101
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Ma Q, Huang B, Wang J, Seger C, Yang W, Li C, Wang J, Feng J, Weng L, Jiang W, Huang R. Altered modular organization of intrinsic brain functional networks in patients with Parkinson's disease. Brain Imaging Behav 2018; 11:430-443. [PMID: 26860909 DOI: 10.1007/s11682-016-9524-7] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Although previous studies reported altered topology of brain functional networks in patients with Parkinson's disease (PD), the modular organization of brain functional networks in PD patients remains largely unknown. Using the resting-state functional MRI (R-fMRI) and graph theory, we examined the modular organization of brain functional networks in 32 unmedicated patients with early-to-mid motor stage PD and 31 healthy controls. Compared to the controls, the PD patients tended to show decreased integrity and segregation, both within and between modules. This was inferred by significantly increased intra-modular characteristic path length (L p) within four modules: mPFC, SN, SMN, and FPN, decreased inter-modular functional connectivity (FC) between mPFC and SN, SMN, and VN, and decreased intra-modular clustering in the PD patients. Intra-modular characteristic path length within the mPFC showed significantly positive correlation with general cognitive ability in the PD group. Receiver operating characteristic (ROC) analysis revealed that FC between mPFC and SN had the highest significant accuracy in differentiating the patients from the controls. Our findings may provide new insight in understanding the pathological changes that underlie impairment in cognition and movement in Parkinson's disease.
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Affiliation(s)
- Qing Ma
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, 510631, China
| | - Biao Huang
- Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China.
| | - Jinhui Wang
- Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, 310015, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou, 310015, China
| | - Carol Seger
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, 510631, China.,Department of Psychology and Program in Molecular, Cellular, and Integrative Neurosciences, Colorado State University, Fort Collins, CO, USA
| | - Wanqun Yang
- Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China
| | - Changhong Li
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, 510631, China
| | - Junjing Wang
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, 510631, China
| | - Jieying Feng
- Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China
| | - Ling Weng
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, 510631, China
| | - Wenjie Jiang
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, 510631, China
| | - Ruiwang Huang
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, 510631, China.
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102
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Distinct manifestation of cognitive deficits associate with different resting-state network disruptions in non-demented patients with Parkinson’s disease. J Neurol 2018; 265:688-700. [DOI: 10.1007/s00415-018-8755-5] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2017] [Revised: 12/11/2017] [Accepted: 01/17/2018] [Indexed: 12/13/2022]
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103
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Li C, Huang B, Zhang R, Ma Q, Yang W, Wang L, Wang L, Xu Q, Feng J, Liu L, Zhang Y, Huang R. Impaired topological architecture of brain structural networks in idiopathic Parkinson's disease: a DTI study. Brain Imaging Behav 2018; 11:113-128. [PMID: 26815739 DOI: 10.1007/s11682-015-9501-6] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Parkinson's disease (PD) is considered as a neurodegenerative disorder of the brain central nervous system. But, to date, few studies adopted the network model to reveal topological changes in brain structural networks in PD patients. Additionally, although the concept of rich club organization has been widely used to study brain networks in various brain disorders, there is no study to report the changed rich club organization of brain networks in PD patients. Thus, we collected diffusion tensor imaging (DTI) data from 35 PD patients and 26 healthy controls and adopted deterministic tractography to construct brain structural networks. During the network analysis, we calculated their topological properties, and built the rich club organization of brain structural networks for both subject groups. By comparing the between-group differences in topological properties and rich club organizations, we found that the connectivity strength of the feeder and local connections are lower in PD patients compared to those of the healthy controls. Furthermore, using a network-based statistic (NBS) approach, we identified uniformly significantly decreased connections in two modules, the limbic/paralimbic/subcortical module and the cognitive control/attention module, in patients compared to controls. In addition, for the topological properties of brain network topology in the PD patients, we found statistically increased shortest path length and decreased global efficiency. Statistical comparisons of nodal properties were also widespread in the frontal and parietal regions for the PD patients. These findings may provide useful information to better understand the abnormalities of brain structural networks in PD patients.
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Affiliation(s)
- Changhong Li
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Brain Study Institute, South China Normal University, Guangzhou, 510631, China
| | - Biao Huang
- Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China.
| | - Ruibin Zhang
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Brain Study Institute, South China Normal University, Guangzhou, 510631, China
| | - Qing Ma
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Brain Study Institute, South China Normal University, Guangzhou, 510631, China
| | - Wanqun Yang
- Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China
| | - Lijuan Wang
- Department of Neurology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China
| | - Limin Wang
- Department of Neurology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China
| | - Qin Xu
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Brain Study Institute, South China Normal University, Guangzhou, 510631, China
| | - Jieying Feng
- Department of Radiology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China
| | - Liqing Liu
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Brain Study Institute, South China Normal University, Guangzhou, 510631, China
| | - Yuhu Zhang
- Department of Neurology, Guangdong Academy of Medical Sciences, Guangdong General Hospital, Guangzhou, China
| | - Ruiwang Huang
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Brain Study Institute, South China Normal University, Guangzhou, 510631, China.
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104
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Abstract
The topological organization underlying the human brain was extensively investigated using resting-state functional magnetic resonance imaging, focusing on a low frequency of signal oscillation from 0.01 to 0.1 Hz. However, the frequency specificities with regard to the topological properties of the brain networks have not been fully revealed. In this study, a novel complementary ensemble empirical mode decomposition (CEEMD) method was used to separate the fMRI time series into five characteristic oscillations with distinct frequencies. Then, the small world properties of brain networks were analyzed for each of these five oscillations in patients (n = 67) with depressed Parkinson’s disease (DPD, n = 20) , non-depressed Parkinson’s disease (NDPD, n = 47) and healthy controls (HC, n = 46). Compared with HC, the results showed decreased network efficiency in characteristic oscillations from 0.05 to 0.12 Hz and from 0.02 to 0.05 Hz for the DPD and NDPD patients, respectively. Furthermore, compared with HC, the most significant inter-group difference across five brain oscillations was found in the basal ganglia (0.01 to 0.05 Hz) and paralimbic-limbic network (0.02 to 0.22 Hz) for the DPD patients, and in the visual cortex (0.02 to 0.05 Hz) for the NDPD patients. Compared with NDPD, the DPD patients showed reduced efficiency of nodes in the basal ganglia network (0.01 to 0.05 Hz). Our results demonstrated that DPD is characterized by a disrupted topological organization in large-scale brain functional networks. Moreover, the CEEMD analysis suggested a prominent dissociation in the topological organization of brain networks between DPD and NDPD in both space and frequency domains. Our findings indicated that these characteristic oscillatory activities in different functional circuits may contribute to distinct motor and non-motor components of clinical impairments in Parkinson’s disease.
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105
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Large-scale cortical volume correlation networks reveal disrupted small world patterns in Parkinson’s disease. Neurosci Lett 2018; 662:374-380. [DOI: 10.1016/j.neulet.2017.10.032] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2017] [Revised: 09/24/2017] [Accepted: 10/17/2017] [Indexed: 11/21/2022]
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106
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Díez-Cirarda M, Strafella AP, Kim J, Peña J, Ojeda N, Cabrera-Zubizarreta A, Ibarretxe-Bilbao N. Dynamic functional connectivity in Parkinson's disease patients with mild cognitive impairment and normal cognition. Neuroimage Clin 2017; 17:847-855. [PMID: 29527489 PMCID: PMC5842729 DOI: 10.1016/j.nicl.2017.12.013] [Citation(s) in RCA: 100] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2017] [Revised: 11/24/2017] [Accepted: 12/07/2017] [Indexed: 11/25/2022]
Abstract
The objective was to assess dynamic functional connectivity (FC) and local/global connectivity in Parkinson's disease (PD) patients with mild cognitive impairment (PD-MCI) and with normal cognition (PD-NC). The sample included 35 PD patients and 26 healthy controls (HC). Cognitive assessment followed an extensive neuropsychological battery. For resting-state functional MRI (rs-fMRI) analysis, independent component analysis (ICA) was performed and components were located in 7 networks: Subcortical (SC), Auditory (AUD), Somatomotor (SM), visual (VI), cognitive-control (CC), default-mode (DMN), and cerebellar (CB). Dynamic FC analysis was performed using the GIFT toolbox. FC differences between groups in each FC state were analysed with the network-based statistic (NBS) approach. Finally, a graph-theoretical analysis for local/global parameters was performed. The whole sample showed 2 dynamic FC states during the rs-fMRI. PD-MCI patients showed decreased mean dwell time in the hypo-connectivity state (p = 0.030) and showed increased number of state transitions (p = 0.007) compared with the HC. In addition, in the hypo-connectivity state, PD-MCI patients showed reduced inter-network FC between the SM-CC, SM-VI, SM-AUD, CC-VI and SC-DMN compared with the HC (p < 0.05-FDR). These FC alterations in PD-MCI were accompanied by graph-topological alterations in nodes located in the SM network (p < 0.001). In contrast, no differences were found between the PD-NC and HC. Findings suggest the presence of dynamic functional brain deteriorations in PD-MCI that are not present in PD-NC, showing the PD-MCI group dynamic FC dysfunctions, reduced FC mostly between SM-CC networks and graph-topological deteriorations in the SM network. A dynamic FC approach could be helpful to understand cognitive deterioration in PD.
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Affiliation(s)
- María Díez-Cirarda
- Department of Methods and Experimental Psychology, Faculty of Psychology and Education, University of Deusto, Bilbao, Basque Country, Spain.
| | - Antonio P Strafella
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada; Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, UHN, University of Toronto, Ontario, Canada.
| | - Jinhee Kim
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, Canada; Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, UHN, University of Toronto, Ontario, Canada.
| | - Javier Peña
- Department of Methods and Experimental Psychology, Faculty of Psychology and Education, University of Deusto, Bilbao, Basque Country, Spain.
| | - Natalia Ojeda
- Department of Methods and Experimental Psychology, Faculty of Psychology and Education, University of Deusto, Bilbao, Basque Country, Spain.
| | | | - Naroa Ibarretxe-Bilbao
- Department of Methods and Experimental Psychology, Faculty of Psychology and Education, University of Deusto, Bilbao, Basque Country, Spain.
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107
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Jiang J, Zhou H, Duan H, Liu X, Zuo C, Huang Z, Yu Z, Yan Z. A novel individual-level morphological brain networks constructing method and its evaluation in PET and MR images. Heliyon 2017; 3:e00475. [PMID: 29322101 PMCID: PMC5753611 DOI: 10.1016/j.heliyon.2017.e00475] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2017] [Revised: 11/14/2017] [Accepted: 11/27/2017] [Indexed: 12/17/2022] Open
Abstract
Mapping the human brain is one of the great scientific challenges of the 21st century. Brain network analysis is an effective technique based on graph theory that is widely used to investigate network patterns in the human brain. Currently, mapping an individual brain network using a single image has been a hotspot in the field of brain science; techniques, such as the Kullback-Leibler (KL) method, have applications in structural Magnetic Resonance (MR) imaging. However, maintaining an image's intensity, shape, texture and gradient information during feature extraction is very challenging. In this study, we propose a novel method for individual-level network construction based on the high-resolution Brainnetome Atlas, which shows 246 brain regions. Principal components (PCs) were obtained for each brain region using principal component analysis (PCA) for feature extraction. Individual brain networks were followed and used to construct the PC similarity measurement based on the mutual information (MI) method. To evaluate the robustness of the proposed method, three independent experiments were carried out. In the first, 34 healthy subjects underwent two Carbon 11-labeled Pittsburgh compound B Positron emission tomography (11C-PiB PET) scans; in the second, 32 healthy subjects underwent two structural MRI scans; and in the last, 10 Alzheimer's disease (AD) subjects and 10Healthy Control (HC) subjects underwent 11C-PiB PET scans. For each subject, network metrics including clustering coefficient, path length, small-world coefficient, efficiency and node betweenness centrality were calculated. The results suggested that both the individual PET and structural MRI networks exhibited a good small-word property, and the variances within subjects was also quite small in all metrics, The average value of Coefficient of variation (CV) map was 0.33 and 0.32 for PiB PET and MR images respectively, and intra-class correlation coefficients (ICC) range from approximately 0.4 to 0.7, indicating that the new method was well adapted to the subjects. The results of intra-class correlation coefficients from the test-retest experiment were consistent with previous research employing KL divergence, but with low computational complexity. Further, differences between AD subjects and HC subjects can be observed in network metrics. The method proposed herein provides a new perspective for investigating individual brain connectivity; it would enable neuroscientists to further understand the functions of the human brain.
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Affiliation(s)
- Jiehui Jiang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
- Institute of Biomedical Engineering, School of Communication and Information Technology, Shanghai University, Shanghai, China
| | - Hucheng Zhou
- Institute of Biomedical Engineering, School of Communication and Information Technology, Shanghai University, Shanghai, China
| | - Huoqiang Duan
- Institute of Biomedical Engineering, School of Communication and Information Technology, Shanghai University, Shanghai, China
| | - Xin Liu
- Institute of Biomedical Engineering, School of Communication and Information Technology, Shanghai University, Shanghai, China
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhemin Huang
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhihua Yu
- Shanghai Geriatric Institute of Chinese Medicine, Shanghai, China
| | - Zhuangzhi Yan
- Institute of Biomedical Engineering, School of Communication and Information Technology, Shanghai University, Shanghai, China
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108
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Suo X, Lei D, Li N, Cheng L, Chen F, Wang M, Kemp GJ, Peng R, Gong Q. Functional Brain Connectome and Its Relation to Hoehn and Yahr Stage in Parkinson Disease. Radiology 2017; 285:904-913. [DOI: 10.1148/radiol.2017162929] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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109
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Badea L, Onu M, Wu T, Roceanu A, Bajenaru O. Exploring the reproducibility of functional connectivity alterations in Parkinson's disease. PLoS One 2017; 12:e0188196. [PMID: 29182621 PMCID: PMC5705108 DOI: 10.1371/journal.pone.0188196] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Accepted: 11/02/2017] [Indexed: 12/31/2022] Open
Abstract
Since anatomic MRI is presently not able to directly discern neuronal loss in Parkinson's Disease (PD), studying the associated functional connectivity (FC) changes seems a promising approach toward developing non-invasive and non-radioactive neuroimaging markers for this disease. While several groups have reported such FC changes in PD, there are also significant discrepancies between studies. Investigating the reproducibility of PD-related FC changes on independent datasets is therefore of crucial importance. We acquired resting-state fMRI scans for 43 subjects (27 patients and 16 normal controls, with 2 replicate scans per subject) and compared the observed FC changes with those obtained in two independent datasets, one made available by the PPMI consortium (91 patients, 18 controls) and a second one by the group of Tao Wu (20 patients, 20 controls). Unfortunately, PD-related functional connectivity changes turned out to be non-reproducible across datasets. This could be due to disease heterogeneity, but also to technical differences. To distinguish between the two, we devised a method to directly check for disease heterogeneity using random splits of a single dataset. Since we still observe non-reproducibility in a large fraction of random splits of the same dataset, we conclude that functional heterogeneity may be a dominating factor behind the lack of reproducibility of FC alterations in different rs-fMRI studies of PD. While global PD-related functional connectivity changes were non-reproducible across datasets, we identified a few individual brain region pairs with marginally consistent FC changes across all three datasets. However, training classifiers on each one of the three datasets to discriminate PD scans from controls produced only low accuracies on the remaining two test datasets. Moreover, classifiers trained and tested on random splits of the same dataset (which are technically homogeneous) also had low test accuracies, directly substantiating disease heterogeneity.
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Affiliation(s)
- Liviu Badea
- Artificial Intelligence and Bioinformatics Group, National Institute for Research and Development in Informatics, Bucharest, Romania
- * E-mail:
| | - Mihaela Onu
- Medical Imaging Department, Clinical Hospital Prof. Dr. Th. Burghele, Bucharest, Romania
- University of Medicine and Pharmacy “Carol Davila”, Biophysics Department, Bucharest, Romania
| | - Tao Wu
- Department of Neurobiology, Key Laboratory on Neurodegenerative Disorders of Ministry of Education, Beijing Institute of Geriatrics, Xuanwu Hospital, Capital Medical University, Beijing, China
- Beijing Key Laboratory on Parkinson’s Disease, Parkinson Disease Centre of Beijing Institute for Brain Disorders, Beijing, China
| | - Adina Roceanu
- University Emergency Hospital Bucharest, Neurology Department, Bucharest, Romania
| | - Ovidiu Bajenaru
- University Emergency Hospital Bucharest, Neurology Department, Bucharest, Romania
- University of Medicine and Pharmacy “Carol Davila”, Department of Clinical Neurosciences, Bucharest, Romania
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110
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Tang Y, Xiao X, Xie H, Wan CM, Meng L, Liu ZH, Liao WH, Tang BS, Guo JF. Altered Functional Brain Connectomes between Sporadic and Familial Parkinson's Patients. Front Neuroanat 2017; 11:99. [PMID: 29163072 PMCID: PMC5681528 DOI: 10.3389/fnana.2017.00099] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Accepted: 10/19/2017] [Indexed: 11/30/2022] Open
Abstract
Familial Parkinson's disease (PD) is often caused by mutation of a certain gene, while sporadic PD is associated with variants of genes which can influence the susceptibility to PD. The goal of this study was to investigate the difference between the two forms of PD in terms of brain abnormalities using resting-state functional MRI and graph theory. Thirty-one familial PD patients and 36 sporadic PD patients underwent resting-state functional MRI scanning. Frequency-dependent functional connectivity was calculated for each subject using wavelet-based correlations of BOLD signal over 246 brain regions from Brainnetome Atlas. Graph theoretical analysis was then performed to analyze the topology of the functional network, and functional connectome differences were identified with a network-based statistical approach. Our results revealed a frequency-specific (0.016 and 0.031 Hz) connectome difference between familial and sporadic forms of PD, as indicated by an increase in assortativity and decrease in the nodal strength in the left medial amygdala of the familial PD group. In addition, the familial PD patients also showed a distinctive functional network between the left medial amygdala and regions related to retrieval of motion information. The present study indicates that the medial amygdala might be most vulnerable to both sporadic and familial PD. Our findings provide some new insights into disrupted resting-state functional connectomes between sporadic PD and familial PD.
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Affiliation(s)
- Yan Tang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,School of Information Science and Engineering, Central South University, Changsha, China
| | - Xue Xiao
- School of Basic Medical Science, Central South University, Changsha, China
| | - Hua Xie
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, United States
| | - Chang-Min Wan
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Li Meng
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Zhen-Hua Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
| | - Wei-Hua Liao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, China
| | - Bei-Sha Tang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,School of Basic Medical Science, Central South University, Changsha, China.,Parkinson's Disease Center of Beijing Institute for Brain Disorders, Beijing, China.,Collaborative Innovation Center for Brain Science, Shanghai, China.,State Key Laboratory of Medical Genetics, Changsha, China
| | - Ji-Feng Guo
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China.,State Key Laboratory of Medical Genetics, Changsha, China
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111
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Kim J, Criaud M, Cho SS, Díez-Cirarda M, Mihaescu A, Coakeley S, Ghadery C, Valli M, Jacobs MF, Houle S, Strafella AP. Abnormal intrinsic brain functional network dynamics in Parkinson's disease. Brain 2017; 140:2955-2967. [PMID: 29053835 PMCID: PMC5841202 DOI: 10.1093/brain/awx233] [Citation(s) in RCA: 232] [Impact Index Per Article: 33.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2017] [Revised: 06/22/2017] [Accepted: 07/17/2017] [Indexed: 01/17/2023] Open
Abstract
See Nieuwhof and Helmich (doi:10.1093/brain/awx267 ) for a scientific commentary on this article . Parkinson’s disease is a neurodegenerative disorder characterized by nigrostriatal dopamine depletion. Previous studies measuring spontaneous brain activity using resting state functional magnetic resonance imaging have reported abnormal changes in broadly distributed whole-brain networks. Although resting state functional connectivity, estimating temporal correlations between brain regions, is measured with the assumption that intrinsic fluctuations throughout the scan are stable, dynamic changes of functional connectivity have recently been suggested to reflect aspects of functional capacity of neural systems, and thus may serve as biomarkers of disease. The present work is the first study to investigate the dynamic functional connectivity in patients with Parkinson’s disease, with a focus on the temporal properties of functional connectivity states as well as the variability of network topological organization using resting state functional magnetic resonance imaging. Thirty-one Parkinson’s disease patients and 23 healthy controls were studied using group spatial independent component analysis, a sliding windows approach, and graph-theory methods. The dynamic functional connectivity analyses suggested two discrete connectivity configurations: a more frequent, sparsely connected within-network state (State I) and a less frequent, more strongly interconnected between-network state (State II). In patients with Parkinson’s disease, the occurrence of the sparsely connected State I dropped by 12.62%, while the expression of the more strongly interconnected State II increased by the same amount. This was consistent with the altered temporal properties of the dynamic functional connectivity characterized by a shortening of the dwell time of State I and by a proportional increase of the dwell time pattern in State II. These changes are suggestive of a reduction in functional segregation among networks and are correlated with the clinical severity of Parkinson’s disease symptoms. Additionally, there was a higher variability in the network global efficiency, suggesting an abnormal global integration of the brain networks. The altered functional segregation and abnormal global integration in brain networks confirmed the vulnerability of functional connectivity networks in Parkinson’s disease.
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Affiliation(s)
- Jinhee Kim
- Morton and Gloria Shulman Movement Disorder Unit and E.J. Safra Parkinson Disease Program, Neurology Division, Department of Medicine, Toronto Western Hospital, University Health Network, University of Toronto, Ontario, M5G 2C4, Canada
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, M5T 2S8, Canada
- Division of Brain, Imaging and Behaviour – Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Ontario, M5T 2S8, Canada
| | - Marion Criaud
- Morton and Gloria Shulman Movement Disorder Unit and E.J. Safra Parkinson Disease Program, Neurology Division, Department of Medicine, Toronto Western Hospital, University Health Network, University of Toronto, Ontario, M5G 2C4, Canada
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, M5T 2S8, Canada
- Division of Brain, Imaging and Behaviour – Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Ontario, M5T 2S8, Canada
| | - Sang Soo Cho
- Morton and Gloria Shulman Movement Disorder Unit and E.J. Safra Parkinson Disease Program, Neurology Division, Department of Medicine, Toronto Western Hospital, University Health Network, University of Toronto, Ontario, M5G 2C4, Canada
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, M5T 2S8, Canada
- Division of Brain, Imaging and Behaviour – Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Ontario, M5T 2S8, Canada
| | - María Díez-Cirarda
- Morton and Gloria Shulman Movement Disorder Unit and E.J. Safra Parkinson Disease Program, Neurology Division, Department of Medicine, Toronto Western Hospital, University Health Network, University of Toronto, Ontario, M5G 2C4, Canada
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, M5T 2S8, Canada
- Division of Brain, Imaging and Behaviour – Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Ontario, M5T 2S8, Canada
| | - Alexander Mihaescu
- Morton and Gloria Shulman Movement Disorder Unit and E.J. Safra Parkinson Disease Program, Neurology Division, Department of Medicine, Toronto Western Hospital, University Health Network, University of Toronto, Ontario, M5G 2C4, Canada
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, M5T 2S8, Canada
- Division of Brain, Imaging and Behaviour – Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Ontario, M5T 2S8, Canada
| | - Sarah Coakeley
- Morton and Gloria Shulman Movement Disorder Unit and E.J. Safra Parkinson Disease Program, Neurology Division, Department of Medicine, Toronto Western Hospital, University Health Network, University of Toronto, Ontario, M5G 2C4, Canada
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, M5T 2S8, Canada
- Division of Brain, Imaging and Behaviour – Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Ontario, M5T 2S8, Canada
| | - Christine Ghadery
- Morton and Gloria Shulman Movement Disorder Unit and E.J. Safra Parkinson Disease Program, Neurology Division, Department of Medicine, Toronto Western Hospital, University Health Network, University of Toronto, Ontario, M5G 2C4, Canada
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, M5T 2S8, Canada
- Division of Brain, Imaging and Behaviour – Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Ontario, M5T 2S8, Canada
| | - Mikaeel Valli
- Morton and Gloria Shulman Movement Disorder Unit and E.J. Safra Parkinson Disease Program, Neurology Division, Department of Medicine, Toronto Western Hospital, University Health Network, University of Toronto, Ontario, M5G 2C4, Canada
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, M5T 2S8, Canada
- Division of Brain, Imaging and Behaviour – Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Ontario, M5T 2S8, Canada
| | - Mark F Jacobs
- Morton and Gloria Shulman Movement Disorder Unit and E.J. Safra Parkinson Disease Program, Neurology Division, Department of Medicine, Toronto Western Hospital, University Health Network, University of Toronto, Ontario, M5G 2C4, Canada
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, M5T 2S8, Canada
- Division of Brain, Imaging and Behaviour – Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Ontario, M5T 2S8, Canada
| | - Sylvain Houle
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, M5T 2S8, Canada
| | - Antonio P Strafella
- Morton and Gloria Shulman Movement Disorder Unit and E.J. Safra Parkinson Disease Program, Neurology Division, Department of Medicine, Toronto Western Hospital, University Health Network, University of Toronto, Ontario, M5G 2C4, Canada
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, Ontario, M5T 2S8, Canada
- Division of Brain, Imaging and Behaviour – Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Ontario, M5T 2S8, Canada
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112
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Impaired brain network architecture in newly diagnosed Parkinson's disease based on graph theoretical analysis. Neurosci Lett 2017; 657:151-158. [PMID: 28789983 DOI: 10.1016/j.neulet.2017.08.002] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2017] [Revised: 07/22/2017] [Accepted: 08/01/2017] [Indexed: 12/14/2022]
Abstract
BACKGROUND Resting state functional magnetic resonance imaging (rs-fMRI) has been applied to investigate topographic structure in Parkinson's disease (PD). Alteration of topographic architecture has been inconsistent in PD AIM: To investigate the network profile of PD using graph theoretical analysis. METHOD Twenty six newly diagnosed PD and 19 age- and gender- matched healthy controls (HC) were included in our analysis. Small-world profile and topographic profiles (nodal degree, global efficiency, local efficiency, cluster coefficient, shortest path length, betweenness centrality) were measured and compared between groups, with age and gender as covariates. We also performed correlation analysis between topographic features with motor severity measured by UPDRS III. RESULTS Small-world property was present in PD. Nodal degree, global efficiency, local efficiency and characteristic path length consistently revealed disruptive sensorimotor network, and visual network to a less degree in PD. By contrast, default mode network (DMN) and cerebellum in PD showed higher nodal degree, global efficiency and local efficiency, and lower characteristic path length. Global and local efficiency in the midbrain was higher in PD excluding substantia nigra. PD group also exhibited lower cluster coefficient in the subcortical motor network (thalamus and caudate nucleus). No significant correlation was found between topographic properties and motor severity. CONCLUSION PD exhibited disruptive sensorimotor and visual networks in early disease stage. DMN, a certain areas in the cerebellum and midbrain may compensate for disruptive sensorimotor and visual network in PD. Disruptive network architecture may be an early alteration of PD pathophysiology but may not serve as a valid biomarker yet.
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113
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Choi JW, Jeong MH, Her SJ, Lee BU, Cha KS, Jung KY, Kim KH. Abnormal Sleep Delta Rhythm and Interregional Phase Synchrony in Patients with Restless Legs Syndrome and Their Reversal by Dopamine Agonist Treatment. J Clin Neurol 2017; 13:340-350. [PMID: 28831786 PMCID: PMC5653621 DOI: 10.3988/jcn.2017.13.4.340] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 05/13/2017] [Accepted: 05/15/2017] [Indexed: 02/05/2023] Open
Abstract
Background and Purpose The purpose of this study was to characterize abnormal cortical activity during sleep in restless legs syndrome (RLS) patients and to determine the effects of treatment with a dopamine agonist. Based on whole-brain electroencephalograms, we attempted to verify alterations in the functional network as well as the spectral power of neural activities during sleep in RLS patients and to determine whether the changes are reversed by treatment with pramipexole. Methods Twelve drug-naïve RLS patients participated in the study. Overnight polysomnography was performed before and after treatment: the first recording was made immediately prior to administering the first dose of pramipexole, and the second recording was made 12–16 weeks after commencing pramipexole administration. Sixteen age-matched healthy participants served as a control group. The spectral power and interregional phase synchrony were analyzed in 30-s epochs. The functional characteristics of the cortical network were quantified using graph-theory measures. Results The delta-band power was significantly increased and the small-world network characteristics in the delta band were disrupted in RLS patients compared to the healthy controls. These abnormalities were successfully treated by dopaminergic medication. The delta-band power was significantly correlated with the RLS severity score in the RLS patients prior to treatment. Conclusions Our findings suggest that the spectral and functional network characteristics of neural activities during sleep become abnormal in RLS patients, and these abnormalities can be successfully treated by a dopamine agonist.
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Affiliation(s)
- Jeong Woo Choi
- Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju, Korea
| | - Min Hee Jeong
- Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju, Korea
| | - Seong Jin Her
- Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju, Korea
| | - Byeong Uk Lee
- Department of Neurology, Seoul National University College of Medicine, Seoul, Korea
| | - Kwang Su Cha
- Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju, Korea
| | - Ki Young Jung
- Department of Neurology, Seoul National University College of Medicine, Seoul, Korea.
| | - Kyung Hwan Kim
- Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju, Korea.
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114
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Sörös P, Doñamayor N, Wittke C, Al-Khaled M, Brüggemann N, Münte TF. Increase in Beta-Band Activity during Preparation for Overt Speech in Patients with Parkinson's Disease. Front Hum Neurosci 2017; 11:371. [PMID: 28790903 PMCID: PMC5522874 DOI: 10.3389/fnhum.2017.00371] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2017] [Accepted: 07/03/2017] [Indexed: 12/25/2022] Open
Abstract
Speech impairment is a frequent and often serious symptom of Parkinson's disease (PD), characterized by a disorder of phonation, articulation and prosody. While research on the pathogenesis of the prominent limb motor symptoms has made considerable progress in recent years, the pathophysiology of PD speech impairment is still incompletely understood. To investigate the neural correlates of speech production in PD, EEG was recorded in 14 non-demented patients with idiopathic PD and preserved verbal fluency on regular dopaminergic medication (8 women; mean age ± SD: 69.5 ± 8.0 years). The control group consisted of 15 healthy age-matched individuals (7 women; age: 69.7 ± 7.0 years). All participants performed a visually-cued, overt speech production task; required utterances were papapa and pataka. During the preparatory phase of speech production, in a time window of 200-400 ms after presentation of the visual cue, β-power was significantly increased in PD patients compared to healthy controls. Previous research has shown that the physiological decrease of β-power preceding limb movement onset is delayed and smaller in PD patients off medication and normalizes under dopaminergic treatment. By contrast, our study demonstrates that β-power during preparation for speech production is higher in patients on dopaminergic therapy than controls. Thus, our results suggest that the mechanisms that regulate β-activity preceding limb movement and speech production differ in PD. The pathophysiological role of this increase in β-power during speech preparation needs to be determined.
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Affiliation(s)
- Peter Sörös
- Department of Neurology, University of LübeckLübeck, Germany
- Psychiatry and Psychotherapy, School of Medicine and Health Sciences, University Hospital Karl-Jaspers-Klinik, University of OldenburgOldenburg, Germany
- Neuroimaging Unit, University of OldenburgOldenburg, Germany
- Research Center Neurosensory Science, University of OldenburgOldenburg, Germany
| | - Nuria Doñamayor
- Department of Neurology, University of LübeckLübeck, Germany
- Department of Psychiatry, University of CambridgeCambridge, United Kingdom
| | | | | | | | - Thomas F. Münte
- Department of Neurology, University of LübeckLübeck, Germany
- Institute of Psychology II, University of LübeckLübeck, Germany
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115
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Nonmotor fluctuations: phenotypes, pathophysiology, management, and open issues. J Neural Transm (Vienna) 2017; 124:1029-1036. [PMID: 28702850 DOI: 10.1007/s00702-017-1757-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Accepted: 07/06/2017] [Indexed: 02/07/2023]
Abstract
Parkinson's disease (PD) is a neurodegenerative multisystem disorder characterized by progressive motor symptoms such as bradykinesia, tremor and muscle rigidity. Over the course of the disease, numerous non-motor symptoms, sometimes preceding the onset of motor symptoms, significantly impair patients' quality of life. The significance of non-motor symptoms may outweigh the burden through progressive motor incapacity, especially in later stages of the disease. The advanced stage of the disease is characterized by motor complications such as fluctuations and dyskinesias induced by the long-term application of levodopa therapy. In recent years, it became evident that various non-motor symptoms such as psychiatric symptoms, fatigue and pain also show fluctuations after chronic levodopa therapy (named non-motor fluctuations or NMFs). Although NMFs have moved into the focus of interest, current national guidelines on the treatment of PD may refer to non-motor symptoms and their management, but do not mention NMF, and do not contain recommendations on their management. The present article summarizes major issues related to NMF including clinical phenomenology and pathophysiology, and outlines a number of open issues and topics for future research.
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116
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Mohl B, Berman BD, Shelton E, Tanabe J. Levodopa response differs in Parkinson's motor subtypes: A task-based effective connectivity study. J Comp Neurol 2017; 525:2192-2201. [PMID: 28256710 PMCID: PMC6301039 DOI: 10.1002/cne.24197] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2016] [Revised: 02/06/2017] [Accepted: 02/08/2017] [Indexed: 11/08/2022]
Abstract
Parkinson's disease (PD) is a circuit-level disorder with clinically-determined motor subtypes. Despite evidence suggesting each subtype may have different pathophysiology, few neuroimaging studies have examined levodopa-induced differences in neural activation between tremor dominant (TD) and postural instability/gait difficulty (PIGD) subtype patients during a motor task. The goal of this functional MRI (fMRI) study was to examine task-induced activation and connectivity in the cortico-striatal-thalamo-cortical motor circuit in healthy controls, TD patients, and PIGD patients before and after levodopa administration. Fourteen TD and 12 PIGD cognitively-intact patients and 21 age- and sex-matched healthy controls completed a right-hand, paced tapping fMRI paradigm. Collectively, PD patients off medication (OFF) showed hypoactivation of the motor cortex relative to healthy controls, even when controlling for performance. After levodopa intake, the PIGD patients had significantly increased activation in the left putamen compared with TD patients and healthy controls. Psychophysiological interaction analysis revealed that levodopa increased effective connectivity between the posterior putamen and other areas of the motor circuit during tapping in TD patients, but not in PIGD patients. This novel, levodopa-induced difference in the neural responses between PD motor subtypes may have significant implications for elucidating the mechanisms underlying the distinct phenotypic manifestations and enabling the classification of motor subtypes objectively using fMRI.
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Affiliation(s)
- Brianne Mohl
- Departments of Radiology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Brian D Berman
- Departments of Radiology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Departments of Neurology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Departments of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Departments of Neurology Section, Denver VA Medical Center, Denver, CO USA
| | - Erika Shelton
- Departments of Neurology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Jody Tanabe
- Departments of Radiology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Departments of Neurology, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Departments of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
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Paldino MJ, Chu ZD, Chapieski ML, Golriz F, Zhang W. Repeatability of graph theoretical metrics derived from resting-state functional networks in paediatric epilepsy patients. Br J Radiol 2017; 90:20160656. [PMID: 28406312 PMCID: PMC5602170 DOI: 10.1259/bjr.20160656] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 03/29/2017] [Accepted: 04/12/2017] [Indexed: 01/26/2023] Open
Abstract
OBJECTIVE To measure the repeatability of metrics that quantify brain network architecture derived from resting-state functional MRI in a cohort of paediatric patients with epilepsy. METHODS We identified patients with: (1) epilepsy; (2) brain MRI at 3 T; (3) two identical resting-state functional MRI acquisitions performed on the same day. Undirected, weighted networks were constructed based on the resting-state time series using a range of processing parameters including parcellation size and graph threshold. The following topological properties were calculated: degree, strength, characteristic path length, global efficiency, clustering coefficient, modularity and small worldness. Based on repeated measures, we then calculated: (1) Pearson correlation coefficient; (2) intraclass correlation coefficient; (3) root-mean-square coefficient of variation; (4) repeatability coefficient; and (5) 95% confidence limits for change. RESULTS 26 patients were included (age range: 4-21 years). Correlation coefficients demonstrated a highly consistent relationship between repeated observations for all metrics, and the intraclass correlation coefficients were generally in the excellent range. Repeatability in the data set was not significantly influenced by parcellation size. However, trends towards decreased repeatability were observed at higher graph thresholds. CONCLUSION These findings demonstrate the reliability of network metrics in a cohort of paediatric patients with epilepsy. Advances in knowledge: Our results point to the potential for graph theoretical analyses of resting-state data to provide reliable markers of network architecture in children with epilepsy. At the level of an individual patient, change over time greater than the repeatability coefficient or 95% confidence limits for change is unlikely to be related to intrinsic variability of the method.
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Affiliation(s)
- Michael J Paldino
- Department of Radiology, Texas Children's Hospital, Houston, TX, USA
| | - Zili D Chu
- Department of Radiology, Texas Children's Hospital, Houston, TX, USA
| | - Mary L Chapieski
- Department of Pediatric Medicine, Texas Children's Hospital, Houston, TX, USA
| | - Farahnaz Golriz
- Department of Radiology, Texas Children's Hospital, Houston, TX, USA
| | - Wei Zhang
- Department of Radiology, Texas Children's Hospital, Houston, TX, USA
- Outcomes and Impact Service, Texas Children's Hospital, Houston, TX, USA
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Tahmasian M, Eickhoff SB, Giehl K, Schwartz F, Herz DM, Drzezga A, van Eimeren T, Laird AR, Fox PT, Khazaie H, Zarei M, Eggers C, Eickhoff CR. Resting-state functional reorganization in Parkinson's disease: An activation likelihood estimation meta-analysis. Cortex 2017; 92:119-138. [PMID: 28467917 DOI: 10.1016/j.cortex.2017.03.016] [Citation(s) in RCA: 90] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Revised: 01/15/2017] [Accepted: 03/31/2017] [Indexed: 12/21/2022]
Abstract
Parkinson's disease (PD) is a common progressive neurodegenerative disorder. Studies using resting-state functional magnetic resonance imaging (fMRI) to investigate underlying pathophysiology of motor and non-motor symptoms in PD yielded largely inconsistent results. This quantitative neuroimaging meta-analysis aims to identify consistent abnormal intrinsic functional patterns in PD across studies. We used PubMed to retrieve suitable resting-state studies and stereotactic data were extracted from 28 individual between-group comparisons. Convergence across their findings was tested using the activation likelihood estimation (ALE) approach. We found convergent evidence for intrinsic functional disturbances in bilateral inferior parietal lobule (IPL) and the supramarginal gyrus in PD patients compared to healthy subjects. In follow-up task-based and task-independent functional connectivity (FC) analyses using two independent healthy subject data sets, we found that the regions showing convergent aberrations in PD formed an interconnected network mainly with the default mode network (DMN). Behavioral characterization of these regions using the BrainMap database suggested associated dysfunction of perception and executive processes. Taken together, our findings highlight the role of parietal cortex in the pathophysiology of PD.
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Affiliation(s)
- Masoud Tahmasian
- Department of Neurology, University Hospital Cologne, Germany; Department of Nuclear Medicine, University Hospital Cologne, Cologne, Germany; Institute of Medical Sciences and Technology, Shahid Beheshti University, Tehran, Iran; Sleep Disorders Research Center, Kermanshah University of Medical Sciences (KUMS), Kermanshah, Iran.
| | - Simon B Eickhoff
- Institute of Clinical Neuroscience & Medical Psychology, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Institute of Neuroscience and Medicine (INM-1, INM-7), Research Center Jülich, Jülich, Germany
| | - Kathrin Giehl
- Department of Nuclear Medicine, University Hospital Cologne, Cologne, Germany
| | - Frank Schwartz
- Department of Neurology, University Hospital Cologne, Germany
| | - Damian M Herz
- Medical Research Council Brain Network Dynamics Unit at the University of Oxford, Oxford, United Kingdom; Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Alexander Drzezga
- Department of Nuclear Medicine, University Hospital Cologne, Cologne, Germany
| | - Thilo van Eimeren
- Department of Neurology, University Hospital Cologne, Germany; Department of Nuclear Medicine, University Hospital Cologne, Cologne, Germany
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center, San Antonio, TX, USA; South Texas Veterans Health Care System, San Antonio, TX, USA
| | - Habibolah Khazaie
- Sleep Disorders Research Center, Kermanshah University of Medical Sciences (KUMS), Kermanshah, Iran
| | - Mojtaba Zarei
- Institute of Medical Sciences and Technology, Shahid Beheshti University, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Carsten Eggers
- Department of Neurology, University Hospital Cologne, Germany; Department of Neurology, Phillips University Marburg, Germany
| | - Claudia R Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Germany; Department of Psychiatry, Psychotherapy, and Psychosomatics, RWTH Aachen University, Aachen, Germany
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119
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Caminiti SP, Tettamanti M, Sala A, Presotto L, Iannaccone S, Cappa SF, Magnani G, Perani D. Metabolic connectomics targeting brain pathology in dementia with Lewy bodies. J Cereb Blood Flow Metab 2017; 37:1311-1325. [PMID: 27306756 PMCID: PMC5453453 DOI: 10.1177/0271678x16654497] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Revised: 03/24/2016] [Accepted: 05/17/2016] [Indexed: 12/21/2022]
Abstract
Dementia with Lewy bodies is characterized by α-synuclein accumulation and degeneration of dopaminergic and cholinergic pathways. To gain an overview of brain systems affected by neurodegeneration, we characterized the [18F]FDG-PET metabolic connectivity in 42 dementia with Lewy bodies patients, as compared to 42 healthy controls, using sparse inverse covariance estimation method and graph theory. We performed whole-brain and anatomically driven analyses, targeting cholinergic and dopaminergic pathways, and the α-synuclein spreading. The first revealed substantial alterations in connectivity indexes, brain modularity, and hubs configuration. Namely, decreases in local metabolic connectivity within occipital cortex, thalamus, and cerebellum, and increases within frontal, temporal, parietal, and basal ganglia regions. There were also long-range disconnections among these brain regions, all supporting a disruption of the functional hierarchy characterizing the normal brain. The anatomically driven analysis revealed alterations within brain structures early affected by α-synuclein pathology, supporting Braak's early pathological staging in dementia with Lewy bodies. The dopaminergic striato-cortical pathway was severely affected, as well as the cholinergic networks, with an extensive decrease in connectivity in Ch1-Ch2, Ch5-Ch6 networks, and the lateral Ch4 capsular network significantly towards the occipital cortex. These altered patterns of metabolic connectivity unveil a new in vivo scenario for dementia with Lewy bodies underlying pathology in terms of changes in whole-brain metabolic connectivity, spreading of α-synuclein, and neurotransmission impairment.
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Affiliation(s)
- Silvia P Caminiti
- Vita-Salute San Raffaele University, Faculty of Medicine and Surgery, Milan, Italy
- Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy
| | - Marco Tettamanti
- Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy
- Nuclear Medicine Unit, San Raffaele Hospital, Milan, Italy
| | - Arianna Sala
- Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy
| | - Luca Presotto
- Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy
| | - Sandro Iannaccone
- Neurological Rehabilitation Department, San Raffaele Hospital, Milan, Italy
| | - Stefano F Cappa
- Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy
- IUSS Pavia, Piazza della Vittoria, Pavia, Italy
| | | | - Daniela Perani
- Vita-Salute San Raffaele University, Faculty of Medicine and Surgery, Milan, Italy
- Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy
- Nuclear Medicine Unit, San Raffaele Hospital, Milan, Italy
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120
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Xu J, Zhang J, Zhang J, Wang Y, Zhang Y, Wang J, Li G, Hu Q, Zhang Y. Abnormalities in Structural Covariance of Cortical Gyrification in Parkinson's Disease. Front Neuroanat 2017; 11:12. [PMID: 28326021 PMCID: PMC5339339 DOI: 10.3389/fnana.2017.00012] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Accepted: 02/17/2017] [Indexed: 01/21/2023] Open
Abstract
Although abnormal cortical morphology and connectivity between brain regions (structural covariance) have been reported in Parkinson's disease (PD), the topological organizations of large-scale structural brain networks are still poorly understood. In this study, we investigated large-scale structural brain networks in a sample of 37 PD patients and 34 healthy controls (HC) by assessing the structural covariance of cortical gyrification with local gyrification index (lGI). We demonstrated prominent small-world properties of the structural brain networks for both groups. Compared with the HC group, PD patients showed significantly increased integrated characteristic path length and integrated clustering coefficient, as well as decreased integrated global efficiency in structural brain networks. Distinct distributions of hub regions were identified between the two groups, showing more hub regions in the frontal cortex in PD patients. Moreover, the modular analyses revealed significantly decreased integrated regional efficiency in lateral Fronto-Insula-Temporal module, and increased integrated regional efficiency in Parieto-Temporal module in the PD group as compared to the HC group. In summary, our study demonstrated altered topological properties of structural networks at a global, regional and modular level in PD patients. These findings suggests that the structural networks of PD patients have a suboptimal topological organization, resulting in less effective integration of information between brain regions.
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Affiliation(s)
- Jinping Xu
- Key Laboratory for Neuroinformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengdu, China; Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhen, China
| | - Jiuquan Zhang
- Department of Radiology, Southwest Hospital, Third Military Medical University Chongqing, China
| | - Jinlei Zhang
- Key Laboratory for Neuroinformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China
| | - Yue Wang
- Key Laboratory for Neuroinformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China
| | - Yanling Zhang
- Department of Neurology, Southwest Hospital, Third Military Medical University Chongqing, China
| | - Jian Wang
- Department of Radiology, Southwest Hospital, Third Military Medical University Chongqing, China
| | - Guanglin Li
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhen, China; Key Laboratory of Human-Machine Intelligence Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhen, China
| | - Qingmao Hu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhen, China; Key Laboratory of Human-Machine Intelligence Synergy Systems, Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhen, China
| | - Yuanchao Zhang
- Key Laboratory for Neuroinformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengdu, China; Center for Information in Medicine, University of Electronic Science and Technology of ChinaChengdu, China
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Monteiro C, Cardoso-Cruz H, Matos M, Dourado M, Lima D, Galhardo V. Increased fronto-hippocampal connectivity in the Prrxl1 knockout mouse model of congenital hypoalgesia. Pain 2017; 157:2045-2056. [PMID: 27168359 DOI: 10.1097/j.pain.0000000000000611] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Despite the large number of studies addressing how prolonged painful stimulation affects brain functioning, there are only a handful of studies aimed at uncovering if persistent conditions of reduced pain perception would also result in brain plasticity. Permanent hypoalgesia induced by neonatal injection of capsaicin or carrageenan has already been shown to affect learning and memory and to induce alterations in brain gene expression. In this study, we used the Prrxl1 model of congenital mild hypoalgesia to conduct a detailed study of the neurophysiological and behavioral consequences of reduced pain experience. Prrxl1 knockout animals are characterized by selective depletion of small diameter primary afferents and abnormal development of the superficial dorsal laminae of the spinal cord, resulting in diminished pain perception but normal tactile and motor behaviour. Behavioral testing of Prrxl1 mice revealed that these animals have reduced anxiety levels, enhanced memory performance, and improved fear extinction. Neurophysiological recordings from awake behaving Prrxl1 mice show enhanced altered fronto-hippocampal connectivity in the theta- and gamma-bands. Importantly, although inflammatory pain by Complete Freund Adjuvant injection caused a decrease in fronto-hippocampal connectivity in the wild-type animals, Prrxl1 mice maintained the baseline levels. The onset of inflammatory pain also reverted the differences in forebrain expression of stress- and monoamine-related genes in Prrxl1 mice. Altogether our results suggest that congenital hypoalgesia may have an effect on brain plasticity that is the inverse of what is usually observed in animal models of chronic pain.
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Affiliation(s)
- Clara Monteiro
- Departamento de Biologia Experimental, Faculdade de Medicina, Universidade do Porto, Porto, Portugal.,Instituto de Biologia Molecular e Celular-IBMC, Universidade do Porto, Porto, Portugal.,Instituto de Investigação e Inovação em Saúde-i3S, Universidade do Porto, Porto, Portugal
| | - Helder Cardoso-Cruz
- Departamento de Biologia Experimental, Faculdade de Medicina, Universidade do Porto, Porto, Portugal.,Instituto de Biologia Molecular e Celular-IBMC, Universidade do Porto, Porto, Portugal.,Instituto de Investigação e Inovação em Saúde-i3S, Universidade do Porto, Porto, Portugal
| | - Mariana Matos
- Departamento de Biologia Experimental, Faculdade de Medicina, Universidade do Porto, Porto, Portugal.,Instituto de Biologia Molecular e Celular-IBMC, Universidade do Porto, Porto, Portugal.,Instituto de Investigação e Inovação em Saúde-i3S, Universidade do Porto, Porto, Portugal
| | - Margarida Dourado
- Departamento de Biologia Experimental, Faculdade de Medicina, Universidade do Porto, Porto, Portugal.,Instituto de Biologia Molecular e Celular-IBMC, Universidade do Porto, Porto, Portugal.,Instituto de Investigação e Inovação em Saúde-i3S, Universidade do Porto, Porto, Portugal
| | - Deolinda Lima
- Departamento de Biologia Experimental, Faculdade de Medicina, Universidade do Porto, Porto, Portugal.,Instituto de Biologia Molecular e Celular-IBMC, Universidade do Porto, Porto, Portugal.,Instituto de Investigação e Inovação em Saúde-i3S, Universidade do Porto, Porto, Portugal
| | - Vasco Galhardo
- Departamento de Biologia Experimental, Faculdade de Medicina, Universidade do Porto, Porto, Portugal.,Instituto de Biologia Molecular e Celular-IBMC, Universidade do Porto, Porto, Portugal.,Instituto de Investigação e Inovação em Saúde-i3S, Universidade do Porto, Porto, Portugal
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Göttlich M, Ye Z, Rodriguez-Fornells A, Münte TF, Krämer UM. Viewing socio-affective stimuli increases connectivity within an extended default mode network. Neuroimage 2017; 148:8-19. [DOI: 10.1016/j.neuroimage.2016.12.044] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2016] [Revised: 11/10/2016] [Accepted: 12/15/2016] [Indexed: 01/14/2023] Open
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Westphal R, Simmons C, Mesquita MB, Wood TC, Williams SCR, Vernon AC, Cash D. Characterization of the resting-state brain network topology in the 6-hydroxydopamine rat model of Parkinson's disease. PLoS One 2017; 12:e0172394. [PMID: 28249008 PMCID: PMC5382982 DOI: 10.1371/journal.pone.0172394] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2016] [Accepted: 02/03/2017] [Indexed: 01/21/2023] Open
Abstract
Resting-state functional MRI (rsfMRI) is an imaging technology that has recently gained attention for its ability to detect disruptions in functional brain networks in humans, including in patients with Parkinson's disease (PD), revealing early and widespread brain network abnormalities. This methodology is now readily applicable to experimental animals offering new possibilities for cross-species translational imaging. In this context, we herein describe the application of rsfMRI to the unilaterally-lesioned 6-hydroxydopamine (6-OHDA) rat, a robust experimental model of the dopamine depletion implicated in PD. Using graph theory to analyse the rsfMRI data, we were able to provide meaningful and translatable measures of integrity, influence and segregation of the underlying functional brain architecture. Specifically, we confirm that rats share a similar functional brain network topology as observed in humans, characterised by small-worldness and modularity. Interestingly, we observed significantly reduced functional connectivity in the 6-OHDA rats, primarily in the ipsilateral (lesioned) hemisphere as evidenced by significantly lower node degree, local efficiency and clustering coefficient in the motor, orbital and sensorimotor cortices. In contrast, we found significantly, and bilaterally, increased thalamic functional connectivity in the lesioned rats. The unilateral deficits in the cortex are consistent with the unilateral nature of this model and further support the validity of the rsfMRI technique in rodents. We thereby provide a methodological framework for the investigation of brain networks in other rodent experimental models of PD, as well as of animal models in general, for cross-comparison with human data.
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Affiliation(s)
- Robert Westphal
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Camilla Simmons
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Michel B. Mesquita
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Tobias C. Wood
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Steve C. R. Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Anthony C. Vernon
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Diana Cash
- Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
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Functional connectomics from a "big data" perspective. Neuroimage 2017; 160:152-167. [PMID: 28232122 DOI: 10.1016/j.neuroimage.2017.02.031] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2016] [Revised: 01/21/2017] [Accepted: 02/13/2017] [Indexed: 01/10/2023] Open
Abstract
In the last decade, explosive growth regarding functional connectome studies has been observed. Accumulating knowledge has significantly contributed to our understanding of the brain's functional network architectures in health and disease. With the development of innovative neuroimaging techniques, the establishment of large brain datasets and the increasing accumulation of published findings, functional connectomic research has begun to move into the era of "big data", which generates unprecedented opportunities for discovery in brain science and simultaneously encounters various challenging issues, such as data acquisition, management and analyses. Big data on the functional connectome exhibits several critical features: high spatial and/or temporal precision, large sample sizes, long-term recording of brain activity, multidimensional biological variables (e.g., imaging, genetic, demographic, cognitive and clinic) and/or vast quantities of existing findings. We review studies regarding functional connectomics from a big data perspective, with a focus on recent methodological advances in state-of-the-art image acquisition (e.g., multiband imaging), analysis approaches and statistical strategies (e.g., graph theoretical analysis, dynamic network analysis, independent component analysis, multivariate pattern analysis and machine learning), as well as reliability and reproducibility validations. We highlight the novel findings in the application of functional connectomic big data to the exploration of the biological mechanisms of cognitive functions, normal development and aging and of neurological and psychiatric disorders. We advocate the urgent need to expand efforts directed at the methodological challenges and discuss the direction of applications in this field.
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Paldino MJ, Golriz F, Chapieski ML, Zhang W, Chu ZD. Brain Network Architecture and Global Intelligence in Children with Focal Epilepsy. AJNR Am J Neuroradiol 2017; 38:349-356. [PMID: 27737853 PMCID: PMC7963842 DOI: 10.3174/ajnr.a4975] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2016] [Accepted: 08/29/2016] [Indexed: 01/25/2023]
Abstract
BACKGROUND AND PURPOSE The biologic basis for intelligence rests to a large degree on the capacity for efficient integration of information across the cerebral network. We aimed to measure the relationship between network architecture and intelligence in the pediatric, epileptic brain. MATERIALS AND METHODS Patients were retrospectively identified with the following: 1) focal epilepsy; 2) brain MR imaging at 3T, including resting-state functional MR imaging; and 3) full-scale intelligence quotient measured by a pediatric neuropsychologist. The cerebral cortex was parcellated into approximately 700 gray matter network "nodes." The strength of a connection between 2 nodes was defined by the correlation between their blood oxygen level-dependent time-series. We calculated the following topologic properties: clustering coefficient, transitivity, modularity, path length, and global efficiency. A machine learning algorithm was used to measure the independent contribution of each metric to the intelligence quotient after adjusting for all other metrics. RESULTS Thirty patients met the criteria (4-18 years of age); 20 patients required anesthesia during MR imaging. After we accounted for age and sex, clustering coefficient and path length were independently associated with full-scale intelligence quotient. Neither motion parameters nor general anesthesia was an important variable with regard to accurate intelligence quotient prediction by the machine learning algorithm. A longer history of epilepsy was associated with shorter path lengths (P = .008), consistent with reorganization of the network on the basis of seizures. Considering only patients receiving anesthesia during machine learning did not alter the patterns of network architecture contributing to global intelligence. CONCLUSIONS These findings support the physiologic relevance of imaging-based metrics of network architecture in the pathologic, developing brain.
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Affiliation(s)
- M J Paldino
- From the Departments of Radiology (M.J.P., F.G., Z.D.C.)
| | - F Golriz
- From the Departments of Radiology (M.J.P., F.G., Z.D.C.)
| | | | - W Zhang
- Outcomes and Impact Service (W.Z.), Texas Children's Hospital, Houston, Texas
| | - Z D Chu
- From the Departments of Radiology (M.J.P., F.G., Z.D.C.)
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Using ‘swallow-tail’ sign and putaminal hypointensity as biomarkers to distinguish multiple system atrophy from idiopathic Parkinson’s disease: A susceptibility-weighted imaging study. Eur Radiol 2017; 27:3174-3180. [DOI: 10.1007/s00330-017-4743-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Revised: 12/29/2016] [Accepted: 01/04/2017] [Indexed: 12/27/2022]
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127
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Guimarães RP, Arci Santos MC, Dagher A, Campos LS, Azevedo P, Piovesana LG, De Campos BM, Larcher K, Zeighami Y, Scarparo Amato-Filho AC, Cendes F, D'Abreu ACF. Pattern of Reduced Functional Connectivity and Structural Abnormalities in Parkinson's Disease: An Exploratory Study. Front Neurol 2017; 7:243. [PMID: 28133455 PMCID: PMC5233672 DOI: 10.3389/fneur.2016.00243] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Accepted: 12/21/2016] [Indexed: 02/03/2023] Open
Abstract
Background MRI brain changes in Parkinson’s disease (PD) are controversial. Objectives We aimed to describe structural and functional changes in PD. Methods Sixty-six patients with PD (57.94 ± 10.25 years) diagnosed according to the UK Brain Bank criteria were included. We performed a whole brain analysis using voxel-based morphometry (VBM–SPM 8 software), cortical thickness (CT) using CIVET, and resting-state fMRI using the Neuroimaging Analysis Kit software to compare patients and controls. For VBM and CT we classified subjects into three groups according to disease severity: mild PD [Hoehn and Yahr scale (HY) 1–1.5], moderate PD (HY 2–2.5), and severe PD (HY 3–5). Results We observed gray matter atrophy in the insula and inferior frontal gyrus in the moderate PD and in the insula, frontal gyrus, putamen, cingulated, and paracingulate gyri in the severe groups. In the CT analysis, in mild PD, cortical thinning was restricted to the superior temporal gyrus, gyrus rectus, and olfactory cortex; in the moderate group, the postcentral gyrus, supplementary motor area, and inferior frontal gyrus were also affected; in the severe PD, areas such as the precentral and postentral gyrus, temporal pole, fusiform, and occipital gyrus had reduced cortical thinning. We observed altered connectivity at the default mode, visual, sensorimotor, and cerebellar networks. Conclusion Subjects with mild symptoms already have cortical involvement; however, further cerebral involvement seems to follow Braak’s proposed mechanism. Similar regions are affected both structurally and functionally. We believe the combination of different MRI techniques may be useful in evaluating progressive brain involvement and they may eventually be used as surrogate markers of disease progression.
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Affiliation(s)
- Rachel Paes Guimarães
- Department of Neurology, University of Campinas, Campinas, Brazil; Laboratory of Neuroimaging, University of Campinas, Campinas, Brazil; Montreal Neurological Institute, Brain Imaging Center, McGill University, Montreal, QC, Canada
| | | | - Alain Dagher
- Montreal Neurological Institute, Brain Imaging Center, McGill University , Montreal, QC , Canada
| | | | - Paula Azevedo
- Department of Neurology, University of Campinas , Campinas , Brazil
| | | | | | - Kevin Larcher
- Montreal Neurological Institute, Brain Imaging Center, McGill University , Montreal, QC , Canada
| | - Yashar Zeighami
- Montreal Neurological Institute, Brain Imaging Center, McGill University , Montreal, QC , Canada
| | | | - Fernando Cendes
- Department of Neurology, University of Campinas, Campinas, Brazil; Laboratory of Neuroimaging, University of Campinas, Campinas, Brazil
| | - Anelyssa Cysne Frota D'Abreu
- Department of Neurology, University of Campinas, Campinas, Brazil; Laboratory of Neuroimaging, University of Campinas, Campinas, Brazil
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128
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Chen X, Wang M, He J, Li W. Dynamic brain network evolution in normal aging based on computational experiments. Neurocomputing 2017. [DOI: 10.1016/j.neucom.2016.09.056] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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129
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Tinaz S, Lauro PM, Ghosh P, Lungu C, Horovitz SG. Changes in functional organization and white matter integrity in the connectome in Parkinson's disease. NEUROIMAGE-CLINICAL 2016; 13:395-404. [PMID: 28116232 PMCID: PMC5226806 DOI: 10.1016/j.nicl.2016.12.019] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Revised: 12/08/2016] [Accepted: 12/16/2016] [Indexed: 11/27/2022]
Abstract
Parkinson's disease (PD) leads to dysfunction in multiple cortico-striatal circuits. The neurodegeneration has also been associated with impaired white matter integrity. This structural and functional “disconnection” in PD needs further characterization. We investigated the structural and functional organization of the PD whole brain connectome consisting of 200 nodes using diffusion tensor imaging and resting-state functional MRI, respectively. Data from 20 non-demented PD patients on dopaminergic medication and 20 matched controls were analyzed using graph theory-based methods. We focused on node strength, clustering coefficient, and local efficiency as measures of local network properties; and network modularity as a measure of information flow. PD patients showed reduced white matter connectivity in frontoparietal-striatal nodes compared to controls, but no change in modular organization of the white matter tracts. PD group also showed reduction in functional local network metrics in many nodes distributed across the connectome. There was also decreased functional modularity in the core cognitive networks including the default mode and dorsal attention networks, and sensorimotor network, as well as a lack of modular distinction in the orbitofrontal and basal ganglia nodes in the PD group compared to controls. Our results suggest that despite subtle white matter connectivity changes, the overall structural organization of the PD connectome remains robust at relatively early disease stages. However, there is a breakdown in the functional modular organization of the PD connectome. DTI and rs-fMRI investigation of the connectome in Parkinson's disease (PD) Local network properties and modularity were examined using graph theory. White matter connectivity was decreased in frontoparietal-striatal nodes in PD. Functional modularity of cognitive and motor networks was reduced in PD.
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Affiliation(s)
- Sule Tinaz
- Division of Movement Disorders, Department of Neurology, Yale School of Medicine, New Haven, CT, USA; Human Motor Control Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Peter M Lauro
- Office of the Clinical Director, National Institute of Neurologic Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Pritha Ghosh
- Office of the Clinical Director, National Institute of Neurologic Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA; Parkinson's Disease and Movement Disorders Program, Department of Neurology, George Washington University, Washington, DC, USA
| | - Codrin Lungu
- Office of the Clinical Director, National Institute of Neurologic Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
| | - Silvina G Horovitz
- Human Motor Control Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
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130
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Resting-state functional connectivity of subthalamic nucleus in different Parkinson's disease phenotypes. J Neurol Sci 2016; 371:137-147. [DOI: 10.1016/j.jns.2016.10.035] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Revised: 08/27/2016] [Accepted: 10/21/2016] [Indexed: 11/21/2022]
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131
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Functional brain mapping using specific sensory-circuit stimulation and a theoretical graph network analysis in mice with neuropathic allodynia. Sci Rep 2016; 6:37802. [PMID: 27898057 PMCID: PMC5127182 DOI: 10.1038/srep37802] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2016] [Accepted: 11/02/2016] [Indexed: 11/08/2022] Open
Abstract
Allodynia, a form of neuropathic pain, is defined as pain in response to a non-nociceptive stimulus. The brain regions responsible for pain, which are not normally activated, can be activated in allodynic mice by providing a suitable stimulus to Aβ-fibers, which transmit signals from tactile sensory fibers. Functional MRI (fMRI) can be used to objectively observe abnormal brain activation. In the present study, fMRI was conducted to investigate allodynia in mice; allodynia was generated by surgical injury at the L4 spinal nerve root, thus selectively stimulating sensory nerve fibers. In intact mice, only the primary somatosensory cortex (S1) was activated by stimulation of Aβ-fibers. Meanwhile, allodynic mice showed significantly higher BOLD signals in the anterior cingulate area (ACA) and thalamus. Using resting state fMRI, both degree and eigenvector centrality were significantly decreased in the contralateral S1, clustering coefficient and local efficiency were significantly increased in the ACA, and betweenness centrality was significantly higher in the ventral posterolateral nucleus of the thalamus. These results suggest that the observed abnormal BOLD activation is associated with defects in Aβ-fibers when Aβ-fibers in allodynic mice are selectively stimulated. The objective approach enabled by fMRI can improve our understanding of pathophysiological mechanisms and therapeutic efficacy.
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132
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Weil RS, Schrag AE, Warren JD, Crutch SJ, Lees AJ, Morris HR. Visual dysfunction in Parkinson's disease. Brain 2016; 139:2827-2843. [PMID: 27412389 PMCID: PMC5091042 DOI: 10.1093/brain/aww175] [Citation(s) in RCA: 242] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2016] [Revised: 05/23/2016] [Accepted: 06/05/2016] [Indexed: 01/09/2023] Open
Abstract
Patients with Parkinson's disease have a number of specific visual disturbances. These include changes in colour vision and contrast sensitivity and difficulties with complex visual tasks such as mental rotation and emotion recognition. We review changes in visual function at each stage of visual processing from retinal deficits, including contrast sensitivity and colour vision deficits to higher cortical processing impairments such as object and motion processing and neglect. We consider changes in visual function in patients with common Parkinson's disease-associated genetic mutations including GBA and LRRK2 . We discuss the association between visual deficits and clinical features of Parkinson's disease such as rapid eye movement sleep behavioural disorder and the postural instability and gait disorder phenotype. We review the link between abnormal visual function and visual hallucinations, considering current models for mechanisms of visual hallucinations. Finally, we discuss the role of visuo-perceptual testing as a biomarker of disease and predictor of dementia in Parkinson's disease.
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Affiliation(s)
- Rimona S. Weil
- 1 Institute of Neurology, University College London, London, UK
- 2 National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
- 3 Department of Clinical Neurosciences, Royal Free Hospital NHS Trust, London, UK
| | - Anette E. Schrag
- 1 Institute of Neurology, University College London, London, UK
- 2 National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Jason D. Warren
- 2 National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
- 4 Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
| | - Sebastian J. Crutch
- 4 Dementia Research Centre, UCL Institute of Neurology, University College London, London, UK
| | - Andrew J. Lees
- 1 Institute of Neurology, University College London, London, UK
- 2 National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Huw R. Morris
- 1 Institute of Neurology, University College London, London, UK
- 2 National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
- 3 Department of Clinical Neurosciences, Royal Free Hospital NHS Trust, London, UK
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133
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Schirmer A, Meck WH, Penney TB. The Socio-Temporal Brain: Connecting People in Time. Trends Cogn Sci 2016; 20:760-772. [DOI: 10.1016/j.tics.2016.08.002] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Revised: 08/05/2016] [Accepted: 08/08/2016] [Indexed: 10/21/2022]
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134
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Abstract
PURPOSE OF REVIEW Recent developments in functional magnetic resonance imaging (fMRI) have catalyzed a new field of translational neuroscience. Using fMRI to monitor the aspects of task-related changes in neural activation or brain connectivity, investigators can offer feedback of simple or complex neural signals/patterns back to the participant on a quasireal-time basis [real-time-fMRI-based neurofeedback (rt-fMRI-NF)]. Here, we introduce some background methodology of the new developments in this field and give a perspective on how they may be used in neurorehabilitation in the future. RECENT FINDINGS The development of rt-fMRI-NF has been used to promote self-regulation of activity in several brain regions and networks. In addition, and unlike other noninvasive techniques, rt-fMRI-NF can access specific subcortical regions and in principle any region that can be monitored using fMRI including the cerebellum, brainstem and spinal cord. In Parkinson's disease and stroke, rt-fMRI-NF has been demonstrated to alter neural activity after the self-regulation training was completed and to modify specific behaviours. SUMMARY Future exploitation of rt-fMRI-NF could be used to induce neuroplasticity in brain networks that are involved in certain neurological conditions. However, currently, the use of rt-fMRI-NF in randomized, controlled clinical trials is in its infancy.
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Affiliation(s)
- David E.J. Linden
- MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, and Cardiff University Brain Imaging Centre, Cardiff
| | - Duncan L. Turner
- Neurorehabilitation Unit, School of Health, Sport and Bioscience, University of East London, London, UK
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135
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Berman BD, Smucny J, Wylie KP, Shelton E, Kronberg E, Leehey M, Tregellas JR. Levodopa modulates small-world architecture of functional brain networks in Parkinson's disease. Mov Disord 2016; 31:1676-1684. [PMID: 27461405 DOI: 10.1002/mds.26713] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2015] [Revised: 05/23/2016] [Accepted: 05/30/2016] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND PD is associated with disrupted connectivity to a large number of distributed brain regions. How the disease alters the functional topological organization of the brain, however, remains poorly understood. Furthermore, how levodopa modulates network topology in PD is largely unknown. The objective of this study was to use resting-state functional MRI and graph theory to determine how small-world architecture is altered in PD and affected by levodopa administration. METHODS Twenty-one PD patients and 20 controls underwent functional MRI scanning. PD patients were scanned off medication and 1 hour after 200 mg levodopa. Imaging data were analyzed using 226 nodes comprising 10 intrinsic brain networks. Correlation matrices were generated for each subject and converted into cost-thresholded, binarized adjacency matrices. Cost-integrated whole-brain global and local efficiencies were compared across groups and tested for relationships with disease duration and severity. RESULTS Data from 2 patients and 4 controls were excluded because of excess motion. Patients off medication showed no significant changes in global efficiency and overall local efficiency, but in a subnetwork analysis did show increased local efficiency in executive (P = 0.006) and salience (P = 0.018) networks. Levodopa significantly decreased local efficiency (P = 0.039) in patients except within the subcortical network, in which it significantly increased local efficiency (P = 0.007). CONCLUSIONS Levodopa modulates global and local efficiency measures of small-world topology in PD, suggesting that degeneration of nigrostriatal neurons in PD may be associated with a large-scale network reorganization and that levodopa tends to normalize the disrupted network topology in PD. © 2016 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Brian D Berman
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.,Neurology Section, Denver VA Medical Center, Denver, Colorado, USA
| | - Jason Smucny
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Korey P Wylie
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Erika Shelton
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Eugene Kronberg
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Maureen Leehey
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Jason R Tregellas
- Department of Psychiatry, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA.,Research Service, Denver VA Medical Center, Denver, Colorado, USA
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136
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Functional Brain Networks: Does the Choice of Dependency Estimator and Binarization Method Matter? Sci Rep 2016; 6:29780. [PMID: 27417262 PMCID: PMC4945914 DOI: 10.1038/srep29780] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Accepted: 06/23/2016] [Indexed: 11/08/2022] Open
Abstract
The human brain can be modelled as a complex networked structure with brain regions as individual nodes and their anatomical/functional links as edges. Functional brain networks are constructed by first extracting weighted connectivity matrices, and then binarizing them to minimize the noise level. Different methods have been used to estimate the dependency values between the nodes and to obtain a binary network from a weighted connectivity matrix. In this work we study topological properties of EEG-based functional networks in Alzheimer’s Disease (AD). To estimate the connectivity strength between two time series, we use Pearson correlation, coherence, phase order parameter and synchronization likelihood. In order to binarize the weighted connectivity matrices, we use Minimum Spanning Tree (MST), Minimum Connected Component (MCC), uniform threshold and density-preserving methods. We find that the detected AD-related abnormalities highly depend on the methods used for dependency estimation and binarization. Topological properties of networks constructed using coherence method and MCC binarization show more significant differences between AD and healthy subjects than the other methods. These results might explain contradictory results reported in the literature for network properties specific to AD symptoms. The analysis method should be seriously taken into account in the interpretation of network-based analysis of brain signals.
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137
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Thomas J, Seo D, Sael L. Review on Graph Clustering and Subgraph Similarity Based Analysis of Neurological Disorders. Int J Mol Sci 2016; 17:ijms17060862. [PMID: 27258269 PMCID: PMC4926396 DOI: 10.3390/ijms17060862] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Revised: 05/10/2016] [Accepted: 05/24/2016] [Indexed: 01/03/2023] Open
Abstract
How can complex relationships among molecular or clinico-pathological entities of neurological disorders be represented and analyzed? Graphs seem to be the current answer to the question no matter the type of information: molecular data, brain images or neural signals. We review a wide spectrum of graph representation and graph analysis methods and their application in the study of both the genomic level and the phenotypic level of the neurological disorder. We find numerous research works that create, process and analyze graphs formed from one or a few data types to gain an understanding of specific aspects of the neurological disorders. Furthermore, with the increasing number of data of various types becoming available for neurological disorders, we find that integrative analysis approaches that combine several types of data are being recognized as a way to gain a global understanding of the diseases. Although there are still not many integrative analyses of graphs due to the complexity in analysis, multi-layer graph analysis is a promising framework that can incorporate various data types. We describe and discuss the benefits of the multi-layer graph framework for studies of neurological disease.
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Affiliation(s)
- Jaya Thomas
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA.
- Department of Computer Science, State University New York Korea, Incheon 406-840, Korea.
| | - Dongmin Seo
- Korea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea.
| | - Lee Sael
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA.
- Department of Computer Science, State University New York Korea, Incheon 406-840, Korea.
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138
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Vo A, Sako W, Fujita K, Peng S, Mattis PJ, Skidmore FM, Ma Y, Uluğ AM, Eidelberg D. Parkinson's disease-related network topographies characterized with resting state functional MRI. Hum Brain Mapp 2016; 38:617-630. [PMID: 27207613 DOI: 10.1002/hbm.23260] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2015] [Revised: 03/24/2016] [Accepted: 05/03/2016] [Indexed: 11/10/2022] Open
Abstract
Spatial covariance mapping can be used to identify and measure the activity of disease-related functional brain networks. While this approach has been widely used in the analysis of cerebral blood flow and metabolic PET scans, it is not clear whether it can be reliably applied to resting state functional MRI (rs-fMRI) data. In this study, we present a novel method based on independent component analysis (ICA) to characterize specific network topographies associated with Parkinson's disease (PD). Using rs-fMRI data from PD and healthy subjects, we used ICA with bootstrap resampling to identify a PD-related pattern that reliably discriminated the two groups. This topography, termed rs-MRI PD-related pattern (fPDRP), was similar to previously characterized disease-related patterns identified using metabolic PET imaging. Following pattern identification, we validated the fPDRP by computing its expression in rs-fMRI testing data on a prospective case basis. Indeed, significant increases in fPDRP expression were found in separate sets of PD and control subjects. In addition to providing a similar degree of group separation as PET, fPDRP values correlated with motor disability and declined toward normal with levodopa administration. Finally, we used this approach in conjunction with neuropsychological performance measures to identify a separate PD cognition-related pattern in the patients. This pattern, termed rs-fMRI PD cognition-related pattern (fPDCP), was topographically similar to its PET-derived counterpart. Subject scores for the fPDCP correlated with executive function in both training and testing data. These findings suggest that ICA can be used in conjunction with bootstrap resampling to identify and validate stable disease-related network topographies in rs-fMRI. Hum Brain Mapp 38:617-630, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- An Vo
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York
| | - Wataru Sako
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York
| | - Koji Fujita
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York
| | - Shichun Peng
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York
| | - Paul J Mattis
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York.,Department of Neurology, Northwell Health, Manhasset, New York
| | - Frank M Skidmore
- Department of Neurology, University of Alabama School of Medicine, Birmingham, Alabama
| | - Yilong Ma
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York
| | - Aziz M Uluğ
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York
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139
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Hou Y, Luo C, Yang J, Ou R, Song W, Wei Q, Cao B, Zhao B, Wu Y, Shang HF, Gong Q. Prediction of individual clinical scores in patients with Parkinson's disease using resting-state functional magnetic resonance imaging. J Neurol Sci 2016; 366:27-32. [PMID: 27288771 DOI: 10.1016/j.jns.2016.04.030] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2016] [Revised: 03/27/2016] [Accepted: 04/16/2016] [Indexed: 02/05/2023]
Abstract
Neuroimaging holds the promise that it may one day aid the clinical assessment. However, the vast majority of studies using resting-state functional magnetic resonance imaging (fMRI) have reported average differences between Parkinson's disease (PD) patients and healthy controls, which do not permit inferences at the level of individuals. This study was to develop a model for the prediction of PD illness severity ratings from individual fMRI brain scan. The resting-state fMRI scans were obtained from 84 patients with PD and the Unified Parkinson's Disease Rating Scale-III (UPDRS-III) scores were obtained before scanning. The RVR method was used to predict clinical scores (UPDRS-III) from fMRI scans. The application of RVR to whole-brain resting-state fMRI data allowed prediction of UPDRS-III scores with statistically significant accuracy (correlation=0.35, P-value=0.001; mean sum of squares=222.17, P-value=0.002). This prediction was informed strongly by negative weight areas including prefrontal lobe and medial occipital lobe, and positive weight areas including medial parietal lobe. It was suggested that fMRI scans contained sufficient information about neurobiological change in patients with PD to permit accurate prediction about illness severity, on an individual subject basis. Our results provided preliminary evidence, as proof-of-concept, to support that fMRI might be possible to be a clinically useful quantitative assessment aid in PD at individual level. This may enable clinicians to target those uncooperative patients and machines to replace human for a more efficient use of health care resources.
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Affiliation(s)
- YanBing Hou
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - ChunYan Luo
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jing Yang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - RuWei Ou
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wei Song
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - QianQian Wei
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Bei Cao
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Bi Zhao
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Ying Wu
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Hui-Fang Shang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
| | - QiYong Gong
- Huaxi MR Research Center, Department of Radiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
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140
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Bowman FD, Drake DF, Huddleston DE. Multimodal Imaging Signatures of Parkinson's Disease. Front Neurosci 2016; 10:131. [PMID: 27147942 PMCID: PMC4834347 DOI: 10.3389/fnins.2016.00131] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2015] [Accepted: 03/15/2016] [Indexed: 11/13/2022] Open
Abstract
Parkinson's disease (PD) is a complex neurodegenerative disorder that manifests through hallmark motor symptoms, often accompanied by a range of non-motor symptoms. There is a putative delay between the onset of the neurodegenerative process, marked by the death of dopamine-producing cells, and the onset of motor symptoms, creating an urgent need to develop biomarkers that may yield early PD detection. Neuroimaging offers a non-invasive approach to examining the potential utility of a vast number of functional and structural brain characteristics as biomarkers. We present a statistical framework for analyzing neuroimaging data from multiple modalities to determine features that reliably distinguish PD patients from healthy control (HC) subjects. Our approach builds on elastic net, performing regularization and variable selection, while introducing additional criteria centering on parsimony and reproducibility. We apply our method to data from 42 subjects (28 PD patients and 14 HC). Our approach demonstrates extremely high accuracy, assessed via cross-validation, and isolates brain regions that are implicated in the neurodegenerative PD process.
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Affiliation(s)
- F DuBois Bowman
- Department of Biostatistics, The Mailman School of Public Health, Columbia University New York, NY, USA
| | - Daniel F Drake
- Department of Biostatistics, The Mailman School of Public Health, Columbia University New York, NY, USA
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141
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Contrasting variability patterns in the default mode and sensorimotor networks balance in bipolar depression and mania. Proc Natl Acad Sci U S A 2016; 113:4824-9. [PMID: 27071087 DOI: 10.1073/pnas.1517558113] [Citation(s) in RCA: 151] [Impact Index Per Article: 18.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023] Open
Abstract
Depressive and manic phases in bipolar disorder show opposite constellations of affective, cognitive, and psychomotor symptoms. At a neural level, these may be related to topographical disbalance between large-scale networks, such as the default mode network (DMN) and sensorimotor network (SMN). We investigated topographical patterns of variability in the resting-state signal-measured by fractional SD (fSD) of the BOLD signal-of the DMN and SMN (and other networks) in two frequency bands (Slow5 and Slow4) with their ratio and clinical correlations in depressed (n = 20), manic (n = 20), euthymic (n = 20) patients, and healthy controls (n = 40). After controlling for global signal changes, the topographical balance between the DMN and SMN, specifically in the lowest frequency band, as calculated by the Slow5 fSD DMN/SMN ratio, was significantly increased in depression, whereas the same ratio was significantly decreased in mania. Additionally, Slow5 variability was increased in the DMN and decreased in the SMN in depressed patients, whereas the opposite topographical pattern was observed in mania. Finally, the Slow5 fSD DMN/SMN ratio correlated positively with clinical scores of depressive symptoms and negatively with those of mania. Results were replicated in a smaller independent bipolar disorder sample. We demonstrated topographical abnormalities in frequency-specific resting-state variability in the balance between DMN and SMN with opposing patterns in depression and mania. The Slow5 DMN/SMN ratio was tilted toward the DMN in depression but was shifted toward the SMN in mania. The Slow5 fSD DMN/SMN pattern could constitute a state-biomarker in diagnosis and therapy.
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142
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Utianski RL, Caviness JN, van Straaten ECW, Beach TG, Dugger BN, Shill HA, Driver-Dunckley ED, Sabbagh MN, Mehta S, Adler CH, Hentz JG. Graph theory network function in Parkinson's disease assessed with electroencephalography. Clin Neurophysiol 2016; 127:2228-36. [PMID: 27072094 DOI: 10.1016/j.clinph.2016.02.017] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2015] [Revised: 02/05/2016] [Accepted: 02/22/2016] [Indexed: 12/12/2022]
Abstract
OBJECTIVES To determine what differences exist in graph theory network measures derived from electroencephalography (EEG), between Parkinson's disease (PD) patients who are cognitively normal (PD-CN) and matched healthy controls; and between PD-CN and PD dementia (PD-D). METHODS EEG recordings were analyzed via graph theory network analysis to quantify changes in global efficiency and local integration. This included minimal spanning tree analysis. T-tests and correlations were used to assess differences between groups and assess the relationship with cognitive performance. RESULTS Network measures showed increased local integration across all frequency bands between control and PD-CN; in contrast, decreased local integration occurred in PD-D when compared to PD-CN in the alpha1 frequency band. Differences found in PD-MCI mirrored PD-D. Correlations were found between network measures and assessments of global cognitive performance in PD. CONCLUSIONS Our results reveal distinct patterns of band and network measure type alteration and breakdown for PD, as well as with cognitive decline in PD. SIGNIFICANCE These patterns suggest specific ways that interaction between cortical areas becomes abnormal and contributes to PD symptoms at various stages. Graph theory analysis by EEG suggests that network alteration and breakdown are robust attributes of PD cortical dysfunction pathophysiology.
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Affiliation(s)
| | | | | | | | - Brittany N Dugger
- Institute of Neurodegenerative Diseases, University of California in San Francisco, San Francisco, CA, USA
| | - Holly A Shill
- Department of Neurology, Barrow Neurological Institute, Phoenix, AZ, USA
| | | | - Marwan N Sabbagh
- Department of Neurology, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Shyamal Mehta
- Department of Neurology, Mayo Clinic, Scottsdale, AZ, USA
| | | | - Joseph G Hentz
- Department of Neurology, Mayo Clinic, Scottsdale, AZ, USA
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143
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A systematic review on the applications of resting-state fMRI in Parkinson's disease: Does dopamine replacement therapy play a role? Cortex 2015; 73:80-105. [DOI: 10.1016/j.cortex.2015.08.005] [Citation(s) in RCA: 127] [Impact Index Per Article: 14.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Revised: 06/08/2015] [Accepted: 08/05/2015] [Indexed: 01/16/2023]
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144
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Weingarten CP, Sundman MH, Hickey P, Chen NK. Neuroimaging of Parkinson's disease: Expanding views. Neurosci Biobehav Rev 2015; 59:16-52. [PMID: 26409344 PMCID: PMC4763948 DOI: 10.1016/j.neubiorev.2015.09.007] [Citation(s) in RCA: 101] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2015] [Revised: 09/07/2015] [Accepted: 09/15/2015] [Indexed: 12/14/2022]
Abstract
Advances in molecular and structural and functional neuroimaging are rapidly expanding the complexity of neurobiological understanding of Parkinson's disease (PD). This review article begins with an introduction to PD neurobiology as a foundation for interpreting neuroimaging findings that may further lead to more integrated and comprehensive understanding of PD. Diverse areas of PD neuroimaging are then reviewed and summarized, including positron emission tomography, single photon emission computed tomography, magnetic resonance spectroscopy and imaging, transcranial sonography, magnetoencephalography, and multimodal imaging, with focus on human studies published over the last five years. These included studies on differential diagnosis, co-morbidity, genetic and prodromal PD, and treatments from L-DOPA to brain stimulation approaches, transplantation and gene therapies. Overall, neuroimaging has shown that PD is a neurodegenerative disorder involving many neurotransmitters, brain regions, structural and functional connections, and neurocognitive systems. A broad neurobiological understanding of PD will be essential for translational efforts to develop better treatments and preventive strategies. Many questions remain and we conclude with some suggestions for future directions of neuroimaging of PD.
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Affiliation(s)
- Carol P Weingarten
- Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, United States.
| | - Mark H Sundman
- Brain Imaging and Analysis Center, Duke University Medical Center, United States
| | - Patrick Hickey
- Department of Neurology, Duke University School of Medicine, United States
| | - Nan-kuei Chen
- Brain Imaging and Analysis Center, Duke University Medical Center, United States; Department of Radiology, Duke University School of Medicine, United States
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145
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Shah C, Beall EB, Frankemolle AMM, Penko A, Phillips MD, Lowe MJ, Alberts JL. Exercise Therapy for Parkinson's Disease: Pedaling Rate Is Related to Changes in Motor Connectivity. Brain Connect 2015; 6:25-36. [PMID: 26414696 DOI: 10.1089/brain.2014.0328] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Forced-rate lower-extremity exercise has recently emerged as a potential safe and low-cost therapy for Parkinson's disease (PD). The efficacy is believed to be dependent on pedaling rate, with rates above the subjects' voluntary exercise rates being most beneficial. In this study, we use functional connectivity magnetic resonance imaging (MRI) to further elucidate the mechanism underlying this effect. Twenty-seven PD patients were randomized to complete 8 weeks of forced-rate exercise (FE) or voluntary-rate exercise (VE). Exercise was delivered using a specialized stationary bicycle, which can augment patients' voluntary exercise rates. The FE group received assistance from the cycle. Imaging was conducted at baseline, end of therapy, and after 4 weeks of follow-up. Functional connectivity (FC) was determined via seed-based correlation analysis, using activation-based seeds in the primary motor cortex (M1). The change in FC after exercise was compared using linear correlation with pedaling rate. Results of the correlation analysis showed a strong positive correlation between pedaling rate and change in FC from the most affected M1 to the ipsilateral thalamus. This effect persisted after 4 weeks of follow-up. These results indicate that a plausible mechanism for the therapeutic efficacy of high-rate exercise in PD is that it improves thalamo-cortical connectivity.
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Affiliation(s)
- Chintan Shah
- 1 Department of Radiology, Hospital of the University of Pennsylvania , Philadelphia, Pennsylvania
| | - Erik B Beall
- 2 Imaging Institute , Cleveland Clinic, Cleveland, Ohio
| | - Anneke M M Frankemolle
- 3 Department of Biomedical Engineering, Case Western Reserve University , Cleveland, Ohio
| | - Amanda Penko
- 4 Center for Neurological Restoration , Cleveland Clinic, Cleveland, Ohio
| | | | - Mark J Lowe
- 2 Imaging Institute , Cleveland Clinic, Cleveland, Ohio
| | - Jay L Alberts
- 3 Department of Biomedical Engineering, Case Western Reserve University , Cleveland, Ohio.,4 Center for Neurological Restoration , Cleveland Clinic, Cleveland, Ohio.,5 Cleveland FES Center, L. Stokes Cleveland VA Medical Center , Cleveland, Ohio
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146
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Hu Y, Mi X, Xu X, Fang W, Zeng K, Yang M, Li C, Wang S, Li M, Wang X. The Brain Activity in Brodmann Area 17: A Potential Bio-Marker to Predict Patient Responses to Antiepileptic Drugs. PLoS One 2015; 10:e0139819. [PMID: 26439500 PMCID: PMC4595505 DOI: 10.1371/journal.pone.0139819] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2014] [Accepted: 07/28/2015] [Indexed: 12/29/2022] Open
Abstract
In this study, we aimed to predict newly diagnosed patient responses to antiepileptic drugs (AEDs) using resting-state functional magnetic resonance imaging tools to explore changes in spontaneous brain activity. We recruited 21 newly diagnosed epileptic patients, 8 drug-resistant (DR) patients, 11 well-healed (WH) patients, and 13 healthy controls. After a 12-month follow-up, 11 newly diagnosed epileptic patients who showed a poor response to AEDs were placed into the seizures uncontrolled (SUC) group, while 10 patients were enrolled in the seizure-controlled (SC) group. By calculating the amplitude of fractional low-frequency fluctuations (fALFF) of blood oxygen level-dependent signals to measure brain activity during rest, we found that the SUC patients showed increased activity in the bilateral occipital lobe, particularly in the cuneus and lingual gyrus compared with the SC group and healthy controls. Interestingly, DR patients also showed increased activity in the identical cuneus and lingual gyrus regions, which comprise Brodmann's area 17 (BA17), compared with the SUC patients; however, these abnormalities were not observed in SC and WH patients. The receiver operating characteristic (ROC) curves indicated that the fALFF value of BA17 could differentiate SUC patients from SC patients and healthy controls with sufficient sensitivity and specificity prior to the administration of medication. Functional connectivity analysis was subsequently performed to evaluate the difference in connectivity between BA17 and other brain regions in the SUC, SC and control groups. Regions nearby the cuneus and lingual gyrus were found positive connectivity increased changes or positive connectivity changes with BA17 in the SUC patients, while remarkably negative connectivity increased changes or positive connectivity decreased changes were found in the SC patients. Additionally, default mode network (DMN) regions showed negative connectivity increased changes or negative changes with BA17 in the SUC patients. The abnormal increased in BA17 activity may be a key point that plays a substantial role in facilitating seizure onset.
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Affiliation(s)
- Yida Hu
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Xiujuan Mi
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Xin Xu
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Weidong Fang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Kebin Zeng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Mingming Yang
- Department of Pediatrics, Chongqing City Hospital of Traditional Chinese Medicine, Chongqing, People’s Republic of China
| | - Chenyu Li
- Department of Neurology, Chongqing City Hospital of Traditional Chinese Medicine, Chongqing, People’s Republic of China
| | - Shasha Wang
- The Nursing Department, Chongqing Three Gorges Central Hospital, Chongqing, People’s Republic of China
| | - Minghui Li
- The Nursing Department, First Hospital of Shanxi Medical University, Taiyuan, People’s Republic of China
| | - Xuefeng Wang
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
- * E-mail:
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147
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Yan LR, Wu YB, Zeng XH, Gao LC. Dysfunctional putamen modulation during bimanual finger-to-thumb movement in patients with Parkinson's disease. Front Hum Neurosci 2015; 9:516. [PMID: 26483652 PMCID: PMC4588113 DOI: 10.3389/fnhum.2015.00516] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2015] [Accepted: 09/04/2015] [Indexed: 11/14/2022] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder affecting middle-aged and elderly people. PD can be viewed as “circuit disorder,” indicating that large scale cortico-subcortical pathways were involved in its pathophysiology. The brain network in an experimental context is emerging as an important biomarker in disease diagnosis and prognosis prediction. This context-dependent network for PD and the underling functional mechanism remains unclear. In this paper, the brain network profiles in 11 PD patients without dementia were studied and compared with 12 healthy controls. The functional magnetic resonance imaging (fMRI) data were acquired when the subjects were performing a pseudorandomized unimanual or bimanual finger-to-thumb movement task. The activation was detected and the network profiles were analyzed by psychophysiological interaction (PPI) toolbox. For the controls and PD patients, the motor areas including the primary motor and premotor areas, supplementary motor area, the cerebellum and parts of the frontal, temporal and parietal gyrus were activated. The right putamen exhibited significant control > PD activation and weaker activity during the bimanual movement relative to the unimanual movement in the control group. The decreased putamen modulation on some nucleus in basal ganglia, such as putamen, thalamus and caudate, and some cortical areas, such as cingulate, parietal, angular, frontal, temporal and occipital gyrus was detected in the bimanual movement condition relative to the unimanual movement condition. Between-group PPI difference was detected in cingulate gyrus, angular gyrus and precuneus (control > PD) and inferior frontal gyrus (PD > control). The deficient putamen activation and its enhanced connectivity with the frontal gyrus could be a correlate of impaired basal ganglia inhibition and frontal gyrus compensation to maintain the task performance during the motor programs of PD patients.
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Affiliation(s)
- Li-Rong Yan
- Department of Information, Wuhan General Hospital of Guangzhou Command Wuhan, China
| | - Yi-Bo Wu
- Department of Unmanned Aerial Vehicle, Wuhan Mechanical Technology College Wuhan, China
| | - Xiao-Hua Zeng
- Department of Radiology, Wuhan General Hospital of Guangzhou Command Wuhan, China
| | - Li-Chen Gao
- Department of Radiology, Wuhan General Hospital of Guangzhou Command Wuhan, China
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148
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Göttlich M, Krämer UM, Kordon A, Hohagen F, Zurowski B. Resting-state connectivity of the amygdala predicts response to cognitive behavioral therapy in obsessive compulsive disorder. Biol Psychol 2015; 111:100-9. [PMID: 26388257 DOI: 10.1016/j.biopsycho.2015.09.004] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2014] [Revised: 08/31/2015] [Accepted: 09/14/2015] [Indexed: 11/26/2022]
Abstract
BACKGROUND Obsessive-compulsive disorder (OCD) is a psychiatric disorder which is characterized by recurrent intrusive thoughts (obsessions) and ritualized, repetitive behaviors or mental acts (compulsions). The gold standard for the treatment of OCD is cognitive behavioral therapy (CBT) with exposure and response prevention. This is the first study exploring the predictive value of resting-state functional connectivity for the outcome of CBT. METHODS We assessed whole-brain resting-state functional connectivity in a group of 17 un-medicated OCD inpatients prior to CBT compared to 19 healthy controls using functional magnetic resonance imaging. The graph theoretical metric degree centrality served as indicator for altered voxel-wise whole-brain functional connectivity. The relative change in the Yale-Brown Obsessive Compulsive Scale (YBOCS) score was used to evaluate treatment outcome. RESULTS The degree centrality of the right basolateral nuclei group of the amygdala was positively correlated with the response to subsequent CBT. OCD patients showed a lower degree centrality of the superficial amygdala (bilateral). CONCLUSIONS Our results suggest that two different sub-regions of the amygdala and their respective neural networks are affected in OCD: the superficial amygdala and networks related to evaluation of reinforcers and risk anticipation and the basolateral amygdala which is implicated in fear processing. The diminished CBT response in patients showing a lower degree centrality of the basolateral amygdala reflects a deficient fear circuit in these patients which may impact fear extinction as a core mechanism of exposure-based CBT.
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Affiliation(s)
- Martin Göttlich
- Department of Neurology, University of Lübeck, Lübeck, Germany.
| | - Ulrike M Krämer
- Department of Neurology, University of Lübeck, Lübeck, Germany; Institute for Psychology II, University of Lübeck, Lübeck, Germany
| | - Andreas Kordon
- Department of Psychiatry, University of Lübeck, Lübeck, Germany
| | - Fritz Hohagen
- Department of Psychiatry, University of Lübeck, Lübeck, Germany
| | - Bartosz Zurowski
- Department of Psychiatry, University of Lübeck, Lübeck, Germany; University of Hamburg, Institute for Systems Neuroscience, Germany
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149
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Göttlich M, Beyer F, Krämer UM. BASCO: a toolbox for task-related functional connectivity. Front Syst Neurosci 2015; 9:126. [PMID: 26441558 PMCID: PMC4565057 DOI: 10.3389/fnsys.2015.00126] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2015] [Accepted: 08/28/2015] [Indexed: 12/22/2022] Open
Abstract
BASCO (BetA Series COrrelation) is a user-friendly MATLAB toolbox with a graphical user interface (GUI) which allows investigating functional connectivity in event-related functional magnetic resonance imaging (fMRI) data. Connectivity analyses extend and compliment univariate activation analyses since the actual interaction between brain regions involved in a task can be explored. BASCO supports seed-based functional connectivity as well as brain network analyses. Although there are a multitude of advanced toolboxes for investigating resting-state functional connectivity, BASCO is the first toolbox for evaluating task-related whole-brain functional connectivity employing a large number of network nodes. Thus, BASCO allows investigating task-specific rather than resting-state networks. Here, we summarize the main features of the toolbox and describe the methods and algorithms.
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Affiliation(s)
- Martin Göttlich
- Department of Neurology, University of Lübeck Lübeck, Germany
| | - Frederike Beyer
- Department of Neurology, University of Lübeck Lübeck, Germany ; Institute for Psychology II, University of Lübeck Lübeck, Germany
| | - Ulrike M Krämer
- Department of Neurology, University of Lübeck Lübeck, Germany ; Institute for Psychology II, University of Lübeck Lübeck, Germany
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150
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Zhang D, Wang J, Liu X, Chen J, Liu B. Aberrant Brain Network Efficiency in Parkinson's Disease Patients with Tremor: A Multi-Modality Study. Front Aging Neurosci 2015; 7:169. [PMID: 26379547 PMCID: PMC4553412 DOI: 10.3389/fnagi.2015.00169] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2015] [Accepted: 08/17/2015] [Indexed: 01/18/2023] Open
Abstract
The coordination of spontaneous brain activity is widely enhanced relative to compensation activity in Parkinson’s disease (PD) with tremor; however, the associated topological organization remains unclear. This study collected magnetic resonance imaging data from 36 participants [i.e., 16 PD patients and 20 matched normal controls (NCs)] and constructed wavelet-based functional and morphological brain networks for individual participants. Graph-based network analysis indicated that the information translation efficiency in the functional brain network was disrupted within the wavelet scale 2 (i.e., 0.063–0.125 Hz) in PD patients. Compared with the NCs, the network local efficiency was decreased and the network global efficiency was increased in PD patients. Network local efficiency could effectively discriminate PD patients from the NCs using multivariate pattern analysis, and could also describe the variability of tremor based on a multiple linear regression model (MLRM). However, these observations were not identified in the network global efficiency. Notably, the global and local efficiency were both significantly increased in the morphological brain network of PD patients. We further found that the global and local network efficiency both worked well on PD classifications (i.e., using MVPA) and clinical performance descriptions (i.e., using MLRM). More importantly, functional and morphological brain networks were highly associated in terms of network local efficiency in PD patients. This study sheds lights on network disorganization in PD with tremor and helps for understanding the neural basis underlying this type of PD.
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Affiliation(s)
- Delong Zhang
- Department of Radiology, Guangdong Provincial Hospital of Chinese Medicine , Guangzhou , China ; Guangzhou University of Chinese Medicine Postdoctoral Mobile Research Station , Guangzhou , China
| | - Jinhui Wang
- Center for Cognition and Brain Disorders, Hangzhou Normal University , Hangzhou , China ; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments , Hangzhou , China
| | - Xian Liu
- Department of Radiology, Guangdong Provincial Hospital of Chinese Medicine , Guangzhou , China
| | - Jun Chen
- Department of Radiology, Guangdong Provincial Hospital of Chinese Medicine , Guangzhou , China
| | - Bo Liu
- Department of Radiology, Guangdong Provincial Hospital of Chinese Medicine , Guangzhou , China
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