/hbm.25799] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 12/23/2021] [Accepted: 01/24/2022] [Indexed: 11/17/2022] Open
Abstract
Dynamic functional network connectivity (dFNC) analysis is a widely used approach for capturing brain activation patterns, connectivity states, and network organization. However, a typical sliding window plus clustering (SWC) approach for analyzing dFNC models the system through a fixed sequence of connectivity states. SWC assumes connectivity patterns span throughout the brain, but they are relatively spatially constrained and temporally short‐lived in practice. Thus, SWC is neither designed to capture transient dynamic changes nor heterogeneity across subjects/time. We propose a state‐space time series summarization framework called “statelets” to address these shortcomings. It models functional connectivity dynamics at fine‐grained timescales, adapting time series motifs to changes in connectivity strength, and constructs a concise yet informative representation of the original data that conveys easily comprehensible information about the phenotypes. We leverage the earth mover distance in a nonstandard way to handle scale differences and utilize kernel density estimation to build a probability density profile for local motifs. We apply the framework to study dFNC of patients with schizophrenia (SZ) and healthy control (HC). Results demonstrate SZ subjects exhibit reduced modularity in their brain network organization relative to HC. Statelets in the HC group show an increased recurrence across the dFNC time‐course compared to the SZ. Analyzing the consistency of the connections across time reveals significant differences within visual, sensorimotor, and default mode regions where HC subjects show higher consistency than SZ. The introduced approach also enables handling dynamic information in cross‐modal and multimodal applications to study healthy and disordered brains.
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Affiliation(s)
- Md Abdur Rahaman
- Georgia Institute of Technology, Atlanta, Georgia, USA.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Eswar Damaraju
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Debbrata K Saha
- Georgia Institute of Technology, Atlanta, Georgia, USA.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Sergey M Plis
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Vince D Calhoun
- Georgia Institute of Technology, Atlanta, Georgia, USA.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
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16
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Huang J, Cheng R, Liu X, Chen L, Luo T. Abnormal static and dynamic functional connectivity of networks related to cognition in patients with subcortical ischemic vascular disease. Neuroradiology 2022; 64:1201-1211. [DOI: 10.1007/s00234-022-02895-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 12/20/2021] [Indexed: 12/01/2022]
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17
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Qin Y, Liu X, Guo X, Liu M, Li H, Xu S. Low-Frequency Repetitive Transcranial Magnetic Stimulation Restores Dynamic Functional Connectivity in Subcortical Stroke. Front Neurol 2021; 12:771034. [PMID: 34950102 PMCID: PMC8689061 DOI: 10.3389/fneur.2021.771034] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 10/27/2021] [Indexed: 01/09/2023] Open
Abstract
Background and Purpose: Strokes consistently result in brain network dysfunction. Previous studies have focused on the resting-state characteristics over the study period, while dynamic recombination remains largely unknown. Thus, we explored differences in dynamics between brain networks in patients who experienced subcortical stroke and the effects of low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) on dynamic functional connectivity (dFC). Methods: A total of 41 patients with subcortical stroke were randomly divided into the LF-rTMS (n = 23) and the sham stimulation groups (n = 18). Resting-state functional MRI data were collected before (1 month after stroke) and after (3 months after stroke) treatment; a total of 20 age- and sex-matched healthy controls were also included. An independent component analysis, sliding window approach, and k-means clustering were used to identify different functional networks, estimate dFC matrices, and analyze dFC states before treatment. We further assessed the effect of LF-rTMS on dFCs in patients with subcortical stroke. Results: Compared to healthy controls, patients with stroke spent significantly more time in state I [p = 0.043, effect size (ES) = 0.64] and exhibited shortened stay in state II (p = 0.015, ES = 0.78); the dwell time gradually returned to normal after LF-rTMS treatment (p = 0.015, ES = 0.55). Changes in dwell time before and after LF-rTMS treatment were positively correlated with changes in the Fugl-Meyer Assessment for Upper Extremity (pr = 0.48, p = 0.028). Moreover, patients with stroke had decreased dFCs between the sensorimotor and cognitive control domains, yet connectivity within the cognitive control network increased. These abnormalities were partially improved after LF-rTMS treatment. Conclusion: Abnormal changes were noted in temporal and spatial characteristics of sensorimotor domains and cognitive control domains of patients who experience subcortical stroke; LF-rTMS can promote the partial recovery of dFC. These findings offer new insight into the dynamic neural mechanisms underlying effect of functional recombination and rTMS in subcortical stroke. Registration: http://www.chictr.org.cn/index.aspx, Unique.identifier: ChiCTR1800019452.
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Affiliation(s)
- Yin Qin
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China
- Department of Rehabilitation Medicine, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, China
| | - Xiaoying Liu
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China
- Department of Rehabilitation Medicine, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, China
| | - Xiaoping Guo
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China
- Department of Rehabilitation Medicine, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, China
| | - Minhua Liu
- Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China
- Department of Rehabilitation Medicine, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, China
| | - Hui Li
- Department of Radiology, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, China
| | - Shangwen Xu
- Department of Radiology, The 900th Hospital of Joint Logistic Support Force, PLA, Fuzhou, China
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18
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Zhang X, Liu J, Yang Y, Zhao S, Guo L, Han J, Hu X. Test-retest reliability of dynamic functional connectivity in naturalistic paradigm functional magnetic resonance imaging. Hum Brain Mapp 2021; 43:1463-1476. [PMID: 34870361 PMCID: PMC8837589 DOI: 10.1002/hbm.25736] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 11/19/2021] [Accepted: 11/24/2021] [Indexed: 01/30/2023] Open
Abstract
Dynamic functional connectivity (dFC) has been increasingly used to characterize the brain transient temporal functional patterns and their alterations in diseased brains. Meanwhile, naturalistic neuroimaging paradigms have been an emerging approach for cognitive neuroscience with high ecological validity. However, the test–retest reliability of dFC in naturalistic paradigm neuroimaging is largely unknown. To address this issue, we examined the test–retest reliability of dFC in functional magnetic resonance imaging (fMRI) under natural viewing condition. The intraclass correlation coefficients (ICC) of four dFC statistics including standard deviation (Std), coefficient of variation (COV), amplitude of low frequency fluctuation (ALFF), and excursion (Excursion) were used to measure the test–retest reliability. The test–retest reliability of dFC in naturalistic viewing condition was then compared with that under resting state. Our experimental results showed that: (a) Global test–retest reliability of dFC was much lower than that of static functional connectivity (sFC) in both resting‐state and naturalistic viewing conditions; (b) Both global and local (including visual, limbic and default mode networks) test–retest reliability of dFC could be significantly improved in naturalistic viewing condition compared to that in resting state; (c) There existed strong negative correlation between sFC and dFC, weak negative correlation between dFC and dFC‐ICC (i.e., ICC of dFC), as well as weak positive correlation between dFC‐ICC and sFC‐ICC (i.e., ICC of sFC). The present study provides novel evidence for the promotion of naturalistic paradigm fMRI in functional brain network studies.
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Affiliation(s)
- Xin Zhang
- Institute of Medical Research, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Jiayue Liu
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Yang Yang
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Shijie Zhao
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Xintao Hu
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China
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19
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Zang F, Zhu Y, Zhang Q, Tan C, Wang Q, Xie C. APOE genotype moderates the relationship between LRP1 polymorphism and cognition across the Alzheimer's disease spectrum via disturbing default mode network. CNS Neurosci Ther 2021; 27:1385-1395. [PMID: 34387022 PMCID: PMC8504518 DOI: 10.1111/cns.13716] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 07/31/2021] [Accepted: 08/03/2021] [Indexed: 12/23/2022] Open
Abstract
AIMS This study aims to investigate the mechanisms by which apolipoprotein E (APOE) genotype modulates the relationship between low-density lipoprotein receptor-related protein 1 (LRP1) rs1799986 variant on the default mode network (DMN) and cognition in Alzheimer's disease (AD) spectrum populations. METHODS Cross-sectional 168 subjects of AD spectrum were obtained from Alzheimer's Disease Neuroimaging Initiative database with resting-state fMRI scans and neuropsychological scores data. Multivariable linear regression analysis was adopted to investigate the main effects and interaction of LRP1 and disease on the DMN. Moderation and interactive analyses were performed to assess the relationships among APOE, LRP1, and cognition. A support vector machine model was used to classify AD spectrum with altered connectivity as an objective diagnostic biomarker. RESULTS The main effects and interaction of LRP1 and disease were mainly focused on the core hubs of frontal-parietal network. Several brain regions with altered connectivity were correlated with cognitive scores in LRP1-T carriers, but not in non-carriers. APOE regulated the effect of LRP1 on cognitive performance. The functional connectivity of numerous brain regions within LRP1-T carriers yielded strong power for classifying AD spectrum. CONCLUSION These findings suggested LRP1 could affect DMN and provided a stage-dependent neuroimaging biomarker for classifying AD spectrum populations.
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Affiliation(s)
- Feifei Zang
- Department of NeurologyAffiliated ZhongDa HospitalSchool of MedicineSoutheast UniversityNanjingChina
| | - Yao Zhu
- Department of NeurologyAffiliated ZhongDa HospitalSchool of MedicineSoutheast UniversityNanjingChina
| | - Qianqian Zhang
- Department of NeurologyAffiliated ZhongDa HospitalSchool of MedicineSoutheast UniversityNanjingChina
| | - Chang Tan
- Department of NeurologyAffiliated ZhongDa HospitalSchool of MedicineSoutheast UniversityNanjingChina
| | - Qing Wang
- Department of NeurologyAffiliated ZhongDa HospitalSchool of MedicineSoutheast UniversityNanjingChina
| | - Chunming Xie
- Department of NeurologyAffiliated ZhongDa HospitalSchool of MedicineSoutheast UniversityNanjingChina
- Neuropsychiatric InstituteAffiliated ZhongDa HospitalSoutheast UniversityNanjingChina
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20
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Using multiband multi-echo imaging to improve the robustness and repeatability of co-activation pattern analysis for dynamic functional connectivity. Neuroimage 2021; 243:118555. [PMID: 34492293 PMCID: PMC10018461 DOI: 10.1016/j.neuroimage.2021.118555] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 08/24/2021] [Accepted: 09/03/2021] [Indexed: 02/04/2023] Open
Abstract
Emerging evidence has shown that functional connectivity is dynamic and changes over the course of a scan. Furthermore, connectivity patterns can arise from short periods of co-activation on the order of seconds. Recently, a dynamic co-activation patterns (CAPs) analysis was introduced to examine the co-activation of voxels resulting from individual timepoints. The goal of this study was to apply CAPs analysis on resting state fMRI data collected using an advanced multiband multi-echo (MBME) sequence, in comparison with a multiband (MB) sequence with a single echo. Data from 28 healthy control subjects were examined. Subjects underwent two resting state scans, one MBME and one MB, and 19 subjects returned within two weeks for a repeat scan session. Data preprocessing included advanced denoising namely multi-echo independent component analysis (ME-ICA) for the MBME data and an ICA-based strategy for Automatic Removal of Motion Artifacts (ICA-AROMA) for the MB data. The CAPs analysis was conducted using the newly published TbCAPs toolbox. CAPs were extracted using both seed-based and seed-free approaches. Timepoints were clustered using k-means clustering. The following metrics were compared between MBME and MB datasets: mean activation in each CAP, the spatial correlation and mean squared error (MSE) between each timepoint and the centroid CAP it was assigned to, within-dataset variance across timepoints assigned to the same CAP, and the between-session spatial correlation of each CAP. Co-activation was heightened for MBME data for the majority of CAPs. Spatial correlation and MSE between each timepoint and its assigned centroid CAP were higher and lower respectively for MBME data. The within-dataset variance was also lower for MBME data. Finally, the between-session spatial correlation was higher for MBME data. Overall, our findings suggest that the advanced MBME sequence is a promising avenue for the measurement of dynamic co-activation patterns by increasing the robustness and reproducibility of the CAPs.
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Lin S, Li J, Chen S, Lin X, Ye M, Qiu Y. Progressive Disruption of Dynamic Functional Network Connectivity in Patients With Hepatitis B Virus-related cirrhosis. J Magn Reson Imaging 2021; 54:1830-1840. [PMID: 34031950 DOI: 10.1002/jmri.27740] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 05/13/2021] [Accepted: 05/13/2021] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND The diseased-related dynamic functional network connectivity (dFNC) disruption and its relationship with cognitive impairment in hepatitis B virus-related cirrhosis (HBV-RC) patients with minimal hepatic encephalopathy (MHE) and no MHE (NMHE) remain unknown. This knowledge would help identify MHE pathophysiology and monitor disease progression in HBV-RC patients. PURPOSE To investigate the dFNC in patients with NMHE and MHE and the relationship between dFNC indices with the psychometric hepatic encephalopathy score (PHES). STUDY TYPE Prospective. POPULATION Thirty HBV-RC patients (including 17 NMHE and 13 MHE) and 38 healthy controls (HC). FIELD STRENGTH/SEQUENCE A 1.5 T MRI and gradient-echo echo-planar imaging and fast field echo three-dimensional T1-weighted imaging. ASSESSMENT The independent components, dFNC matrix and dFNC indices (mean dwell times [DT], number of states, number of transitions, and fraction time in each state), were obtained through GIFT package. Cognitive measurement and patients grouping were based on PHES tests. STATISTICAL TESTS One-way ANOVA, chi-square test, two-sample t-test, Kruskal-Wallis test, spearman's correlation analysis and the false discovery rate. Significance level: P < 0.05. RESULTS Compared to HC (21.1 ± 4.02), the DT of state 1 decreased in NMHE (9.0 ± 3.04, P = 0.062, 95% confidence interval [CI] is -0.65 to 24.88) and significantly in MHE stage (1.2 ± 1.01) and was significantly correlated with PHES (r = 0.5) for all patients. The DT of state 2 increased gradually in NMHE (75.2 ± 13.10, P = 0.052, 95% CI, -54.23 to 0.28) and significantly in MHE stage (94.6 ± 15.61) when compared to HC (48.2 ± 6.97). Moreover, the connectivity between cognitive control network (CCN) and visual network (VIS) in state 1 (0.7 ± 0.79) and between default mode (DMN) and VIS in state 2 (-0.2 ± 0.29) decreased significantly in MHE when compared to HC (0.1 ± 0.68 for CCN-VIS in state 1 and 0.1 ± 0.17 for DMN-VIS for state 2). DATA CONCLUSION: dFNC exhibited progressive impairment as the disease advances in patients with HBV-RC. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Shiwei Lin
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Jing Li
- Department of Medical Imaging, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China
| | - Shengli Chen
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Xiaoshan Lin
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Min Ye
- Department of Geriatrics, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China.,Department of Geriatrics, Guangzhou First People's Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Yingwei Qiu
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong, China
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22
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Bonkhoff AK, Schirmer MD, Bretzner M, Etherton M, Donahue K, Tuozzo C, Nardin M, Giese A, Wu O, D. Calhoun V, Grefkes C, Rost NS. Abnormal dynamic functional connectivity is linked to recovery after acute ischemic stroke. Hum Brain Mapp 2021; 42:2278-2291. [PMID: 33650754 PMCID: PMC8046120 DOI: 10.1002/hbm.25366] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 01/26/2021] [Accepted: 01/29/2021] [Indexed: 12/30/2022] Open
Abstract
The aim of the current study was to explore the whole-brain dynamic functional connectivity patterns in acute ischemic stroke (AIS) patients and their relation to short and long-term stroke severity. We investigated resting-state functional MRI-based dynamic functional connectivity of 41 AIS patients two to five days after symptom onset. Re-occurring dynamic connectivity configurations were obtained using a sliding window approach and k-means clustering. We evaluated differences in dynamic patterns between three NIHSS-stroke severity defined groups (mildly, moderately, and severely affected patients). Furthermore, we built Bayesian hierarchical models to evaluate the predictive capacity of dynamic connectivity and examine the interrelation with clinical measures, such as white matter hyperintensity lesions. Finally, we established correlation analyses between dynamic connectivity and AIS severity as well as 90-day neurological recovery (ΔNIHSS). We identified three distinct dynamic connectivity configurations acutely post-stroke. More severely affected patients spent significantly more time in a configuration that was characterized by particularly strong connectivity and isolated processing of functional brain domains (three-level ANOVA: p < .05, post hoc t tests: p < .05, FDR-corrected). Configuration-specific time estimates possessed predictive capacity of stroke severity in addition to the one of clinical measures. Recovery, as indexed by the realized change of the NIHSS over time, was significantly linked to the dynamic connectivity between bilateral intraparietal lobule and left angular gyrus (Pearson's r = -.68, p = .003, FDR-corrected). Our findings demonstrate transiently increased isolated information processing in multiple functional domains in case of severe AIS. Dynamic connectivity involving default mode network components significantly correlated with recovery in the first 3 months poststroke.
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Affiliation(s)
- Anna K. Bonkhoff
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
- Cognitive NeuroscienceInstitute of Neuroscience and Medicine (INM‐3), Research Centre JuelichJuelichGermany
| | - Markus D. Schirmer
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
- Department of Population Health SciencesGerman Centre for Neurodegenerative Diseases (DZNE)Germany
| | - Martin Bretzner
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
- Neurosciences and CognitionUniversity of LilleLilleFrance
| | - Mark Etherton
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
| | - Kathleen Donahue
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
| | - Carissa Tuozzo
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
| | - Marco Nardin
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
| | - Anne‐Katrin Giese
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | - Vince D. Calhoun
- Tri‐institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of TechnologyEmory UniversityAtlantaGeorgiaUSA
| | - Christian Grefkes
- Cognitive NeuroscienceInstitute of Neuroscience and Medicine (INM‐3), Research Centre JuelichJuelichGermany
- Department of NeurologyUniversity Hospital CologneCologneGermany
| | - Natalia S. Rost
- J. Philip Kistler Stroke Research CenterMassachusetts General HospitalBostonMassachusettsUSA
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23
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Dynamic Functional Network Connectivity Changes Associated with fMRI Neurofeedback of Right Premotor Cortex. Brain Sci 2021; 11:brainsci11050582. [PMID: 33946251 PMCID: PMC8147082 DOI: 10.3390/brainsci11050582] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 04/27/2021] [Accepted: 04/27/2021] [Indexed: 01/03/2023] Open
Abstract
Neurofeedback of real-time functional magnetic resonance imaging (rtfMRI) can enable people to self-regulate motor-related brain regions and lead to alteration of motor performance and functional connectivity (FC) underlying motor execution tasks. Numerous studies suggest that FCs dynamically fluctuate over time. However, little is known about the impact of neurofeedback training of the motor-related region on the dynamic characteristics of FC underlying motor execution tasks. This study aims to investigate the mechanism of self-regulation of the right premotor area (PMA) on the underlying dynamic functional network connectivity (DFNC) of motor execution (ME) tasks and reveal the relationship between DFNC, training effect, and motor performance. The results indicate that the experimental group spent less time on state 2, with overall weak connections, and more time on state 6, having strong positive connections between motor-related networks than the control group after the training. For the experimental group’s state 2, the mean dwell time after the training showed negative correlation with the tapping frequency and the amount of upregulation of PMA. The findings show that rtfMRI neurofeedback can change the temporal properties of DFNC, and the DFNC changes in state with overall weak connections were associated with the training effect and the improvement in motor performance.
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24
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Li K, Fu Z, Luo X, Zeng Q, Huang P, Zhang M, Vince CD. The Influence of Cerebral Small Vessel Disease on Static and Dynamic Functional Network Connectivity in Subjects Along Alzheimer's Disease Continuum. Brain Connect 2021; 11:189-200. [PMID: 33198482 DOI: 10.1089/brain.2020.0819] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Background: Alzheimer's disease (AD) is a chronic neurodegenerative disorder frequently accompanied by cerebral small vessel disease (CSVD). However, the influence of CSVD on the brain functional connectivity in subjects along the AD continuum is still largely unknown. The current study combined the static and dynamic functional network connectivity (FNC) to explore the underlying mechanism. Materials and Methods: In this study, we included 182 healthy controls, 27 individuals with subjective cognitive decline (SCD), 27 with SCD+CSVD, 104 with mild cognitive impairment (MCI), 123 with MCI+CSVD, 16 with AD, and 62 with AD+CSVD. We examined the static and dynamic FNC within the default mode, salience, and cognitive control domains. We also assessed the association between atypical FNC patterns and cognitive impairments, as well as the pathologies. Results: Static FNC results showed progressively increased within-domain connectivity and decreased between-domain connectivity along the AD continuum, especially in CSVD subjects. Dynamic FNC in CSVD subjects showed more occurrences in a highly modularized state and fewer occurrences in the diffusely connected state. Further analysis showed that neuropathology and CSVD burden divergently affect the FNC changes. Conclusions: The overall results demonstrate divergent abnormalities of FNC in CSVD and non-CSVD individuals along the AD continuum, which were divergently affected by neuropathology and CSVD burden. Specifically, those with CSVD show more static and dynamic FNC impairments, associated with cognitive decline. These findings may advance our understanding of the effect of CSVD on AD onset and progression, and provide potential hints for clinical treatment.
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Affiliation(s)
- Kaicheng Li
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.,Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Zening Fu
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Xiao Luo
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Qingze Zeng
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The 2nd Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Calhoun D Vince
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.,Department of Psychology, Computer Science, Neuroscience Institute, and Physics, Georgia State University, Atlanta, Georgia, China.,Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, China
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25
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Chen HJ, Zou ZY, Zhang XH, Shi JY, Huang NX, Lin YJ. Dynamic Changes in Functional Network Connectivity Involving Amyotrophic Lateral Sclerosis and Its Correlation With Disease Severity. J Magn Reson Imaging 2021; 54:239-248. [PMID: 33559360 DOI: 10.1002/jmri.27521] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Revised: 12/28/2020] [Accepted: 12/29/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Aberrant static functional connectivity (FC) has been well demonstrated in amyotrophic lateral sclerosis (ALS); however, ALS-related alterations in FC dynamic properties remain unclear, although dynamic FC analyses contribute to uncover mechanisms underlying neurodegenerative disorders. PURPOSE To explore dynamic functional network connectivity (dFNC) in ALS and its correlation with disease severity. STUDY TYPE Prospective. SUBJECTS Thirty-two ALS patients and 45 healthy controls. FIELD STRENGTH/SEQUENCE Multiband resting-state functional images using gradient echo echo-planar imaging and T1-weighted images were acquired at 3.0 T. ASSESSMENT Disease severity was evaluated with the revised ALS Functional Rating Scale (ALSFRS-R) and patients were stratified according to diagnostic category. Independent component analysis was conducted to identify the components of seven intrinsic brain networks (ie, visual/sensorimotor (SMN)/auditory/cognitive-control (CCN)/default-mode (DMN)/subcortical/cerebellar networks). A sliding-window correlation approach was used to compute dFNC. FNC states were determined by k-mean clustering, and state-specific FNC and dynamic indices (fraction time/mean dwell time/transition number) were calculated. STATISTICAL TESTS Two-sample t test used for comparisons on dynamic measures and Spearman's correlation analysis. RESULTS ALS patients showed increased FNC between DMN-SMN in state 1 and between CCN-SMN in state 4. Patients remained in state 2 (showing the weakest FNC) for a significantly longer time (mean dwell time: 49.8 ± 40.1 vs. 93.6 ± 126.3; P < 0.05) and remained in state 1 (showing a relatively strong FNC) for a shorter time (fraction time: 0.27 ± 0.25 vs. 0.13 ± 0.20; P < 0.05). ALS patients exhibited less temporal variability in their FNC (transition number: 10.2 ± 4.4 vs. 7.8 ± 3.8; P < 0.05). A significant correlation was observed between ALSFRS-R and mean dwell time in state 2 (r = -0.414, P < 0.05) and transition number (r = 0.452, P < 0.05). No significant between-subgroup difference in dFNC properties was found (all P > 0.05). DATA CONCLUSION Our findings suggest aberrant dFNC properties in ALS, which is associated with disease severity. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
- Hua-Jun Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Zhang-Yu Zou
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Xiao-Hong Zhang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Jia-Yan Shi
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Nao-Xin Huang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Yan-Juan Lin
- Department of Cardiovascular Surgery, Fujian Medical University Union Hospital, Fuzhou, China.,Department of Nursing, Fujian Medical University Union Hospital, Fuzhou, China
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26
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Wang Y, Wang C, Miao P, Liu J, Wei Y, Wu L, Wang K, Cheng J. An imbalance between functional segregation and integration in patients with pontine stroke: A dynamic functional network connectivity study. NEUROIMAGE-CLINICAL 2020; 28:102507. [PMID: 33395996 PMCID: PMC7714678 DOI: 10.1016/j.nicl.2020.102507] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 10/24/2020] [Accepted: 11/12/2020] [Indexed: 11/04/2022]
Abstract
Pontine stroke patients show abnormal time-varying brain activity. Pontine stroke patients exist aberrant functional segregation and integration. The alterations of dFNC may lead to a poor functional recovery after stroke.
Background Previous studies on brain functional connectivity have revealed the neural physiopathology in patients with pontine stroke (PS). However, those studies focused only on the static features of intrinsic fluctuations, rather than on the time-varying effects throughout the entire scan. In the present study, we sought to explore the underlying mechanism of PS using the dynamic functional network connectivity (dFNC) method. Methods Resting-state functional magnetic resonance imaging (fMRI) data were collected from 58 patients with PS and 52 healthy controls (HC). Independent component analysis (ICA), the sliding window method, and k-means clustering analysis were performed to extract different functional networks, to calculate dFNC matrices, and to estimate distinct dynamic connectivity states. Additionally, temporal features were compared between the two groups in each state to explore the brain’s preference for different dynamic connectivity states in PS, and global and local efficiency were compared among states to explore the differences of topologic organization across different dFNC states. The correlations between clinical scales and the temporal features that differed between the two groups also were calculated. Results The dFNC analyses suggested four recurring states; in two of these states, the PS group showed a different duration from that of the HC group. Patients with PS spent significantly more time in a sparsely connected state (State 1), which was characterized by relatively low levels of connectivity within and between all brain networks. In contrast, patients with PS spent significantly less time in a highly segregated state (State 2), which was characterized by high levels of positive connectivities within primary perceptional domains and within higher cognitive control domains, and by high levels of negative inter-functional connectivities (inter-FCs) among primary perceptional and higher cognitive control domains. Additionally, the dwell time in State 2 was positively correlated with HC group’s long-term memory scores in the Rey Auditory Verbal Learning Test (RAVLT-L), whereas there was no correlation between the State-2 dwell time and RAVLT-L scores in the PS group. Furthermore, the sparsely connected state and the highly segregated state mentioned above had the highest global efficiency and the highest local efficiency among the four states, respectively. Conclusions In summary, we observed a preference in the aberrant brain for dynamic connectivity states with different network topologic organization in patients with PS, indicating abnormal functional segregation and integration of the whole brain and confirming the imperfection of functional network connectivity in patients with PS. These findings provide new evidence for the dynamic neural mechanisms underlying clinical symptoms in patients with PS.
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Affiliation(s)
- Yingying Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Caihong Wang
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Peifang Miao
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingchun Liu
- Department of Radiology, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Ying Wei
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Luobing Wu
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kaiyu Wang
- GE Healthcare MR Research, Beijing, China
| | - Jingliang Cheng
- Department of MRI, Henan Key Laboratory of Magnetic Resonance Function and Molecular Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
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27
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Agcaoglu O, Wilson TW, Wang YP, Stephen JM, Calhoun VD. Dynamic Resting-State Connectivity Differences in Eyes Open Versus Eyes Closed Conditions. Brain Connect 2020; 10:504-519. [PMID: 32892633 DOI: 10.1089/brain.2020.0768] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Introduction: Previous studies have shown significant conditional differences between eyes open, fixated at an image (EO) and eyes closed (EC) in the acquired resting-state functional magnetic resonance imaging (rs-fMRI) data. Aim: We recently showed significant functional network connectivity (FNC) differences between EO and EC across a variety of networks. In this study, we aim at further evaluating differences in dynamic FNC (dFNC) between EO and EC. Materials and Methods: Rs-fMRI were collected from adolescents aged 9-15 years old during both EO and EC conditions, and dFNC was calculated by using the independent component analysis framework. Results: We found that out of five states (clusters), state 1 was observed to be more dominant in the EO condition, whereas state 2 was observed to be more dominant in the EC condition. States 1 and 2 showed significant differences in the mean dwell time based on false discovery rate, and states 1, 2, 3, and 4 differed in the frequency of occurrences. These results are consistent with our previous study of static connectivity in suggesting that EO and EC differences not only are relatively strong but also importantly reveal that these differences vary over time, especially in one particularly transient connectivity pattern. Conclusion: Our results manifest as changes in the proportion of time spent in unique functional connectivity patterns, and they show unique transient functional connectivity patterns in a subset of identified states. Overall, our findings indicate that both static and dynamic rs-fMRI connectivity patterns are strongly impacted by basic conditional differences such as EO and EC. Impact statement Our findings not only suggest that eyes open, fixated at an image (EO) and eyes closed (EC) condition-related resting state functional magnetic resonance imaging differences are relatively strong, but they also reveal an important attribute of these conditions that these differences vary over time, especially in one particularly transient connectivity pattern. Our results manifest as changes in the proportion of time spent in unique functional connectivity patterns, and they show unique transient functional connectivity patterns in a subset of identified states. We believe there is benefit in having the EO/EC as a contrast of interest in future studies, if time allows.
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Affiliation(s)
- Oktay Agcaoglu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Tony W Wilson
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, Louisiana, USA.,Department of Global Biostatistics and Data Science, Tulane University, New Orleans, Louisiana, USA
| | | | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA.,The Mind Research Network, Albuquerque, New Mexico, USA
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28
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Aberrant dynamic functional network connectivity in cirrhotic patients without overt hepatic encephalopathy. Eur J Radiol 2020; 132:109324. [PMID: 33038576 DOI: 10.1016/j.ejrad.2020.109324] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 09/16/2020] [Accepted: 09/25/2020] [Indexed: 02/08/2023]
Abstract
PURPOSE Neurocognitive impairment is a common complication in cirrhosis and is associated with alterations in static functional network connectivity (FNC) between distinct brain systems. However, accumulating evidence suggests temporal variability in FNC even at rest. This study aimed to explore dynamic FNC (dFNC) differences and to elucidate their association with neurocognitive changes in cirrhotic patients. METHODS Fifty-four cirrhotic patients and 42 controls underwent resting-state functional magnetic resonance imaging. Psychometric hepatic encephalopathy score (PHES) was used to assess neurocognitive function. Independent component analysis was performed to identify the components of seven intrinsic brain networks, including sensorimotor (SMN), auditory, visual, cognitive control (CCN), default mode (DMN), subcortical (SC), and cerebellar networks. Sliding window correlation approach was employed to calculate dFNC. FNC states were determined by k-means clustering method, and then functional state analysis was conducted to measure dynamic indices. RESULTS The patients showed decreased dFNC in State 2, involving the connectivity between posterior subsystem of DMN and CCN (represented by bilateral insular cortex), and in State 3, involving the connectivity between SMN (represented by bilateral precentral gyrus) and SC (represented by bilateral putamen and caudate). The patients spent significantly longer time in State 4 that was with weakest FNC across all networks. We observed a significant correlation between PHES and fraction time/mean dwell time in State 4. CONCLUSIONS Aberrant dFNC may be the underlying mechanism of neurocognitive impairments in cirrhosis. Dynamic FNC analysis may potentially be utilized in investigating cirrhosis-related neuropathological processes.
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Hilger K, Winter NR, Leenings R, Sassenhagen J, Hahn T, Basten U, Fiebach CJ. Predicting intelligence from brain gray matter volume. Brain Struct Funct 2020; 225:2111-2129. [PMID: 32696074 PMCID: PMC7473979 DOI: 10.1007/s00429-020-02113-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 07/04/2020] [Indexed: 12/21/2022]
Abstract
A positive association between brain size and intelligence is firmly established, but whether region-specific anatomical differences contribute to general intelligence remains an open question. Results from voxel-based morphometry (VBM) - one of the most widely used morphometric methods - have remained inconclusive so far. Here, we applied cross-validated machine learning-based predictive modeling to test whether out-of-sample prediction of individual intelligence scores is possible on the basis of voxel-wise gray matter volume. Features were derived from structural magnetic resonance imaging data (N = 308) using (a) a purely data-driven method (principal component analysis) and (b) a domain knowledge-based approach (atlas parcellation). When using relative gray matter (corrected for total brain size), only the atlas-based approach provided significant prediction, while absolute gray matter (uncorrected) allowed for above-chance prediction with both approaches. Importantly, in all significant predictions, the absolute error was relatively high, i.e., greater than ten IQ points, and in the atlas-based models, the predicted IQ scores varied closely around the sample mean. This renders the practical value even of statistically significant prediction results questionable. Analyses based on the gray matter of functional brain networks yielded significant predictions for the fronto-parietal network and the cerebellum. However, the mean absolute errors were not reduced in contrast to the global models, suggesting that general intelligence may be related more to global than region-specific differences in gray matter volume. More generally, our study highlights the importance of predictive statistical analysis approaches for clarifying the neurobiological bases of intelligence and provides important suggestions for future research using predictive modeling.
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Affiliation(s)
- Kirsten Hilger
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany.
- Department of Psychology, Julius Maximilian University Würzburg, Würzburg, Germany.
- IDeA Center for Individual Development and Adaptive Education, Frankfurt am Main, Germany.
- Department of Psychology I, University Wuerzburg, Marcusstr. 9-11, 97070, Würzburg, Germany.
| | - Nils R Winter
- Institute of Translational Psychiatry, University Hospital Münster, Münster, Germany
| | - Ramona Leenings
- Institute of Translational Psychiatry, University Hospital Münster, Münster, Germany
| | - Jona Sassenhagen
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Tim Hahn
- Institute of Translational Psychiatry, University Hospital Münster, Münster, Germany
| | - Ulrike Basten
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Christian J Fiebach
- Department of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany
- IDeA Center for Individual Development and Adaptive Education, Frankfurt am Main, Germany
- Brain Imaging Center, Goethe University Frankfurt, Frankfurt am Main, Germany
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Weng Y, Liu X, Hu H, Huang H, Zheng S, Chen Q, Song J, Cao B, Wang J, Wang S, Huang R. Open eyes and closed eyes elicit different temporal properties of brain functional networks. Neuroimage 2020; 222:117230. [PMID: 32771616 DOI: 10.1016/j.neuroimage.2020.117230] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2019] [Revised: 07/24/2020] [Accepted: 07/31/2020] [Indexed: 12/16/2022] Open
Abstract
The eyes are our windows to the brain. There are differences in brain activity between people who have their eyes closed (EC) and eyes open (EO). Previous studies focused on differences in brain functional properties between these eyes conditions based on an assumption that brain activity is a static phenomenon. However, the dynamic nature of the brain activity in different eyes conditions is still unclear. In this study, we collected resting-state fMRI data from 21 healthy subjects in the EC and EO conditions. Using a sliding time window approach and a k-means clustering algorithm, we calculated the temporal properties of dynamic functional connectivity (dFC) states in the eyes conditions. We also used graph theory to estimate the dynamic topological properties of functional networks in the two conditions. We detected two dFC states, a hyper-connected State 1 and a hypo-connected State 2. We showed the following results: (i) subjects in the EC condition stayed longer in the hyper-connected State 1 than those in the EO; (ii) subjects in the EO condition stayed longer in the hypo-connected State 2 than those in the EC; and (iii) the dFC state transformed into the other state more frequently during EC than during EO. We also found the variance of the characteristic path length was higher during EC than during EO in the hyper-connected State 1. These results indicate that brain activity may be more active and unstable during EC than during EO. Our findings may provide insights into the dynamic nature of the resting-state brain and could be a useful reference for future rs-fMRI studies.
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Affiliation(s)
- Yihe Weng
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, 510631 Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Xiaojin Liu
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, 510631 Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Huiqing Hu
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, 510631 Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Huiyuan Huang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, 510631 Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Senning Zheng
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, 510631 Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Qinyuan Chen
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Jie Song
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, 510631 Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Bolin Cao
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, 510631 Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Junjing Wang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, 510631 Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Shuai Wang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, 510631 Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Ruiwang Huang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, China; School of Psychology, South China Normal University, 510631 Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China.
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31
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Cheong RY, Gabery S, Petersén Å. The Role of Hypothalamic Pathology for Non-Motor Features of Huntington's Disease. J Huntingtons Dis 2020; 8:375-391. [PMID: 31594240 PMCID: PMC6839491 DOI: 10.3233/jhd-190372] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Huntington’s disease (HD) is a fatal genetic neurodegenerative disorder. It has mainly been considered a movement disorder with cognitive symptoms and these features have been associated with pathology of the striatum and cerebral cortex. Importantly, individuals with the mutant huntingtin gene suffer from a spectrum of non-motor features often decades before the motor disorder manifests. These symptoms and signs include a range of psychiatric symptoms, sleep problems and metabolic changes with weight loss particularly in later stages. A higher body mass index at diagnosis is associated with slower disease progression. The common psychiatric symptom of apathy progresses with the disease. The fact that non-motor features are present early in the disease and that they show an association to disease progression suggest that unravelling the underlying neurobiological mechanisms may uncover novel targets for early disease intervention and better symptomatic treatment. The hypothalamus and the limbic system are important brain regions that regulate emotion, social cognition, sleep and metabolism. A number of studies using neuroimaging, postmortem human tissue and genetic manipulation in animal models of the disease has collectively shown that the hypothalamus and the limbic system are affected in HD. These findings include the loss of neuropeptide-expressing neurons such as orexin (hypocretin), oxytocin, vasopressin, somatostatin and VIP, and increased levels of SIRT1 in distinct nuclei of the hypothalamus. This review provides a summary of the results obtained so far and highlights the potential importance of these changes for the understanding of non-motor features in HD.
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Affiliation(s)
- Rachel Y Cheong
- Translational Neuroendocrine Research Unit, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Sanaz Gabery
- Translational Neuroendocrine Research Unit, Department of Experimental Medical Science, Lund University, Lund, Sweden
| | - Åsa Petersén
- Translational Neuroendocrine Research Unit, Department of Experimental Medical Science, Lund University, Lund, Sweden
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Zhou Z, Cai B, Zhang G, Zhang A, Calhoun VD, Wang YP. Prediction and classification of sleep quality based on phase synchronization related whole-brain dynamic connectivity using resting state fMRI. Neuroimage 2020; 221:117190. [PMID: 32711063 DOI: 10.1016/j.neuroimage.2020.117190] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2020] [Revised: 07/15/2020] [Accepted: 07/19/2020] [Indexed: 12/15/2022] Open
Abstract
Recently, functional network connectivity (FNC) has been extended from static to dynamic analysis to explore the time-varying functional organization of brain networks. Nowadays, a majority of dynamic FNC (dFNC) analysis frameworks identified recurring FNC patterns with linear correlations based on the amplitude of fMRI time series. However, the brain is a complex dynamical system and phase synchronization provides more informative measures. This paper proposes a novel framework for the prediction/classification of behaviors and cognitions based on the dFNCs derived from phase locking value. When applying to the analysis of fMRI data from Human Connectome Project (HCP), four dFNC states are identified for the study of sleep quality. State 1 exhibits most intense phase synchronization across the whole brain. States 2 and 3 have low and weak connections, respectively. State 4 exhibits strong phase synchronization in intra and inter-connections of somatomotor, visual and cognitive control networks. Through the two-sample t-test, we reveal that for the group with bad sleep quality, state 4 shows decreased phase synchronization within and between networks such as subcortical, auditory, somatomotor and visual, but increased phase synchronization within cognitive control network, and between this network and somatomotor/visual/default-mode/cerebellar networks. The networks with increased phase synchronization in state 4 behave oppositely in state 2. Group differences are absent in state 3, and weak in state 1. We establish a prediction model by linear regression of FNC against sleep quality, and adopt a support vector machine approach for the classification. We compare the performance between conventional FNC and PLV-based dFNC with cross-validation. Results show that the PLV-based dFNC significantly outperforms the conventional FNC in terms of both predictive power and classification accuracy. We also observe that combining static and dynamic features does not significantly improve the classification over using dFNC features alone. Overall, the proposed approach provides a novel means to assess dFNC, which can be used as brain fingerprints to facilitate prediction and classification.
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Affiliation(s)
- Zhongxing Zhou
- Biomedical Engineering Department, Tulane University, New Orleans, LA, United States; Tianjin University, School of Precision Instruments and Optoelectronics Engineering, Tianjin, China
| | - Biao Cai
- Biomedical Engineering Department, Tulane University, New Orleans, LA, United States
| | - Gemeng Zhang
- Biomedical Engineering Department, Tulane University, New Orleans, LA, United States
| | - Aiying Zhang
- Biomedical Engineering Department, Tulane University, New Orleans, LA, United States
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, United States; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico, United States
| | - Yu-Ping Wang
- Biomedical Engineering Department, Tulane University, New Orleans, LA, United States.
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Xue T, Dong F, Huang R, Tao Z, Tang J, Cheng Y, Zhou M, Hu Y, Li X, Yu D, Ju H, Yuan K. Dynamic Neuroimaging Biomarkers of Smoking in Young Smokers. Front Psychiatry 2020; 11:663. [PMID: 32754067 PMCID: PMC7367415 DOI: 10.3389/fpsyt.2020.00663] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Accepted: 06/26/2020] [Indexed: 01/06/2023] Open
Abstract
OBJECTIVE To examine potential changes in the dynamic characteristics of regional neural activity in young smokers and to detect whether the changes were associated with smoking behavior. METHODS The dynamic regional homogeneity (dReHo) and dynamic amplitude of low-frequency fluctuations (dALFF) in 40 young smokers and 42 nonsmokers were compared. Correlation analyses were also performed between dReHo and dALFF in areas showing group differences and smoking behavior [e.g., the Fagerström Test for Nicotine dependence (FTND) scores and pack-years]. RESULTS Significantly differences in dReHo variability were observed in the inferior frontal gyrus (IFG), superior frontal gyrus (SFG), medial frontal gyrus (MFG), insula, cuneus, postcentral gyrus, inferior semi-lunar lobule, orbitofrontal gyrus, and inferior temporal gyrus (ITG). Young smokers also showed significantly increased dALFF variability in the anterior cingulate cortex (ACC) and ITG. Furthermore, a significant positive correlation was found between dALFF variability in the ACC and the pack-years; whereas a significant negative correlation between dReHo variability in the IFG and the FTND scores was found in young smokers. CONCLUSION The pattern of resting state regional neural activity variability was different between young smokers and nonsmokers. Dynamic regional indexes might be a novel neuroimaging biomarker of smoking behavior in young smokers.
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Affiliation(s)
- Ting Xue
- School of Science, Inner Mongolia University of Science and Technology, Baotou, China
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Fang Dong
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Ruoyan Huang
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Zhanlong Tao
- School of Science, Inner Mongolia University of Science and Technology, Baotou, China
| | - Jun Tang
- School of Science, Inner Mongolia University of Science and Technology, Baotou, China
| | - Yongxin Cheng
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Mi Zhou
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Yiting Hu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Xiaojian Li
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Dahua Yu
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
| | - Haitao Ju
- Department of Neurosurgery, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, China
| | - Kai Yuan
- Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, China
- Life Sciences Research Center, School of Life Science and Technology, Xidian University, Xi’an, China
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34
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Wilton DK, Stevens B. The contribution of glial cells to Huntington's disease pathogenesis. Neurobiol Dis 2020; 143:104963. [PMID: 32593752 DOI: 10.1016/j.nbd.2020.104963] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 05/07/2020] [Accepted: 06/10/2020] [Indexed: 12/20/2022] Open
Abstract
Glial cells play critical roles in the normal development and function of neural circuits, but in many neurodegenerative diseases, they become dysregulated and may contribute to the development of brain pathology. In Huntington's disease (HD), glial cells both lose normal functions and gain neuropathic phenotypes. In addition, cell-autonomous dysfunction elicited by mutant huntingtin (mHTT) expression in specific glial cell types is sufficient to induce both pathology and Huntington's disease-related impairments in motor and cognitive performance, suggesting that these cells may drive the development of certain aspects of Huntington's disease pathogenesis. In support of this imaging studies in pre-symptomatic HD patients and work on mouse models have suggested that glial cell dysfunction occurs at a very early stage of the disease, prior to the onset of motor and cognitive deficits. Furthermore, selectively ablating mHTT from specific glial cells or correcting for HD-induced changes in their transcriptional profile rescues some HD-related phenotypes, demonstrating the potential of targeting these cells for therapeutic intervention. Here we review emerging research focused on understanding the involvement of different glial cell types in specific aspects of HD pathogenesis. This work is providing new insight into how HD impacts biological functions of glial cells in the healthy brain as well as how HD induced dysfunction in these cells might change the way they integrate into biological circuits.
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Affiliation(s)
- Daniel K Wilton
- Department of Neurology, F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Beth Stevens
- Department of Neurology, F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA; Stanley Center, Broad Institute, Cambridge, MA 02142, USA; Howard Hughes Medical Institute, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.
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35
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Chakroborty S, Manfredsson FP, Dec AM, Campbell PW, Stutzmann GE, Beaumont V, West AR. Phosphodiesterase 9A Inhibition Facilitates Corticostriatal Transmission in Wild-Type and Transgenic Rats That Model Huntington's Disease. Front Neurosci 2020; 14:466. [PMID: 32581668 PMCID: PMC7283904 DOI: 10.3389/fnins.2020.00466] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 04/15/2020] [Indexed: 12/18/2022] Open
Abstract
Huntington's disease (HD) results from abnormal expansion in CAG trinucleotide repeats within the HD gene, a mutation which leads to degeneration of striatal medium-sized spiny neurons (MSNs), deficits in corticostriatal transmission, and loss of motor control. Recent studies also indicate that metabolism of cyclic nucleotides by phosphodiesterases (PDEs) is dysregulated in striatal networks in a manner linked to deficits in corticostriatal transmission. The current study assessed cortically-evoked firing in electrophysiologically-identified MSNs and fast-spiking interneurons (FSIs) in aged (9-11 months old) wild-type (WT) and BACHD transgenic rats (TG5) treated with vehicle or the selective PDE9A inhibitor PF-04447943. WT and TG5 rats were anesthetized with urethane and single-unit activity was isolated during low frequency electrical stimulation of the ipsilateral motor cortex. Compared to WT controls, MSNs recorded in TG5 animals exhibited decreased spike probability during cortical stimulation delivered at low to moderate stimulation intensities. Moreover, large increases in onset latency of cortically-evoked spikes and decreases in spike probability were observed in FSIs recorded in TG5 animals. Acute systemic administration of the PDE9A inhibitor PF-04447943 significantly decreased the onset latency of cortically-evoked spikes in MSNs recorded in WT and TG5 rats. PDE9A inhibition also increased the proportion of MSNs responding to cortical stimulation and reversed deficits in spike probability observed in TG5 rats. As PDE9A is a cGMP specific enzyme, drugs such as PF-04447943 which act to facilitate striatal cGMP signaling and glutamatergic corticostriatal transmission could be useful therapeutic agents for restoring striatal function and alleviating motor and cognitive symptoms associated with HD.
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Affiliation(s)
- Shreaya Chakroborty
- Department of Neuroscience, Rosalind Franklin University of Medicine and Science, North Chicago, IL, United States
| | - Fredric P Manfredsson
- Parkinson's Disease Research Unit, Department of Neurobiology, Barrow Neurological Institute, Phoenix, AZ, United States
| | - Alexander M Dec
- Department of Neuroscience, Rosalind Franklin University of Medicine and Science, North Chicago, IL, United States
| | - Peter W Campbell
- Department of Neuroscience, Rosalind Franklin University of Medicine and Science, North Chicago, IL, United States
| | - Grace E Stutzmann
- Department of Neuroscience, Rosalind Franklin University of Medicine and Science, North Chicago, IL, United States
| | - Vahri Beaumont
- CHDI Management/CHDI Foundation, Los Angeles, CA, United States
| | - Anthony R West
- Department of Neuroscience, Rosalind Franklin University of Medicine and Science, North Chicago, IL, United States
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36
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Bonkhoff AK, Espinoza FA, Gazula H, Vergara VM, Hensel L, Michely J, Paul T, Rehme AK, Volz LJ, Fink GR, Calhoun VD, Grefkes C. Acute ischaemic stroke alters the brain's preference for distinct dynamic connectivity states. Brain 2020; 143:1525-1540. [PMID: 32357220 PMCID: PMC7241954 DOI: 10.1093/brain/awaa101] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Revised: 01/26/2020] [Accepted: 02/16/2020] [Indexed: 01/01/2023] Open
Abstract
Acute ischaemic stroke disturbs healthy brain organization, prompting subsequent plasticity and reorganization to compensate for the loss of specialized neural tissue and function. Static resting state functional MRI studies have already furthered our understanding of cerebral reorganization by estimating stroke-induced changes in network connectivity aggregated over the duration of several minutes. In this study, we used dynamic resting state functional MRI analyses to increase temporal resolution to seconds and explore transient configurations of motor network connectivity in acute stroke. To this end, we collected resting state functional MRI data of 31 patients with acute ischaemic stroke and 17 age-matched healthy control subjects. Stroke patients presented with moderate to severe hand motor deficits. By estimating dynamic functional connectivity within a sliding window framework, we identified three distinct connectivity configurations of motor-related networks. Motor networks were organized into three regional domains, i.e. a cortical, subcortical and cerebellar domain. The dynamic connectivity patterns of stroke patients diverged from those of healthy controls depending on the severity of the initial motor impairment. Moderately affected patients (n = 18) spent significantly more time in a weakly connected configuration that was characterized by low levels of connectivity, both locally as well as between distant regions. In contrast, severely affected patients (n = 13) showed a significant preference for transitions into a spatially segregated connectivity configuration. This configuration featured particularly high levels of local connectivity within the three regional domains as well as anti-correlated connectivity between distant networks across domains. A third connectivity configuration represented an intermediate connectivity pattern compared to the preceding two, and predominantly encompassed decreased interhemispheric connectivity between cortical motor networks independent of individual deficit severity. Alterations within this third configuration thus closely resembled previously reported ones originating from static resting state functional MRI studies post-stroke. In summary, acute ischaemic stroke not only prompted changes in connectivity between distinct networks, but it also caused characteristic changes in temporal properties of large-scale network interactions depending on the severity of the individual deficit. These findings offer new vistas on the dynamic neural mechanisms underlying acute neurological symptoms, cortical reorganization and treatment effects in stroke patients.
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Affiliation(s)
- Anna K Bonkhoff
- Department of Neurology, University Hospital Cologne and Medical Faculty, University of Cologne, Germany
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
- Queen Square Institute of Neurology, University College London, London, UK
| | | | - Harshvardhan Gazula
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Victor M Vergara
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Lukas Hensel
- Department of Neurology, University Hospital Cologne and Medical Faculty, University of Cologne, Germany
| | - Jochen Michely
- Department of Neurology, University Hospital Cologne and Medical Faculty, University of Cologne, Germany
| | - Theresa Paul
- Department of Neurology, University Hospital Cologne and Medical Faculty, University of Cologne, Germany
| | - Anne K Rehme
- Department of Neurology, University Hospital Cologne and Medical Faculty, University of Cologne, Germany
| | - Lukas J Volz
- Department of Neurology, University Hospital Cologne and Medical Faculty, University of Cologne, Germany
| | - Gereon R Fink
- Department of Neurology, University Hospital Cologne and Medical Faculty, University of Cologne, Germany
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, New Mexico, USA
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia, USA
| | - Christian Grefkes
- Department of Neurology, University Hospital Cologne and Medical Faculty, University of Cologne, Germany
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
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Vergara VM, Salman M, Abrol A, Espinoza FA, Calhoun VD. Determining the number of states in dynamic functional connectivity using cluster validity indexes. J Neurosci Methods 2020; 337:108651. [PMID: 32109439 DOI: 10.1016/j.jneumeth.2020.108651] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Revised: 02/01/2020] [Accepted: 02/24/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND Clustering analysis is employed in brain dynamic functional connectivity (dFC) to cluster the data into a set of dynamic states. These states correspond to different patterns of functional connectivity that iterate through time. Although several cluster validity index (CVI) methods to determine the best clustering partition exists, the appropriateness of methods to apply in the case of dynamic connectivity analysis has not been determined. NEW METHOD Currently employed indexes do not provide a crisp answer on what is the best number of clusters. In addition, there is a lack of CVI testing in the context of dFC data. This work tests a comprehensive set of twenty four cluster validity indexes applied to addiction data and suggest the best ones for clustering dynamic functional connectivity. RESULTS Out of the twenty four considered CVIs, Davies-Bouldin and Ray-Turi were the most suitable methods to find the number of clusters in both simulation and real data. The solution for these two CVIs is to find a local minimum critical point, which can be automated using computational algorithms. COMPARISON WITH EXISTING METHODS Elbow-Criterion, Silhouette and GAP-Statistic methods have been widely used in dFC studies. These methods are included among the tested CVIs where the performances of all twenty four CVIs are compared. CONCLUSIONS Davies-Bouldin and Ray-Turi CVIs showed better performance among a group of twenty four CVIs in determining the number of clusters to use in dFC analysis.
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Affiliation(s)
- Victor M Vergara
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, USA.
| | - Mustafa Salman
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
| | - Anees Abrol
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA.
| | - Flor A Espinoza
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, USA.
| | - Vince D Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA; The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, USA; School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
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38
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Vergara VM, Abrol A, Espinoza FA, Calhoun VD. Selection of Efficient Clustering Index to Estimate the Number of Dynamic Brain States from Functional Network Connectivity. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:632-635. [PMID: 31945977 DOI: 10.1109/embc.2019.8856284] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Clustering analysis is employed in brain dynamic functional connectivity to cluster the data into a set of dynamic states. These states correspond to different patterns of functional connectivity that iterate through time. Although several methods to determine the best clustering partition exists, the appropriateness of methods to apply in the case of dynamic connectivity analysis has not been determined. In this work we examine the use of the Davies-Bouldin clustering validity index via simulation and real data analysis. Currently employed indexes, such as the Silhouette index, do not provide an effective estimation requiring the use of an elbow criterion. All elbow criteria rely on users experience and introduce uncertainty into the estimation. We demonstrate the feasibility of using the Davies-Bouldin index as a method delivering a unique discrete response to provide automated selection of the number of clusters.
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Espinoza FA, Vergara VM, Damaraju E, Henke KG, Faghiri A, Turner JA, Belger AA, Ford JM, McEwen SC, Mathalon DH, Mueller BA, Potkin SG, Preda A, Vaidya JG, van Erp TGM, Calhoun VD. Characterizing Whole Brain Temporal Variation of Functional Connectivity via Zero and First Order Derivatives of Sliding Window Correlations. Front Neurosci 2019; 13:634. [PMID: 31316333 PMCID: PMC6611425 DOI: 10.3389/fnins.2019.00634] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Accepted: 06/03/2019] [Indexed: 11/13/2022] Open
Abstract
Brain functional connectivity has been shown to change over time during resting state fMRI experiments. Close examination of temporal changes have revealed a small set of whole-brain connectivity patterns called dynamic states. Dynamic functional network connectivity (dFNC) studies have demonstrated that it is possible to replicate the dynamic states across several resting state experiments. However, estimation of states and their temporal dynamicity still suffers from noisy and imperfect estimations. In regular dFNC implementations, states are estimated by comparing connectivity patterns through the data without considering time, in other words only zero order changes are examined. In this work we propose a method that includes first order variations of dFNC in the searching scheme of dynamic connectivity patterns. Our approach, referred to as temporal variation of functional network connectivity (tvFNC), estimates the derivative of dFNC, and then searches for reoccurring patterns of concurrent dFNC states and their derivatives. The tvFNC method is first validated using a simulated dataset and then applied to a resting-state fMRI sample including healthy controls (HC) and schizophrenia (SZ) patients and compared to the standard dFNC approach. Our dynamic approach reveals extra patterns in the connectivity derivatives complementing the already reported state patterns. State derivatives consist of additional information about increment and decrement of connectivity among brain networks not observed by the original dFNC method. The tvFNC shows more sensitivity than regular dFNC by uncovering additional FNC differences between the HC and SZ groups in each state. In summary, the tvFNC method provides a new and enhanced approach to examine time-varying functional connectivity.
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Affiliation(s)
- Flor A Espinoza
- Mind Research Network, Albuquerque, NM, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Victor M Vergara
- Mind Research Network, Albuquerque, NM, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Eswar Damaraju
- Mind Research Network, Albuquerque, NM, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States
| | - Kyle G Henke
- Mind Research Network, Albuquerque, NM, United States.,Department of Mathematics and Statistics, The University of New Mexico, Albuquerque, NM, United States
| | - Ashkan Faghiri
- Mind Research Network, Albuquerque, NM, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.,Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM, United States
| | - Jessica A Turner
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.,Department of Psychology and Neuroscience, Georgia State University, Atlanta, GA, United States
| | - Aysenil A Belger
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Judith M Ford
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States.,San Francisco VA Medical Center, San Francisco, CA, United States
| | - Sarah C McEwen
- Pacific Neuroscience Institute, Santa Monica, CA, United States.,John Wayne Cancer Institute, Department of Translational Neurosciences and Neurotherapeutics, Santa Monica, CA, United States
| | - Daniel H Mathalon
- Department of Psychiatry, University of California, San Francisco, San Francisco, CA, United States.,San Francisco VA Medical Center, San Francisco, CA, United States
| | - Bryon A Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, United States
| | - Steven G Potkin
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
| | - Adrian Preda
- Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States
| | - Jatin G Vaidya
- Department of Psychiatry, The University of Iowa, Iowa City, IA, United States
| | - Theo G M van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California, Irvine, Irvine, CA, United States.,Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, CA, United States
| | - Vince D Calhoun
- Mind Research Network, Albuquerque, NM, United States.,Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, United States.,Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, NM, United States.,Department of Psychology and Neuroscience, Georgia State University, Atlanta, GA, United States
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Espinoza FA, Liu J, Ciarochi J, Turner JA, Vergara VM, Caprihan A, Misiura M, Johnson HJ, Long JD, Bockholt JH, Paulsen JS, Calhoun VD. Dynamic functional network connectivity in Huntington's disease and its associations with motor and cognitive measures. Hum Brain Mapp 2019; 40:1955-1968. [PMID: 30618191 PMCID: PMC6865767 DOI: 10.1002/hbm.24504] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 12/12/2018] [Accepted: 12/14/2018] [Indexed: 02/03/2023] Open
Abstract
Dynamic functional network connectivity (dFNC) is an expansion of traditional, static FNC that measures connectivity variation among brain networks throughout scan duration. We used a large resting-state fMRI (rs-fMRI) sample from the PREDICT-HD study (N = 183 Huntington disease gene mutation carriers [HDgmc] and N = 78 healthy control [HC] participants) to examine whole-brain dFNC and its associations with CAG repeat length as well as the product of scaled CAG length and age, a variable representing disease burden. We also tested for relationships between functional connectivity and motor and cognitive measurements. Group independent component analysis was applied to rs-fMRI data to obtain whole-brain resting state networks. FNC was defined as the correlation between RSN time-courses. Dynamic FNC behavior was captured using a sliding time window approach, and FNC results from each window were assigned to four clusters representing FNC states, using a k-means clustering algorithm. HDgmc individuals spent significantly more time in State-1 (the state with the weakest FNC pattern) compared to HC. However, overall HC individuals showed more FNC dynamism than HDgmc. Significant associations between FNC states and genetic and clinical variables were also identified. In FNC State-4 (the one that most resembled static FNC), HDgmc exhibited significantly decreased connectivity between the putamen and medial prefrontal cortex compared to HC, and this was significantly associated with cognitive performance. In FNC State-1, disease burden in HDgmc participants was significantly associated with connectivity between the postcentral gyrus and posterior cingulate cortex, as well as between the inferior occipital gyrus and posterior parietal cortex.
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Affiliation(s)
- Flor A. Espinoza
- Department of Translational Neuroscience, The Mind Research NetworkAlbuquerqueNew Mexico
| | - Jingyu Liu
- Department of Translational Neuroscience, The Mind Research NetworkAlbuquerqueNew Mexico
| | - Jennifer Ciarochi
- Department of Psychology and NeuroscienceGeorgia State UniversityAtlantaGeorgia
| | - Jessica A. Turner
- Department of Psychology and NeuroscienceGeorgia State UniversityAtlantaGeorgia
| | - Victor M. Vergara
- Department of Translational Neuroscience, The Mind Research NetworkAlbuquerqueNew Mexico
| | - Arvind Caprihan
- Department of Translational Neuroscience, The Mind Research NetworkAlbuquerqueNew Mexico
| | - Maria Misiura
- Department of Psychology and NeuroscienceGeorgia State UniversityAtlantaGeorgia
| | - Hans J. Johnson
- Department of Electrical and Computer EngineeringUniversity of IowaIowa CityIowa
- Department of PsychiatryUniversity of IowaIowa CityIowa
| | - Jeffrey D. Long
- Department of PsychiatryUniversity of IowaIowa CityIowa
- Department of BiostatisticsUniversity of IowaIowa CityIowa
| | - Jeremy H. Bockholt
- Department of Translational Neuroscience, The Mind Research NetworkAlbuquerqueNew Mexico
| | | | - Vince D. Calhoun
- Department of Translational Neuroscience, The Mind Research NetworkAlbuquerqueNew Mexico
- Department of Psychology and NeuroscienceGeorgia State UniversityAtlantaGeorgia
- Department of Electrical and Computer EngineeringUniversity of New MexicoAlbuquerqueNew Mexico
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