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Ragothaman A, Mancini M, Nutt JG, Wang J, Fair DA, Horak FB, Miranda-Dominguez O. Motor networks, but also non-motor networks predict motor signs in Parkinson's disease. Neuroimage Clin 2023; 40:103541. [PMID: 37972450 PMCID: PMC10685308 DOI: 10.1016/j.nicl.2023.103541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 10/31/2023] [Accepted: 11/10/2023] [Indexed: 11/19/2023]
Abstract
OBJECTIVE Investigate the brain functional networks associated with motor impairment in people with Parkinson's disease (PD). BACKGROUND PD is primarily characterized by motor dysfunction. Resting-state functional connectivity (RsFC) offers a unique opportunity to non-invasively characterize brain function. In this study, we hypothesized that the motor dysfunction observed in people with PD involves atypical connectivity not only in motor but also in higher-level attention networks. Understanding the interaction between motor and non-motor RsFC that are related to the motor signs could provide insights into PD pathophysiology. METHODS We used data from 88 people with PD (mean age: 68.2(SD:10), 55 M/33F) coming from 2 cohorts. Motor severity was assessed in practical OFF-medication state, using MDS-UPDRS Part-III motor scores (mean: 49 (SD:10)). RsFC was characterized using an atlas of 384 regions that were grouped into 13 functional networks. Associations between RsFC and motor severity were assessed independently for each RsFC using predictive modeling. RESULTS The top 5 % models that predicted the MDS-UPDRS-III motor scores with effect size >0.5 were the connectivity between (1) the somatomotor and Subcortical-Basal-ganglia, (2) somatomotor and Visual and (3) CinguloOpercular (CiO) and language/Ventral attention (Lan/VeA) network pairs. DISCUSSION Our findings suggest that, along with motor networks, visual- and attention-related cortical networks are also associated with the motor symptoms of PD. Non-motor networks may be involved indirectly in motor-coordination. When people with PD have deficits in motor networks, more attention may be needed to carry out formerly automatic motor functions, consistent with compensatory mechanisms in parkinsonian movement disorders.
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Affiliation(s)
| | - Martina Mancini
- Department of Neurology, Oregon Health and Science University, Portland, OR 97239, USA
| | - John G Nutt
- Department of Neurology, Oregon Health and Science University, Portland, OR 97239, USA
| | - Junping Wang
- Department of Radiology, Tianjin Medical University General Hospital, China
| | - Damien A Fair
- Masonic Institute for the Developing Brain (MIDB), University of Minnesota, Minneapolis, MN 55455, USA; Institute of Child Development, College of Education and Human Development, University of Minnesota, Minneapolis, MN 55455, USA; Department of Pediatrics, University of Minnesota Medical School, University of Minnesota, Minneapolis, MN 55455, USA; Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97239, USA
| | - Fay B Horak
- Department of Neurology, Oregon Health and Science University, Portland, OR 97239, USA; Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR 97239, USA.
| | - Oscar Miranda-Dominguez
- Masonic Institute for the Developing Brain (MIDB), University of Minnesota, Minneapolis, MN 55455, USA; Department of Pediatrics, University of Minnesota Medical School, University of Minnesota, Minneapolis, MN 55455, USA
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Li J, Tan C, Zhang L, Cai S, Shen Q, Liu Q, Wang M, Song C, Zhou F, Yuan J, Liu Y, Lan B, Liao H. Neural functional network of early Parkinson's disease based on independent component analysis. Cereb Cortex 2023; 33:11025-11035. [PMID: 37746803 DOI: 10.1093/cercor/bhad342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 08/08/2023] [Indexed: 09/26/2023] Open
Abstract
This work explored neural network changes in early Parkinson's disease: Resting-state functional magnetic resonance imaging was used to investigate functional alterations in different stages of Parkinson's disease (PD). Ninety-five PD patients (50 early/mild and 45 early/moderate) and 37 healthy controls (HCs) were included. Independent component analysis revealed significant differences in intra-network connectivity, specifically in the default mode network (DMN) and right frontoparietal network (RFPN), in both PD groups compared to HCs. Inter-network connectivity analysis showed reduced connectivity between the executive control network (ECN) and DMN, as well as ECN-left frontoparietal network (LFPN), in early/mild PD. Early/moderate PD exhibited decreased connectivity in ECN-LFPN, ECN-RFPN, ECN-DMN, and DMN-auditory network, along with increased connectivity in LFPN-cerebellar network. Correlations were found between ECN-DMN and ECN-LFPN connections with UPDRS-III scores in early/mild PD. These findings suggest that PD progression involves dysfunction in multiple intra- and inter-networks, particularly implicating the ECN, and a wider range of abnormal functional networks may mark the progression of the disease.
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Affiliation(s)
- Junli Li
- Department of Medical Imaging, Huizhou Central People's Hospital, Eling North Road, Huicheng District, Huizhou, Guangdong 516001, China
| | - Changlian Tan
- Department of Radiology, The Second Xiangya Hospital, Central South University, Renmin Middle Road, Furong District, Changsha, Hunan 410011, China
| | - Lin Zhang
- Department of Radiology, Chengdu Fifth People's Hospital, Mashi Street, Wenjiang District, Chengdu, Sichuan 611130, China
| | - Sainan Cai
- Department of Radiology, The Second Xiangya Hospital, Central South University, Renmin Middle Road, Furong District, Changsha, Hunan 410011, China
| | - Qin Shen
- Department of Radiology, The Second Xiangya Hospital, Central South University, Renmin Middle Road, Furong District, Changsha, Hunan 410011, China
| | - Qinru Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Renmin Middle Road, Furong District, Changsha, Hunan 410011, China
| | - Min Wang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Renmin Middle Road, Furong District, Changsha, Hunan 410011, China
| | - ChenDie Song
- Department of Radiology, The Second Xiangya Hospital, Central South University, Renmin Middle Road, Furong District, Changsha, Hunan 410011, China
| | - Fan Zhou
- Department of Radiology, The Second Xiangya Hospital, Central South University, Renmin Middle Road, Furong District, Changsha, Hunan 410011, China
| | - Jiaying Yuan
- Department of Radiology, The Second Xiangya Hospital, Central South University, Renmin Middle Road, Furong District, Changsha, Hunan 410011, China
| | - Yujing Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Renmin Middle Road, Furong District, Changsha, Hunan 410011, China
| | - Bowen Lan
- Department of Medical Imaging, Huizhou Central People's Hospital, Eling North Road, Huicheng District, Huizhou, Guangdong 516001, China
| | - Haiyan Liao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Renmin Middle Road, Furong District, Changsha, Hunan 410011, China
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Zarghami TS. A new causal centrality measure reveals the prominent role of subcortical structures in the causal architecture of the extended default mode network. Brain Struct Funct 2023; 228:1917-1941. [PMID: 37658184 DOI: 10.1007/s00429-023-02697-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 08/09/2023] [Indexed: 09/03/2023]
Abstract
Network representation has been an incredibly useful concept for understanding the behavior of complex systems in social sciences, biology, neuroscience, and beyond. Network science is mathematically founded on graph theory, where nodal importance is gauged using measures of centrality. Notably, recent work suggests that the topological centrality of a node should not be over-interpreted as its dynamical or causal importance in the network. Hence, identifying the influential nodes in dynamic causal models (DCM) remains an open question. This paper introduces causal centrality for DCM, a dynamics-sensitive and causally-founded centrality measure based on the notion of intervention in graphical models. Operationally, this measure simplifies to an identifiable expression using Bayesian model reduction. As a proof of concept, the average DCM of the extended default mode network (eDMN) was computed in 74 healthy subjects. Next, causal centralities of different regions were computed for this causal graph, and compared against several graph-theoretical centralities. The results showed that the subcortical structures of the eDMN were more causally central than the cortical regions, even though the graph-theoretical centralities unanimously favored the latter. Importantly, model comparison revealed that only the pattern of causal centrality was causally relevant. These results are consistent with the crucial role of the subcortical structures in the neuromodulatory systems of the brain, and highlight their contribution to the organization of large-scale networks. Potential applications of causal centrality-to study causal models of other neurotypical and pathological functional networks-are discussed, and some future lines of research are outlined.
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Affiliation(s)
- Tahereh S Zarghami
- Bio-Electric Department, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran.
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4
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Li T, Wang L, Piao Z, Chen K, Yu X, Wen Q, Suo D, Zhang C, Funahashi S, Pei G, Fang B, Yan T. Altered Neurovascular Coupling for Multidisciplinary Intensive Rehabilitation in Parkinson's Disease. J Neurosci 2023; 43:1256-1266. [PMID: 36609454 PMCID: PMC9962778 DOI: 10.1523/jneurosci.1204-22.2023] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 12/31/2022] [Accepted: 01/03/2023] [Indexed: 01/07/2023] Open
Abstract
Effective rehabilitation in Parkinson's disease (PD) is related to brain reorganization with restoration of cortico-subcortical networks and compensation of frontoparietal networks; however, further neural rehabilitation evidence from a multidimensional perspective is needed. To investigate how multidisciplinary intensive rehabilitation treatment affects neurovascular coupling, 31 PD patients (20 female) before and after treatment and 30 healthy controls (17 female) underwent blood oxygenation level-dependent functional magnetic resonance imaging and arterial spin labeling scans. Cerebral blood flow (CBF) was used to measure perfusion, and fractional amplitude of low-frequency fluctuation (fALFF) was used to measure neural activity. The global CBF-fALFF correlation and regional CBF/fALFF ratio were calculated as neurovascular coupling. Dynamic causal modeling (DCM) was used to evaluate treatment-related alterations in the strength and directionality of information flow. Treatment reduced CBF-fALFF correlations. The altered CBF/fALFF exhibited increases in the left angular gyrus and the right inferior parietal gyrus and decreases in the bilateral thalamus and the right superior frontal gyrus. The CBF/fALFF alteration in right superior frontal gyrus showed correlations with motor improvement. Further, DCM indicated increases in connectivity from the superior frontal gyrus and decreases from the thalamus to the inferior parietal gyrus. The benefits of rehabilitation were reflected in the dual mechanism, with restoration of executive control occurring in the initial phase of motor learning and compensation of information integration occurring in the latter phase. These findings may yield multimodal insights into the role of rehabilitation in disease modification and identify the dorsolateral superior frontal gyrus as a potential target for noninvasive neuromodulation in PD.SIGNIFICANCE STATEMENT Although rehabilitation has been proposed as a promising supplemental treatment for PD as it results in brain reorganization, restoring cortico-subcortical networks and eliciting compensatory activation of frontoparietal networks, further multimodal evidence of the neural mechanisms underlying rehabilitation is needed. We measured the ratio of perfusion and neural activity derived from arterial spin labeling and blood oxygenation level-dependent fMRI data and found that benefits of rehabilitation seem to be related to the dual mechanism, restoring executive control in the initial phase of motor learning and compensating for information integration in the latter phase. We also identified the dorsolateral superior frontal gyrus as a potential target for noninvasive neuromodulation in PD patients.
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Affiliation(s)
- Ting Li
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Li Wang
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Zhixin Piao
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Keke Chen
- Parkinson Medical Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Xin Yu
- Parkinson Medical Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Qiping Wen
- Parkinson Medical Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Dingjie Suo
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Chunyu Zhang
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Shintaro Funahashi
- Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing 100081, China
| | - Guangying Pei
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
| | - Boyan Fang
- Parkinson Medical Center, Beijing Rehabilitation Hospital, Capital Medical University, Beijing 100144, China
| | - Tianyi Yan
- School of Life Science, Beijing Institute of Technology, Beijing 100081, China
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5
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Filippi M, Spinelli EG, Cividini C, Ghirelli A, Basaia S, Agosta F. The human functional connectome in neurodegenerative diseases: relationship to pathology and clinical progression. Expert Rev Neurother 2023; 23:59-73. [PMID: 36710600 DOI: 10.1080/14737175.2023.2174016] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
INTRODUCTION Neurodegenerative diseases can be considered as 'disconnection syndromes,' in which a communication breakdown prompts cognitive or motor dysfunction. Mathematical models applied to functional resting-state MRI allow for the organization of the brain into nodes and edges, which interact to form the functional brain connectome. AREAS COVERED The authors discuss the recent applications of functional connectomics to neurodegenerative diseases, from preclinical diagnosis, to follow up along with the progressive changes in network organization, to the prediction of the progressive spread of neurodegeneration, to stratification of patients into prognostic groups, and to record responses to treatment. The authors searched PubMed using the terms 'neurodegenerative diseases' AND 'fMRI' AND 'functional connectome' OR 'functional connectivity' AND 'connectomics' OR 'graph metrics' OR 'graph analysis.' The time range covered the past 20 years. EXPERT OPINION Considering the great pathological and phenotypical heterogeneity of neurodegenerative diseases, identifying a common framework to diagnose, monitor and elaborate prognostic models is challenging. Graph analysis can describe the complexity of brain architectural rearrangements supporting the network-based hypothesis as unifying pathogenetic mechanism. Although a multidisciplinary team is needed to overcome the limit of methodologic complexity in clinical application, advanced methodologies are valuable tools to better characterize functional disconnection in neurodegeneration.
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Affiliation(s)
- Massimo Filippi
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy.,Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Edoardo Gioele Spinelli
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Camilla Cividini
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Alma Ghirelli
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Silvia Basaia
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federica Agosta
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
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6
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Wen X, Yang M, Hsu L, Zhang D. Test-retest reliability of modular-relevant analysis in brain functional network. Front Neurosci 2022; 16:1000863. [PMID: 36570835 PMCID: PMC9770801 DOI: 10.3389/fnins.2022.1000863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 11/22/2022] [Indexed: 12/13/2022] Open
Abstract
Introduction The human brain could be modeled as a complex network via functional magnetic resonance imaging (fMRI), and the architecture of these brain functional networks can be studied from multiple spatial scales with different graph theory tools. Detecting modules is an important mesoscale network measuring approach that has provided crucial insights for uncovering how brain organizes itself among different functional subsystems. Despite its successful application in a wide range of brain network studies, the lack of comprehensive reliability assessment prevents its potential extension to clinical trials. Methods To fill this gap, this paper, using resting-state test-retest fMRI data, systematically explored the reliabilities of five popular network metrics derived from modular structure. Considering the repeatability of network partition depends heavily on network size and module detection algorithm, we constructed three types of brain functional networks for each subject by using a set of coarse-to-fine brain atlases and adopted four methods for single-subject module detection and twelve methods for group-level module detection. Results The results reported moderate-to-good reliability in modularity, intra- and inter-modular functional connectivities, within-modular degree and participation coefficient at both individual and group levels, indicating modular-relevant network metrics can provide robust evaluation results. Further analysis identified the significant influence of module detection algorithm and node definition approach on reliabilities of network partitions and its derived network analysis results. Discussion This paper provides important guidance for choosing reliable modular-relevant network metrics and analysis strategies in future studies.
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Affiliation(s)
- Xuyun Wen
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, Jiangsu, China
| | - Mengting Yang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, Jiangsu, China
| | - Liming Hsu
- Center for Animal MRI, University of North Carolina, Chapel Hill, Chapel Hill, NC, United States
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, Jiangsu, China
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Bagarinao E, Kawabata K, Watanabe H, Hara K, Ohdake R, Ogura A, Masuda M, Kato T, Maesawa S, Katsuno M, Sobue G. Connectivity impairment of cerebellar and sensorimotor connector hubs in Parkinson’s disease. Brain Commun 2022; 4:fcac214. [PMID: 36072644 PMCID: PMC9438962 DOI: 10.1093/braincomms/fcac214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 05/25/2022] [Accepted: 08/19/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Cognitive and movement processes involved integration of several large-scale brain networks. Central to these integrative processes are connector hubs, brain regions characterized by strong connections with multiple networks. Growing evidence suggests that many neurodegenerative and psychiatric disorders are associated with connector hub dysfunctions. Using a network metric called functional connectivity overlap ratio, we investigated connector hub alterations in Parkinson’s disease. Resting-state functional MRI data from 99 patients (male/female = 44/55) and 99 age- and sex-matched healthy controls (male/female = 39/60) participating in our cross-sectional study were used in the analysis. We have identified two sets of connector hubs, mainly located in the sensorimotor cortex and cerebellum, with significant connectivity alterations with multiple resting-state networks. Sensorimotor connector hubs have impaired connections primarily with primary processing (sensorimotor, visual), visuospatial, and basal ganglia networks, whereas cerebellar connector hubs have impaired connections with basal ganglia and executive control networks. These connectivity alterations correlated with patients’ motor symptoms. Specifically, values of the functional connectivity overlap ratio of the cerebellar connector hubs were associated with tremor score, whereas that of the sensorimotor connector hubs with postural instability and gait disturbance score, suggesting potential association of each set of connector hubs with the disorder’s two predominant forms, the akinesia/rigidity and resting tremor subtypes. In addition, values of the functional connectivity overlap ratio of the sensorimotor connector hubs were highly predictive in classifying patients from controls with an accuracy of 75.76%. These findings suggest that, together with the basal ganglia, cerebellar and sensorimotor connector hubs are significantly involved in Parkinson’s disease with their connectivity dysfunction potentially driving the clinical manifestations typically observed in this disorder.
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Affiliation(s)
- Epifanio Bagarinao
- Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine , Nagoya, Aichi, 461–8673 Japan
- Brain & Mind Research Center, Nagoya University , Nagoya, Aichi, 466–8550 Japan
| | - Kazuya Kawabata
- Brain & Mind Research Center, Nagoya University , Nagoya, Aichi, 466–8550 Japan
- Department of Neurology, Nagoya University Graduate School of Medicine , Nagoya, Aichi, 466-8550 Japan
| | - Hirohisa Watanabe
- Brain & Mind Research Center, Nagoya University , Nagoya, Aichi, 466–8550 Japan
- Department of Neurology, Nagoya University Graduate School of Medicine , Nagoya, Aichi, 466-8550 Japan
- Department of Neurology, Fujita Health University School of Medicine , Toyoake, Aichi, 470-1192 Japan
| | - Kazuhiro Hara
- Department of Neurology, Nagoya University Graduate School of Medicine , Nagoya, Aichi, 466-8550 Japan
| | - Reiko Ohdake
- Department of Neurology, Fujita Health University School of Medicine , Toyoake, Aichi, 470-1192 Japan
| | - Aya Ogura
- Brain & Mind Research Center, Nagoya University , Nagoya, Aichi, 466–8550 Japan
- Department of Neurology, Nagoya University Graduate School of Medicine , Nagoya, Aichi, 466-8550 Japan
| | - Michihito Masuda
- Department of Neurology, Nagoya University Graduate School of Medicine , Nagoya, Aichi, 466-8550 Japan
| | - Toshiyasu Kato
- Department of Neurology, Nagoya University Graduate School of Medicine , Nagoya, Aichi, 466-8550 Japan
| | - Satoshi Maesawa
- Brain & Mind Research Center, Nagoya University , Nagoya, Aichi, 466–8550 Japan
- Department of Neurosurgery, Nagoya University Graduate School of Medicine , Nagoya, Aichi, 466-8550 Japan
| | - Masahisa Katsuno
- Department of Neurology, Nagoya University Graduate School of Medicine , Nagoya, Aichi, 466-8550 Japan
| | - Gen Sobue
- Brain & Mind Research Center, Nagoya University , Nagoya, Aichi, 466–8550 Japan
- Aichi Medical University , Nagakute, Aichi, 480-1195 Japan
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Lu J, Wang Y, Shu Z, Zhang X, Wang J, Cheng Y, Zhu Z, Yu Y, Wu J, Han J, Yu N. fNIRS-based brain state transition features to signify functional degeneration after Parkinson's disease. J Neural Eng 2022; 19. [PMID: 35917809 DOI: 10.1088/1741-2552/ac861e] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 08/01/2022] [Indexed: 11/12/2022]
Abstract
OBJECTIVE Parkinson's disease (PD) is a common neurodegenerative brain disorder, and early diagnosis is of vital importance for treatment. Existing methods are mainly focused on behavior examination, while the functional neurodegeneration after PD has not been well explored. This paper aims to investigate the brain functional variation of PD patients in comparison with healthy controls. APPROACH In this work, we propose brain hemodynamic states and state transition features to signify functional degeneration after PD. Firstly, a functional near-infrared spectroscopy (fNIRS)-based experimental paradigm was designed to capture brain activation during dual-task walking from PD patients and healthy controls. Then, three brain states, named expansion, contraction, and intermediate states, were defined with respect to the oxyhemoglobin and deoxyhemoglobin responses. After that, two features were designed from a constructed transition factor and concurrent variations of oxy- and deoxy-hemoglobin over time, to quantify the transitions of brain states. Further, a support vector machine classifier was trained with the proposed features to distinguish PD patients and healthy controls. RESULTS Experimental results showed that our method with the proposed brain state transition features achieved classification accuracy of 0:8200 and F score of 0:9091, and outperformed existing fNIRS-based methods. Compared with healthy controls, PD patients had significantly smaller transition acceleration and transition angle. SIGNIFICANCE The proposed brain state transition features well signify functional degeneration of PD patients and may serve as promising functional biomarkers for PD diagnosis.
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Affiliation(s)
- Jiewei Lu
- College of Artificial Intelligence, Nankai University, Haihe Education Park, Tongyan Road No.38, Tianjin, 300350, CHINA
| | - Yue Wang
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, No22.Qixiangtai Rd.,Heping Dist, Tianjin, Tianjin, 300070, CHINA
| | - Zhilin Shu
- College of Artificial Intelligence, Nankai University, Haihe Education Park, Tongyan Road No.38, Tianjin, 300350, CHINA
| | - Xinyuan Zhang
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, No22.Qixiangtai Rd.,Heping Dist, Tianjin, 300070, CHINA
| | - Jin Wang
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, No22.Qixiangtai Rd.,Heping Dist, Tianjin, 300070, CHINA
| | - Yuanyuan Cheng
- Department of Rehabilitation Medicine, Tianjin Huanhu Hospital, No.122, Qixiangtai Road, Hexi District, Tianjin, 300060, CHINA
| | - Zhizhong Zhu
- Department of Rehabilitation Medicine, Tianjin Huanhu Hospital, Tianjin Huanhu Hospital, No.122, Qixiangtai Road, Hexi District, Tianjin, 300060, CHINA
| | - Yang Yu
- Department of Rehabilitation Medicine, Tianjin Huanhu Hospital, Tianjin Huanhu Hospital, No.122, Qixiangtai Road, Hexi District, Tianjin, 300060, CHINA
| | - Jialing Wu
- Department of Neurology, Tianjin Huanhu Hospital, No.122, Qixiangtai Road, Hexi District, Tianjin, 300060, CHINA
| | - Jianda Han
- College of Artificial Intelligence, Nankai University, Haihe Education Park, Tongyan Road No.38, Tianjin, 300350, CHINA
| | - Ningbo Yu
- College of Artificial Intelligence, Nankai University, Haihe Education Park, Tongyan Road No.38, Tianjin, 300350, CHINA
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Avvaru S, Parhi KK. Betweenness Centrality in Resting-State Functional Networks Distinguishes Parkinson's Disease. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:4785-4788. [PMID: 36086073 DOI: 10.1109/embc48229.2022.9870988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The goal of this paper is to use graph theory network measures derived from non-invasive electroencephalography (EEG) to develop neural decoders that can differentiate Parkinson's disease (PD) patients from healthy controls (HC). EEG signals from 27 patients and 27 demographically matched controls from New Mexico were analyzed by estimating their functional networks. Data recorded from the patients during ON and OFF levodopa sessions were included in the analysis for comparison. We used betweenness centrality of estimated functional networks to classify the HC and PD groups. The classifiers were evaluated using leave-one-out cross-validation. We observed that the PD patients (on and off medication) could be distinguished from healthy controls with 89% accuracy - approximately 4% higher than the state-of-the-art on the same dataset. This work shows that brain network analysis using extracranial resting-state EEG can discover patterns of interactions indicative of PD. This approach can also be extended to other neurological disorders.
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10
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Zhang J, Villringer A, Nikulin VV. Dopaminergic Modulation of Local Non-oscillatory Activity and Global-Network Properties in Parkinson’s Disease: An EEG Study. Front Aging Neurosci 2022; 14:846017. [PMID: 35572144 PMCID: PMC9106139 DOI: 10.3389/fnagi.2022.846017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
Dopaminergic medication for Parkinson’s disease (PD) modulates neuronal oscillations and functional connectivity (FC) across the basal ganglia-thalamic-cortical circuit. However, the non-oscillatory component of the neuronal activity, potentially indicating a state of excitation/inhibition balance, has not yet been investigated and previous studies have shown inconsistent changes of cortico-cortical connectivity as a response to dopaminergic medication. To further elucidate changes of regional non-oscillatory component of the neuronal power spectra, FC, and to determine which aspects of network organization obtained with graph theory respond to dopaminergic medication, we analyzed a resting-state electroencephalography (EEG) dataset including 15 PD patients during OFF and ON medication conditions. We found that the spectral slope, typically used to quantify the broadband non-oscillatory component of power spectra, steepened particularly in the left central region in the ON compared to OFF condition. In addition, using lagged coherence as a FC measure, we found that the FC in the beta frequency range between centro-parietal and frontal regions was enhanced in the ON compared to the OFF condition. After applying graph theory analysis, we observed that at the lower level of topology the node degree was increased, particularly in the centro-parietal area. Yet, results showed no significant difference in global topological organization between the two conditions: either in global efficiency or clustering coefficient for measuring global and local integration, respectively. Interestingly, we found a close association between local/global spectral slope and functional network global efficiency in the OFF condition, suggesting a crucial role of local non-oscillatory dynamics in forming the functional global integration which characterizes PD. These results provide further evidence and a more complete picture for the engagement of multiple cortical regions at various levels in response to dopaminergic medication in PD.
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Affiliation(s)
- Juanli Zhang
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
- *Correspondence: Juanli Zhang,
| | - Arno Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Department of Cognitive Neurology, University Hospital Leipzig, Leipzig, Germany
| | - Vadim V. Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Neurophysics Group, Department of Neurology, Charité – Universitätsmedizin Berlin, Berlin, Germany
- Vadim V. Nikulin,
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11
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Caravaggio F, Barnett AJ, Nakajima S, Iwata Y, Kim J, Borlido C, Mar W, Gerretsen P, Remington G, Graff-Guerrero A. The effects of acute dopamine depletion on resting-state functional connectivity in healthy humans. Eur Neuropsychopharmacol 2022; 57:39-49. [PMID: 35091322 DOI: 10.1016/j.euroneuro.2022.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 01/05/2022] [Accepted: 01/10/2022] [Indexed: 11/24/2022]
Abstract
Alpha-methyl-para-tyrosine (AMPT), a competitive inhibitor of tyrosine hydroxylase, can be used to deplete endogenous dopamine in humans. We examined how AMPT-induced dopamine depletion alters resting-state functional connectivity of the basal ganglia, and canonical resting-state networks, in healthy humans. Fourteen healthy participants (8 females; age [mean ± SD] = 27.93 ± 9.86) completed the study. Following dopamine depletion, the caudate showed reduced connectivity with the medial prefrontal cortex (mPFC) (Cohen's d = 1.89, p<.0001). Moreover, the caudate, putamen, globus pallidus, and midbrain all showed reduced connectivity with the occipital cortex (Cohen's d = 1.48-1.90; p<.0001-0.001). Notably, the dorsal caudate showed increased connectivity with the sensorimotor network (Cohen's d = 2.03, p=.002). AMPT significantly decreased self-reported motivation (t(13)=4.19, p=.001) and increased fatigue (t(13)=4.79, p=.0004). A greater increase in fatigue was associated with a greater reduction in connectivity between the substantia nigra and the mPFC (Cohen's d = 3.02, p<.00001), while decreased motivation was correlated with decreased connectivity between the VTA and left sensorimotor cortex (Cohen's d = 2.03, p=.00004). These findings help us to better understand the role of dopamine in basal ganglia function and may help us better understand neuropsychiatric diseases where abnormal dopamine levels are observed.
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Affiliation(s)
- Fernando Caravaggio
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8, Canada; Department of Psychiatry, University of Toronto, 250 College Street, Toronto, ON M5T 1R8, Canada.
| | - Alexander J Barnett
- Center for Neuroscience, University of California, Davis, 1515 Newton Ct, Davis, California 95618, United States of America
| | - Shinichiro Nakajima
- Department of Neuropsychiatry, Keio University, 2 Chome-15-45 Mita, Tokyo 108-8345, Japan
| | - Yusuke Iwata
- Department of Neuropsychiatry, University of Yamanashi, 4 Chome-4-37 Takeda, Kofu 400-8510, Japan
| | - Julia Kim
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8, Canada
| | - Carol Borlido
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8, Canada
| | - Wanna Mar
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8, Canada
| | - Philip Gerretsen
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8, Canada; Department of Psychiatry, University of Toronto, 250 College Street, Toronto, ON M5T 1R8, Canada
| | - Gary Remington
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8, Canada; Department of Psychiatry, University of Toronto, 250 College Street, Toronto, ON M5T 1R8, Canada
| | - Ariel Graff-Guerrero
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, 250 College Street, Toronto, ON M5T 1R8, Canada; Department of Psychiatry, University of Toronto, 250 College Street, Toronto, ON M5T 1R8, Canada
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12
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Mijalkov M, Volpe G, Pereira JB. Directed Brain Connectivity Identifies Widespread Functional Network Abnormalities in Parkinson's Disease. Cereb Cortex 2022; 32:593-607. [PMID: 34331060 PMCID: PMC8805861 DOI: 10.1093/cercor/bhab237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 05/19/2021] [Accepted: 06/17/2021] [Indexed: 11/14/2022] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder characterized by topological abnormalities in large-scale functional brain networks, which are commonly analyzed using undirected correlations in the activation signals between brain regions. This approach assumes simultaneous activation of brain regions, despite previous evidence showing that brain activation entails causality, with signals being typically generated in one region and then propagated to other ones. To address this limitation, here, we developed a new method to assess whole-brain directed functional connectivity in participants with PD and healthy controls using antisymmetric delayed correlations, which capture better this underlying causality. Our results show that whole-brain directed connectivity, computed on functional magnetic resonance imaging data, identifies widespread differences in the functional networks of PD participants compared with controls, in contrast to undirected methods. These differences are characterized by increased global efficiency, clustering, and transitivity combined with lower modularity. Moreover, directed connectivity patterns in the precuneus, thalamus, and cerebellum were associated with motor, executive, and memory deficits in PD participants. Altogether, these findings suggest that directional brain connectivity is more sensitive to functional network differences occurring in PD compared with standard methods, opening new opportunities for brain connectivity analysis and development of new markers to track PD progression.
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Affiliation(s)
- Mite Mijalkov
- Address correspondence to Mite Mijalkov and Joana B. Pereira, Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Karolinska Institutet, Neo 7th floor, Blickagången 16, 141 83 Huddinge, Sweden. (M.M.); (J.B.P.)
| | | | - Joana B Pereira
- Address correspondence to Mite Mijalkov and Joana B. Pereira, Department of Neurobiology, Care Sciences and Society, Division of Clinical Geriatrics, Karolinska Institutet, Neo 7th floor, Blickagången 16, 141 83 Huddinge, Sweden. (M.M.); (J.B.P.)
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13
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Arya N, Vaish A, Zhao K, Rao H. Neural Mechanisms Underlying Breast Cancer Related Fatigue: A Systematic Review of Neuroimaging Studies. Front Neurosci 2021; 15:735945. [PMID: 34858127 PMCID: PMC8631399 DOI: 10.3389/fnins.2021.735945] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 10/19/2021] [Indexed: 12/03/2022] Open
Abstract
Introduction: Fatigue and cognitive dysfunction commonly co-occur in breast cancer patients and survivors. However, the underlying neural mechanism is not clear. We performed a systematic review of studies that used neuroimaging methods to investigate structural and functional changes in the brain associated with fatigue in breast cancer patients and survivors. Methods: We searched PubMed, Scopus, EmBase, and Cochrane CENTRAL from January 2009 to May 2021 for studies that reported brain neuroimaging findings in relationship to fatigue in breast cancer patients or survivors. Neuroimaging methods included magnetic resonance imaging (MRI), positron emission tomography (PET), and electroencephalogram (EEG). We summarized structural and functional neuroimaging changes associated with fatigue. Results: Of the 176 articles retrieved, ten MRI studies reported neuroimaging findings in relationship to fatigue. Together these studies compared 385 breast cancer patients or survivors to 205 controls. Fatigue was associated with reduced white matter integrity and increased glutamate in the insula but changes in gray matter volume were not associated with fatigue score. Nine of the ten studies found significant associations between fatigue and functional changes in the frontoparietal cortex. In response to memory and planning tasks, fatigue was associated with increased activations in several regions of the frontoparietal cortex, however, overall performance on tasks was not reduced. Fatigue was also associated with extensive changes in the connectivity of brain networks that filter endogenous signals (salience network), internal attention (default mode network), and external attention (dorsal attention network). Subcortical regions associated with fatigue included insula (interoception), superior colliculus (sleep regulation), and thalamus (alertness). Functional brain changes before initiation of chemotherapy were a better predictor of post-treatment fatigue than chemotherapy itself. Conclusions: Fatigue in breast cancer is associated with widespread functional changes of brain regions and networks that affect executive function including memory, planning, internal and external attention. Observed changes likely represent a compensatory mechanism through which breast cancer patients and survivors try to maintain adequate executive function. Breast cancer patients scheduled to undergo chemotherapy are at high risk for developing fatigue even before the start of treatment.
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Affiliation(s)
- Nisha Arya
- Department of Neurology, Center for Functional Neuroimaging, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Anya Vaish
- Department of Neurology, Center for Functional Neuroimaging, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Ke Zhao
- Department of Neurology, Center for Functional Neuroimaging, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Hengyi Rao
- Department of Neurology, Center for Functional Neuroimaging, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
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14
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Ma L, Tian L, Hu T, Jiang T, Zuo N. Development of Individual Variability in Brain Functional Connectivity and Capability across the Adult Lifespan. Cereb Cortex 2021; 31:3925-3938. [PMID: 33822909 DOI: 10.1093/cercor/bhab059] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 01/26/2021] [Accepted: 02/07/2021] [Indexed: 11/14/2022] Open
Abstract
Individual variability exists in both brain function and behavioral performance. However, changes in individual variability in brain functional connectivity and capability across adult development and aging have not yet been clearly examined. Based on resting-state functional magnetic resonance imaging data from a large cohort of participants (543 adults, aged 18-88 years), brain functional connectivity was analyzed to characterize the spatial distribution and differences in individual variability across the adult lifespan. Results showed high individual variability in the association cortex over the adult lifespan, whereas individual variability in the primary cortex was comparably lower in the initial stage but increased with age. Individual variability was also negatively correlated with the strength/number of short-, medium-, and long-range functional connections in the brain, with long-range connections playing a more critical role in increasing global individual variability in the aging brain. More importantly, in regard to specific brain regions, individual variability in the motor cortex was significantly correlated with differences in motor capability. Overall, we identified specific patterns of individual variability in brain functional structure during the adult lifespan and demonstrated that functional variability in the brain can reflect behavioral performance. These findings advance our understanding of the underlying principles of the aging brain across the adult lifespan and suggest how to characterize degenerating behavioral capability using imaging biomarkers.
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Affiliation(s)
- Liying Ma
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Lixia Tian
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
| | - Tianyu Hu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China.,Chinese Institute for Brain Research, Beijing 102206, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,Key Laboratory for Neuro-Information of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 625014, China.,Queensland Brain Institute, University of Queensland, Brisbane, QLD 4072, Australia
| | - Nianming Zuo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100190, China.,Chinese Institute for Brain Research, Beijing 102206, China
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15
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Mancini A, Ghiglieri V, Parnetti L, Calabresi P, Di Filippo M. Neuro-Immune Cross-Talk in the Striatum: From Basal Ganglia Physiology to Circuit Dysfunction. Front Immunol 2021; 12:644294. [PMID: 33953715 PMCID: PMC8091963 DOI: 10.3389/fimmu.2021.644294] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 03/16/2021] [Indexed: 01/02/2023] Open
Abstract
The basal ganglia network is represented by an interconnected group of subcortical nuclei traditionally thought to play a crucial role in motor learning and movement execution. During the last decades, knowledge about basal ganglia physiology significantly evolved and this network is now considered as a key regulator of important cognitive and emotional processes. Accordingly, the disruption of basal ganglia network dynamics represents a crucial pathogenic factor in many neurological and psychiatric disorders. The striatum is the input station of the circuit. Thanks to the synaptic properties of striatal medium spiny neurons (MSNs) and their ability to express synaptic plasticity, the striatum exerts a fundamental integrative and filtering role in the basal ganglia network, influencing the functional output of the whole circuit. Although it is currently established that the immune system is able to regulate neuronal transmission and plasticity in specific cortical areas, the role played by immune molecules and immune/glial cells in the modulation of intra-striatal connections and basal ganglia activity still needs to be clarified. In this manuscript, we review the available evidence of immune-based regulation of synaptic activity in the striatum, also discussing how an abnormal immune activation in this region could be involved in the pathogenesis of inflammatory and degenerative central nervous system (CNS) diseases.
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Affiliation(s)
- Andrea Mancini
- Section of Neurology, Department of Medicine and Surgery, Università degli Studi di Perugia, Perugia, Italy
| | | | - Lucilla Parnetti
- Section of Neurology, Department of Medicine and Surgery, Università degli Studi di Perugia, Perugia, Italy
| | - Paolo Calabresi
- Section of Neurology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.,Department of Neuroscience, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Massimiliano Di Filippo
- Section of Neurology, Department of Medicine and Surgery, Università degli Studi di Perugia, Perugia, Italy
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16
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Suo X, Lei D, Li N, Li W, Kemp GJ, Sweeney JA, Peng R, Gong Q. Disrupted morphological grey matter networks in early-stage Parkinson's disease. Brain Struct Funct 2021; 226:1389-1403. [PMID: 33825053 PMCID: PMC8096749 DOI: 10.1007/s00429-020-02200-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 12/16/2020] [Indexed: 02/05/2023]
Abstract
While previous structural-covariance studies have an advanced understanding of brain alterations in Parkinson's disease (PD), brain–behavior relationships have not been examined at the individual level. This study investigated the topological organization of grey matter (GM) networks, their relation to disease severity, and their potential imaging diagnostic value in PD. Fifty-four early-stage PD patients and 54 healthy controls (HC) underwent structural T1-weighted magnetic resonance imaging. GM networks were constructed by estimating interregional similarity in the distributions of regional GM volume using the Kullback–Leibler divergence measure. Results were analyzed using graph theory and network-based statistics (NBS), and the relationship to disease severity was assessed. Exploratory support vector machine analyses were conducted to discriminate PD patients from HC and different motor subtypes. Compared with HC, GM networks in PD showed a higher clustering coefficient (P = 0.014) and local efficiency (P = 0.014). Locally, nodal centralities in PD were lower in postcentral gyrus and temporal-occipital regions, and higher in right superior frontal gyrus and left putamen. NBS analysis revealed decreased morphological connections in the sensorimotor and default mode networks and increased connections in the salience and frontoparietal networks in PD. Connection matrices and graph-based metrics allowed single-subject classification of PD and HC with significant accuracy of 73.1 and 72.7%, respectively, while graph-based metrics allowed single-subject classification of tremor-dominant and akinetic–rigid motor subtypes with significant accuracy of 67.0%. The topological organization of GM networks was disrupted in early-stage PD in a way that suggests greater segregation of information processing. There is potential for application to early imaging diagnosis.
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Affiliation(s)
- Xueling Suo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, PR China
| | - Du Lei
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, PR China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Nannan Li
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Wenbin Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, PR China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - John A Sweeney
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, PR China
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Rong Peng
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, No. 37 Guo Xue Xiang, Chengdu, 610041, PR China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
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17
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Chen X, Liu M, Wu Z, Cheng H. Topological Abnormalities of Functional Brain Network in Early-Stage Parkinson's Disease Patients With Mild Cognitive Impairment. Front Neurosci 2020; 14:616872. [PMID: 33424546 PMCID: PMC7793724 DOI: 10.3389/fnins.2020.616872] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 11/18/2020] [Indexed: 11/17/2022] Open
Abstract
Recent studies have demonstrated structural and functional alterations in Parkinson's disease (PD) with mild cognitive impairment (MCI). However, the topological patterns of functional brain networks in newly diagnosed PD patients with MCI are unclear so far. In this study, we used functional magnetic resonance imaging (fMRI) and graph theory approaches to explore the functional brain network in 45 PD patients with MCI (PD-MCI), 22 PD patients without MCI (PD-nMCI), and 18 healthy controls (HC). We found that the PD-MCI, PD-nMCI, and HC groups exhibited a small-world architecture in the functional brain network. However, early-stage PD-MCI patients had decreased clustering coefficient, increased characteristic path length, and changed nodal centrality in the default mode network (DMN), control network (CN), somatomotor network (SMN), and visual network (VN), which might contribute to factors for MCI symptoms in PD patients. Our results demonstrated that PD-MCI patients were associated with disrupted topological organization in the functional network, thus providing a topological network insight into the role of information exchange in the underlying development of MCI symptoms in PD patients.
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Affiliation(s)
- Xiangbin Chen
- Department of TCM Internal Medicine, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, China
| | - Mengting Liu
- School of Music, Jimei University, Xiamen, China
| | - Zhibing Wu
- Department of TCM Internal Medicine, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, China
| | - Hao Cheng
- Department of Ultrasonography, Shaanxi Cancer Hospital Affiliated to Xi’an Jiaotong University, Xi’an, China
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18
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Kok JG, Leemans A, Teune LK, Leenders KL, McKeown MJ, Appel-Cresswell S, Kremer HPH, de Jong BM. Structural Network Analysis Using Diffusion MRI Tractography in Parkinson's Disease and Correlations With Motor Impairment. Front Neurol 2020; 11:841. [PMID: 32982909 PMCID: PMC7492210 DOI: 10.3389/fneur.2020.00841] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 07/07/2020] [Indexed: 11/13/2022] Open
Abstract
Functional impairment of spatially distributed brain regions in Parkinson's disease (PD) suggests changes in integrative and segregative network characteristics, for which novel analysis methods are available. To assess underlying structural network differences between PD patients and controls, we employed MRI T1 gray matter segmentation and diffusion MRI tractography to construct connectivity matrices to compare patients and controls with data originating from two different centers. In the Dutch dataset (Data-NL), 14 PD patients, and 15 healthy controls were analyzed, while 19 patients and 18 controls were included in the Canadian dataset (Data-CA). All subjects underwent T1 and diffusion-weighted MRI. Patients were assessed with Part 3 of the Unified Parkinson's Disease Rating Scale (UPDRS). T1 images were segmented using FreeSurfer, while tractography was performed using ExploreDTI. The regions of interest from the FreeSurfer segmentation were combined with the white matter streamline sets resulting from the tractography, to construct connectivity matrices. From these matrices, both global and local efficiencies were calculated, which were compared between the PD and control groups and related to the UPDRS motor scores. The connectivity matrices showed consistent patterns among the four groups, without significant differences between PD patients and control subjects, either in Data-NL or in Data-CA. In Data-NL, however, global and local efficiencies correlated negatively with UPDRS scores at both the whole-brain and the nodal levels [false discovery rate (FDR) 0.05]. At the nodal level, particularly, the posterior parietal cortex showed a negative correlation between UPDRS and local efficiency, while global efficiency correlated negatively with the UPDRS in the sensorimotor cortex. The spatial patterns of negative correlations between UPDRS and parameters for network efficiency seen in Data-NL suggest subtle structural differences in PD that were below sensitivity thresholds in Data-CA. These correlations are in line with previously described functional differences. The methodological approaches to detect such differences are discussed.
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Affiliation(s)
- Jelmer G Kok
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Laura K Teune
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Klaus L Leenders
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Martin J McKeown
- Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, BC, Canada
| | - Silke Appel-Cresswell
- Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, BC, Canada
| | - Hubertus P H Kremer
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Bauke M de Jong
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
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19
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Tessitore A, Cirillo M, De Micco R. Functional Connectivity Signatures of Parkinson's Disease. JOURNAL OF PARKINSONS DISEASE 2020; 9:637-652. [PMID: 31450512 PMCID: PMC6839494 DOI: 10.3233/jpd-191592] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Resting-state functional magnetic resonance imaging (RS-fMRI) studies have been extensively applied to analyze the pathophysiology of neurodegenerative disorders such as Parkinson’s disease (PD). In the present narrative review, we attempt to summarize the most recent RS-fMRI findings highlighting the role of brain networks re-organization and adaptation in the course of PD. We also discuss limitations and potential definition of early functional connectivity signatures to track and predict future PD progression. Understanding the neural correlates and potential predisposing factors of clinical progression and complication will be crucial to guide novel clinical trials and to foster preventive strategies.
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Affiliation(s)
- Alessandro Tessitore
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy.,MRI Research Center SUN-FISM, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Mario Cirillo
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy.,MRI Research Center SUN-FISM, University of Campania "Luigi Vanvitelli", Naples, Italy
| | - Rosa De Micco
- Department of Advanced Medical and Surgical Sciences, University of Campania "Luigi Vanvitelli", Naples, Italy.,MRI Research Center SUN-FISM, University of Campania "Luigi Vanvitelli", Naples, Italy
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20
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Frequency-Specific Changes of Resting Brain Activity in Parkinson’s Disease: A Machine Learning Approach. Neuroscience 2020; 436:170-183. [DOI: 10.1016/j.neuroscience.2020.01.049] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 01/30/2020] [Accepted: 01/31/2020] [Indexed: 12/24/2022]
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21
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Freeman DM, Lou D, Li Y, Martos SN, Wang Z. The conserved DNMT1-dependent methylation regions in human cells are vulnerable to neurotoxicant rotenone exposure. Epigenetics Chromatin 2020; 13:17. [PMID: 32178731 PMCID: PMC7076959 DOI: 10.1186/s13072-020-00338-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Accepted: 03/06/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Allele-specific DNA methylation (ASM) describes genomic loci that maintain CpG methylation at only one inherited allele rather than having coordinated methylation across both alleles. The most prominent of these regions are germline ASMs (gASMs) that control the expression of imprinted genes in a parent of origin-dependent manner and are associated with disease. However, our recent report reveals numerous ASMs at non-imprinted genes. These non-germline ASMs are dependent on DNA methyltransferase 1 (DNMT1) and strikingly show the feature of random, switchable monoallelic methylation patterns in the mouse genome. The significance of these ASMs to human health has not been explored. Due to their shared allelicity with gASMs, herein, we propose that non-traditional ASMs are sensitive to exposures in association with human disease. RESULTS We first explore their conservancy in the human genome. Our data show that our putative non-germline ASMs were in conserved regions of the human genome and located adjacent to genes vital for neuronal development and maturation. We next tested the hypothesized vulnerability of these regions by exposing human embryonic kidney cell HEK293 with the neurotoxicant rotenone for 24 h. Indeed,14 genes adjacent to our identified regions were differentially expressed from RNA-sequencing. We analyzed the base-resolution methylation patterns of the predicted non-germline ASMs at two neurological genes, HCN2 and NEFM, with potential to increase the risk of neurodegeneration. Both regions were significantly hypomethylated in response to rotenone. CONCLUSIONS Our data indicate that non-germline ASMs seem conserved between mouse and human genomes, overlap important regulatory factor binding motifs, and regulate the expression of genes vital to neuronal function. These results support the notion that ASMs are sensitive to environmental factors such as rotenone and may alter the risk of neurological disease later in life by disrupting neuronal development.
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Affiliation(s)
- Dana M Freeman
- Laboratory of Environmental Epigenomes, Department of Environmental Health & Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Dan Lou
- Laboratory of Environmental Epigenomes, Department of Environmental Health & Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Yanqiang Li
- Laboratory of Environmental Epigenomes, Department of Environmental Health & Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Suzanne N Martos
- Laboratory of Environmental Epigenomes, Department of Environmental Health & Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Zhibin Wang
- Laboratory of Environmental Epigenomes, Department of Environmental Health & Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
- The State Key Laboratory of Biocatalysis and Enzyme Engineering, School of Life Sciences, Hubei University, Wuhan, 430062, Hubei, China.
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
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22
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Munia TTK, Aviyente S. Graph-to-signal transformation based classification of functional connectivity brain networks. PLoS One 2019; 14:e0212470. [PMID: 31437168 PMCID: PMC6705775 DOI: 10.1371/journal.pone.0212470] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Accepted: 07/26/2019] [Indexed: 11/19/2022] Open
Abstract
Complex network theory has been successful at unveiling the topology of the brain and showing alterations to the network structure due to brain disease, cognitive function and behavior. Functional connectivity networks (FCNs) represent different brain regions as the nodes and the connectivity between them as the edges of a graph. Graph theoretic measures provide a way to extract features from these networks enabling subsequent characterization and discrimination of networks across conditions. However, these measures are constrained mostly to binary networks and highly dependent on the network size. In this paper, we propose a novel graph-to-signal transform that overcomes these shortcomings to extract features from functional connectivity networks. The proposed transformation is based on classical multidimensional scaling (CMDS) theory and transforms a graph into signals such that the Euclidean distance between the nodes of the network is preserved. In this paper, we propose to use the resistance distance matrix for transforming weighted functional connectivity networks into signals. Our results illustrate how well-known network structures transform into distinct signals using the proposed graph-to-signal transformation. We then compute well-known signal features on the extracted graph signals to discriminate between FCNs constructed across different experimental conditions. Based on our results, the signals obtained from the graph-to-signal transformation allow for the characterization of functional connectivity networks, and the corresponding features are more discriminative compared to graph theoretic measures.
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Affiliation(s)
- Tamanna Tabassum Khan Munia
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, United States of America
| | - Selin Aviyente
- Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, United States of America
- * E-mail:
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23
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Khambhati AN, Kahn AE, Costantini J, Ezzyat Y, Solomon EA, Gross RE, Jobst BC, Sheth SA, Zaghloul KA, Worrell G, Seger S, Lega BC, Weiss S, Sperling MR, Gorniak R, Das SR, Stein JM, Rizzuto DS, Kahana MJ, Lucas TH, Davis KA, Tracy JI, Bassett DS. Functional control of electrophysiological network architecture using direct neurostimulation in humans. Netw Neurosci 2019; 3:848-877. [PMID: 31410383 PMCID: PMC6663306 DOI: 10.1162/netn_a_00089] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Accepted: 04/14/2019] [Indexed: 01/30/2023] Open
Abstract
Chronically implantable neurostimulation devices are becoming a clinically viable option for treating patients with neurological disease and psychiatric disorders. Neurostimulation offers the ability to probe and manipulate distributed networks of interacting brain areas in dysfunctional circuits. Here, we use tools from network control theory to examine the dynamic reconfiguration of functionally interacting neuronal ensembles during targeted neurostimulation of cortical and subcortical brain structures. By integrating multimodal intracranial recordings and diffusion-weighted imaging from patients with drug-resistant epilepsy, we test hypothesized structural and functional rules that predict altered patterns of synchronized local field potentials. We demonstrate the ability to predictably reconfigure functional interactions depending on stimulation strength and location. Stimulation of areas with structurally weak connections largely modulates the functional hubness of downstream areas and concurrently propels the brain towards more difficult-to-reach dynamical states. By using focal perturbations to bridge large-scale structure, function, and markers of behavior, our findings suggest that stimulation may be tuned to influence different scales of network interactions driving cognition. Brain stimulation devices capable of perturbing the physiological state of neural systems are rapidly gaining popularity for their potential to treat neurological and psychiatric disease. A root problem is that underlying dysfunction spans a large-scale network of brain regions, requiring the ability to control the complex interactions between multiple brain areas. Here, we use tools from network control theory to examine the dynamic reconfiguration of functionally interacting neuronal ensembles during targeted neurostimulation of cortical and subcortical brain structures. We demonstrate the ability to predictably reconfigure patterns of interactions between functional brain areas by modulating the strength and location of stimulation. Our findings have high significance for designing stimulation protocols capable of modulating distributed neural circuits in the human brain.
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Affiliation(s)
- Ankit N Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Ari E Kahn
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Julia Costantini
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Youssef Ezzyat
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Ethan A Solomon
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert E Gross
- Department of Neurosurgery, Emory University Hospital, Atlanta, GA, USA
| | - Barbara C Jobst
- Department of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Kareem A Zaghloul
- Surgical Neurology Branch, National Institutes of Health, Bethesda, MD, USA
| | | | - Sarah Seger
- Department of Neurosurgery, University of Texas, Southwestern Medical Center, Dallas, TX, USA
| | - Bradley C Lega
- Department of Neurosurgery, University of Texas, Southwestern Medical Center, Dallas, TX, USA
| | - Shennan Weiss
- Department of Neurology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Michael R Sperling
- Department of Neurology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Richard Gorniak
- Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Sandhitsu R Das
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Joel M Stein
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel S Rizzuto
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael J Kahana
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Timothy H Lucas
- Department of Neurosurgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Kathryn A Davis
- Department of Neurology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Joseph I Tracy
- Department of Neurology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
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24
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Cognitive performance in mid-stage Parkinson's disease: functional connectivity under chronic antiparkinson treatment. Brain Imaging Behav 2019; 13:200-209. [PMID: 28942477 DOI: 10.1007/s11682-017-9765-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Cognitive impairment in Parkinson's disease (PD) is related to the reorganization of brain topology. Although drug challenge studies have proven how levodopa treatment can modulate functional connectivity in brain circuits, the role of chronic dopaminergic therapy on cognitive status and functional connectivity has never been investigated. We sought to characterize brain functional topology in mid-stage PD patients under chronic antiparkinson treatment and explore the presence of correlation between reorganization of brain architecture and specific cognitive deficits. We explored networks topology and functional connectivity in 16 patients with PD and 16 matched controls through a graph theoretical analysis of resting state-functional MRI data, and evaluated the relationships between network metrics and cognitive performance. PD patients showed a preserved small-world network topology but a lower clustering coefficient in comparison with healthy controls. Locally, PD patients showed lower degree of connectivity and local efficiency in many hubs corresponding to functionally relevant areas. Four disconnected subnetworks were also identified in regions responsible for executive control, sensory-motor control and planning, motor coordination and visual elaboration. Executive functions and information processing speed were directly correlated with degree of connectivity and local efficiency in frontal, parietal and occipital areas. While functional reorganization appears in both motor and cognitive areas, the clinical expression of network imbalance seems to be partially compensated by the chronic levodopa treatment with regards to the motor but not to the cognitive performance. In a context of reduced network segregation, the presence of higher local efficiency in hubs regions correlates with a better cognitive performance.
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25
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Maidan I, Jacob Y, Giladi N, Hausdorff JM, Mirelman A. Altered organization of the dorsal attention network is associated with freezing of gait in Parkinson's disease. Parkinsonism Relat Disord 2019; 63:77-82. [DOI: 10.1016/j.parkreldis.2019.02.036] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Revised: 02/20/2019] [Accepted: 02/22/2019] [Indexed: 01/29/2023]
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26
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Cerebellar resting-state functional connectivity in Parkinson's disease and multiple system atrophy: Characterization of abnormalities and potential for differential diagnosis at the single-patient level. NEUROIMAGE-CLINICAL 2019; 22:101720. [PMID: 30785051 PMCID: PMC6383182 DOI: 10.1016/j.nicl.2019.101720] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Revised: 02/03/2019] [Accepted: 02/12/2019] [Indexed: 01/15/2023]
Abstract
Background Recent studies using resting-state functional connectivity and machine-learning to distinguish patients with neurodegenerative diseases from other groups of subjects show promising results. This approach has not been tested to discriminate between Parkinson's disease (PD) and multiple system atrophy (MSA) patients. Objectives Our first aim is to characterize possible abnormalities in resting-state functional connectivity between the cerebellum and a set of intrinsic-connectivity brain networks and between the cerebellum and different regions of the striatum in PD and MSA. The second objective of this study is to assess the potential of cerebellar connectivity measures to distinguish between PD and MSA patients at the single-patient level. Methods Fifty-nine healthy controls, 62 PD patients, and 30 MSA patients underwent resting-state functional MRI with a 3T scanner. Independent component analysis and dual regression were used to define seven resting-state networks of interest. To assess striatal connectivity, a seed-to-voxel approach was used after dividing the striatum into six regions bilaterally. Measures of cerebellar-brain network and cerebellar-striatal connectivity were then used as features in a support vector machine to discriminate between PD and MSA patients. Results MSA patients displayed reduced cerebellar connectivity with different brain networks and with the striatum compared with PD patients and with controls. The classification procedure achieved an overall accuracy of 77.17% with 83.33% of the MSA subjects and 74.19% of the PD patients correctly classified. Conclusion Our findings suggest that measures of cerebellar functional connectivity have the potential to distinguish between PD and MSA patients. Reduced cerebellar functional connectivity in MSA compared with healthy controls. Reduced cerebellar-striatal functional connectivity in MSA compared with PD. Reduced connectivity between cerebellum and brain networks in MSA compared with PD. Cerebellar connectivity might help discriminate between MSA and PD patients.
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27
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Kang MG, Yun SJ, Shin HI, Kim E, Lee HH, Oh BM, Seo HG. Effects of robot-assisted gait training in patients with Parkinson's disease: study protocol for a randomized controlled trial. Trials 2019; 20:15. [PMID: 30616685 PMCID: PMC6322303 DOI: 10.1186/s13063-018-3123-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Accepted: 12/10/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Robot-assisted gait training (RAGT) was developed to restore gait function by promoting neuroplasticity through repetitive locomotor training and has been utilized in gait training. However, contradictory outcomes of RAGT have been reported for patients with Parkinson's disease (PD). In addition, the mechanism of the RAGT treatment effect is still unknown. This study aims to investigate the effects of RAGT on gait velocity in patients with PD and to unveil the mechanisms of these effects. METHODS This is a prospective, single-blind, single-center, randomized controlled trial. Eligible participants will be randomly allocated to: 1) a Walkbot-S™ RAGT group or 2) a treadmill training group. The participants will receive three 45-min sessions of each intervention per week for 4 weeks. Gait speed during RAGT will be targeted to the maximal speed depending on the participant's height; the same principle will be applied to the treadmill training group to match the training intensity. The primary outcome measure is gait speed measured by the 10-Meter Walk Test at a comfortable pace under single-task conditions. Secondary outcomes include dual-task interference, the Berg Balance Scale, Timed Up and Go test, the Korean version of the Falls Efficacy Scale-International, New Freezing of Gait Questionnaire, Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale, and functional connectivity measured by resting-state functional magnetic resonance imaging. Baseline assessments (T0) will be conducted to acquire clinical characteristics and outcome measure values before the intervention. Postintervention assessments (T1) will compare immediate efficacies within 3 days after the intervention. Follow-up assessments (T2) will be conducted 1 month after the intervention. Considering an alpha of 0.05 and a power of 80%, the total number of participants to be recruited is 44. DISCUSSION This study will reveal the effect of RAGT using an exoskeletal robot, not only on gait speed, but also on gait automaticity, balance function, fall risk, quality of life, and disease severity. In addition, the study will shed new light on the mechanism of the RAGT effect by evaluating changes in gait automaticity and brain functional networks. TRIAL REGISTRATION ClinicalTrials.gov, NCT03490578 . Registered on 21 March 2018.
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Affiliation(s)
- Min-Gu Kang
- Department of Rehabilitation Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, Republic of Korea
| | - Seo Jung Yun
- Department of Rehabilitation Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, Republic of Korea
| | - Hyun Iee Shin
- Department of Rehabilitation Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, Republic of Korea
| | - Eunkyung Kim
- Department of Rehabilitation Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, Republic of Korea
| | - Hyun Haeng Lee
- Department of Rehabilitation Medicine, Konkuk University Medical Center, Seoul, Republic of Korea
| | - Byung-Mo Oh
- Department of Rehabilitation Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, Republic of Korea
| | - Han Gil Seo
- Department of Rehabilitation Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, Republic of Korea.
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28
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Burciu RG, Vaillancourt DE. Imaging of Motor Cortex Physiology in Parkinson's Disease. Mov Disord 2018; 33:1688-1699. [PMID: 30280416 PMCID: PMC6261674 DOI: 10.1002/mds.102] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2018] [Revised: 06/26/2018] [Accepted: 06/29/2018] [Indexed: 12/13/2022] Open
Abstract
There is abundant evidence that the pathophysiology of Parkinson's disease (PD) is not confined to the nigrostriatal dopaminergic pathway but propagates along the cortico‐basal ganglia‐thalamo‐cortical neural network. A critical node in this functional circuit impacted by PD is the primary motor cortex (M1), which plays a key role in generating neural impulses that regulate movements. The past several decades have lay witness to numerous in vivo neuroimaging techniques that provide a window into the function and structure of M1. A consistent observation from numerous studies is that during voluntary movement, but also at rest, the functional activity of M1 is altered in PD relative to healthy individuals, and it relates to many of the motor signs. Although this abnormal functional activity can be partially restored with acute dopaminergic medication, it continues to deteriorate with disease progression and may predate structural degeneration of M1. The current review discusses the evidence that M1 is fundamental to the pathophysiology of PD, as measured by neuroimaging techniques such as positron emission tomography, single‐photon emission computed tomography, electroencephalography, magnetoencephalography, and functional and structural MRI. Although novel treatments that target the cortex will not cure PD, they could significantly slow down and alter the progressive course of the disease and thus improve clinical care for this degenerative disease. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society
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Affiliation(s)
- Roxana G Burciu
- Department of Kinesiology and Applied Physiology, University of Delaware, Newark, Delaware, USA
| | - David E Vaillancourt
- Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, Florida, USA.,Department of Neurology, University of Florida, Gainesville, Florida, USA.,Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA
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29
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Marshall LJ, Willett C. Parkinson's disease research: adopting a more human perspective to accelerate advances. Drug Discov Today 2018; 23:1950-1961. [PMID: 30240875 DOI: 10.1016/j.drudis.2018.09.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 08/20/2018] [Accepted: 09/12/2018] [Indexed: 12/21/2022]
Abstract
Parkinson's disease (PD) affects 1% of the population over 60 years old and, with global increases in the aging population, presents huge economic and societal burdens. The etiology of PD remains unknown; most cases are idiopathic, presumed to result from genetic and environmental risk factors. Despite 200 years since the first description of PD, the mechanisms behind initiation and progression of the characteristic neurodegenerative processes are not known. Here, we review progress and limitations of the multiple PD animal models available and identify advances that could be implemented to better understand pathological processes, improve disease outcome, and reduce dependence on animal models. Lessons learned from reducing animal use in PD research could serve as guideposts for wider biomedical research.
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Affiliation(s)
- Lindsay J Marshall
- Humane Society International, The Humane Society of the United States, 700 Professional Drive, Gaithersburg, MD 20879, USA
| | - Catherine Willett
- Humane Society International, The Humane Society of the United States, 700 Professional Drive, Gaithersburg, MD 20879, USA.
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30
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Lei H, Huang Z, Zhou F, Elazab A, Tan EL, Li H, Qin J, Lei B. Parkinson's Disease Diagnosis via Joint Learning From Multiple Modalities and Relations. IEEE J Biomed Health Inform 2018; 23:1437-1449. [PMID: 30183649 DOI: 10.1109/jbhi.2018.2868420] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Parkinson's disease (PD) is a neurodegenerative progressive disease that mainly affects the motor systems of patients. To slow this disease deterioration, early and accurate diagnosis of PD is an effective way, which alleviates mental and physical sufferings by clinical intervention. In this paper, we propose a joint regression and classification framework for PD diagnosis via magnetic resonance and diffusion tensor imaging data. Specifically, we devise a unified multitask feature selection model to explore multiple relationships among features, samples, and clinical scores. We regress four clinical variables of depression, sleep, olfaction, cognition scores, as well as perform the classification of PD disease from the multimodal data. The multitask model explores the relationships at the level of clinical scores, image features, and subjects, to select the most informative and diseased-related features for diagnosis. The proposed method is evaluated on the public Parkinson's progression markers initiative dataset. The extensive experimental results show that the multitask framework can effectively boost the performance of regression and classification and outperforms other state-of-the-art methods. The computerized predictions of clinical scores and label for PD diagnosis may offer quantitative reference for decision support as well.
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31
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Vriend C, van den Heuvel OA, Berendse HW, van der Werf YD, Douw L. Global and Subnetwork Changes of the Structural Connectome in de novo Parkinson’s Disease. Neuroscience 2018; 386:295-308. [DOI: 10.1016/j.neuroscience.2018.06.050] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 05/25/2018] [Accepted: 06/27/2018] [Indexed: 12/21/2022]
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32
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Guo T, Guan X, Zeng Q, Xuan M, Gu Q, Huang P, Xu X, Zhang M. Alterations of Brain Structural Network in Parkinson's Disease With and Without Rapid Eye Movement Sleep Behavior Disorder. Front Neurol 2018; 9:334. [PMID: 29867741 PMCID: PMC5958180 DOI: 10.3389/fneur.2018.00334] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2017] [Accepted: 04/26/2018] [Indexed: 11/29/2022] Open
Abstract
Background and objective Rapid eye movement sleep behavior disorder (RBD) has a strong association with alpha synucleinpathies such as Parkinson’s disease (PD) and PD patients with RBD tend to have a poorer prognosis. However, we still know little about the pathogenesis of RBD in PD. Therefore, we aim to detect the alterations of structural correlation network (SCN) in PD patients with and without RBD. Materials and methods A total of 191 PD patients, including 51 patients with possible RBD (pRBD) and 140 patients with non-possible RBD, and 76 normal controls were included in the present study. Structural brain networks were constructed by thresholding gray matter volume correlation matrices of 116 regions and analyzed using graph theoretical approaches. Results There was no difference in global properties among the three groups. Significant enhanced regional nodal measures in limbic system, frontal-temporal regions, and occipital regions and decreased nodal measures in cerebellum were found in PD patients with pRBD (PD-pRBD) compared with PD patients without pRBD. Besides, nodes in frontal lobe, temporal lobe, and limbic system were served as hubs in both two PD groups, and PD-pRBD exhibited additionally recruited hubs in limbic regions. Conclusion Based on the SCN analysis, we found PD-pRBD exhibited a reorganization of nodal properties as well as the remapping of the hub distribution in whole brain especially in limbic system, which may shed light to the pathophysiology of PD with RBD.
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Affiliation(s)
- Tao Guo
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Qiaoling Zeng
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Min Xuan
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Quanquan Gu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
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33
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The reorganization of functional architecture in the early-stages of Parkinson's disease. Parkinsonism Relat Disord 2018; 50:61-68. [DOI: 10.1016/j.parkreldis.2018.02.013] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Revised: 01/18/2018] [Accepted: 02/07/2018] [Indexed: 01/01/2023]
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34
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Hohenfeld C, Werner CJ, Reetz K. Resting-state connectivity in neurodegenerative disorders: Is there potential for an imaging biomarker? Neuroimage Clin 2018; 18:849-870. [PMID: 29876270 PMCID: PMC5988031 DOI: 10.1016/j.nicl.2018.03.013] [Citation(s) in RCA: 139] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2017] [Revised: 02/06/2018] [Accepted: 03/14/2018] [Indexed: 12/14/2022]
Abstract
Biomarkers in whichever modality are tremendously important in diagnosing of disease, tracking disease progression and clinical trials. This applies in particular for disorders with a long disease course including pre-symptomatic stages, in which only subtle signs of clinical progression can be observed. Magnetic resonance imaging (MRI) biomarkers hold particular promise due to their relative ease of use, cost-effectiveness and non-invasivity. Studies measuring resting-state functional MR connectivity have become increasingly common during recent years and are well established in neuroscience and related fields. Its increasing application does of course also include clinical settings and therein neurodegenerative diseases. In the present review, we critically summarise the state of the literature on resting-state functional connectivity as measured with functional MRI in neurodegenerative disorders. In addition to an overview of the results, we briefly outline the methods applied to the concept of resting-state functional connectivity. While there are many different neurodegenerative disorders cumulatively affecting a substantial number of patients, for most of them studies on resting-state fMRI are lacking. Plentiful amounts of papers are available for Alzheimer's disease (AD) and Parkinson's disease (PD), but only few works being available for the less common neurodegenerative diseases. This allows some conclusions on the potential of resting-state fMRI acting as a biomarker for the aforementioned two diseases, but only tentative statements for the others. For AD, the literature contains a relatively strong consensus regarding an impairment of the connectivity of the default mode network compared to healthy individuals. However, for AD there is no considerable documentation on how that alteration develops longitudinally with the progression of the disease. For PD, the available research points towards alterations of connectivity mainly in limbic and motor related regions and networks, but drawing conclusions for PD has to be done with caution due to a relative heterogeneity of the disease. For rare neurodegenerative diseases, no clear conclusions can be drawn due to the few published results. Nevertheless, summarising available data points towards characteristic connectivity alterations in Huntington's disease, frontotemporal dementia, dementia with Lewy bodies, multiple systems atrophy and the spinocerebellar ataxias. Overall at this point in time, the data on AD are most promising towards the eventual use of resting-state fMRI as an imaging biomarker, although there remain issues such as reproducibility of results and a lack of data demonstrating longitudinal changes. Improved methods providing more precise classifications as well as resting-state network changes that are sensitive to disease progression or therapeutic intervention are highly desirable, before routine clinical use could eventually become a reality.
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Affiliation(s)
- Christian Hohenfeld
- RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany
| | - Cornelius J Werner
- RWTH Aachen University, Department of Neurology, Aachen, Germany; RWTH Aachen University, Section Interdisciplinary Geriatrics, Aachen, Germany
| | - Kathrin Reetz
- RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich GmbH and RWTH Aachen University, Aachen, Germany.
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35
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Zhang J, Zhang Y, Wang L, Sang L, Yang J, Yan R, Li P, Wang J, Qiu M. Disrupted structural and functional connectivity networks in ischemic stroke patients. Neuroscience 2017; 364:212-225. [PMID: 28918259 DOI: 10.1016/j.neuroscience.2017.09.009] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Revised: 09/04/2017] [Accepted: 09/05/2017] [Indexed: 01/10/2023]
Abstract
Local lesions caused by stroke may result in extensive structural and functional reorganization in the brain. Previous studies of this phenomenon have focused on specific brain networks. Here, we aimed to discover abnormalities in whole-brain networks and to explore the decoupling between structural and functional connectivity in patients with stroke. Fifteen ischemic stroke patients and 23 normal controls (NCs) were recruited in this study. A graph theoretical analysis was employed to investigate the abnormal topological properties of structural and functional brain networks in patients with stroke. Both patients with stroke and NCs exhibited small-world organization in brain networks. However, compared to NCs, patients with stroke exhibited abnormal global properties characterized by a higher characteristic path length and lower global efficiency. Furthermore, patients with stroke showed altered nodal characteristics, primarily in certain motor- and cognition-related regions. Positive correlations between the nodal degree of the inferior parietal lobule and the Fugl-Meyer Assessment (FMA) score and between the nodal betweenness centrality of the posterior cingulate gyrus (PCG) and immediate recall were observed in patients with stroke. Most importantly, the strength of the structural-functional connectivity network coupling was decreased, and the coupling degree was related to the FMA score of patients, suggesting that decoupling may provide a novel biomarker for the assessment of motor impairment in patients with stroke. Thus, the topological organization of brain networks is altered in patients with stroke, and our results provide insights into the structural and functional organization of the brain after stroke from the viewpoint of network topology.
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Affiliation(s)
- Jingna Zhang
- Department of Medical Imaging, College of Biomedical Engineering, Third Military Medical University, 30 Gaotanyan Road, Chongqing 40038, China
| | - Ye Zhang
- Department of Medical Imaging, College of Biomedical Engineering, Third Military Medical University, 30 Gaotanyan Road, Chongqing 40038, China
| | - Li Wang
- Department of Medical Imaging, College of Biomedical Engineering, Third Military Medical University, 30 Gaotanyan Road, Chongqing 40038, China
| | - Linqiong Sang
- Department of Medical Imaging, College of Biomedical Engineering, Third Military Medical University, 30 Gaotanyan Road, Chongqing 40038, China
| | - Jun Yang
- Department of Radiology, Southwest Hospital, Third Military Medical University, 30 Gaotanyan Road, Chongqing 400038, China
| | - Rubing Yan
- Department of Rehabilitation, Southwest Hospital, Third Military Medical University, Chongqing 400038, China
| | - Pengyue Li
- Department of Medical Imaging, College of Biomedical Engineering, Third Military Medical University, 30 Gaotanyan Road, Chongqing 40038, China
| | - Jian Wang
- Department of Radiology, Southwest Hospital, Third Military Medical University, 30 Gaotanyan Road, Chongqing 400038, China.
| | - Mingguo Qiu
- Department of Medical Imaging, College of Biomedical Engineering, Third Military Medical University, 30 Gaotanyan Road, Chongqing 40038, China.
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36
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Kam TE, Suk HI, Lee SW. Multiple functional networks modeling for autism spectrum disorder diagnosis. Hum Brain Mapp 2017; 38:5804-5821. [PMID: 28845892 DOI: 10.1002/hbm.23769] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2016] [Revised: 07/25/2017] [Accepted: 08/07/2017] [Indexed: 11/07/2022] Open
Abstract
Despite countless studies on autism spectrum disorder (ASD), diagnosis relies on specific behavioral criteria and neuroimaging biomarkers for the disorder are still relatively scarce and irrelevant for diagnostic workup. Many researchers have focused on functional networks of brain activities using resting-state functional magnetic resonance imaging (rsfMRI) to diagnose brain diseases, including ASD. Although some existing methods are able to reveal the abnormalities in functional networks, they are either highly dependent on prior assumptions for modeling these networks or do not focus on latent functional connectivities (FCs) by considering discriminative relations among FCs in a nonlinear way. In this article, we propose a novel framework to model multiple networks of rsfMRI with data-driven approaches. Specifically, we construct large-scale functional networks with hierarchical clustering and find discriminative connectivity patterns between ASD and normal controls (NC). We then learn features and classifiers for each cluster through discriminative restricted Boltzmann machines (DRBMs). In the testing phase, each DRBM determines whether a test sample is ASD or NC, based on which we make a final decision with a majority voting strategy. We assess the diagnostic performance of the proposed method using public datasets and describe the effectiveness of our method by comparing it to competing methods. We also rigorously analyze FCs learned by DRBMs on each cluster and discover dominant FCs that play a major role in discriminating between ASD and NC. Hum Brain Mapp 38:5804-5821, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Tae-Eui Kam
- Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
| | - Heung-Il Suk
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Seong-Whan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
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37
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Abstract
Important advances are afoot in the field of neurosurgery-particularly in the realms of deep brain stimulation (DBS), deep brain manipulation (DBM), and the newly introduced refinement "closed-loop" deep brain stimulation (CLDBS). Use of closed-loop technology will make both DBS and DBM more precise as procedures and will broaden their indications. CLDBS utilizes as feedback a variety of sources of electrophysiological and neurochemical afferent information about the function of the brain structures to be treated or studied. The efferent actions will be either electric, i.e. the classic excitatory or inhibitory ones, or micro-injection of such things as neural proteins and transmitters, neural grafts, implants of pluripotent stem cells or mesenchymal stem cells, and some variants of gene therapy. The pathologies to be treated, beside Parkinson's disease and movement disorders, include repair of neural tissues, neurodegenerative pathologies, psychiatric and behavioral dysfunctions, i.e. schizophrenia in its various guises, bipolar disorders, obesity, anorexia, drug addiction, and alcoholism. The possibility of using these new modalities to treat a number of cognitive dysfunctions is also under consideration. Because the DBS-CLDBS technology brings about a cross-fertilization between scientific investigation and surgical practice, it will also contribute to an enhanced understanding of brain function.
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Affiliation(s)
- Stylianos Nicolaidis
- Retired from Collège de France and CNRS, 84 Boulevard du Maréchal Joffre, 92340 Bourg-la-Reine, France.
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38
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Smitha KA, Akhil Raja K, Arun KM, Rajesh PG, Thomas B, Kapilamoorthy TR, Kesavadas C. Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks. Neuroradiol J 2017; 30:305-317. [PMID: 28353416 DOI: 10.1177/1971400917697342] [Citation(s) in RCA: 319] [Impact Index Per Article: 45.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
The inquisitiveness about what happens in the brain has been there since the beginning of humankind. Functional magnetic resonance imaging is a prominent tool which helps in the non-invasive examination, localisation as well as lateralisation of brain functions such as language, memory, etc. In recent years, there is an apparent shift in the focus of neuroscience research to studies dealing with a brain at 'resting state'. Here the spotlight is on the intrinsic activity within the brain, in the absence of any sensory or cognitive stimulus. The analyses of functional brain connectivity in the state of rest have revealed different resting state networks, which depict specific functions and varied spatial topology. However, different statistical methods have been introduced to study resting state functional magnetic resonance imaging connectivity, yet producing consistent results. In this article, we introduce the concept of resting state functional magnetic resonance imaging in detail, then discuss three most widely used methods for analysis, describe a few of the resting state networks featuring the brain regions, associated cognitive functions and clinical applications of resting state functional magnetic resonance imaging. This review aims to highlight the utility and importance of studying resting state functional magnetic resonance imaging connectivity, underlining its complementary nature to the task-based functional magnetic resonance imaging.
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Affiliation(s)
- K A Smitha
- 1 Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Science and Technology, India
| | - K Akhil Raja
- 1 Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Science and Technology, India
| | - K M Arun
- 1 Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Science and Technology, India
| | - P G Rajesh
- 2 Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, India
| | - Bejoy Thomas
- 1 Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Science and Technology, India
| | - T R Kapilamoorthy
- 1 Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Science and Technology, India
| | - Chandrasekharan Kesavadas
- 1 Department of Imaging Sciences and Interventional Radiology, Sree Chitra Tirunal Institute for Medical Science and Technology, India
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Algarin C, Karunakaran KD, Reyes S, Morales C, Lozoff B, Peirano P, Biswal B. Differences on Brain Connectivity in Adulthood Are Present in Subjects with Iron Deficiency Anemia in Infancy. Front Aging Neurosci 2017; 9:54. [PMID: 28326037 PMCID: PMC5339238 DOI: 10.3389/fnagi.2017.00054] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2016] [Accepted: 02/23/2017] [Indexed: 11/13/2022] Open
Abstract
Iron deficiency continues to be the most prevalent micronutrient deficit worldwide. Since iron is involved in several processes including myelination, dopamine neurotransmission and neuronal metabolism, the presence of iron deficiency anemia (IDA) in infancy relates to long-lasting neurofunctional effects. There is scarce data regarding whether these effects would extend to former iron deficient anemic human adults. Resting state functional magnetic resonance imaging (fMRI) is a novel technique to explore patterns of functional connectivity. Default Mode Network (DMN), one of the resting state networks, is deeply involved in memory, social cognition and self-referential processes. The four core regions consistently identified in the DMN are the medial prefrontal cortex, posterior cingulate/retrosplenial cortex and left and right inferior parietal cortex. Therefore to investigate the DMN in former iron deficient anemic adults is a particularly useful approach to elucidate de long term effects on functional brain. We conducted this research to explore the connection between IDA in infancy and altered patterns of resting state brain functional networks in young adults. Resting-state fMRI studies were performed to 31 participants that belong to a follow-up study since infancy. Of them, 14 participants were former iron deficient anemic in infancy and 17 were controls, with mean age of 21.5 years (±1.5) and 54.8% were males. Resting-state fMRI protocol was used and the data was analyzed using the seed based connectivity statistical analysis to assess the DMN. We found that compared to controls, former iron deficient anemic subjects showed posterior DMN decreased connectivity to the left posterior cingulate cortex (PCC), whereas they exhibited increased anterior DMN connectivity to the right PCC. Differences between groups were also apparent in the left medial frontal gyrus, with former iron deficient anemic participants having increased connectivity with areas included in DMN and dorsal attention networks. These preliminary results suggest different patterns of functional connectivity between former iron deficient anemic and control young adults. Indeed, IDA in infancy, a common nutritional problem among human infants, may turn out to be important for understanding the mechanisms of cognitive alterations, common in adulthood.
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Affiliation(s)
- Cecilia Algarin
- Sleep and Neurofunctional Laboratory, Institute of Nutrition and Food Technology, University of Chile Santiago, Chile
| | | | - Sussanne Reyes
- Sleep and Neurofunctional Laboratory, Institute of Nutrition and Food Technology, University of Chile Santiago, Chile
| | - Cristian Morales
- Sleep and Neurofunctional Laboratory, Institute of Nutrition and Food Technology, University of Chile Santiago, Chile
| | - Betsy Lozoff
- Center for Human Growth and Development, University of Michigan Ann Arbor, MI, USA
| | - Patricio Peirano
- Sleep and Neurofunctional Laboratory, Institute of Nutrition and Food Technology, University of Chile Santiago, Chile
| | - Bharat Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights Newark, NJ, USA
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40
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Llufriu S, Martinez-Heras E, Solana E, Sola-Valls N, Sepulveda M, Blanco Y, Martinez-Lapiscina EH, Andorra M, Villoslada P, Prats-Galino A, Saiz A. Structural networks involved in attention and executive functions in multiple sclerosis. NEUROIMAGE-CLINICAL 2016; 13:288-296. [PMID: 28050344 PMCID: PMC5192049 DOI: 10.1016/j.nicl.2016.11.026] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Revised: 11/22/2016] [Accepted: 11/26/2016] [Indexed: 12/18/2022]
Abstract
Attention and executive deficits are disabling symptoms in multiple sclerosis (MS) that have been related to disconnection mechanisms. We aimed to investigate changes in structural connectivity in MS and their association with attention and executive performance applying an improved framework that combines high order probabilistic tractography and anatomical exclusion criteria postprocessing. We compared graph theory metrics of structural networks and fractional anisotropy (FA) of white matter (WM) connections or edges between 72 MS subjects and 38 healthy volunteers (HV) and assessed their correlation with cognition. Patients displayed decreased network transitivity, global efficiency and increased path length compared with HV (p < 0.05, corrected). Also, nodal strength was decreased in 26 of 84 gray matter regions. The distribution of nodes with stronger connections or hubs of the network was similar among groups except for the right pallidum and left insula, which became hubs in patients. MS subjects presented reduced edge FA widespread in the network, while FA was increased in 24 connections (p < 0.05, corrected). Decreased integrity of frontoparietal networks, deep gray nuclei and insula correlated with worse attention and executive performance (r between 0.38 and 0.55, p < 0.05, corrected). Contrarily, higher strength in the right transverse temporal cortex and increased FA of several connections (mainly from cingulate, frontal and occipital cortices) were associated with worse functioning (r between − 0.40 and − 0.47, p < 0.05 corrected). In conclusion, structural brain connectivity is disturbed in MS due to widespread impairment of WM connections and gray matter structures. The increased edge connectivity suggests the presence of reorganization mechanisms at the structural level. Importantly, attention and executive performance relates to frontoparietal networks, deep gray nuclei and insula. These results support the relevance of network integrity to maintain optimal cognitive skills. High order tractography and anatomical exclusion criteria improve connectivity analyses. Structural connectivity is less efficient in multiple sclerosis. Attentional and executive functions relate to integrity of strategic networks. Increased connectivity suggests structural reorganization mechanisms.
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Affiliation(s)
- Sara Llufriu
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Eloy Martinez-Heras
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Elisabeth Solana
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Nuria Sola-Valls
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Maria Sepulveda
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Yolanda Blanco
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Elena H Martinez-Lapiscina
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Magi Andorra
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Pablo Villoslada
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
| | - Alberto Prats-Galino
- Laboratory of Surgical NeuroAnatomy (LSNA), Facultat de Medicina, Universitat de Barcelona, Barcelona, Spain
| | - Albert Saiz
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Barcelona, Spain
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41
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Bell PT, Shine JM. Subcortical contributions to large-scale network communication. Neurosci Biobehav Rev 2016; 71:313-322. [DOI: 10.1016/j.neubiorev.2016.08.036] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2015] [Accepted: 08/29/2016] [Indexed: 01/20/2023]
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42
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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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