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Cui R, Zheng Z, Jiang L, Ma W, Gong D, Yao D. Co-activation patterns during viewing of different video game genres. Brain Res Bull 2024; 213:110974. [PMID: 38710311 DOI: 10.1016/j.brainresbull.2024.110974] [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: 12/29/2023] [Revised: 04/13/2024] [Accepted: 05/02/2024] [Indexed: 05/08/2024]
Abstract
Past research has revealed cognitive improvements resulting from engagement with both traditional action video games and newer action-like video games, such as action real-time strategy games (ARSG). However, the cortical dynamics elicited by different video gaming genres remain unclear. This study explored the temporal dynamics of cortical networks in response to different gaming genres. Functional magnetic resonance imaging (fMRI) data were obtained during eye-closed resting and passive viewing of gameplay videos of three genres: life simulation games (LSG), first-person shooter games (FPS), and ARSG. Data analysis used a seed-free Co-Activation Pattern (CAP) based on Regions of Interest (ROIs). When comparing the viewing of action-like video games (FPS and ARSG) to LSG viewing, significant dynamic distinctions were observed in both primary and higher-order networks. Within action-like video games, compared to FPS viewing, ARSG viewing elicited a more pronounced increase in the Fraction of Time and Counts of attentional control-related CAPs, along with an increased Transition Probability from sensorimotor-related CAPs to attentional control-related CAPs. Compared to ARSG viewing, FPS viewing elicited a significant increase in the Fraction of Time of sensorimotor-related CAPs, when gaming experience was considered as a covariate. Thus, different video gaming genres, including distinct action-like video gaming genres, elicited unique dynamic patterns in whole-brain CAPs, potentially influencing the development of various cognitive processes.
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Affiliation(s)
- Ruifang Cui
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zihao Zheng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lijun Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Weiyi Ma
- School of Human Environmental Sciences, University of Arkansas, Fayetteville, AR, USA.
| | - Diankun Gong
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
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Wang R, Liu X, Sun C, Hu B, Yang L, Liu Y, Geng D, Lin J, Li Y. Altered Neurovascular Coupling in Patients With Mitochondrial Myopathy, Encephalopathy, Lactic Acidosis, and Stroke-Like Episodes (MELAS): A Combined Resting-State fMRI and Arterial Spin Labeling Study. J Magn Reson Imaging 2024; 60:327-336. [PMID: 37795920 DOI: 10.1002/jmri.29035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 09/19/2023] [Accepted: 09/19/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Coupling between neuronal activity and blood perfusion is termed neurovascular coupling (NVC), and it provides a potentially new mechanistic perspective into understanding numerous brain diseases. Although abnormal brain activity and blood supply have been separately reported in mitochondrial myopathy, encephalopathy, lactic acidosis, and stroke-like episodes (MELAS), whether anomalous NVC would be present is unclear. PURPOSE To investigate NVC changes and potential neural basis in MELAS by combining resting-state functional MRI (rs-fMRI) and arterial spin labeling (ASL). STUDY TYPE Prospective. SUBJECTS Twenty-four patients with MELAS (age: 29.8 ± 7.3 years) in the acute stage and 24 healthy controls (HCs, age: 26.4 ± 8.1 years). Additionally, 12 patients in the chronic stage were followed up. FIELD STRENGTH/SEQUENCE 3.0 T, resting-state gradient-recalled echo-planar imaging and pseudo-continuous 3D ASL sequences. ASSESSMENT Amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and functional connectivity strength (FCS) were calculated from rs-fMRI, and cerebral blood flow (CBF) was computed from ASL. Global NVC was assessed by correlation coefficients of CBF-ALFF, CBF-fALFF, CBF-ReHo, and CBF-FCS. Regional NVC was also evaluated by voxel-wise and lesion-wise ratios of CBF/ALFF, CBF/fALFF, CBF/ReHo, and CBF/FCS. STATISTICAL TESTS Two-sample t-test, paired-sample t-test, Gaussian random fields correction. A P value <0.05 was considered statistically significant. RESULTS Compared with HC, MELAS patients in acute stage showed significantly reduced global CBF-ALFF, CBF-fALFF, CBF-ReHo, and CBF-FCS coupling (P < 0.001). Altered CBF/ALFF, CBF/fALFF, CBF/ReHo, and CBF/FCS ratios were found mainly distributed in the middle cerebral artery territory in MELAS patients. In addition, significantly increased NVC ratios were found in the acute stroke-like lesions in acute stage (P < 0.001), with a recovery trend in chronic stage. DATA CONCLUSIONS This study showed dynamic alterations in NVC in MELAS patients from acute to chronic stage, which may provide a novel insight for understanding the pathogenesis of MELAS. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Rong Wang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Xueling Liu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Chong Sun
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Bin Hu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Liqin Yang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Yiru Liu
- Luhang High School, Shanghai, China
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Jie Lin
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
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3
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Zhang X, Liu L, Li Y, Li X, Wang K, Han S, Wang M, Zhang Y, Zheng G, Cheng J, Wen B. Integrative neurovascular coupling and neurotransmitter analyses in anisometropic and visual deprivation amblyopia children. iScience 2024; 27:109988. [PMID: 38883835 PMCID: PMC11177132 DOI: 10.1016/j.isci.2024.109988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/14/2024] [Accepted: 05/13/2024] [Indexed: 06/18/2024] Open
Abstract
The association between visual abnormalities and impairments in cerebral blood flow and brain region potentially results in neural dysfunction of amblyopia. Nevertheless, the differences in the complex mechanisms of brain neural network coupling and its relationship with neurotransmitters remain unclear. Here, the neurovascular coupling mechanism and neurotransmitter activity in children with anisometropic amblyopia (AA) and visual deprivation amblyopia (VDA) was explored. The neurovascular coupling of 17 brain regions in amblyopia children was significantly abnormal than in normal controls. The classification abilities of coupling units in brain regions differed between two types of amblyopia. Correlations between different coupling effects and neurotransmitters were different. The findings of this study demonstrate a correlation between the neurovascular coupling and neurotransmitter in children with AA and VDA, implying their impaired neurovascular coupling function and potential molecular underpinnings. The neuroimaging evidence revealed herein offers potential for the development of neural therapies for amblyopia.
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Affiliation(s)
- Xiaopan Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Liang Liu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yadong Li
- Department of Ophthalmology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiao Li
- Department of Ophthalmology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Kejia Wang
- Department of Ophthalmology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Mengzhu Wang
- MR Research Collaboration, Siemens Healthineers Ltd., Beijing, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Guangying Zheng
- Department of Ophthalmology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Baohong Wen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Weinstein SM, Vandekar SN, Li B, Alexander‐Bloch AF, Raznahan A, Li M, Gur RE, Gur RC, Roalf DR, Park MTM, Chakravarty M, Baller EB, Linn KA, Satterthwaite TD, Shinohara RT. Network enrichment significance testing in brain-phenotype association studies. Hum Brain Mapp 2024; 45:e26714. [PMID: 38878300 PMCID: PMC11179683 DOI: 10.1002/hbm.26714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 04/08/2024] [Accepted: 05/04/2024] [Indexed: 06/19/2024] Open
Abstract
Functional networks often guide our interpretation of spatial maps of brain-phenotype associations. However, methods for assessing enrichment of associations within networks of interest have varied in terms of both scientific rigor and underlying assumptions. While some approaches have relied on subjective interpretations, others have made unrealistic assumptions about spatial properties of imaging data, leading to inflated false positive rates. We seek to address this gap in existing methodology by borrowing insight from a method widely used in genetics research for testing enrichment of associations between a set of genes and a phenotype of interest. We propose network enrichment significance testing (NEST), a flexible framework for testing the specificity of brain-phenotype associations to functional networks or other sub-regions of the brain. We apply NEST to study enrichment of associations with structural and functional brain imaging data from a large-scale neurodevelopmental cohort study.
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Affiliation(s)
- Sarah M. Weinstein
- Department of Epidemiology and BiostatisticsTemple University College of Public HealthPhiladelphiaPennsylvaniaUSA
| | - Simon N. Vandekar
- Department of BiostatisticsVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Bin Li
- Department of Computer and Information SciencesTemple University College of Science and TechnologyPhiladelphiaPennsylvaniaUSA
| | - Aaron F. Alexander‐Bloch
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
- Department of Child and Adolescent Psychiatry and Behavioral ScienceChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Armin Raznahan
- Section on Developmental NeurogenomicsNational Institute of Mental Health Intramural Research ProgramBethesdaMarylandUSA
| | - Mingyao Li
- Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Raquel E. Gur
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Ruben C. Gur
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - David R. Roalf
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Min Tae M. Park
- Department of Psychiatry, Temerty Faculty of MedicineUniversity of TorontoTorontoOntarioCanada
- Integrated Program in NeuroscienceMcGill UniversityQCCanada
| | - Mallar Chakravarty
- Department of PsychiatryMcGill UniversityQCCanada
- Cerebral Imaging Centre, Douglas Research Centre, McGill UniversityQCCanada
| | - Erica B. Baller
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Kristin A. Linn
- Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Theodore D. Satterthwaite
- Department of PsychiatryUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
| | - Russell T. Shinohara
- Department of Biostatistics, Epidemiology, and InformaticsUniversity of Pennsylvania, Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
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Su S, Zhao J, Dai Y, Lin L, Zhou Q, Yan Z, Qian L, Cui W, Liu M, Zhang H, Yang Z, Chen Y. Altered neurovascular coupling in the children with attention-deficit/hyperactivity disorder: a comprehensive fMRI analysis. Eur Child Adolesc Psychiatry 2024; 33:1081-1091. [PMID: 37222790 DOI: 10.1007/s00787-023-02238-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 05/17/2023] [Indexed: 05/25/2023]
Abstract
The coupling between resting-state cerebral blood flow (CBF) and blood oxygenation level-dependent (BOLD) signals reflects the mechanism of neurovascular coupling (NVC), which have not been illustrated in attention-deficit/hyperactivity disorder (ADHD). Fifty ADHD and 42 age- and gender-matched typically developing controls (TDs) were enrolled. The NVC imaging metrics were investigated by exploring the Pearson correlation coefficients between CBF and BOLD-derived quantitative maps (ALFF, fALFF, DCP maps). Three types of NVC metrics (CBF-ALFF, CBF-fALFF, CBF-DCP coupling) were compared between ADHD and TDs group, and the inner association between altered NVC metrics and clinical variables in ADHD group was further analyzed. Compared to TDs, ADHD showed significantly reduced whole-brain CBF-ALFF coupling (P < 0.001). Among regional level (all PFDR < 0.05), ADHD showed significantly lower CBF-ALFF coupling in bilateral thalamus, default-mode network (DMN) involving left anterior cingulate (ACG.L) and right parahippocampal gyrus (PHG.R), execution control network (ECN) involving right middle orbital frontal gyrus (ORBmid.R) and right inferior frontal triangular gyrus (IFGtriang.R), and increased CBF-ALFF coupling in attention network (AN)-related left superior temporal gyrus (STG.L) and somatosensory network (SSN))-related left rolandic operculum (ROL.L). Furthermore, increased CBF-fALFF coupling was found in the visual network (VN)-related left cuneus and negatively correlated with the concentration index of ADHD (R = - 0.299, PFDR = 0.035). Abnormal regional NVC metrics were at widespread neural networks in ADHD, mainly involved in DMN, ECN, SSN, AN, VN and bilateral thalamus. Notably, this study reinforced the insights into the neural basis and pathophysiological mechanism underlying ADHD.
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Affiliation(s)
- Shu Su
- Department of Radiology, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Jing Zhao
- Department of Radiology, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Yan Dai
- Department of Radiology, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Liping Lin
- Department of Radiology, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Qin Zhou
- Department of Radiology, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Zi Yan
- Department of Radiology, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Long Qian
- Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, China
| | - Wei Cui
- Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, China
| | - Meina Liu
- Department of Pediatric, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Hongyu Zhang
- Department of Pediatric, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Zhiyun Yang
- Department of Radiology, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
| | - Yingqian Chen
- Department of Radiology, First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
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Wu C, Wu H, Zhou C, Guan X, Guo T, Wu J, Chen J, Wen J, Qin J, Tan S, Duanmu X, Yuan W, Zheng Q, Zhang B, Xu X, Zhang M. Neurovascular coupling alteration in drug-naïve Parkinson's disease: The underlying molecular mechanisms and levodopa's restoration effects. Neurobiol Dis 2024; 191:106406. [PMID: 38199273 DOI: 10.1016/j.nbd.2024.106406] [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: 11/12/2023] [Revised: 12/25/2023] [Accepted: 01/06/2024] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Parkinson's disease (PD) patients exhibit an imbalance between neuronal activity and perfusion, referred to as abnormal neurovascular coupling (NVC). Nevertheless, the underlying molecular mechanism and how levodopa, the standard treatment in PD, regulates NVC is largely unknown. MATERIAL AND METHODS A total of 52 drug-naïve PD patients and 49 normal controls (NCs) were enrolled. NVC was characterized in vivo by relating cerebral blood flow (CBF) and amplitude of low-frequency fluctuations (ALFF). Motor assessments and MRI scanning were conducted on drug-naïve patients before and after levodopa therapy (OFF/ON state). Regional NVC differences between patients and NCs were identified, followed by an assessment of the associated receptors/transporters. The influence of levodopa on NVC, CBF, and ALFF within these abnormal regions was analyzed. RESULTS Compared to NCs, OFF-state patients showed NVC dysfunction in significantly lower NVC in left precentral, postcentral, superior parietal cortex, and precuneus, along with higher NVC in left anterior cingulate cortex, right olfactory cortex, thalamus, caudate, and putamen (P-value <0.0006). The distribution of NVC differences correlated with the density of dopaminergic, serotonin, MU-opioid, and cholinergic receptors/transporters. Additionally, levodopa ameliorated abnormal NVC in most of these regions, where there were primarily ALFF changes with limited CBF modifications. CONCLUSION Patients exhibited NVC dysfunction primarily in the striato-thalamo-cortical circuit and motor control regions, which could be driven by dopaminergic and nondopaminergic systems, and levodopa therapy mainly restored abnormal NVC by modulating neuronal activity.
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Affiliation(s)
- Chenqing Wu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Haoting Wu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cheng Zhou
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Guan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Guo
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingwen Chen
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiaqi Wen
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianmei Qin
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Sijia Tan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojie Duanmu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Weijin Yuan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qianshi Zheng
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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Liu S, Fan D, He C, Liu X, Zhang H, Zhang H, Zhang Z, Xie C. Resting-state cerebral blood flow and functional connectivity abnormalities in depressed patients with childhood maltreatment: Potential biomarkers of vulnerability? Psychiatry Clin Neurosci 2024; 78:41-50. [PMID: 37781929 DOI: 10.1111/pcn.13603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 10/03/2023]
Abstract
AIM Childhood maltreatment (CM) is an important risk factor for major depressive disorder (MDD). This study aimed to explore the specific effect of CM on cerebral blood flow (CBF) and brain functional connectivity (FC) in MDD patients. METHODS A total of 150 subjects were collected including 55 MDD patients with CM, 34 MDD patients without CM, 19 healthy controls (HC) with CM, and 42 HC without CM. All subjects completed MRI scans and neuropsychological tests. Two-way analysis of covariance was used to detect the main and interactive effects of disease and CM on CBF and FC across subjects. Then, partial correlation analyses were conducted to explore the behavioral significance of altered CBF and FC in MDD patients. Finally, a support vector classifier model was applied to differentiate MDD patients. RESULTS MDD patients represented increased CBF in bilateral temporal lobe and decreased CBF in right visual cortex. Importantly, significant depression-by-CM interactive effects on CBF were primarily located in the frontoparietal regions, including orbitofrontal cortex (OFC), lateral prefrontal cortex (PFC), and parietal cortex. Moreover, significant FC abnormalities were seen in OFC-PFC and frontoparietal-visual cortex. Notably, the abnormal CBF and FC were significantly associated with behavioral performance. Finally, a combination of altered CBF and FC behaved with a satisfactory classification ability to differentiate MDD patients. CONCLUSIONS These results highlight the importance of frontoparietal and visual cortices for MDD with CM experience, proposing a potential neuroimaging biomarker for MDD identification.
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Affiliation(s)
- Sangni Liu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Dandan Fan
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Cancan He
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Xinyi Liu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Haisan Zhang
- Psychology School of Xinxiang Medical University, Xinxiang, China
- Department of Psychiatry, Henan Provincial Mental Hospital, Xinxiang Medical University, Xinxiang, China
- Xinxiang Key Laboratory of Multimodal Brain Imaging, Henan Provincial Mental Hospital, Xinxiang Medical University, Xinxiang, China
| | - Hongxing Zhang
- Psychology School of Xinxiang Medical University, Xinxiang, China
- Department of Psychiatry, Henan Provincial Mental Hospital, Xinxiang Medical University, Xinxiang, China
- Xinxiang Key Laboratory of Multimodal Brain Imaging, Henan Provincial Mental Hospital, Xinxiang Medical University, Xinxiang, China
| | - Zhijun Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
- Neuropsychiatric Institute, Affiliated ZhongDa Hospital, Southeast University, Nanjing, China
- The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, China
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
- Neuropsychiatric Institute, Affiliated ZhongDa Hospital, Southeast University, Nanjing, China
- The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, China
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8
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Tu D, Mahony B, Moore TM, Bertolero MA, Alexander-Bloch AF, Gur R, Bassett DS, Satterthwaite TD, Raznahan A, Shinohara RT. CoCoA: conditional correlation models with association size. Biostatistics 2023; 25:154-170. [PMID: 35939558 PMCID: PMC10724258 DOI: 10.1093/biostatistics/kxac032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/14/2022] [Accepted: 07/18/2022] [Indexed: 11/13/2022] Open
Abstract
Many scientific questions can be formulated as hypotheses about conditional correlations. For instance, in tests of cognitive and physical performance, the trade-off between speed and accuracy motivates study of the two variables together. A natural question is whether speed-accuracy coupling depends on other variables, such as sustained attention. Classical regression techniques, which posit models in terms of covariates and outcomes, are insufficient to investigate the effect of a third variable on the symmetric relationship between speed and accuracy. In response, we propose a conditional correlation model with association size, a likelihood-based statistical framework to estimate the conditional correlation between speed and accuracy as a function of additional variables. We propose novel measures of the association size, which are analogous to effect sizes on the correlation scale while adjusting for confound variables. In simulation studies, we compare likelihood-based estimators of conditional correlation to semiparametric estimators adapted from genomic studies and find that the former achieves lower bias and variance under both ideal settings and model assumption misspecification. Using neurocognitive data from the Philadelphia Neurodevelopmental Cohort, we demonstrate that greater sustained attention is associated with stronger speed-accuracy coupling in a complex reasoning task while controlling for age. By highlighting conditional correlations as the outcome of interest, our model provides complementary insights to traditional regression modeling and partitioned correlation analyses.
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Affiliation(s)
- Danni Tu
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA, 19104, USA
| | - Bridget Mahony
- Section on Developmental Neurogenomics, National Institutes of Mental Health, 10 Center Drive, Bethesda, MD, 20892, USA
| | - Tyler M Moore
- Department of Psychiatry, Perelman School of Medicine, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Maxwell A Bertolero
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, USA and Penn Lifespan Informatics and Neuroimaging Center, 3700 Hamilton Walk, Philadelphia, PA, 19104, USA
| | | | - Ruben Gur
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, USA
| | - Dani S Bassett
- Department of Bioengineering, University of Pennsylvania, 209 South 33rd Street, Philadelphia, PA, 19104, USA, Department of Physics and Astronomy, University of Pennsylvania, 209 South 33rd Street, Philadelphia, PA, 19104, USA, Department of Electrical and Systems Engineering, University of Pennsylvania, 200 South 33rd Street, Philadelphia, PA, 19104, USA and Department of Neurology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, Philadelphia, PA, USA and Penn Lifespan Informatics and Neuroimaging Center, Philadelphia, PA, USA
| | - Armin Raznahan
- Section on Developmental Neurogenomics, National Institutes of Mental Health, Bethesda, MD, USA
| | - Russell T Shinohara
- The Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
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9
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Xie H, Yang Y, Sun Q, Li ZY, Ni MH, Chen ZH, Li SN, Dai P, Cui YY, Cao XY, Jiang N, Du LJ, Yu Y, Yan LF, Cui GB. Abnormalities of cerebral blood flow and the regional brain function in Parkinson's disease: a systematic review and multimodal neuroimaging meta-analysis. Front Neurol 2023; 14:1289934. [PMID: 38162449 PMCID: PMC10755479 DOI: 10.3389/fneur.2023.1289934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 11/15/2023] [Indexed: 01/03/2024] Open
Abstract
Background Parkinson's disease (PD) is a neurodegenerative disease with high incidence rate. Resting state functional magnetic resonance imaging (rs-fMRI), as a widely used method for studying neurodegenerative diseases, has not yet been combined with two important indicators, amplitude low-frequency fluctuation (ALFF) and cerebral blood flow (CBF), for standardized analysis of PD. Methods In this study, we used seed-based d-mapping and permutation of subject images (SDM-PSI) software to investigate the changes in ALFF and CBF of PD patients. After obtaining the regions of PD with changes in ALFF or CBF, we conducted a multimodal analysis to identify brain regions where ALFF and CBF changed together or could not synchronize. Results The final study included 31 eligible trials with 37 data sets. The main analysis results showed that the ALFF of the left striatum and left anterior thalamic projection decreased in PD patients, while the CBF of the right superior frontal gyrus decreased. However, the results of multimodal analysis suggested that there were no statistically significant brain regions. In addition, the decrease of ALFF in the left striatum and the decrease of CBF in the right superior frontal gyrus was correlated with the decrease in clinical cognitive scores. Conclusion PD patients had a series of spontaneous brain activity abnormalities, mainly involving brain regions related to the striatum-thalamic-cortex circuit, and related to the clinical manifestations of PD. Among them, the left striatum and right superior frontal gyrus are more closely related to cognition. Systematic review registration https://www.crd.york.ac.uk/ PROSPERO (CRD42023390914).
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Affiliation(s)
- Hao Xie
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), Xi’an, Shaanxi, China
| | - Yang Yang
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), Xi’an, Shaanxi, China
| | - Qian Sun
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), Xi’an, Shaanxi, China
| | - Ze-Yang Li
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), Xi’an, Shaanxi, China
| | - Min-Hua Ni
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), Xi’an, Shaanxi, China
| | - Zhu-Hong Chen
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), Xi’an, Shaanxi, China
| | - Si-Ning Li
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), Xi’an, Shaanxi, China
- Faculty of Medical Technology, Xi’an Medical University, Xi’an, Shaanxi, China
| | - Pan Dai
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), Xi’an, Shaanxi, China
- Faculty of Medical Technology, Xi’an Medical University, Xi’an, Shaanxi, China
| | - Yan-Yan Cui
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), Xi’an, Shaanxi, China
- Faculty of Medical Technology, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China
| | - Xin-Yu Cao
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), Xi’an, Shaanxi, China
- Faculty of Medical Technology, Medical School of Yan’an University, Yan’an, Shaanxi, China
| | - Nan Jiang
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), Xi’an, Shaanxi, China
| | - Li-Juan Du
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), Xi’an, Shaanxi, China
| | - Ying Yu
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), Xi’an, Shaanxi, China
| | - Lin-Feng Yan
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), Xi’an, Shaanxi, China
| | - Guang-Bin Cui
- Department of Radiology and Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University (Air Force Medical University), Xi’an, Shaanxi, China
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10
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Wang R, Lin J, Li Y. Response to "The Neurovascular Coupling Concept Does Not Sufficiently Explain the Pathophysiology of Stroke-Like Lesions". J Magn Reson Imaging 2023. [PMID: 37966890 DOI: 10.1002/jmri.29133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 10/31/2023] [Indexed: 11/16/2023] Open
Affiliation(s)
- Rong Wang
- Department of Radiology, HuaShan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Jie Lin
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yuxin Li
- Department of Radiology, HuaShan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
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11
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Li T, Liu T, Zhang J, Ma Y, Wang G, Suo D, Yang B, Wang X, Funahashi S, Zhang K, Fang B, Yan T. Neurovascular coupling dysfunction of visual network organization in Parkinson's disease. Neurobiol Dis 2023; 188:106323. [PMID: 37838006 DOI: 10.1016/j.nbd.2023.106323] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 10/11/2023] [Accepted: 10/11/2023] [Indexed: 10/16/2023] Open
Abstract
Parkinson's disease (PD) has been showed perfusion and neural activity alterations in specific regions, such as the motor and visual networks; however, the clinical significance of coupling changes is still unknown. To identify how neurovascular coupling changes during the pathophysiology of PD, patients and healthy controls underwent multiparametric magnetic resonance imaging to measure neural activity organization of segregation and integration using amplitude of low-frequency fluctuation (ALFF) and functional connectivity strength (FCS), and measure vascular responses using cerebral blood flow (CBF). Neurovascular coupling was calculated as the global CBF-ALFF and CBF-FCS coupling and the regional CBF/ALFF and CBF/FCS ratio. Correlations and dynamic causal modeling was then used to evaluate relationships with disease-alterations to clinical variables and information flow. Neurovascular coupling was impaired in PD with decreased global CBF-ALFF and CBF-FCS coupling, as well as decreased CBF/ALFF in the parieto-occipital cortex (dorsal visual stream) and CBF/FCS in the temporo-occipital cortex (ventral visual stream); these decouplings were associated with motor and non-motor impairments. The distinctive patterns of neurovascular coupling alterations within the dorsal and ventral visual streams of the visual system could potentially provide additional understanding into the pathophysiological mechanisms of PD.
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Affiliation(s)
- Ting Li
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Tiantian Liu
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
| | - Jian Zhang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Yunxiao Ma
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Gongshu Wang
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Dingjie Suo
- School of Medical Technology, Beijing Institute of Technology, Beijing, China
| | - Bowen Yang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xiu Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shintaro Funahashi
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, China
| | - Kai Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Boyan Fang
- Parkinson Medical Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, 100144, China
| | - Tianyi Yan
- School of Medical Technology, Beijing Institute of Technology, Beijing, China.
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12
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Xue H, Xu X, Yan Z, Cheng J, Zhang L, Zhu W, Cui G, Zhang Q, Qiu S, Yao Z, Qin W, Liu F, Liang M, Fu J, Xu Q, Xu J, Xie Y, Zhang P, Li W, Wang C, Shen W, Zhang X, Xu K, Zuo XN, Ye Z, Yu Y, Xian J, Yu C. Genome-wide association study of hippocampal blood-oxygen-level-dependent-cerebral blood flow correlation in Chinese Han population. iScience 2023; 26:108005. [PMID: 37822511 PMCID: PMC10562876 DOI: 10.1016/j.isci.2023.108005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 07/29/2023] [Accepted: 09/18/2023] [Indexed: 10/13/2023] Open
Abstract
Correlation between blood-oxygen-level-dependent (BOLD) and cerebral blood flow (CBF) has been used as an index of neurovascular coupling. Hippocampal BOLD-CBF correlation is associated with neurocognition, and the reduced correlation is associated with neuropsychiatric disorders. We conducted the first genome-wide association study of the hippocampal BOLD-CBF correlation in 4,832 Chinese Han subjects. The hippocampal BOLD-CBF correlation had an estimated heritability of 16.2-23.9% and showed reliable genome-wide significant association with a locus at 3q28, in which many variants have been linked to neuroimaging and cerebrospinal fluid markers of Alzheimer's disease. Gene-based association analyses showed four significant genes (GMNC, CRTC2, DENND4B, and GATAD2B) and revealed enrichment for mast cell calcium mobilization, microglial cell proliferation, and ubiquitin-related proteolysis pathways that regulate different cellular components of the neurovascular unit. This is the first unbiased identification of the association of hippocampal BOLD-CBF correlation, providing fresh insights into the genetic architecture of hippocampal neurovascular coupling.
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Affiliation(s)
- Hui Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou 310009, China
| | - Zhihan Yan
- Department of Radiology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou 325027, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Longjiang Zhang
- Department of Radiology, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210002, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Guangbin Cui
- Functional and Molecular Imaging Key Lab of Shaanxi Province & Department of Radiology, Tangdu Hospital, Air Force Medical University, Xi’an 710038, China
| | - Quan Zhang
- Department of Radiology, Characteristic Medical Center of Chinese People’s Armed Police Force, Tianjin 300162, China
| | - Shijun Qiu
- Department of Medical Imaging, the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou 510405, China
| | - Zhenwei Yao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Meng Liang
- School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin 300203, China
| | - Jilian Fu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Qiang Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Jiayuan Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Peng Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Wei Li
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Caihong Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Wen Shen
- Department of Radiology, Tianjin First Center Hospital, Tianjin 300192, China
| | - Xiaochu Zhang
- Division of Life Science and Medicine, University of Science & Technology of China, Hefei 230027, China
| | - Kai Xu
- Department of Radiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou 221006, China
| | - Xi-Nian Zuo
- Developmental Population Neuroscience Research Center at IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University, Ministry of Education, Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China
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13
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Zhang X, Liu L, Yang F, Liu Z, Jin X, Han S, Zhang Y, Cheng J, Wen B. Neurovascular coupling dysfunction in high myopia patients: Evidence from a multi-modal magnetic resonance imaging analysis. J Neuroradiol 2023:S0150-9861(23)00242-0. [PMID: 37777086 DOI: 10.1016/j.neurad.2023.09.005] [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: 05/29/2023] [Revised: 09/09/2023] [Accepted: 09/24/2023] [Indexed: 10/02/2023]
Abstract
BACKGROUND AND PURPOSE To investigate neurovascular coupling dysfunction in high myopia (HM) patients. MATERIALS AND METHODS A total of 37 HM patients and 36 healthy controls were included in this study. Degree centrality (DC), regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF), and fractional ALFF (fALFF) maps were employed to represent neuronal activity. Cerebral blood perfusion was characterized by cerebral blood flow (CBF). The correlation coefficient was calculated to reflect the relationship between neuronal activity and cerebral blood perfusion. Pearson partial correlation analysis was utilized to evaluate the association between HM dysfunction and clinical indicators. RESULTS HM patients exhibited significant alterations in neurovascular coupling across 37 brain regions compared to healthy controls. The brain regions with marked changes varied among the four neurovascular coupling patterns, including the middle frontal gyrus, superior occipital gyrus, middle occipital gyrus, and fusiform gyrus. Additionally, the superior frontal gyrus orbital part, medial superior frontal gyrus, inferior occipital gyrus, and dorsolateral superior frontal gyrus displayed significant changes in three coupling patterns. In HM patients, the ReHo-CBF changes in the inferior frontal gyrus orbital part were positively correlated with best-corrected visual acuity (BCVA) and refractive diopter changes. Similarly, the ALFF-CBF changes in the inferior frontal gyrus orbital part showed a positive correlation with refractive diopter changes. ReHo-CBF and ALFF-CBF alterations in the paracentral lobule were positively correlated with BCVA and refractive diopter changes. CONCLUSION Our findings underscore the abnormal alterations in neurovascular coupling across multiple brain regions in HM patients. These results suggest that neurovascular dysfunction in HM patients may be associated with an aberrant visual regulation mechanism.
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Affiliation(s)
- Xiaopan Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China; Henan Key Laboratory of magnetic resonance and brain function, Zhengzhou 450052, China
| | - Liang Liu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Fan Yang
- Department of Ophthalmology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Zijun Liu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Xuemin Jin
- Department of Ophthalmology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China; Henan Key Laboratory of magnetic resonance and brain function, Zhengzhou 450052, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China; Henan Key Laboratory of magnetic resonance and brain function, Zhengzhou 450052, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Baohong Wen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China.
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14
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Talbot JS, Perkins DR, Dawkins TG, Douglas AJM, Griffiths TD, Richards CT, Owen K, Lord RN, Pugh CJA, Oliver JL, Lloyd RS, Ainslie PN, McManus AM, Stembridge M. Neurovascular coupling and cerebrovascular hemodynamics are modified by exercise training status at different stages of maturation during youth. Am J Physiol Heart Circ Physiol 2023; 325:H510-H521. [PMID: 37450291 PMCID: PMC10538977 DOI: 10.1152/ajpheart.00302.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 07/10/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023]
Abstract
Neurovascular coupling (NVC) is mediated via nitric oxide signaling, which is independently influenced by sex hormones and exercise training. Whether exercise training differentially modifies NVC pre- versus postpuberty, where levels of circulating sex hormones will differ greatly within and between sexes, remains to be determined. Therefore, we investigated the influence of exercise training status on resting intracranial hemodynamics and NVC at different stages of maturation. Posterior and middle cerebral artery velocities (PCAv and MCAv) and pulsatility index (PCAPI and MCAPI) were assessed via transcranial Doppler ultrasound at rest and during visual NVC stimuli. N = 121 exercise-trained (males, n = 32; females, n = 32) and untrained (males, n = 28; females, n = 29) participants were characterized as pre (males, n = 33; females, n = 29)- or post (males, n = 27; females, n = 32)-peak height velocity (PHV). Exercise-trained youth demonstrated higher resting MCAv (P = 0.010). Maturity and training status did not affect the ΔPCAv and ΔMCAv during NVC. However, pre-PHV untrained males (19.4 ± 13.5 vs. 6.8 ± 6.0%; P ≤ 0.001) and females (19.3 ± 10.8 vs. 6.4 ± 7.1%; P ≤ 0.001) had a higher ΔPCAPI during NVC than post-PHV untrained counterparts, whereas the ΔPCAPI was similar in pre- and post-PHV trained youth. Pre-PHV untrained males (19.4 ± 13.5 vs. 7.9 ± 6.0%; P ≤ 0.001) and females (19.3 ± 10.8 vs. 11.1 ± 7.3%; P = 0.016) also had a larger ΔPCAPI than their pre-PHV trained counterparts during NVC, but the ΔPCAPI was similar in trained and untrained post-PHV youth. Collectively, our data indicate that exercise training elevates regional cerebral blood velocities during youth, but training-mediated adaptations in NVC are only attainable during early stages of adolescence. Therefore, childhood provides a unique opportunity for exercise-mediated adaptations in NVC.NEW & NOTEWORTHY We report that the change in cerebral blood velocity during a neurovascular coupling task (NVC) is similar in pre- and postpubertal youth, regardless of exercise-training status. However, prepubertal untrained youth demonstrated a greater increase in cerebral blood pulsatility during the NVC task when compared with their trained counterparts. Our findings highlight that childhood represents a unique opportunity for exercise-mediated adaptations in cerebrovascular hemodynamics during NVC, which may confer long-term benefits in cerebrovascular function.
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Affiliation(s)
- Jack S Talbot
- Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, United Kingdom
- Centre for Health, Activity and Wellbeing Research, Cardiff Metropolitan University, Cardiff, United Kingdom
| | - Dean R Perkins
- Department of Sport Science, University of Innsbruck, Innsbruck, Austria
| | - Tony G Dawkins
- Centre for Heart, Lung and Vascular Health, School of Health and Exercise Sciences, University of British Columbia Okanagan, Kelowna, British Columbia, Canada
| | - Andrew J M Douglas
- Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, United Kingdom
- Centre for Health, Activity and Wellbeing Research, Cardiff Metropolitan University, Cardiff, United Kingdom
| | - Thomas D Griffiths
- Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, United Kingdom
- Centre for Health, Activity and Wellbeing Research, Cardiff Metropolitan University, Cardiff, United Kingdom
| | - Cory T Richards
- Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, United Kingdom
- Centre for Health, Activity and Wellbeing Research, Cardiff Metropolitan University, Cardiff, United Kingdom
| | - Kerry Owen
- Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, United Kingdom
- Windsor Clive Primary School, Cardiff, United Kingdom
| | - Rachel N Lord
- Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, United Kingdom
- Centre for Health, Activity and Wellbeing Research, Cardiff Metropolitan University, Cardiff, United Kingdom
| | - Christopher J A Pugh
- Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, United Kingdom
- Centre for Health, Activity and Wellbeing Research, Cardiff Metropolitan University, Cardiff, United Kingdom
| | - Jon L Oliver
- Youth Physical Development Centre, Cardiff Metropolitan University, Cardiff, United Kingdom
- Sports Performance Research Institute New Zealand, AUT University, Auckland, New Zealand
| | - Rhodri S Lloyd
- Youth Physical Development Centre, Cardiff Metropolitan University, Cardiff, United Kingdom
- Sports Performance Research Institute New Zealand, AUT University, Auckland, New Zealand
- Centre for Sport Science and Human Performance, Waikato Institute of Technology, Waikato, New Zealand
| | - Philip N Ainslie
- Centre for Heart, Lung and Vascular Health, School of Health and Exercise Sciences, University of British Columbia Okanagan, Kelowna, British Columbia, Canada
| | - Ali M McManus
- Centre for Heart, Lung and Vascular Health, School of Health and Exercise Sciences, University of British Columbia Okanagan, Kelowna, British Columbia, Canada
| | - Mike Stembridge
- Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, United Kingdom
- Centre for Health, Activity and Wellbeing Research, Cardiff Metropolitan University, Cardiff, United Kingdom
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15
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Liu ZQ, Shafiei G, Baillet S, Misic B. Spatially heterogeneous structure-function coupling in haemodynamic and electromagnetic brain networks. Neuroimage 2023; 278:120276. [PMID: 37451374 DOI: 10.1016/j.neuroimage.2023.120276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 07/04/2023] [Accepted: 07/11/2023] [Indexed: 07/18/2023] Open
Abstract
The relationship between structural and functional connectivity in the brain is a key question in connectomics. Here we quantify patterns of structure-function coupling across the neocortex, by comparing structural connectivity estimated using diffusion MRI with functional connectivity estimated using both neurophysiological (MEG-based) and haemodynamic (fMRI-based) recordings. We find that structure-function coupling is heterogeneous across brain regions and frequency bands. The link between structural and functional connectivity is generally stronger in multiple MEG frequency bands compared to resting state fMRI. Structure-function coupling is greater in slower and intermediate frequency bands compared to faster frequency bands. We also find that structure-function coupling systematically follows the archetypal sensorimotor-association hierarchy, as well as patterns of laminar differentiation, peaking in granular layer IV. Finally, structure-function coupling is better explained using structure-informed inter-regional communication metrics than using structural connectivity alone. Collectively, these results place neurophysiological and haemodynamic structure-function relationships in a common frame of reference and provide a starting point for a multi-modal understanding of structure-function coupling in the brain.
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Affiliation(s)
- Zhen-Qi Liu
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Golia Shafiei
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
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16
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Mehta K, Pines A, Adebimpe A, Larsen B, Bassett DS, Calkins ME, Baller EB, Gell M, Patrick LM, Shafiei G, Gur RE, Gur RC, Roalf DR, Romer D, Wolf DH, Kable JW, Satterthwaite TD. Individual differences in delay discounting are associated with dorsal prefrontal cortex connectivity in children, adolescents, and adults. Dev Cogn Neurosci 2023; 62:101265. [PMID: 37327696 PMCID: PMC10285090 DOI: 10.1016/j.dcn.2023.101265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 05/24/2023] [Accepted: 06/11/2023] [Indexed: 06/18/2023] Open
Abstract
Delay discounting is a measure of impulsive choice relevant in adolescence as it predicts many real-life outcomes, including obesity and academic achievement. However, resting-state functional networks underlying individual differences in delay discounting during youth remain incompletely described. Here we investigate the association between multivariate patterns of functional connectivity and individual differences in impulsive choice in a large sample of children, adolescents, and adults. A total of 293 participants (9-23 years) completed a delay discounting task and underwent 3T resting-state fMRI. A connectome-wide analysis using multivariate distance-based matrix regression was used to examine whole-brain relationships between delay discounting and functional connectivity. These analyses revealed that individual differences in delay discounting were associated with patterns of connectivity emanating from the left dorsal prefrontal cortex, a default mode network hub. Greater delay discounting was associated with greater functional connectivity between the dorsal prefrontal cortex and other default mode network regions, but reduced connectivity with regions in the dorsal and ventral attention networks. These results suggest delay discounting in children, adolescents, and adults is associated with individual differences in relationships both within the default mode network and between the default mode and networks involved in attentional and cognitive control.
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Affiliation(s)
- Kahini Mehta
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Adam Pines
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Azeez Adebimpe
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Bart Larsen
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA; Santa Fe Institute, Santa Fe, NM, 87051, USA
| | - Monica E Calkins
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Erica B Baller
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Martin Gell
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany; Institute of Neuroscience and Medicine (INM-7: Brain & Behaviour), Research Centre Jülich, Jülich, Germany
| | - Lauren M Patrick
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Golia Shafiei
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Daniel Romer
- Annenberg Public Policy Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel H Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Joseph W Kable
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA.
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17
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Marshall S, Gabiazon R, Persaud P, Nagamatsu LS. What do functional neuroimaging studies tell us about the association between falls and cognition in older adults? A systematic review. Ageing Res Rev 2023; 85:101859. [PMID: 36669688 DOI: 10.1016/j.arr.2023.101859] [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: 09/26/2022] [Revised: 01/13/2023] [Accepted: 01/16/2023] [Indexed: 01/19/2023]
Abstract
Impaired cognition is a known risk factor for falls in older adults. To enhance prevention strategies and treatment of falls among an aging global population, an understanding of the neural processes and networks involved is required. We present a systematic review investigating how functional neuroimaging techniques have been used to examine the association between falls and cognition in seniors. Peer-reviewed articles were identified through searching five electronic databases: 1) Medline, 2) PsycINFO, 3) CINAHL, 4) EMBASE, and 5) Pubmed. Key author, key paper, and reference searching was also conducted. Nine studies were included in this review. A questionnaire composed of seven questions was used to assess the quality of each study. EEG, fMRI, and PET were utilized across studies to examine brain function in older adults. Consistent evidence demonstrates that cognition is associated with measures of falls/falls risk, specifically visual attention and executive function. Our results show that falls/falls risk may be implicated with specific brain regions and networks. Future studies should be prospective and long-term in nature, with standardized outcome measures. Mobile neuroimaging techniques may also provide insight into brain activity as it pertains to cognition and falls in older adults in real-world settings.
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Affiliation(s)
- Samantha Marshall
- Faculty of Health Sciences, School of Kinesiology, Western University, Ontario, Canada
| | - Raphael Gabiazon
- Schulich School of Medicine and Dentistry, Western University, Ontario, Canada
| | - Priyanka Persaud
- Faculty of Health Sciences, School of Kinesiology, Western University, Ontario, Canada
| | - Lindsay S Nagamatsu
- Faculty of Health Sciences, School of Kinesiology, Western University, Ontario, Canada.
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18
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Li T, Wang L, Piao Z, Chen K, Yu X, Wen Q, Suo D, Zhang C, Funahashi S, Pei G, Fang B, Yan T. Altered Neurovascular Coupling for Multidisciplinary Intensive Rehabilitation in Parkinson's Disease. J Neurosci 2023; 43:1256-1266. [PMID: 36609454 PMCID: PMC9962778 DOI: 10.1523/jneurosci.1204-22.2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 12/31/2022] [Accepted: 01/03/2023] [Indexed: 01/07/2023] Open
Abstract
Effective rehabilitation in Parkinson's disease (PD) is related to brain reorganization with restoration of cortico-subcortical networks and compensation of frontoparietal networks; however, further neural rehabilitation evidence from a multidimensional perspective is needed. To investigate how multidisciplinary intensive rehabilitation treatment affects neurovascular coupling, 31 PD patients (20 female) before and after treatment and 30 healthy controls (17 female) underwent blood oxygenation level-dependent functional magnetic resonance imaging and arterial spin labeling scans. Cerebral blood flow (CBF) was used to measure perfusion, and fractional amplitude of low-frequency fluctuation (fALFF) was used to measure neural activity. The global CBF-fALFF correlation and regional CBF/fALFF ratio were calculated as neurovascular coupling. Dynamic causal modeling (DCM) was used to evaluate treatment-related alterations in the strength and directionality of information flow. Treatment reduced CBF-fALFF correlations. The altered CBF/fALFF exhibited increases in the left angular gyrus and the right inferior parietal gyrus and decreases in the bilateral thalamus and the right superior frontal gyrus. The CBF/fALFF alteration in right superior frontal gyrus showed correlations with motor improvement. Further, DCM indicated increases in connectivity from the superior frontal gyrus and decreases from the thalamus to the inferior parietal gyrus. The benefits of rehabilitation were reflected in the dual mechanism, with restoration of executive control occurring in the initial phase of motor learning and compensation of information integration occurring in the latter phase. These findings may yield multimodal insights into the role of rehabilitation in disease modification and identify the dorsolateral superior frontal gyrus as a potential target for noninvasive neuromodulation in PD.SIGNIFICANCE STATEMENT Although rehabilitation has been proposed as a promising supplemental treatment for PD as it results in brain reorganization, restoring cortico-subcortical networks and eliciting compensatory activation of frontoparietal networks, further multimodal evidence of the neural mechanisms underlying rehabilitation is needed. We measured the ratio of perfusion and neural activity derived from arterial spin labeling and blood oxygenation level-dependent fMRI data and found that benefits of rehabilitation seem to be related to the dual mechanism, restoring executive control in the initial phase of motor learning and compensating for information integration in the latter phase. We also identified the dorsolateral superior frontal gyrus as a potential target for noninvasive neuromodulation in PD patients.
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Affiliation(s)
- Ting Li
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Li Wang
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Zhixin Piao
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Keke Chen
- Parkinson Medical Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Xin Yu
- Parkinson Medical Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Qiping Wen
- Parkinson Medical Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Dingjie Suo
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Chunyu Zhang
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Shintaro Funahashi
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing 100081, China
| | - Guangying Pei
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Boyan Fang
- Parkinson Medical Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
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19
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Weinstein SM, Vandekar SN, Baller EB, Tu D, Adebimpe A, Tapera TM, Gur RC, Gur RE, Detre JA, Raznahan A, Alexander-Bloch AF, Satterthwaite TD, Shinohara RT, Park JY. Spatially-enhanced clusterwise inference for testing and localizing intermodal correspondence. Neuroimage 2022; 264:119712. [PMID: 36309332 PMCID: PMC10062374 DOI: 10.1016/j.neuroimage.2022.119712] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/16/2022] [Accepted: 10/25/2022] [Indexed: 11/05/2022] Open
Abstract
With the increasing availability of neuroimaging data from multiple modalities-each providing a different lens through which to study brain structure or function-new techniques for comparing, integrating, and interpreting information within and across modalities have emerged. Recent developments include hypothesis tests of associations between neuroimaging modalities, which can be used to determine the statistical significance of intermodal associations either throughout the entire brain or within anatomical subregions or functional networks. While these methods provide a crucial foundation for inference on intermodal relationships, they cannot be used to answer questions about where in the brain these associations are most pronounced. In this paper, we introduce a new method, called CLEAN-R, that can be used both to test intermodal correspondence throughout the brain and also to localize this correspondence. Our method involves first adjusting for the underlying spatial autocorrelation structure within each modality before aggregating information within small clusters to construct a map of enhanced test statistics. Using structural and functional magnetic resonance imaging data from a subsample of children and adolescents from the Philadelphia Neurodevelopmental Cohort, we conduct simulations and data analyses where we illustrate the high statistical power and nominal type I error levels of our method. By constructing an interpretable map of group-level correspondence using spatially-enhanced test statistics, our method offers insights beyond those provided by earlier methods.
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Affiliation(s)
- Sarah M Weinstein
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Simon N Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Erica B Baller
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Danni Tu
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Azeez Adebimpe
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA; Strategy Innovation & Deployment Section, Johnson and Johnson, Raritan, NJ, 08869, USA
| | - Tinashe M Tapera
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Armin Raznahan
- Section on Developmental Neurogenomics, National Institute of Mental Health Intramural Research Program, Bethesda, MD 20892, USA
| | - Aaron F Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA; Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jun Young Park
- Department of Statistical Sciences and Department of Psychology, University of Toronto, Toronto, ON, M5G 1Z5, Canada.
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20
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Hu F, Weinstein SM, Baller EB, Valcarcel AM, Adebimpe A, Raznahan A, Roalf DR, Robert‐Fitzgerald TE, Gonzenbach V, Gur RC, Gur RE, Vandekar S, Detre JA, Linn KA, Alexander‐Bloch A, Satterthwaite TD, Shinohara RT. Voxel-wise intermodal coupling analysis of two or more modalities using local covariance decomposition. Hum Brain Mapp 2022; 43:4650-4663. [PMID: 35730989 PMCID: PMC9491276 DOI: 10.1002/hbm.25980] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 05/05/2022] [Accepted: 05/31/2022] [Indexed: 12/28/2022] Open
Abstract
When individual subjects are imaged with multiple modalities, biological information is present not only within each modality, but also between modalities - that is, in how modalities covary at the voxel level. Previous studies have shown that local covariance structures between modalities, or intermodal coupling (IMCo), can be summarized for two modalities, and that two-modality IMCo reveals otherwise undiscovered patterns in neurodevelopment and certain diseases. However, previous IMCo methods are based on the slopes of local weighted linear regression lines, which are inherently asymmetric and limited to the two-modality setting. Here, we present a generalization of IMCo estimation which uses local covariance decompositions to define a symmetric, voxel-wise coupling coefficient that is valid for two or more modalities. We use this method to study coupling between cerebral blood flow, amplitude of low frequency fluctuations, and local connectivity in 803 subjects ages 8 through 22. We demonstrate that coupling is spatially heterogeneous, varies with respect to age and sex in neurodevelopment, and reveals patterns that are not present in individual modalities. As availability of multi-modal data continues to increase, principal-component-based IMCo (pIMCo) offers a powerful approach for summarizing relationships between multiple aspects of brain structure and function. An R package for estimating pIMCo is available at: https://github.com/hufengling/pIMCo.
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Affiliation(s)
- Fengling Hu
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sarah M. Weinstein
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Erica B. Baller
- Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- The Penn Lifespan Informatics and Neuroimaging Center, Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Alessandra M. Valcarcel
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Azeez Adebimpe
- Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- The Penn Lifespan Informatics and Neuroimaging Center, Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Armin Raznahan
- National Institute of Mental Health, Intramural Research ProgramNational Institute of HealthBethesdaMarylandUSA
| | - David R. Roalf
- Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Timothy E. Robert‐Fitzgerald
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Virgilio Gonzenbach
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ruben C. Gur
- Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of RadiologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of NeurologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Raquel E. Gur
- Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of RadiologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of NeurologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Simon Vandekar
- Department of BiostatisticsVanderbilt UniversityNashvilleTennesseeUSA
| | - John A. Detre
- Department of NeurologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Kristin A. Linn
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Aaron Alexander‐Bloch
- Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Theodore D. Satterthwaite
- Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- The Penn Lifespan Informatics and Neuroimaging Center, Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Russell T. Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing and Analytics (CBICA)Perelman School of MedicinePhiladelphiaPennsylvaniaUSA
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21
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Schmithorst VJ, Adams PS, Badaly D, Lee VK, Wallace J, Beluk N, Votava-Smith JK, Weinberg JG, Beers SR, Detterich J, Wood JC, Lo CW, Panigrahy A. Impaired Neurovascular Function Underlies Poor Neurocognitive Outcomes and Is Associated with Nitric Oxide Bioavailability in Congenital Heart Disease. Metabolites 2022; 12:metabo12090882. [PMID: 36144286 PMCID: PMC9504090 DOI: 10.3390/metabo12090882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 09/14/2022] [Accepted: 09/15/2022] [Indexed: 12/03/2022] Open
Abstract
We use a non-invasive MRI proxy of neurovascular function (pnvf) to assess the ability of the vasculature to supply baseline metabolic demand, to compare pediatric and young adult congenital heart disease (CHD) patients to normal referents and relate the proxy to neurocognitive outcomes and nitric oxide bioavailability. In a prospective single-center study, resting-state blood-oxygen-level-dependent (BOLD) and arterial spin labeling (ASL) MRI scans were successfully obtained from 24 CHD patients (age = 15.4 ± 4.06 years) and 63 normal referents (age = 14.1 ± 3.49) years. Pnvf was computed on a voxelwise basis as the negative of the ratio of functional connectivity strength (FCS) estimated from the resting-state BOLD acquisition to regional cerebral blood flow (rCBF) as estimated from the ASL acquisition. Pnvf was used to predict end-tidal CO2 (PETCO2) levels and compared to those estimated from the BOLD data. Nitric oxide availability was obtained via nasal measurements (nNO). Pnvf was compared on a voxelwise basis between CHD patients and normal referents and correlated with nitric oxide availability and neurocognitive outcomes as assessed via the NIH Toolbox. Pnvf was shown as highly predictive of PETCO2 using theoretical modeling. Pnvf was found to be significantly reduced in CHD patients in default mode network (DMN, comprising the ventromedial prefrontal cortex and posterior cingulate/precuneus), salience network (SN, comprising the insula and dorsal anterior cingulate), and central executive network (CEN, comprising posterior parietal and dorsolateral prefrontal cortex) regions with similar findings noted in single cardiac ventricle patients. Positive correlations of Pnvf in these brain regions, as well as the hippocampus, were found with neurocognitive outcomes. Similarly, positive correlations between Pnvf and nitric oxide availability were found in frontal DMN and CEN regions, with particularly strong correlations in subcortical regions (putamen). Reduced Pnvf in CHD patients was found to be mediated by nNO. Mediation analyses further supported that reduced Pnvf in these regions underlies worse neurocognitive outcome in CHD patients and is associated with nitric oxide bioavailability. Impaired neuro-vascular function, which may be non-invasively estimated via combined arterial-spin label and BOLD MR imaging, is a nitric oxide bioavailability dependent factor implicated in adverse neurocognitive outcomes in pediatric and young adult CHD.
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Affiliation(s)
| | - Phillip S. Adams
- Department of Pediatric Anesthesiology, UPMC Children’s Hospital, Pittsburgh, PA 15224, USA
| | - Daryaneh Badaly
- Learning and Development Center, Child Mind Institute, New York, NY 10022, USA
| | - Vincent K. Lee
- Department of Pediatric Radiology, UPMC Children’s Hospital, Pittsburgh, PA 15224, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Julia Wallace
- Department of Pediatric Radiology, UPMC Children’s Hospital, Pittsburgh, PA 15224, USA
| | - Nancy Beluk
- Department of Pediatric Radiology, UPMC Children’s Hospital, Pittsburgh, PA 15224, USA
| | | | | | - Sue R. Beers
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Jon Detterich
- Heart Institute, Children’s Hospital Los Angeles, Los Angeles, CA 90027, USA
| | - John C. Wood
- Heart Institute, Children’s Hospital Los Angeles, Los Angeles, CA 90027, USA
| | - Cecilia W. Lo
- Department of Developmental Biology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Ashok Panigrahy
- Department of Pediatric Radiology, UPMC Children’s Hospital, Pittsburgh, PA 15224, USA
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
- Correspondence: ; Tel.: +1-412-692-5510; Fax: +1-412-692-6929
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22
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Li T, Pei Z, Zhu Z, Wu X, Feng C. Intrinsic brain activity patterns across large-scale networks predict reciprocity propensity. Hum Brain Mapp 2022; 43:5616-5629. [PMID: 36054523 PMCID: PMC9704792 DOI: 10.1002/hbm.26038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 06/06/2022] [Accepted: 07/25/2022] [Indexed: 01/15/2023] Open
Abstract
Reciprocity is prevalent across human societies, but individuals are heterogeneous regarding their reciprocity propensity. Although a large body of task-based brain imaging measures has shed light on the neural underpinnings of reciprocity at group level, the neural basis underlying the individual differences in reciprocity propensity remains largely unclear. Here, we combined brain imaging and machine learning techniques to individually predict reciprocity propensity from resting-state brain activity measured by fractional amplitude of low-frequency fluctuation. The brain regions contributing to the prediction were then analyzed for functional connectivity and decoding analyses, allowing for a data-driven quantitative inference on psychophysiological functions. Our results indicated that patterns of resting-state brain activity across multiple brain systems were capable of predicting individual reciprocity propensity, with the contributing regions distributed across the salience (e.g., ventrolateral prefrontal cortex), fronto-parietal (e.g., dorsolateral prefrontal cortex), default mode (e.g., ventromedial prefrontal cortex), and sensorimotor (e.g., supplementary motor area) networks. Those contributing brain networks are implicated in emotion and cognitive control, mentalizing, and motor-based processes, respectively. Collectively, these findings provide novel evidence on the neural signatures underlying the individual differences in reciprocity, and lend support the assertion that reciprocity emerges from interactions among regions embodied in multiple large-scale brain networks.
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Affiliation(s)
- Ting Li
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University)Ministry of EducationGuangzhouChina,School of Psychology, Institute of Brain Research and Rehabilitation (IBRR), Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive ScienceSouth China Normal UniversityGuangzhouChina,Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
| | - Zhaodi Pei
- School of Artificial IntelligenceBeijing Normal UniversityBeijingChina,Engineering Research Center of Intelligent Technology and Educational Application of Ministry of EducationBeijing Normal UniversityBeijingChina
| | - Zhiyuan Zhu
- School of Artificial IntelligenceBeijing Normal UniversityBeijingChina,Engineering Research Center of Intelligent Technology and Educational Application of Ministry of EducationBeijing Normal UniversityBeijingChina
| | - Xia Wu
- School of Artificial IntelligenceBeijing Normal UniversityBeijingChina,Engineering Research Center of Intelligent Technology and Educational Application of Ministry of EducationBeijing Normal UniversityBeijingChina
| | - Chunliang Feng
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University)Ministry of EducationGuangzhouChina,School of Psychology, Institute of Brain Research and Rehabilitation (IBRR), Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive ScienceSouth China Normal UniversityGuangzhouChina
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