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Holtbernd F, Hohenfeld C, Oertel WH, Knake S, Sittig E, Romanzetti S, Heidbreder A, Michels J, Dogan I, Schulz JB, Schiefer J, Janzen A, Reetz K. The functional brain connectome in isolated rapid eye movement sleep behavior disorder and Parkinson's disease. Sleep Med 2024; 117:184-191. [PMID: 38555837 DOI: 10.1016/j.sleep.2024.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 02/29/2024] [Accepted: 03/10/2024] [Indexed: 04/02/2024]
Abstract
BACKGROUND Isolated rapid-eye-movement behavior disorder (iRBD) often precedes the development of alpha-synucleinopathies such as Parkinson's disease (PD). Magnetic resonance imaging (MRI) studies have revealed structural brain alterations in iRBD partially resembling those observed in PD. However, relatively little is known about whole-brain functional brain alterations in iRBD. Here, we characterize the functional brain connectome of iRBD compared with PD patients and healthy controls (HC) using resting-state functional MRI (rs-fMRI). METHODS Eighteen iRBD subjects (67.3 ± 6.6 years), 18 subjects with PD (65.4 ± 5.8 years), and 39 age- and sex-matched HC (64.4 ± 9.2 years) underwent rs-fMRI at 3 T. We applied a graph theoretical approach to analyze the brain functional connectome at the global and regional levels. Data were analyzed using both frequentist and Bayesian statistics. RESULTS Global connectivity was largely preserved in iRBD and PD individuals. In contrast, both disease groups displayed altered local connectivity mainly in the motor network, temporal cortical regions including the limbic system, and the visual system. There were some group specific alterations, and connectivity changes were pronounced in PD individuals. Overall, however, there was a good agreement of the connectome changes observed in both disease groups. CONCLUSIONS This study provides evidence for widespread functional brain connectivity alterations in iRBD, including motor circuitry, despite normal motor function. Connectome alterations showed substantial resemblance with those observed in PD, underlining a close pathophysiological relationship of iRBD and PD.
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Affiliation(s)
- Florian Holtbernd
- RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Juelich Research Center GmbH and RWTH Aachen University, Aachen, Germany; Institute of Neuroscience and Medicine (INM-4/INM-11), Juelich Research Center, Juelich, Germany
| | - Christian Hohenfeld
- RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Juelich Research Center GmbH and RWTH Aachen University, Aachen, Germany
| | - Wolfgang H Oertel
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
| | - Susanne Knake
- Department of Neurology, Philipps-University Marburg, Marburg, Germany; CMBB, Center for Mind, Brain and Behavior, University Hospital Marburg, Marburg, Germany
| | - Elisabeth Sittig
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
| | - Sandro Romanzetti
- RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Juelich Research Center GmbH and RWTH Aachen University, Aachen, Germany
| | - Anna Heidbreder
- Department of Neurology with Institute of Translational Neurology, University Hospital Muenster, Muenster, Germany; Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Jennifer Michels
- RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Juelich Research Center GmbH and RWTH Aachen University, Aachen, Germany
| | - Imis Dogan
- RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Juelich Research Center GmbH and RWTH Aachen University, Aachen, Germany
| | - Jörg B Schulz
- RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Juelich Research Center GmbH and RWTH Aachen University, Aachen, Germany
| | | | - Annette Janzen
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
| | - Kathrin Reetz
- RWTH Aachen University, Department of Neurology, Aachen, Germany; JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Juelich Research Center GmbH and RWTH Aachen University, Aachen, Germany.
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Rong D, Hu CP, Yang J, Guo Z, Liu W, Yu M. Consistent abnormal activity in the putamen by dopamine modulation in Parkinson's disease: A resting-state neuroimaging meta-analysis. Brain Res Bull 2024; 210:110933. [PMID: 38508469 DOI: 10.1016/j.brainresbull.2024.110933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 02/16/2024] [Accepted: 03/17/2024] [Indexed: 03/22/2024]
Abstract
OBJECTIVE This study aimed to elucidate brain areas mediated by oral anti-parkinsonian medicine that consistently show abnormal resting-state activation in PD and to reveal their functional connectivity profiles using meta-analytic approaches. METHODS Searches of the PubMed, Web of Science databases identified 78 neuroimaging studies including PD OFF state (PD-OFF) versus (vs.) PD ON state (PD-ON) or PD-ON versus healthy controls (HCs) or PD-OFF versus HCs data. Coordinate-based meta-analysis and functional meta-analytic connectivity modeling (MACM) were performed using the activation likelihood estimation algorithm. RESULTS Brain activation in PD-OFF vs. PD-ON was significantly changed in the right putamen and left inferior parietal lobule (IPL). Contrast analysis indicated that PD-OFF vs. HCs had more consistent activation in the right paracentral lobule, right middle frontal gyrus, right thalamus, left superior parietal lobule and right putamen, whereas PD-ON vs. HCs elicited more consistent activation in the bilateral middle temporal gyrus, left occipital gyrus, right inferior frontal gyrus and right caudate. MACM revealed coactivation of the right putamen in the direct contrast of PD-OFF vs. PD-ON. Subtraction analysis of significant coactivation clusters for PD-OFF vs. PD-ON with the medium of HCs showed effects in the sensorimotor, top-down control, and visual networks. By overlapping the MACM maps of the two analytical strategies, we demonstrated that the coactivated brain region focused on the right putamen. CONCLUSIONS The convergence of local brain regions and co-activation neural networks are involved the putamen, suggesting its potential as a specific imaging biomarker to monitor treatment efficacy. SYSTEMATIC REVIEW REGISTRATION [https://www.crd.york.ac.uk/PROSPERO/], identifier [CRD CRD42022304150].
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Affiliation(s)
- Danyan Rong
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, No.264, Guangzhou Road, Gulou District, Nanjing, Jiangsu 210029, China
| | - Chuan-Peng Hu
- School of Psychology, Nanjing Normal University, No.122, Ninghai Road, Gulou District, Nanjing, Jiangsu 210024, China
| | - Jiaying Yang
- Department of Public Health, School of Medicine & Holistic Integrative Medicine, Nanjing University of Chinese Medicine, No.138, Xianlin Road, Nanjing, Jiangsu 210023, China
| | - Zhiying Guo
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, No.264, Guangzhou Road, Gulou District, Nanjing, Jiangsu 210029, China
| | - Weiguo Liu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, No.264, Guangzhou Road, Gulou District, Nanjing, Jiangsu 210029, China.
| | - Miao Yu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, No.264, Guangzhou Road, Gulou District, Nanjing, Jiangsu 210029, China.
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Candia‐Rivera D, Vidailhet M, Chavez M, De Vico Fallani F. A framework for quantifying the coupling between brain connectivity and heartbeat dynamics: Insights into the disrupted network physiology in Parkinson's disease. Hum Brain Mapp 2024; 45:e26668. [PMID: 38520378 PMCID: PMC10960553 DOI: 10.1002/hbm.26668] [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: 10/04/2023] [Revised: 03/07/2024] [Accepted: 03/12/2024] [Indexed: 03/25/2024] Open
Abstract
Parkinson's disease (PD) often shows disrupted brain connectivity and autonomic dysfunctions, progressing alongside with motor and cognitive decline. Recently, PD has been linked to a reduced sensitivity to cardiac inputs, that is, cardiac interoception. Altogether, those signs suggest that PD causes an altered brain-heart connection whose mechanisms remain unclear. Our study aimed to explore the large-scale network disruptions and the neurophysiology of disrupted interoceptive mechanisms in PD. We focused on examining the alterations in brain-heart coupling in PD and their potential connection to motor symptoms. We developed a proof-of-concept method to quantify relationships between the co-fluctuations of brain connectivity and cardiac sympathetic and parasympathetic activities. We quantified the brain-heart couplings from electroencephalogram and electrocardiogram recordings from PD patients on and off dopaminergic medication, as well as in healthy individuals at rest. Our results show that the couplings of fluctuating alpha and gamma connectivity with cardiac sympathetic dynamics are reduced in PD patients, as compared to healthy individuals. Furthermore, we show that PD patients under dopamine medication recover part of the brain-heart coupling, in proportion with the reduced motor symptoms. Our proposal offers a promising approach to unveil the physiopathology of PD and promoting the development of new evaluation methods for the early stages of the disease.
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Affiliation(s)
- Diego Candia‐Rivera
- Sorbonne Université, Paris Brain Institute (ICM), Inria Paris, CNRS UMR7225, INSERM U1127, AP‐HP Hôpital Pitié‐SalpêtrièreParisFrance
| | - Marie Vidailhet
- Sorbonne Université, Paris Brain Institute (ICM)—Team “Movement Investigations and Therapeutics” (MOV'IT), CNRS UMR7225, INSERM U1127, AP‐HP Hôpital Pitié‐SalpêtrièreParisFrance
| | - Mario Chavez
- Sorbonne Université, Paris Brain Institute (ICM), Inria Paris, CNRS UMR7225, INSERM U1127, AP‐HP Hôpital Pitié‐SalpêtrièreParisFrance
| | - Fabrizio De Vico Fallani
- Sorbonne Université, Paris Brain Institute (ICM), Inria Paris, CNRS UMR7225, INSERM U1127, AP‐HP Hôpital Pitié‐SalpêtrièreParisFrance
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Brooks SJ, Jones VO, Wang H, Deng C, Golding SGH, Lim J, Gao J, Daoutidis P, Stamoulis C. Community detection in the human connectome: Method types, differences and their impact on inference. Hum Brain Mapp 2024; 45:e26669. [PMID: 38553865 PMCID: PMC10980844 DOI: 10.1002/hbm.26669] [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: 08/25/2023] [Revised: 03/06/2024] [Accepted: 03/12/2024] [Indexed: 04/02/2024] Open
Abstract
Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method-dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting-state fMRI fromn $$ n $$ = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), andn $$ n $$ = 5338 synthetic networks with heterogeneous, data-inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization-based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi-optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method-specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method-dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods.
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Affiliation(s)
- Skylar J. Brooks
- Boston Children's HospitalDepartment of PediatricsBostonMassachusettsUSA
- University of California BerkeleyHelen Wills Neuroscience InstituteBerkeleyCaliforniaUSA
| | - Victoria O. Jones
- University of MinnesotaDepartment of Chemical Engineering and Material ScienceMinneapolisMinnesotaUSA
| | - Haotian Wang
- Rutgers UniversityDepartment of Computer SciencePiscatawayNew JerseyUSA
| | - Chengyuan Deng
- Rutgers UniversityDepartment of Computer SciencePiscatawayNew JerseyUSA
| | | | - Jethro Lim
- Boston Children's HospitalDepartment of PediatricsBostonMassachusettsUSA
| | - Jie Gao
- Rutgers UniversityDepartment of Computer SciencePiscatawayNew JerseyUSA
| | - Prodromos Daoutidis
- University of MinnesotaDepartment of Chemical Engineering and Material ScienceMinneapolisMinnesotaUSA
| | - Catherine Stamoulis
- Boston Children's HospitalDepartment of PediatricsBostonMassachusettsUSA
- Harvard Medical SchoolDepartment of PediatricsBostonMassachusettsUSA
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Fiorenzato E, Moaveninejad S, Weis L, Biundo R, Antonini A, Porcaro C. Brain Dynamics Complexity as a Signature of Cognitive Decline in Parkinson's Disease. Mov Disord 2024; 39:305-317. [PMID: 38054573 DOI: 10.1002/mds.29678] [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: 07/26/2023] [Revised: 11/13/2023] [Accepted: 11/17/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND Higuchi's fractal dimension (FD) captures brain dynamics complexity and may be a promising method to analyze resting-state functional magnetic resonance imaging (fMRI) data and detect the neuronal interaction complexity underlying Parkinson's disease (PD) cognitive decline. OBJECTIVES The aim was to compare FD with a more established index of spontaneous neural activity, the fractional amplitude of low-frequency fluctuations (fALFF), and identify through machine learning (ML) models which method could best distinguish across PD-cognitive states, ranging from normal cognition (PD-NC), mild cognitive impairment (PD-MCI) to dementia (PDD). Finally, the aim was to explore correlations between fALFF and FD with clinical and cognitive PD features. METHODS Among 118 PD patients age-, sex-, and education matched with 35 healthy controls, 52 were classified with PD-NC, 46 with PD-MCI, and 20 with PDD based on an extensive cognitive and clinical evaluation. fALFF and FD metrics were computed on rs-fMRI data and used to train ML models. RESULTS FD outperformed fALFF metrics in differentiating between PD-cognitive states, reaching an overall accuracy of 78% (vs. 62%). PD showed increased neuronal dynamics complexity within the sensorimotor network, central executive network (CEN), and default mode network (DMN), paralleled by a reduction in spontaneous neuronal activity within the CEN and DMN, whose increased complexity was strongly linked to the presence of dementia. Further, we found that some DMN critical hubs correlated with worse cognitive performance and disease severity. CONCLUSIONS Our study indicates that PD-cognitive decline is characterized by an altered spontaneous neuronal activity and increased temporal complexity, involving the CEN and DMN, possibly reflecting an increased segregation of these networks. Therefore, we propose FD as a prognostic biomarker of PD-cognitive decline. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Eleonora Fiorenzato
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
| | - Sadaf Moaveninejad
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy
| | - Luca Weis
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
- IRCCS, San Camillo Hospital, Venice, Italy
| | - Roberta Biundo
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
- Department of Neuroscience, Center for Neurodegenerative Disease Research (CESNE), University of Padova, Padova, Italy
- Department of General Psychology, University of Padua, Padua, Italy
| | - Angelo Antonini
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy
- Department of Neuroscience, Center for Neurodegenerative Disease Research (CESNE), University of Padova, Padova, Italy
| | - Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center, University of Padua, Padua, Italy
- Institute of Cognitive Sciences and Technologies-National Research Council, Rome, Italy
- Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, United Kingdom
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Wu C, Wu H, Zhou C, Guan X, Guo T, Wu J, Chen J, Wen J, Qin J, Tan S, Duanmu X, Yuan W, Zheng Q, Zhang B, Xu X, Zhang M. Neurovascular coupling alteration in drug-naïve Parkinson's disease: The underlying molecular mechanisms and levodopa's restoration effects. Neurobiol Dis 2024; 191:106406. [PMID: 38199273 DOI: 10.1016/j.nbd.2024.106406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 12/25/2023] [Accepted: 01/06/2024] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND Parkinson's disease (PD) patients exhibit an imbalance between neuronal activity and perfusion, referred to as abnormal neurovascular coupling (NVC). Nevertheless, the underlying molecular mechanism and how levodopa, the standard treatment in PD, regulates NVC is largely unknown. MATERIAL AND METHODS A total of 52 drug-naïve PD patients and 49 normal controls (NCs) were enrolled. NVC was characterized in vivo by relating cerebral blood flow (CBF) and amplitude of low-frequency fluctuations (ALFF). Motor assessments and MRI scanning were conducted on drug-naïve patients before and after levodopa therapy (OFF/ON state). Regional NVC differences between patients and NCs were identified, followed by an assessment of the associated receptors/transporters. The influence of levodopa on NVC, CBF, and ALFF within these abnormal regions was analyzed. RESULTS Compared to NCs, OFF-state patients showed NVC dysfunction in significantly lower NVC in left precentral, postcentral, superior parietal cortex, and precuneus, along with higher NVC in left anterior cingulate cortex, right olfactory cortex, thalamus, caudate, and putamen (P-value <0.0006). The distribution of NVC differences correlated with the density of dopaminergic, serotonin, MU-opioid, and cholinergic receptors/transporters. Additionally, levodopa ameliorated abnormal NVC in most of these regions, where there were primarily ALFF changes with limited CBF modifications. CONCLUSION Patients exhibited NVC dysfunction primarily in the striato-thalamo-cortical circuit and motor control regions, which could be driven by dopaminergic and nondopaminergic systems, and levodopa therapy mainly restored abnormal NVC by modulating neuronal activity.
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Affiliation(s)
- Chenqing Wu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Haoting Wu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cheng Zhou
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Guan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Guo
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingwen Chen
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiaqi Wen
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianmei Qin
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Sijia Tan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojie Duanmu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Weijin Yuan
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qianshi Zheng
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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Hajebrahimi F, Budak M, Saricaoglu M, Temel Z, Demir TK, Hanoglu L, Yildirim S, Bayraktaroglu Z. Functional neural networks stratify Parkinson's disease patients across the spectrum of cognitive impairment. Brain Behav 2024; 14:e3395. [PMID: 38376051 PMCID: PMC10808882 DOI: 10.1002/brb3.3395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 11/23/2023] [Accepted: 12/26/2023] [Indexed: 02/21/2024] Open
Abstract
INTRODUCTION Cognitive impairment (CI) is a significant non-motor symptoms in Parkinson's disease (PD) that often precedes the emergence of motor symptoms by several years. Patients with PD hypothetically progress from stages without CI (PD-normal cognition [NC]) to stages with Mild CI (PD-MCI) and PD dementia (PDD). CI symptoms in PD are linked to different brain regions and neural pathways, in addition to being the result of dysfunctional subcortical regions. However, it is still unknown how functional dysregulation correlates to progression during the CI. Neuroimaging techniques hold promise in discriminating CI stages of PD and further contribute to the biomarker formation of CI in PD. In this study, we explore disparities in the clinical assessments and resting-state functional connectivity (FC) among three CI stages of PD. METHODS We enrolled 88 patients with PD and 26 healthy controls (HC) for a cross sectional clinical study and performed intra- and inter-network FC analysis in conjunction with comprehensive clinical cognitive assessment. RESULTS Our findings underscore the significance of several neural networks, namely, the default mode network (DMN), frontoparietal network (FPN), dorsal attention network, and visual network (VN) and their inter-intra-network FC in differentiating between PD-MCI and PDD. Additionally, our results showed the importance of sensory motor network, VN, DMN, and salience network (SN) in the discriminating PD-NC from PDD. Finally, in comparison to HC, we found DMN, FPN, VN, and SN as pivotal networks for further differential diagnosis of CI stages of PD. CONCLUSION We propose that resting-state networks (RSN) can be a discriminating factor in distinguishing the CI stages of PD and progressing from PD-NC to MCI or PDD. The integration of clinical and neuroimaging data may enhance the early detection of PD in clinical settings and potentially prevent the disease from advancing to more severe stages.
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Affiliation(s)
- Farzin Hajebrahimi
- Functional Imaging and Cognitive‐Affective Neuroscience Lab (fINCAN), Research Institute for Health Sciences and Technologies (SABITA)Istanbul Medipol UniversityIstanbulTurkey
- Department of Physical Therapy and Rehabilitation, School of Health SciencesIstanbul Medipol UniversityIstanbulTurkey
- Department of Health Informatics, Rutgers University, School of Health ProfessionsRutgers Biomedical and Health SciencesNewarkNew JerseyUSA
| | - Miray Budak
- Functional Imaging and Cognitive‐Affective Neuroscience Lab (fINCAN), Research Institute for Health Sciences and Technologies (SABITA)Istanbul Medipol UniversityIstanbulTurkey
- Department of Ergotherapy, School of Health SciencesIstanbul Medipol UniversityIstanbulTurkey
- Center for Molecular and Behavioral NeuroscienceRutgers University‐NewarkNewarkNew JerseyUSA
| | - Mevhibe Saricaoglu
- Functional Imaging and Cognitive‐Affective Neuroscience Lab (fINCAN), Research Institute for Health Sciences and Technologies (SABITA)Istanbul Medipol UniversityIstanbulTurkey
- Program of Electroneurophysiology, Vocational SchoolIstanbul Medipol UniversityIstanbulTurkey
| | - Zeynep Temel
- Department of PsychologyFatih Sultan Mehmet Vakif UniversityIstanbulTurkey
| | - Tugce Kahraman Demir
- Program of Electroneurophysiology, Vocational SchoolBiruni UniversityIstanbulTurkey
| | - Lutfu Hanoglu
- Department of Neurology, School of MedicineIstanbul Medipol UniversityIstanbulTurkey
- Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA)Istanbul Medipol UniversityIstanbulTurkey
| | - Suleyman Yildirim
- Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA)Istanbul Medipol UniversityIstanbulTurkey
- Department of Medical Microbiology, International School of MedicineIstanbul Medipol UniversityIstanbulTurkey
| | - Zubeyir Bayraktaroglu
- Functional Imaging and Cognitive‐Affective Neuroscience Lab (fINCAN), Research Institute for Health Sciences and Technologies (SABITA)Istanbul Medipol UniversityIstanbulTurkey
- Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA)Istanbul Medipol UniversityIstanbulTurkey
- Department of Physiology, International School of MedicineIstanbul Medipol UniversityIstanbulTurkey
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Guo L, Ma J, Cai M, Zhang M, Xu Q, Wang H, Zhang Y, Yao J, Sun Z, Chen Y, Xue H, Zhang Y, Wang S, Xue K, Zhu D, Liu F. Transcriptional signatures of the whole-brain voxel-wise resting-state functional network centrality alterations in schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2023; 9:87. [PMID: 38104130 PMCID: PMC10725456 DOI: 10.1038/s41537-023-00422-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 12/05/2023] [Indexed: 12/19/2023]
Abstract
Neuroimaging studies have revealed that patients with schizophrenia exhibit disrupted resting-state functional connectivity. However, the inconsistent findings across these studies have hindered our comprehensive understanding of the functional connectivity changes associated with schizophrenia, and the molecular mechanisms associated with these alterations remain largely unclear. A quantitative meta-analysis was first conducted on 21 datasets, involving 1057 patients and 1186 healthy controls, to examine disrupted resting-state functional connectivity in schizophrenia, as measured by whole-brain voxel-wise functional network centrality (FNC). Subsequently, partial least squares regression analysis was employed to investigate the relationship between FNC changes and gene expression profiles obtained from the Allen Human Brain Atlas database. Finally, gene enrichment analysis was performed to unveil the biological significance of the altered FNC-related genes. Compared with healthy controls, patients with schizophrenia show consistently increased FNC in the right inferior parietal cortex extending to the supramarginal gyrus, angular gyrus, bilateral medial prefrontal cortex, and right dorsolateral prefrontal cortex, while decreased FNC in the bilateral insula, bilateral postcentral gyrus, and right inferior temporal gyrus. Meta-regression analysis revealed that increased FNC in the right inferior parietal cortex was positively correlated with clinical score. In addition, these observed functional connectivity changes were found to be spatially associated with the brain-wide expression of specific genes, which were enriched in diverse biological pathways and cell types. These findings highlight the aberrant functional connectivity observed in schizophrenia and its potential molecular underpinnings, providing valuable insights into the neuropathology of dysconnectivity associated with this disorder.
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Affiliation(s)
- Lining Guo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Juanwei Ma
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Mengjing Cai
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Minghui Zhang
- Department of Ultrasound, Tianjin Medical University General Hospital Airport Hospital, Tianjin, China
| | - Qiang Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - He Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yijing Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jia Yao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Zuhao Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yayuan Chen
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Hui Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yujie Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Shaoying Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
- Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China.
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China.
| | - Dan Zhu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
- Department of Radiology, Tianjin Medical University General Hospital Airport Hospital, Tianjin, China.
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
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9
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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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10
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Zang Z, Zhang X, Song T, Li J, Nie B, Mei S, Hu Z, Zhang Y, Lu J. Association between gene expression and functional-metabolic architecture in Parkinson's disease. Hum Brain Mapp 2023; 44:5387-5401. [PMID: 37605831 PMCID: PMC10543112 DOI: 10.1002/hbm.26443] [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: 04/20/2023] [Revised: 06/02/2023] [Accepted: 07/23/2023] [Indexed: 08/23/2023] Open
Abstract
Gene expression plays a critical role in the pathogenesis of Parkinson's disease (PD). How gene expression profiles are correlated with functional-metabolic architecture remains obscure. We enrolled 34 PD patients and 25 age-and-sex-matched healthy controls for simultaneous 18 F-FDG-PET/functional MRI scanning during resting state. We investigated the functional gradients and the ratio of standard uptake value. Principal component analysis was used to further combine the functional gradients and glucose metabolism into functional-metabolic architecture. Using partial least squares (PLS) regression, we introduced the transcriptomic data from the Allen Institute of Brain Sciences to identify gene expression patterns underlying the affected functional-metabolic architecture in PD. Between-group comparisons revealed significantly higher gradient variation in the visual, somatomotor, dorsal attention, frontoparietal, default mode, and subcortical network (pFDR < .048) in PD. Increased FDG-uptake was found in the somatomotor and ventral attention network while decreased FDG-uptake was found in the visual network (pFDR < .008). Spatial correlation analysis showed consistently affected patterns of functional gradients and metabolism (p = 2.47 × 10-8 ). PLS analysis and gene ontological analyses further revealed that genes were mainly enriched for metabolic, catabolic, cellular response to ions, and regulation of DNA transcription and RNA biosynthesis. In conclusion, our study provided genetic pathological mechanism to explain imaging-defined brain functional-metabolic architecture of PD.
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Affiliation(s)
- Zhenxiang Zang
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
| | - Xiaolong Zhang
- Department of Physiology, College of Basic Medical SciencesArmy Medical UniversityChongqingChina
| | - Tianbin Song
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
| | - Jiping Li
- Beijing Institute of Functional NeurosurgeryXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Binbin Nie
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy PhysicsChinese Academy of SciencesBeijingChina
| | - Shanshan Mei
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Zhi'an Hu
- Department of Physiology, College of Basic Medical SciencesArmy Medical UniversityChongqingChina
| | - Yuqing Zhang
- Beijing Institute of Functional NeurosurgeryXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Jie Lu
- Department of Radiology and Nuclear Medicine, Xuanwu HospitalCapital Medical UniversityBeijingChina
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijingChina
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11
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Lan Y, Liu X, Yin C, Lyu J, Xiaoxaio M, Cui Z, Li X, Lou X. Resting-state functional magnetic resonance imaging study comparing tremor-dominant and postural instability/gait difficulty subtypes of Parkinson's disease. LA RADIOLOGIA MEDICA 2023; 128:1138-1147. [PMID: 37474664 DOI: 10.1007/s11547-023-01673-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 06/29/2023] [Indexed: 07/22/2023]
Abstract
PURPOSE The symptom-specific intrinsic neural mechanisms underlying Parkinson's disease (PD) subtypes (tremor dominant [TD] and postural instability gait difficulty [PIGD]) remain unclarified. We examined spontaneous brain activity patterns in TD and PIGD. MATERIAL AND METHODS We included 49 patients with PD (21 with TD/28 with PIGD) and 32 healthy controls (HCs) in this study. We conducted analysis of variance and post-hoc analyses of the amplitude of low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) values of the three groups, with age, sex, and gray matter volume as covariates, and a relationship analysis of the ALFF and ReHo values with clinical variables. RESULTS In comparison with HCs, PIGD PD patients had increased ALFF values in the right middle occipital gyrus and left superior occipital gyrus and decreased values primarily in the bilateral inferior frontal gyrus (triangular part). TD PD patients had lower ALFF values in the right inferior frontal gyrus (triangular part) and left insula. In comparison to TD PD patients, PIGD PD patients had higher ALFF values in the left middle occipital gyrus and left superior occipital gyrus. In contrast to HCs, TD PD patients demonstrated a reduction of ReHo values in the left middle temporal gyrus, and PIGD patients showed a decrease of ReHo values in the left inferior temporal gyrus. CONCLUSION ALFF values increased in the occipital gyrus of the PIGD PD patients, thus providing evidence of a compensatory mechanism of altered motor function in comparison with the TD PD patients.
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Affiliation(s)
- Yina Lan
- Department of Radiology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Xinyun Liu
- Department of Radiology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - ChunYu Yin
- Department of Neurology, Chinese PLA General Hospital, Beijing, China
| | - Jinhao Lyu
- Department of Radiology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Ma Xiaoxaio
- Department of Radiology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China
| | - Zhiqiang Cui
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, China
| | - Xuemei Li
- Department of Neurology, Chinese PLA General Hospital, Beijing, China
| | - Xin Lou
- Department of Radiology, Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, China.
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12
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Spetsieris PG, Eidelberg D. Parkinson's disease progression: Increasing expression of an invariant common core subnetwork. Neuroimage Clin 2023; 39:103488. [PMID: 37660556 PMCID: PMC10491857 DOI: 10.1016/j.nicl.2023.103488] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/31/2023] [Accepted: 08/02/2023] [Indexed: 09/05/2023]
Abstract
Notable success has been achieved in the study of neurodegenerative conditions using reduction techniques such as principal component analysis (PCA) and sparse inverse covariance estimation (SICE) in positron emission tomography (PET) data despite their widely differing approach. In a recent study of SICE applied to metabolic scans from Parkinson's disease (PD) patients, we showed that by using PCA to prespecify disease-related partition layers, we were able to optimize maps of functional metabolic connectivity within the relevant networks. Here, we show the potential of SICE, enhanced by disease-specific subnetwork partitions, to identify key regional hubs and their connections, and track their associations in PD patients with increasing disease duration. This approach enabled the identification of a core zone that included elements of the striatum, pons, cerebellar vermis, and parietal cortex and provided a deeper understanding of progressive changes in their connectivity. This subnetwork constituted a robust invariant disease feature that was unrelated to phenotype. Mean expression levels for this subnetwork increased steadily in a group of 70 PD patients spanning a range of symptom durations between 1 and 21 years. The findings were confirmed in a validation sample of 69 patients with up to 32 years of symptoms. The common core elements represent possible targets for disease modification, while their connections to external regions may be better suited for symptomatic treatment.
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Affiliation(s)
- Phoebe G Spetsieris
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, United States
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, United States; Molecular Medicine and Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, United States.
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13
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Nieto-Escamez F, Obrero-Gaitán E, Cortés-Pérez I. Visual Dysfunction in Parkinson's Disease. Brain Sci 2023; 13:1173. [PMID: 37626529 PMCID: PMC10452537 DOI: 10.3390/brainsci13081173] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 07/11/2023] [Accepted: 08/03/2023] [Indexed: 08/27/2023] Open
Abstract
Non-motor symptoms in Parkinson's disease (PD) include ocular, visuoperceptive, and visuospatial impairments, which can occur as a result of the underlying neurodegenerative process. Ocular impairments can affect various aspects of vision and eye movement. Thus, patients can show dry eyes, blepharospasm, reduced blink rate, saccadic eye movement abnormalities, smooth pursuit deficits, and impaired voluntary and reflexive eye movements. Furthermore, visuoperceptive impairments affect the ability to perceive and recognize visual stimuli accurately, including impaired contrast sensitivity and reduced visual acuity, color discrimination, and object recognition. Visuospatial impairments are also remarkable, including difficulties perceiving and interpreting spatial relationships between objects and difficulties judging distances or navigating through the environment. Moreover, PD patients can present visuospatial attention problems, with difficulties attending to visual stimuli in a spatially organized manner. Moreover, PD patients also show perceptual disturbances affecting their ability to interpret and determine meaning from visual stimuli. And, for instance, visual hallucinations are common in PD patients. Nevertheless, the neurobiological bases of visual-related disorders in PD are complex and not fully understood. This review intends to provide a comprehensive description of visual disturbances in PD, from sensory to perceptual alterations, addressing their neuroanatomical, functional, and neurochemical correlates. Structural changes, particularly in posterior cortical regions, are described, as well as functional alterations, both in cortical and subcortical regions, which are shown in relation to specific neuropsychological results. Similarly, although the involvement of different neurotransmitter systems is controversial, data about neurochemical alterations related to visual impairments are presented, especially dopaminergic, cholinergic, and serotoninergic systems.
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Affiliation(s)
- Francisco Nieto-Escamez
- Department of Psychology, University of Almeria, 04120 Almeria, Spain
- Center for Neuropsychological Assessment and Rehabilitation (CERNEP), 04120 Almeria, Spain
| | - Esteban Obrero-Gaitán
- Department of Health Sciences, University of Jaen, Paraje Las Lagunillas s/n, 23071 Jaen, Spain;
| | - Irene Cortés-Pérez
- Department of Health Sciences, University of Jaen, Paraje Las Lagunillas s/n, 23071 Jaen, Spain;
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14
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Schill J, Simonyan K, Lang S, Mathys C, Thiel C, Witt K. Parkinson's disease speech production network as determined by graph-theoretical network analysis. Netw Neurosci 2023; 7:712-730. [PMID: 37397896 PMCID: PMC10312286 DOI: 10.1162/netn_a_00310] [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: 07/06/2022] [Accepted: 02/13/2023] [Indexed: 08/04/2023] Open
Abstract
Parkinson's disease (PD) can affect speech as well as emotion processing. We employ whole-brain graph-theoretical network analysis to determine how the speech-processing network (SPN) changes in PD, and assess its susceptibility to emotional distraction. Functional magnetic resonance images of 14 patients (aged 59.6 ± 10.1 years, 5 female) and 23 healthy controls (aged 64.1 ± 6.5 years, 12 female) were obtained during a picture-naming task. Pictures were supraliminally primed by face pictures showing either a neutral or an emotional expression. PD network metrics were significantly decreased (mean nodal degree, p < 0.0001; mean nodal strength, p < 0.0001; global network efficiency, p < 0.002; mean clustering coefficient, p < 0.0001), indicating an impairment of network integration and segregation. There was an absence of connector hubs in PD. Controls exhibited key network hubs located in the associative cortices, of which most were insusceptible to emotional distraction. The PD SPN had more key network hubs, which were more disorganized and shifted into auditory, sensory, and motor cortices after emotional distraction. The whole-brain SPN in PD undergoes changes that result in (a) decreased network integration and segregation, (b) a modularization of information flow within the network, and (c) the inclusion of primary and secondary cortical areas after emotional distraction.
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Affiliation(s)
- Jana Schill
- Department of Neurology, School of Medicine and Health Sciences, University of Oldenburg, Oldenburg, Germany
| | - Kristina Simonyan
- Department of Otolaryngology, Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
- Department of Otolaryngology, Head and Neck Surgery, Massachusetts Eye and Ear, Boston, MA, USA
| | - Simon Lang
- Department of Neurology, School of Medicine and Health Sciences, University of Oldenburg, Oldenburg, Germany
| | - Christian Mathys
- Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, University of Oldenburg, Oldenburg, Germany
- Research Center Neurosensory Science, University of Oldenburg, Oldenburg, Germany
- Department of Diagnostic and Interventional Radiology, University of Düsseldorf, Düsseldorf, Germany
| | - Christiane Thiel
- Research Center Neurosensory Science, University of Oldenburg, Oldenburg, Germany
- Department of Psychology, School of Medicine and Health Sciences, University of Oldenburg, Oldenburg, Germany
| | - Karsten Witt
- Department of Neurology, School of Medicine and Health Sciences, University of Oldenburg, Oldenburg, Germany
- Research Center Neurosensory Science, University of Oldenburg, Oldenburg, Germany
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15
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Wang Q, Yu M, Yan L, Xu J, Wang Y, Zhou G, Liu W. Aberrant inter-network functional connectivity in drug-naive Parkinson's disease patients with tremor dominant and postural instability and gait difficulty. Front Hum Neurosci 2023; 17:1100431. [PMID: 36816505 PMCID: PMC9934857 DOI: 10.3389/fnhum.2023.1100431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 01/10/2023] [Indexed: 02/05/2023] Open
Abstract
Background: Insight into neural mechanisms of tremor dominant (TD) and postural instability and gait disorder (PIGD) subtypes in Parkinson's disease (PD) is vital for understanding pathophysiological hypotheses underlying this phenotype. However, network disturbances and their correlation with motor subtypes of PD remain unclear. We aimed to investigate the alterations of intra- and inter-network functional connectivity (FC) in drug-naive PD patients with different motor subtypes. Methods: Resting-state functional magnetic resonance imaging was performed on 25 drug-naive PD patients with TD (PD-TD) and 40 drug-naive PD patients with PIGD (PD-PIGD), and 37 healthy controls (HCs) underwent. The following networks were extracted using independent component analysis: sensorimotor network (SMN), left executive control network (LECN), right executive control network, anterior salience network (aSN), posterior salience network (pSN), ventral attention network (VAN), dorsal attention network (DAN), default mode network (DMN), visual network, and auditory network (AN). We measured FC values within and between these networks. Results: There were no detectable variations in intra-network FC. PD-PIGD group demonstrated lower FC between aSN and pSN, as well as between VAN and DMN, in contrast to PD-TD group. Particularly, the FC strength between VAN and DMN was positively correlated with TD and tremor scores, and the best fitting classification models of TD and PIGD subtypes were based on the FC between aSN and pSN. Compared with HCs, both PD-TD and PD-PIGD patients displayed decreased FC between two SMN subnetworks, while PD-TD patients exhibited increased FC between the SMN subnetwork and pSN, and between LECN and VAN. Furthermore, PD-PIGD patients demonstrated decreased FC between the SMN subnetwork and AN. Conclusions: The altered FC between aSN and pSN can be an imaging marker to distinguish PD-TD from PD-PIGD. We for the first time disclosed that the PD-TD patients compensated by increasing attention resources and the PD-PIGD patients displayed reduced FC between SMN and AN. Our findings provide a basis for identification and precision treatment of PD motor subtypes.
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Affiliation(s)
- Qi Wang
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China,Department of Neurology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
| | - Miao Yu
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Lei Yan
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jianxia Xu
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yajie Wang
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Gaiyan Zhou
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Weiguo Liu
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China,*Correspondence: Weiguo Liu
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Chen S, Zhang X, Lin S, Zhang Y, Xu Z, Li Y, Xu M, Hou G, Qiu Y. Suicide risk stratification among major depressed patients based on a machine learning approach and whole-brain functional connectivity. J Affect Disord 2023; 322:173-179. [PMID: 36370913 DOI: 10.1016/j.jad.2022.11.022] [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: 05/18/2022] [Revised: 08/24/2022] [Accepted: 11/07/2022] [Indexed: 11/11/2022]
Abstract
BACKGROUND Suicide risk stratification and individual-level prediction among major depressive disorder (MDD) is important but unrecognized. Here, we construct models to detect suicidality in MDD using machine learning (ML) and whole-brain functional connectivity (FC). METHODS A cross-sectional assessment was conducted on 200 subjects, including 126 MDD with high suicide risk (HSR; 73 patients with suicidal ideation [SI], 53 patients with suicidal attempts [SA]), 36 patients with low suicide risk (LSR) and 38 healthy controls (HCs). Whole-brain FC features were calculated, the least absolute shrinkage and selection operator (LASSO) method was used for feature selection. A support vector machine (SVM) was performed to build models to distinguish MDD from HCs, and for suicide risk stratification among MDD. Leave-one-out cross-validation (LOOCV) was performed for validation. RESULTS The models constructed using SVM on whole-brain FC had powerful classification efficiency in screening MDD from HCs (accuracy = 88.50 %), and in suicide risk stratification among MDD patients (with accuracy = 84.56 % and 74.60 % in classifying patients with HSR or LSR, and SA or SI, respectively). Subsequent analysis demonstrated that intra-network dysconnectivity in the sensorimotor network and inter-network dysconnectivity between the default and dorsal attention network could characterize HSR and SA in MDD, separately. LIMITATIONS This study was a single center cohort study without external validation. CONCLUSION These findings indicate ML approaches are useful in suicide risk stratification among MDD based on whole-brain FC, which may help to identify individuals with different suicide risks in MDD and provide an individual-level prediction.
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Affiliation(s)
- Shengli Chen
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan district, Shenzhen 518000, PR China
| | - Xiaojing Zhang
- Guangdong Provincial Key Laboratory of Genome Stability and Disease Prevention and Regional Immunity and Diseases, Department of Pathology, Shenzhen University School of Medicine, Shenzhen, Guangdong 518060, PR China
| | - Shiwei Lin
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan district, Shenzhen 518000, PR China
| | - Yingli Zhang
- Department of Depressive Disorders, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Cuizhu AVE 1080, Luohu district, Shenzhen 518020, China
| | - Ziyun Xu
- Department of Radiology, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Cuizhu AVE 1080, Luohu district, Shenzhen 518020, China
| | - Yanqing Li
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Duobao AVE 56, Liwan District, Guangzhou, PR China
| | - Manxi Xu
- Department of Radiology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Duobao AVE 56, Liwan District, Guangzhou, PR China
| | - Gangqiang Hou
- Department of Radiology, Shenzhen Mental Health Center, Shenzhen Kangning Hospital, Cuizhu AVE 1080, Luohu district, Shenzhen 518020, China.
| | - Yingwei Qiu
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Taoyuan AVE 89, Nanshan district, Shenzhen 518000, PR China.
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Sure M, Mertiens S, Vesper J, Schnitzler A, Florin E. Alterations of resting-state networks of Parkinson's disease patients after subthalamic DBS surgery. Neuroimage Clin 2023; 37:103317. [PMID: 36610312 PMCID: PMC9850202 DOI: 10.1016/j.nicl.2023.103317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 12/27/2022] [Accepted: 01/02/2023] [Indexed: 01/05/2023]
Abstract
The implantation of deep brain stimulation (DBS) electrodes in Parkinson's disease (PD) patients can lead to a temporary improvement in motor symptoms, known as the stun effect. However, the network alterations induced by the stun effect are not well characterized. As therapeutic DBS is known to alter resting-state networks (RSN) and subsequent motor symptoms in patients with PD, we aimed to investigate whether the DBS-related stun effect also modulated RSNs. Therefore, we analyzed RSNs of 27 PD patients (8 females, 59.0 +- 8.7 years) using magnetoencephalography and compared them to RSNs of 24 age-matched healthy controls (8 females, 62.8 +- 5.1 years). We recorded 30 min of resting-state activity two days before and one day after implantation of the electrodes with and without dopaminergic medication. RSNs were determined by use of phase-amplitude coupling between a low frequency phase and a high gamma amplitude and examined for differences between conditions (i.e., pre vs post surgery). We identified four RSNs across all conditions: sensory-motor, visual, fronto-occipital, and frontal. Each RSN was altered due to electrode implantation. Importantly, these changes were not restricted to spatially close areas to the electrode trajectory. Interestingly, pre-operative RSNs corresponded better with healthy control RSNs regarding the spatial overlap, although the stun effect is associated with motor improvement. Our findings reveal that the stun effect induced by implantation of electrodes exerts brain wide changes in different functional RSNs.
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Affiliation(s)
- Matthias Sure
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany.
| | - Sean Mertiens
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany.
| | - Jan Vesper
- Department of Functional Neurosurgery and Stereotaxy, Medical Faculty, University Hospital, Düsseldorf, Germany.
| | - Alfons Schnitzler
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany; Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty, University Hospital, Düsseldorf, Germany.
| | - Esther Florin
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany.
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Bosch TJ, Espinoza AI, Mancini M, Horak FB, Singh A. Functional Connectivity in Patients With Parkinson’s Disease and Freezing of Gait Using Resting-State EEG and Graph Theory. Neurorehabil Neural Repair 2022; 36:715-725. [DOI: 10.1177/15459683221129282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Background Although many studies have shown abnormalities in brain structure and function in people with Parkinson’s disease (PD), we still have a poor understanding of how brain structure and function relates to freezing of gait (FOG). Graph theory analysis of electroencephalography (EEG) can explore the relationship between brain network structure and gait function in PD. Methods Scalp EEG signals of 83 PD (42 PDFOG+ and 41 PDFOG−) and 42 healthy controls were recorded in an eyes-opened resting-state. The phase lag index was calculated for each electrode pair in different frequency bands, but we focused our analysis on the theta-band and performed global analyses along with nodal analyses over a midfrontal channel. The resulting connectivity matrices were converted to weighted graphs, whose structure was characterized using strength and clustering coefficient measurements, our main outcomes. Results We observed increased global strength and increased global clustering coefficient in people with PD compared to healthy controls in the theta-band, though no differences were observed in midfrontal nodal strength and midfrontal clustering coefficient. Furthermore, no differences in global nor midfrontal nodal strength nor global clustering coefficients were observed between PDFOG+ and PDFOG− in the theta-band. However, PDFOG+ exhibited a significantly diminished midfrontal nodal clustering coefficient in the theta-band compared to PDFOG−. Furthermore, FOG scores were negatively correlated with midfrontal nodal clustering coefficient in the theta-band. Conclusion The present findings support the involvement of midfrontal theta oscillations in FOG symptoms in PD and the sensitivity of graph metrics to characterize functional networks in PDFOG+.
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Affiliation(s)
- Taylor J. Bosch
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, SD, USA
| | | | - Martina Mancini
- Department of Neurology, School of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Fay B. Horak
- Department of Neurology, School of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Arun Singh
- Division of Basic Biomedical Sciences, Sanford School of Medicine, University of South Dakota, Vermillion, SD, USA
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Combining CRISPR-Cas9 and brain imaging to study the link from genes to molecules to networks. Proc Natl Acad Sci U S A 2022; 119:e2122552119. [PMID: 36161926 DOI: 10.1073/pnas.2122552119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Receptors, transporters, and ion channels are important targets for therapy development in neurological diseases, but their mechanistic role in pathogenesis is often poorly understood. Gene editing and in vivo imaging approaches will help to identify the molecular and functional role of these targets and the consequence of their regional dysfunction on the whole-brain level. We combine CRISPR-Cas9 gene editing with in vivo positron emission tomography (PET) and functional MRI (fMRI) to investigate the direct link between genes, molecules, and the brain connectome. The extensive knowledge of the Slc18a2 gene encoding the vesicular monoamine transporter (VMAT2), involved in the storage and release of dopamine, makes it an excellent target for studying the gene network relationships while structurally preserving neuronal integrity and function. We edited the Slc18a2 in the substantia nigra pars compacta of adult rats and used in vivo molecular imaging besides behavioral, histological, and biochemical assessments to characterize the CRISPR-Cas9-mediated VMAT2 knockdown. Simultaneous PET/fMRI was performed to investigate molecular and functional brain alterations. We found that stage-specific adaptations of brain functional connectivity follow the selective impairment of presynaptic dopamine storage and release. Our study reveals that recruiting different brain networks is an early response to the dopaminergic dysfunction preceding neuronal cell loss. Our combinatorial approach is a tool to investigate the impact of specific genes on brain molecular and functional dynamics, which will help to develop tailored therapies for normalizing brain function.
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Gu L, Shu H, Xu H, Wang Y. Functional brain changes in Parkinson’s disease: a whole brain ALE study. Neurol Sci 2022; 43:5909-5916. [DOI: 10.1007/s10072-022-06272-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 07/03/2022] [Indexed: 11/27/2022]
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Impaired Brain Information Transmission Efficiency and Flexibility in Parkinson’s Disease and Rapid Eye Movement Sleep Behavior Disorder: Evidence from Functional Connectivity and Functional Dynamics. PARKINSON'S DISEASE 2022; 2022:7495371. [PMID: 36160829 PMCID: PMC9499819 DOI: 10.1155/2022/7495371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/29/2022] [Accepted: 08/16/2022] [Indexed: 11/17/2022]
Abstract
Parkinson’s disease (PD) is a common neurodegenerative disorder. Rapid eye movement sleep behavior disorder (RBD) is one of the prodromal symptoms of PD. Studies have shown that brain information transmission is affected in PD patients. Consequently, we hypothesized that brain information transmission is impaired in RBD and PD. To prove our hypothesis, we performed functional connectivity (FC) and functional dynamics analysis of three aspects—based on the whole brain, within the resting-state network (RSN), and the interaction between RSNs—using normal control (NC) (n = 21), RBD (n = 24), and PD (n = 45) resting-state functional magnetic resonance imaging (rs-fMRI) data sets. Furthermore, we tested the explanatory power of FC and functional dynamics for the clinical features. Our results found that the global functional dynamics and FC of RBD and PD were impaired. Within RSN, the impairment concentrated in the visual network (VIS) and sensorimotor network (SMN), and the impaired degree of SMN in RBD was higher than that in PD. On the interaction between RSNs, RBD showed a widespread decrease, and PD showed a focal decrease which concentrated in SMN and VIS. Finally, we proved FC and functional dynamics were related to clinical features. These differences confirmed that brain information transmission efficiency and flexibility are impaired in RBD and PD, and these impairments are associated with the clinical features of patients.
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Chu C, He N, Zeljic K, Zhang Z, Wang J, Li J, Liu Y, Zhang Y, Sun B, Li D, Yan F, Zhang C, Liu C. Subthalamic and pallidal stimulation in Parkinson's disease induce distinct brain topological reconstruction. Neuroimage 2022; 255:119196. [PMID: 35413446 DOI: 10.1016/j.neuroimage.2022.119196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 04/02/2022] [Accepted: 04/07/2022] [Indexed: 10/18/2022] Open
Abstract
The subthalamic nucleus (STN) and globus pallidus internus (GPi) are the two most common and effective target brain areas for deep brain stimulation (DBS) treatment of advanced Parkinson's disease. Although DBS has been shown to restore functional neural circuits of this disorder, the changes in topological organization associated with active DBS of each target remain unknown. To investigate this, we acquired resting-state functional magnetic resonance imaging (fMRI) data from 34 medication-free patients with Parkinson's disease that had DBS electrodes implanted in either the subthalamic nucleus or internal globus pallidus (n = 17 each), in both ON and OFF DBS states. Sixteen age-matched healthy individuals were used as a control group. We evaluated the regional information processing capacity and transmission efficiency of brain networks with and without stimulation, and recorded how stimulation restructured the brain network topology of patients with Parkinson's disease. For both targets, the variation of local efficiency in motor brain regions was significantly correlated (p < 0.05) with improvement rate of the Uniform Parkinson's Disease Rating Scale-III scores, with comparable improvements in motor function for the two targets. However, non-motor brain regions showed changes in topological organization during active stimulation that were target-specific. Namely, targeting the STN decreased the information transmission of association, limbic and paralimbic regions, including the inferior frontal gyrus angle, insula, temporal pole, superior occipital gyri, and posterior cingulate, as evidenced by the simultaneous decrease of clustering coefficient and local efficiency. GPi-DBS had a similar effect on the caudate and lenticular nuclei, but enhanced information transmission in the cingulate gyrus. These effects were not present in the DBS-OFF state for GPi-DBS, but persisted for STN-DBS. Our results demonstrate that DBS to the STN and GPi induce distinct brain network topology reconstruction patterns, providing innovative theoretical evidence for deciphering the mechanism through which DBS affects disparate targets in the human brain.
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Affiliation(s)
- Chunguang Chu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Naying He
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kristina Zeljic
- School of Health Sciences, City, University of London, London, EC1V 0HB, UK
| | - Zhen Zhang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Jiang Wang
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China
| | - Jun Li
- School of Information Science and Technology, Shanghai Tech University, Shanghai, China
| | - Yu Liu
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Youmin Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bomin Sun
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Clinical Neuroscience Center, Ruijin Hospital LuWan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dianyou Li
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Clinical Neuroscience Center, Ruijin Hospital LuWan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Chencheng Zhang
- Department of Neurosurgery, Center for Functional Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Clinical Neuroscience Center, Ruijin Hospital LuWan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Research Center for Brain Science and Brain-Inspired Technology, Shanghai, China.
| | - Chen Liu
- School of Electrical and Information Engineering, Tianjin University, Tianjin, China.
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Fujita Y, Yanagisawa T, Fukuma R, Ura N, Oshino S, Kishima H. Abnormal phase-amplitude coupling characterizes the interictal state in epilepsy. J Neural Eng 2022; 19. [PMID: 35385832 DOI: 10.1088/1741-2552/ac64c4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 04/05/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Diagnosing epilepsy still requires visual interpretation of electroencephalography and magnetoencephalography (MEG) by specialists, which prevents quantification and standardization of diagnosis. Previous studies proposed automated diagnosis by combining various features from electroencephalography and MEG, such as relative power (Power) and functional connectivity. However, the usefulness of interictal phase-amplitude coupling (PAC) in diagnosing epilepsy is still unknown. We hypothesized that resting-state PAC would be different for patients with epilepsy in the interictal state and for healthy participants such that it would improve discrimination between the groups. METHODS We obtained resting-state MEG and magnetic resonance imaging in 90 patients with epilepsy during their preoperative evaluation and in 90 healthy participants. We used the cortical currents estimated from MEG and magnetic resonance imaging to calculate Power in the δ (1-3 Hz), θ (4-7 Hz), α (8-13 Hz), β (13-30 Hz), low γ (35-55 Hz), and high γ (65-90 Hz) bands and functional connectivity in the θ band. PAC was evaluated using the synchronization index (SI) for eight frequency band pairs: the phases of δ, θ, α, and β and the amplitudes of low and high γ. First, we compared the mean SI values for the patients with epilepsy and the healthy participants. Then, using features such as PAC, Power, functional connectivity, and features extracted by deep learning individually or combined, we tested whether PAC improves discrimination accuracy for the two groups. RESULTS The mean SI values were significantly different for the patients with epilepsy and the healthy participants. The SI value difference was highest for θ/low γ in the temporal lobe. Discrimination accuracy was the highest, at 90%, using the combination of PAC and deep learning. SIGNIFICANCE Abnormal PAC characterized the patients with epilepsy in the interictal state compared with the healthy participants, potentially improving the discrimination of epilepsy.
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Affiliation(s)
- Yuya Fujita
- Institute for Advanced co-creation studies, Osaka University, 2-2 Yamadaoka Suita Osaka Japan, Suita, 565-0871, JAPAN
| | - Takufumi Yanagisawa
- Institute for Advanced co-creation studies, Osaka University, 2-2 Yamadaoka Suita Osaka Japan, Suita, 565-0871, JAPAN
| | - Ryohei Fukuma
- Institute for Advanced co-creation studies, Osaka University, 2-2 Yamadaoka Suita Osaka Japan, Suita, 565-0871, JAPAN
| | - Natsuko Ura
- Institute for Advanced co-creation studies, Osaka University, 2-2 Yamadaoka Suita Osaka Japan, Suita, 565-0871, JAPAN
| | - Satoru Oshino
- Department of Neurosurgery, Osaka University Faculty of Medicine Graduate School of Medicine, 2-2 Yamadaoka, suita, Osaka, Japan, Osaka University Graduate School of Medicine, Dept of Neurosurgery, Osaka, Osaka, 5670871, JAPAN
| | - Haruhiko Kishima
- Department of neurosurgery, Osaka University, 2-2, Yamadaoka, Suita, Suita, Osaka, 5650871, JAPAN
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Coluzzi D, Pirastru A, Pelizzari L, Cabinio M, Laganà MM, Baselli G, Baglio F. Development and Testing of SPIDER-NET: An Interactive Tool for Brain Connectogram Visualization, Sub-Network Exploration and Graph Metrics Quantification. Front Neurosci 2022; 16:818385. [PMID: 35368253 PMCID: PMC8968144 DOI: 10.3389/fnins.2022.818385] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 02/17/2022] [Indexed: 11/13/2022] Open
Abstract
Brain connectomics consists in the modeling of human brain as networks, mathematically represented as numerical connectivity matrices. However, this representation may result in difficult interpretation of the data. To overcome this limitation, graphical representation by connectograms is currently used via open-source tools, which, however, lack user-friendly interfaces and options to explore specific sub-networks. In this context, we developed SPIDER-NET (Software Package Ideal for Deriving Enhanced Representations of brain NETworks), an easy-to-use, flexible, and interactive tool for connectograms generation and sub-network exploration. This study aims to present SPIDER-NET and to test its potential impact on pilot cases. As a working example, structural connectivity (SC) was investigated with SPIDER-NET in a group of 17 healthy controls (HCs) and in two subjects with stroke injury (Case 1 and Case 2, both with a focal lesion affecting part of the right frontal lobe, insular cortex and subcortical structures). 165 parcels were determined from individual structural magnetic resonance imaging data by using the Destrieux atlas, and defined as nodes. SC matrices were derived with Diffusion Tensor Imaging tractography. SC matrices of HCs were averaged to obtain a single group matrix. SC matrices were then used as input for SPIDER-NET. First, SPIDER-NET was used to derive the connectogram of the right hemisphere of Case 1 and Case 2. Then, a sub-network of interest (i.e., including gray matter regions affected by the stroke lesions) was interactively selected and the associated connectograms were derived for Case 1, Case 2 and HCs. Finally, graph-based metrics were derived for whole-brain SC matrices of Case 1, Case 2 and HCs. The software resulted effective in representing the expected (dis) connectivity pattern in the hemisphere affected by the stroke lesion in Cases 1 and 2. Furthermore, SPIDER-NET allowed to test an a priori hypothesis by interactively extracting a sub-network of interest: Case 1 showed a sub-network connectivity pattern different from Case 2, reflecting the different clinical severity. Global and local graph-based metrics derived with SPIDER-NET were different between cases with stroke injury and HCs. The tool proved to be accessible, intuitive, and interactive in brain connectivity investigation and provided both qualitative and quantitative evidence.
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Affiliation(s)
- Davide Coluzzi
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
- *Correspondence: Davide Coluzzi,
| | - Alice Pirastru
- IRCCS Fondazione Don Carlo Gnocchi Onlus, Milan, Italy
- Alice Pirastru,
| | | | - Monia Cabinio
- IRCCS Fondazione Don Carlo Gnocchi Onlus, Milan, Italy
| | | | - Giuseppe Baselli
- Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy
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Wapstra NJ, Ketola M, Thompson S, Lee A, Madhyastha T, Grabowski TJ, Stocco A. Increased Basal Ganglia Modulatory Effective Connectivity Observed in Resting-State fMRI in Individuals With Parkinson's Disease. Front Aging Neurosci 2022; 14:719089. [PMID: 35350633 PMCID: PMC8957976 DOI: 10.3389/fnagi.2022.719089] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 02/16/2022] [Indexed: 01/17/2023] Open
Abstract
Alterations to interactions between networked brain regions underlie cognitive impairment in many neurodegenerative diseases, providing an important physiological link between brain structure and cognitive function. Previous attempts to characterize the effects of Parkinson's disease (PD) on network functioning using resting-state functional magnetic resonance imaging (rs-fMRI), however, have yielded inconsistent and contradictory results. Potential problems with prior work arise in the specifics of how the area targeted by the diseases (the basal ganglia) interacts with other brain regions. Specifically, current computational models point to the fact that the basal ganglia contributions should be captured with modulatory (i.e., second-order) rather than direct (i.e., first-order) functional connectivity measures. Following this hypothesis, a principled but manageable large-scale brain architecture, the Common Model of Cognition, was used to identify differences in basal ganglia connectivity in PD by analyzing resting-state fMRI data from 111 participants (70 patients with PD; 41 healthy controls) using Dynamic Causal Modeling (DCM). Specifically, the functional connectivity of the basal ganglia was modeled as two second-level, modulatory connections that control projections from sensory cortices to the prefrontal cortex, and from the hippocampus and medial temporal lobe to the prefrontal cortex. We then examined group differences between patients with PD and healthy controls in estimated modulatory effective connectivity in these connections. The Modulatory variant of the Common Model of Cognition outperformed the Direct model across all subjects. It was also found that these second-level modulatory connections had higher estimates of effective connectivity in the PD group compared to the control group, and that differences in effective connectivity were observed for all direct connections between the PD and control groups.We make the case that accounting for modulatory effective connectivity better captures the effects of PD on network functioning and influences the interpretation of the directionality of the between-group results. Limitations include that the PD group was scanned on dopaminergic medication, results were derived from a reasonable but small number of individuals and the ratio of PD to healthy control participants was relatively unbalanced. Future research will examine if the observed effect holds for individuals with PD scanned off their typical dopaminergic medications.
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Affiliation(s)
- Nicholas J. Wapstra
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Micah Ketola
- Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, United States
| | - Shelby Thompson
- Department of Kinesiology, University of Georgia, Athens, GA, United States
| | - Adel Lee
- Etosha Business and Research Consulting, Mount Berry, GA, United States
| | | | - Thomas J. Grabowski
- Department of Radiology, University of Washington, Seattle, WA, United States,Department of Neurology, University of Washington, Seattle, WA, United States
| | - Andrea Stocco
- Department of Psychology, University of Washington, Seattle, WA, United States,*Correspondence: Andrea Stocco
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Li J, Guo J, Sun W, Mei J, Wang Y, Zhang L, Zhang J, Gao J, Su K, Lv Z, Feng X, Li R. Effects of Exercise on Parkinson’s Disease: A Meta-Analysis of Brain Imaging Studies. Front Hum Neurosci 2022; 16:796712. [PMID: 35250515 PMCID: PMC8889068 DOI: 10.3389/fnhum.2022.796712] [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: 10/17/2021] [Accepted: 01/17/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundExercise is increasingly recognized as a key component of Parkinson’s disease (PD) treatment strategies, but the underlying mechanism of how exercise affects PD is not yet fully understood.ObjectiveThe activation likelihood estimation (ALE) method is used to study the mechanism of exercise affecting PD, providing a theoretical basis for studying exercise and PD, and promoting the health of patients with PD.MethodsRelevant keywords were searched on the PubMed, Cochrane Library, and Web of Science databases. Seven articles were finally included according to the screening criteria, with a total sample size of 97 individuals. Using the GingerALE 3.0.2 software, an ALE meta-analysis was performed using seven studies that met the requirements, and the probability of the cross-experiment activation of each voxel was calculated.ResultsThe meta-analysis produced seven clusters, and major activations were found in the cerebellum, occipital lobe, parietal lobe, and frontal lobe brain regions.ConclusionExercise for PD mainly results in the enhanced activation of the cerebellum, occipital lobe, parietal lobe, and frontal lobe. Exercise for PD does not cause a change in the activation of a single brain area, and the observed improvement may result from coordinated changes in multiple brain areas.
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Affiliation(s)
- Jingwen Li
- College of Rehabilitation Medicine, Henan University of Chinese Medicine, Zhengzhou, China
| | - Jian Guo
- Rehabilitation Center, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Weijuan Sun
- Rehabilitation Center, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Jinjin Mei
- College of Rehabilitation Medicine, Henan University of Chinese Medicine, Zhengzhou, China
| | - Yiying Wang
- College of Rehabilitation Medicine, Henan University of Chinese Medicine, Zhengzhou, China
| | - Lihong Zhang
- College of Rehabilitation Medicine, Henan University of Chinese Medicine, Zhengzhou, China
| | - Jianyun Zhang
- College of Rehabilitation Medicine, Henan University of Chinese Medicine, Zhengzhou, China
| | - Jing Gao
- Rehabilitation Center, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Kaiqi Su
- College of Rehabilitation Medicine, Henan University of Chinese Medicine, Zhengzhou, China
| | - Zhuan Lv
- College of Rehabilitation Medicine, Henan University of Chinese Medicine, Zhengzhou, China
- Rehabilitation Center, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Xiaodong Feng
- College of Rehabilitation Medicine, Henan University of Chinese Medicine, Zhengzhou, China
- Rehabilitation Center, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- Xiaodong Feng,
| | - Ruiqing Li
- College of Rehabilitation Medicine, Henan University of Chinese Medicine, Zhengzhou, China
- Rehabilitation Center, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
- *Correspondence: Ruiqing Li,
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Xu J, Yu M, Wang H, Li Y, Li L, Ren J, Pan C, Liu W. Altered Dynamic Functional Connectivity in de novo Parkinson’s Disease Patients With Depression. Front Aging Neurosci 2022; 13:789785. [PMID: 35237143 PMCID: PMC8882994 DOI: 10.3389/fnagi.2021.789785] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 12/31/2021] [Indexed: 11/13/2022] Open
Abstract
BackgroundDepression is one of the most prevalent and disturbing non-motor symptoms in Parkinson’s disease (PD), with few dynamic functional connectivity (dFC) features measured in previous studies. Our aim was to investigate the alterations of the dynamics in de novo patients with PD with depression (dPD).MethodsWe performed dFC analysis on the data of resting-state functional MRI from 21 de novo dPD, 34 de novo patients with PD without depression (ndPD), and 43 healthy controls (HCs). Group independent component analysis, a sliding window approach, followed by k-means clustering were conducted to assess functional connectivity states (which represented highly structured connectivity patterns reoccurring over time) and temporal properties for comparison between groups. We further performed dynamic graph-theoretical analysis to examine the variability of topological metrics.ResultsFour distinct functional connectivity states were clustered via dFC analysis. Compared to patients with ndPD and HCs, patients with dPD showed increased fractional time and mean dwell time in state 2, characterized by default mode network (DMN)-dominated and cognitive executive network (CEN)-disconnected patterns. Besides, compared to HCs, patients with dPD and patients with ndPD both showed weaker dynamic connectivity within the sensorimotor network (SMN) in state 4, a regionally densely connected state. We additionally observed that patients with dPD presented less variability in the local efficiency of the network.ConclusionsOur study demonstrated that altered network connection over time, mainly involving the DMN and CEN, with abnormal dynamic graph properties, may contribute to the presence of depression in patients with PD.
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Affiliation(s)
- Jianxia Xu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Miao Yu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Hui Wang
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- Department of Neurology, Lianyungang Hospital of Traditional Chinese Medicine, Lianyungang, China
| | - Yuqian Li
- Department of Neurology, Affiliated BenQ Hospital of Nanjing Medical University, Nanjing, China
| | - Lanting Li
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jingru Ren
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chenxi Pan
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Weiguo Liu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
- *Correspondence: Weiguo Liu,
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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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Basaia S, Agosta F, Diez I, Bueichekú E, d'Oleire Uquillas F, Delgado-Alvarado M, Caballero-Gaudes C, Rodriguez-Oroz M, Stojkovic T, Kostic VS, Filippi M, Sepulcre J. Neurogenetic traits outline vulnerability to cortical disruption in Parkinson's disease. Neuroimage Clin 2022; 33:102941. [PMID: 35091253 PMCID: PMC8800137 DOI: 10.1016/j.nicl.2022.102941] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 12/03/2021] [Accepted: 01/10/2022] [Indexed: 01/18/2023]
Abstract
The genetic traits that underlie vulnerability to neuronal damage across specific brain circuits in Parkinson's disease (PD) remain to be elucidated. In this study, we characterized the brain topological intersection between propagating connectivity networks in controls and PD participants and gene expression patterns across the human cortex - such as the SNCA gene. We observed that brain connectivity originated from PD-related pathology epicenters in the brainstem recapitulated the anatomical distribution of alpha-synuclein histopathology in postmortem data. We also discovered that the gene set most related to cortical propagation patterns of PD-related pathology was primarily involved in microtubule cellular components. Thus, this study sheds light on new avenues for enhancing detection of PD neuronal vulnerability via an evaluation of in vivo connectivity trajectories across the human brain and successful integration of neuroimaging-genetic strategies.
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Affiliation(s)
- Silvia Basaia
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Federica Agosta
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy
| | - Ibai Diez
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Elisenda Bueichekú
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Federico d'Oleire Uquillas
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Manuel Delgado-Alvarado
- Neurology Department, Sierrallana Hospital, Torrelavega, Spain; IDIVAL, Valdecilla Biomedical Research Institute, Santander, Spain; Biomedical Research Networking Center for Mental Health (CIBERSAM), Madrid, Spain
| | | | - MariCruz Rodriguez-Oroz
- Neurology Department, Clínica Universidad de Navarra, Neuroscience Unit, CIMA Universidad de Navarra, Spain
| | - Tanja Stojkovic
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Vladimir S Kostic
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy
| | - Jorge Sepulcre
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
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Effects of task prioritization on a postural-motor task in early-stage Parkinson's disease: EEG connectivity and clinical implication. GeroScience 2022; 44:2061-2075. [PMID: 35039998 DOI: 10.1007/s11357-022-00516-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 01/12/2022] [Indexed: 11/04/2022] Open
Abstract
Appropriate attentional resource allocation could minimize exaggerated dual-task interference due to basal ganglia dysfunction in Parkinson's disease (PD). Here, we assessed the electroencephalography (EEG) functional connectivity to investigate how task prioritization affected posture-motor dual-tasks in PD. Sixteen early-stage PD patients and 16 healthy controls maintained balance in narrow stance alone (single-posture task) or while separating two interlocking rings (postural dual-task). The participants applied a posture-focus or supraposture-focus strategy in the postural dual-task. Postural sway dynamics, ring-touching time, and scalp EEG were analyzed. Both groups exhibited smaller postural sway size, postural determinism, and ring-touching time with the supraposture-focus versus posture-focus strategy. PD patients exhibited higher mean inter-regional connectivity strength than control subjects in both single and dual-task postural conditions. To cope with dual-task interference, PD patients increased inter-regional connectivity (especially with the posture-focus strategy), while control subjects reduced inter-regional connectivity. The difference in mean connectivity strength between the dual-task condition with supraposture-focus and single-posture condition was negatively correlated to the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III total scores and hand-related sub-scores. Our findings suggest differential task prioritization effects on dual-task performance and cortical reorganization between early-stage PD and healthy individuals. Early-stage PD patients are advocated to use a supraposture-focus strategy during a postural dual-task. In addition, with a supraposture-focus strategy, PD patients with mild motor severity could increase compensatory inter-regional connectivity to cope with dual-task interference.
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31
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Disrupted functional connectivity in PD with probable RBD and its cognitive correlates. Sci Rep 2021; 11:24351. [PMID: 34934134 PMCID: PMC8692356 DOI: 10.1038/s41598-021-03751-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 12/09/2021] [Indexed: 11/24/2022] Open
Abstract
Recent studies associated rapid eye movement sleep behavior disorder (RBD) in Parkinson’s disease (PD) with severe cognitive impairment and brain atrophy. However, whole-brain functional connectivity has never been explored in this group of PD patients. In this study, whole-brain network-based statistics and graph-theoretical approaches were used to characterize resting-state interregional functional connectivity in PD with probable RBD (PD-pRBD) and its relationship with cognition. Our sample consisted of 30 healthy controls, 32 PD without probable RBD (PD-non pRBD), and 27 PD-pRBD. The PD-pRBD group showed reduced functional connectivity compared with controls mainly involving cingulate areas with temporal, frontal, insular, and thalamic regions (p < 0.001). Also, the PD-pRBD group showed reduced functional connectivity between right ventral posterior cingulate and left medial precuneus compared with PD-non pRBD (p < 0.05). We found increased normalized characteristic path length in PD-pRBD compared with PD-non pRBD. In the PD-pRBD group, mean connectivity strength from reduced connections correlated with visuoperceptual task and normalized characteristic path length correlated with processing speed and verbal memory tasks. This work demonstrates the existence of disrupted functional connectivity in PD-pRBD, together with abnormal network integrity, that supports its consideration as a severe PD subtype.
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32
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Diez-Cirarda M, Gabilondo I, Ibarretxe-Bilbao N, Gómez-Esteban JC, Kim J, Lucas-Jiménez O, Del Pino R, Peña J, Ojeda N, Mihaescu A, Valli M, Acera MA, Cabrera-Zubizarreta A, Gómez-Beldarrain MA, Strafella AP. Contributions of sex, depression, and cognition on brain connectivity dynamics in Parkinson's disease. NPJ Parkinsons Dis 2021; 7:117. [PMID: 34916518 PMCID: PMC8677758 DOI: 10.1038/s41531-021-00257-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Accepted: 11/19/2021] [Indexed: 01/09/2023] Open
Abstract
Alterations in time-varying functional connectivity (FC) have been found in Parkinson's disease (PD) patients. To date, very little is known about the influence of sex on brain FC in PD patients and how this could be related to disease severity. The first objective was to evaluate the influence of sex on dynamic FC characteristics in PD patients and healthy controls (HC), while the second aim was to investigate the temporal patterns of dynamic connectivity related to PD motor and non-motor symptoms. Ninety-nine PD patients and sixty-two HC underwent a neuropsychological and clinical assessment. Rs-fMRI and T1-weighted MRI were also acquired. Dynamic FC analyses were performed in the GIFT toolbox. Dynamic FC analyses identified two States: State I, characterized by within-network positive coupling; and State II that showed between-network connectivity, mostly involving somatomotor and visual networks. Sex differences were found in dynamic indexes in HC but these differences were not observed in PD. Hierarchical clustering analysis identified three phenotypically distinct PD subgroups: (1) Subgroup A was characterized by mild motor symptoms; (2) Subgroup B was characterized by depressive and motor symptoms; (3) Subgroup C was characterized by cognitive and motor symptoms. Results revealed that changes in the temporal properties of connectivity were related to the motor/non-motor outcomes of PD severity. Findings suggest that while in HC sex differences may play a certain role in dynamic connectivity patterns, in PD patients, these effects may be overcome by the neurodegenerative process. Changes in the temporal properties of connectivity in PD were mainly related to the clinical markers of PD severity.
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Affiliation(s)
- Maria Diez-Cirarda
- Brain Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada.
- E.J. Safra Parkinson Disease Program & Movement Disorder Unit, Neurology Division; Krembil Brain Institute, University Health Network, University of Toronto, Toronto, ON, Canada.
- Neurodegenerative Diseases Group, Biocruces Bizkaia Health Research Institute, Barakaldo, Spain.
| | - Iñigo Gabilondo
- Neurodegenerative Diseases Group, Biocruces Bizkaia Health Research Institute, Barakaldo, Spain
- IKERBASQUE, The Basque Foundation for Science, Bilbao, Spain
| | - Naroa Ibarretxe-Bilbao
- Department of Psychology, Faculty of Health Sciences, University of Deusto, Bilbao, Spain
| | - Juan Carlos Gómez-Esteban
- Neurodegenerative Diseases Group, Biocruces Bizkaia Health Research Institute, Barakaldo, Spain
- Neurology Department, Cruces University Hospital, Barakaldo, Spain
| | - Jinhee Kim
- Brain Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
- E.J. Safra Parkinson Disease Program & Movement Disorder Unit, Neurology Division; Krembil Brain Institute, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Olaia Lucas-Jiménez
- Department of Psychology, Faculty of Health Sciences, University of Deusto, Bilbao, Spain
| | - Rocio Del Pino
- Neurodegenerative Diseases Group, Biocruces Bizkaia Health Research Institute, Barakaldo, Spain
| | - Javier Peña
- Department of Psychology, Faculty of Health Sciences, University of Deusto, Bilbao, Spain
| | - Natalia Ojeda
- Department of Psychology, Faculty of Health Sciences, University of Deusto, Bilbao, Spain
| | - Alexander Mihaescu
- Brain Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
- E.J. Safra Parkinson Disease Program & Movement Disorder Unit, Neurology Division; Krembil Brain Institute, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Mikaeel Valli
- Brain Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
- E.J. Safra Parkinson Disease Program & Movement Disorder Unit, Neurology Division; Krembil Brain Institute, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Maria Angeles Acera
- Neurodegenerative Diseases Group, Biocruces Bizkaia Health Research Institute, Barakaldo, Spain
| | | | | | - Antonio P Strafella
- Brain Health Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada.
- E.J. Safra Parkinson Disease Program & Movement Disorder Unit, Neurology Division; Krembil Brain Institute, University Health Network, University of Toronto, Toronto, ON, Canada.
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33
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da Silva Machado CB, da Silva LM, Gonçalves AF, Andrade PRD, Mendes CKTT, de Assis TJCF, Godeiro Júnior CDO, Andrade SM. Multisite non-invasive brain stimulation in Parkinson's disease: A scoping review. NeuroRehabilitation 2021; 49:515-531. [PMID: 34776426 PMCID: PMC8764602 DOI: 10.3233/nre-210190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
BACKGROUND: Parkinson’s disease (PD) is a progressive neurodegenerative disorder, characterized by cardinal motor symptoms in addition to cognitive impairment. New insights concerning multisite non-invasive brain stimulation effects have been gained, which can now be used to develop innovative treatment approaches. OBJECTIVE: Map the researchs involving multisite non-invasive brain stimulation in PD, synthesize the available evidence and discuss future directions. METHODS: The databases PubMed, PsycINFO, CINAHL, LILACS and The Cochrane Library were searched from inception until April 2020, without restrictions on the date of publication or the language in which it was published. The reviewers worked in pairs and sequentially evaluated the titles, abstracts and then the full text of all publications identified as potentially relevant. RESULTS: Twelve articles met the inclusion criteria. The target brain regions included mainly the combination of a motor and a frontal area, such as stimulation of the primary motor córtex associated with the dorsolateral prefrontal cortex. Most of the trials showed that this modality was only more effective for the motor component, or for the cognitive and/or non-motor, separately. CONCLUSIONS: Despite the results being encouraging for the use of the multisite aproach, the indication for PD management should be carried out with caution and deserves scientific deepening.
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Affiliation(s)
| | | | | | | | | | | | - Clécio de Oliveira Godeiro Júnior
- Division of Neurology, CHU of Grenoble, Grenoble Alpes University, La Tronche, Grenoble, France.,Division of Neurology, Hospital Universitario Onofre Lopes, Federal University of Rio Grande do Norte, Natal, Brazil
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34
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Jin C, Qi S, Teng Y, Li C, Yao Y, Ruan X, Wei X. Altered Degree Centrality of Brain Networks in Parkinson's Disease With Freezing of Gait: A Resting-State Functional MRI Study. Front Neurol 2021; 12:743135. [PMID: 34707559 PMCID: PMC8542685 DOI: 10.3389/fneur.2021.743135] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Accepted: 09/06/2021] [Indexed: 12/12/2022] Open
Abstract
Freezing of gait (FOG) in Parkinson's disease (PD) leads to devastating consequences; however, little is known about its functional brain network. We explored the differences in degree centrality (DC) of functional networks among PD with FOG (PD FOG+), PD without FOG (PD FOG–), and healthy control (HC) groups. In all, 24 PD FOG+, 37 PD FOG–, and 22 HCs were recruited and their resting-state functional magnetic imaging images were acquired. The whole brain network was analyzed using graph theory analysis. DC was compared among groups using the two-sample t-test. The DC values of disrupted brain regions were correlated with the FOG Questionnaire (FOGQ) scores. Receiver operating characteristic curve analysis was performed. We found significant differences in DC among groups. Compared with HCs, PD FOG+ patients showed decreased DC in the middle frontal gyrus (MFG), superior temporal gyrus (STG), parahippocampal gyrus (PhG), inferior temporal gyrus (ITG), and middle temporal gyrus (MTG). Compared with HC, PD FOG– presented with decreased DC in the MFG, STG, PhG, and ITG. Compared with PD FOG–, PD FOG+ showed decreased DC in the MFG and ITG. A negative correlation existed between the DC of ITG and FOGQ scores; the DC in ITG could distinguish PD FOG+ from PD FOG– and HC. The calculated AUCs were 81.3, 89.5, and 77.7% for PD FOG+ vs. HC, PD FOG– vs. HC, and PD FOG+ vs. PD FOG–, respectively. In conclusion, decreased DC of ITG in PD FOG+ patients compared to PD FOG– patients and HCs may be a unique feature for PD FOG+ and can likely distinguish PD FOG+ from PD FOG– and HC groups.
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Affiliation(s)
- Chaoyang Jin
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.,Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Yueyang Teng
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Chen Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Yudong Yao
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ, United States
| | - Xiuhang Ruan
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xinhua Wei
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
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35
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Xiong Y, Han D, He J, Zong R, Bian X, Duan C, Zhang D, Zhou X, Pan L, Lou X. Correlation of visual area with tremor improvement after MRgFUS thalamotomy in Parkinson's disease. J Neurosurg 2021; 136:681-688. [PMID: 34479209 DOI: 10.3171/2021.3.jns204329] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 03/01/2021] [Indexed: 11/06/2022]
Abstract
OBJECTIVE MRI-guided focused ultrasound (MRgFUS) thalamotomy is a novel and minimally invasive alternative for medication-refractory tremor in Parkinson's disease (PD). However, the impact of MRgFUS thalamotomy on spontaneous neuronal activity in PD remains unclear. The purpose of the current study was to evaluate the effects of MRgFUS thalamotomy on local fluctuations in neuronal activity as measured by the fractional amplitude of low-frequency fluctuations (fALFF) in patients with PD. METHODS Participants with PD undergoing MRgFUS thalamotomy were recruited. Tremor scores were assessed before and 3 and 12 months after treatment using the Clinical Rating Scale for Tremor. MRI data were collected before and 1 day, 1 week, 1 month, 3 months, and 12 months after thalamotomy. The fALFF was calculated. A whole-brain voxel-wise paired t-test was used to identify significant changes in fALFF at 12 months after treatment compared to baseline. Then fALFF in the regions with significant differences were extracted from fALFF maps of patients for further one-way repeated-measures ANOVA to investigate its dynamic alterations. The association between fALFF changes induced by thalamotomy and tremor improvement were evaluated using the nonparametric Spearman rank test. RESULTS Nine participants with PD (mean age ± SD 64.7 ± 6.1 years, 8 males) were evaluated. Voxel-based analysis showed that fALFF in the left occipital cortex (Brodmann area 17 [BA17]) significantly decreased at 12 months after thalamotomy compared to baseline (voxel p < 0.001, cluster p < 0.05 family-wise error [FWE] corrected). At baseline, fALFF in the left occipital BA17 in patients was elevated compared with that in 9 age- and gender-matched healthy subjects (p < 0.05). Longitudinal analysis displayed the dynamic changes of fALFF in this region (F (5,40) = 3.61, p = 0.009). There was a significant positive correlation between the falling trend in fALFF in the left occipital BA17 and hand tremor improvement after treatment over 3 time points (Spearman's rho = 0.44, p = 0.02). CONCLUSIONS The present study investigated the impact of MRgFUS ventral intermediate nucleus thalamotomy on spontaneous neural activity in medication-refractory tremor-dominant PD. The visual area is, for the first time, reported as relevant to tremor improvement in PD after MRgFUS thalamotomy, suggesting a distant effect of MRgFUS thalamotomy and the involvement of specific visuomotor networks in tremor control in PD.
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Affiliation(s)
- Yongqin Xiong
- 1Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Dongshan Han
- 1Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Jianfeng He
- 1Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Rui Zong
- 2Department of Neurosurgery, Chinese PLA General Hospital, Beijing, China; and
| | - Xiangbing Bian
- 1Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Caohui Duan
- 1Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Dekang Zhang
- 1Department of Radiology, Chinese PLA General Hospital, Beijing, China
| | - Xin Zhou
- 3Innovation Academy for Precision Measurement Science and Technology, The Chinese Academy of Sciences-Wuhan National Laboratory for Optoelectronics, Wuhan, China
| | - Longsheng Pan
- 2Department of Neurosurgery, Chinese PLA General Hospital, Beijing, China; and
| | - Xin Lou
- 1Department of Radiology, Chinese PLA General Hospital, Beijing, China
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36
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Filippi M, Basaia S, Sarasso E, Stojkovic T, Stankovic I, Fontana A, Tomic A, Piramide N, Stefanova E, Markovic V, Kostic VS, Agosta F. Longitudinal brain connectivity changes and clinical evolution in Parkinson's disease. Mol Psychiatry 2021; 26:5429-5440. [PMID: 32409731 DOI: 10.1038/s41380-020-0770-0] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 04/23/2020] [Accepted: 04/29/2020] [Indexed: 11/09/2022]
Abstract
Longitudinal connectivity studies might guide our understanding of the underlying neurodegenerative processes. We report the results of a longitudinal study in patients at different stages of Parkinson's disease (PD), who performed motor and non-motor evaluations and serial resting state (RS) functional MRI (fMRI). Cluster analysis was applied to demographic and clinical data of 146 PD patients to define disease subtypes. Brain network functional alterations were assessed at baseline in PD relative to 60 healthy controls and every year for a maximum of 4 years in PD groups. Progression of brain network changes were compared between patient clusters using RS fMRI. The contribution of network changes in predicting clinical deterioration was explored. Two main PD clusters were identified: mild PD (86 patients) and moderate-to-severe PD (60 patients), with the latter group being older and having earlier onset, longer PD duration, more severe motor, non-motor and cognitive deficits. Within the mild patient cluster, two clinical subtypes were further identified: mild motor-predominant (43) and mild-diffuse (43), with the latter being older and having more frequent non-motor symptoms. Longitudinal functional connectivity changes vary across patients in different disease stages with the coexistence of hypo- and hyper-connectivity in all subtypes. RS fMRI changes were associated with motor, cognitive and non-motor evolution in PD patients. Baseline RS fMRI presaged clinical and cognitive evolution. Our network perspective was able to define trajectories of functional architecture changes according to PD stages and prognosis. RS fMRI may be an early biomarker of PD motor and non-motor progression.
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Affiliation(s)
- Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy. .,Neurology Unit and Neurophysiology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy. .,Vita-Salute San Raffaele University, Milan, Italy.
| | - Silvia Basaia
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisabetta Sarasso
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Tanja Stojkovic
- Clinic of Neurology, School of Medicine, University of Belgrade, Belgrade, Serbia
| | - Iva Stankovic
- Clinic of Neurology, School of Medicine, University of Belgrade, Belgrade, Serbia
| | - Andrea Fontana
- Unit of Biostatistics, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Foggia, Italy
| | - Aleksandra Tomic
- Clinic of Neurology, School of Medicine, University of Belgrade, Belgrade, Serbia
| | - Noemi Piramide
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Elka Stefanova
- Clinic of Neurology, School of Medicine, University of Belgrade, Belgrade, Serbia
| | - Vladana Markovic
- Clinic of Neurology, School of Medicine, University of Belgrade, Belgrade, Serbia
| | - Vladimir S Kostic
- Clinic of Neurology, School of Medicine, University of Belgrade, Belgrade, Serbia
| | - Federica Agosta
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
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37
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Ionescu TM, Amend M, Hafiz R, Biswal BB, Maurer A, Pichler BJ, Wehrl HF, Herfert K. Striatal and prefrontal D2R and SERT distributions contrastingly correlate with default-mode connectivity. Neuroimage 2021; 243:118501. [PMID: 34428573 DOI: 10.1016/j.neuroimage.2021.118501] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 07/23/2021] [Accepted: 08/20/2021] [Indexed: 11/28/2022] Open
Abstract
Although brain research has taken important strides in recent decades, the interaction and coupling of its different physiological levels is still not elucidated. Specifically, the molecular substrates of resting-state functional connectivity (rs-FC) remain poorly understood. The aim of this study was elucidating interactions between dopamine D2 receptors (D2R) and serotonin transporter (SERT) availabilities in the striatum (CPu) and medial prefrontal cortex (mPFC), two of the main dopaminergic and serotonergic projection areas, and the default-mode network. Additionally, we delineated its interaction with two other prominent resting-state networks (RSNs), the salience network (SN) and the sensorimotor network (SMN). To this extent, we performed simultaneous PET/fMRI scans in a total of 59 healthy rats using [11C]raclopride and [11C]DASB, two tracers used to image quantify D2R and SERT respectively. Edge, node and network-level rs-FC metrics were calculated for each subject and potential correlations with binding potentials (BPND) in the CPu and mPFC were evaluated. We found widespread negative associations between CPu D2R availability and all the RSNs investigated, consistent with the postulated role of the indirect basal ganglia pathway. Correlations between D2Rs in the mPFC were weaker and largely restricted to DMN connectivity. Strikingly, medial prefrontal SERT correlated both positively with anterior DMN rs-FC and negatively with rs-FC between and within the SN, SMN and the posterior DMN, underlining the complex role of serotonergic neurotransmission in this region. Here we show direct relationships between rs-FC and molecular properties of the brain as assessed by simultaneous PET/fMRI in healthy rodents. The findings in the present study contribute to the basic understanding of rs-FC by revealing associations between inter-subject variances of rs-FC and receptor and transporter availabilities. Additionally, since current therapeutic strategies typically target neurotransmitter systems with the aim of normalizing brain function, delineating associations between molecular and network-level brain properties is essential and may enhance the understanding of neuropathologies and support future drug development.
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Affiliation(s)
- Tudor M Ionescu
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Mario Amend
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Rakibul Hafiz
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ, USA
| | - Bharat B Biswal
- Department of Biomedical Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ, USA
| | - Andreas Maurer
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Bernd J Pichler
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Hans F Wehrl
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany
| | - Kristina Herfert
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, Tuebingen, Germany.
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38
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Korhonen O, Zanin M, Papo D. Principles and open questions in functional brain network reconstruction. Hum Brain Mapp 2021; 42:3680-3711. [PMID: 34013636 PMCID: PMC8249902 DOI: 10.1002/hbm.25462] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 03/11/2021] [Accepted: 04/10/2021] [Indexed: 12/12/2022] Open
Abstract
Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network representation involves often covert theoretical assumptions and methodological choices which affect the way networks are reconstructed from experimental data, and ultimately the resulting network properties and their interpretation. Here, we review some fundamental conceptual underpinnings and technical issues associated with brain network reconstruction, and discuss how their mutual influence concurs in clarifying the organization of brain function.
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Affiliation(s)
- Onerva Korhonen
- Department of Computer ScienceAalto University, School of ScienceHelsinki
- Centre for Biomedical TechnologyUniversidad Politécnica de MadridPozuelo de Alarcón
| | - Massimiliano Zanin
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC‐UIB), Campus UIBPalma de MallorcaSpain
| | - David Papo
- Fondazione Istituto Italiano di TecnologiaFerrara
- Department of Neuroscience and Rehabilitation, Section of PhysiologyUniversity of FerraraFerrara
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Li Z, Ren G, Liu C, Wang Q, Liang K, Han C, Qiao H, Zhang J, Wang Q, Meng F. Dysfunctional Brain Dynamics of Parkinson's Disease and the Effect of Acute Deep Brain Stimulation. Front Neurosci 2021; 15:697909. [PMID: 34354564 PMCID: PMC8331088 DOI: 10.3389/fnins.2021.697909] [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: 04/20/2021] [Accepted: 06/09/2021] [Indexed: 12/25/2022] Open
Abstract
Background: Parkinson's disease (PD) is the second most common neurodegenerative disorder after Alzheimer's disease, and deep brain stimulation (DBS) can effectively alleviate PD symptoms. Although previous studies have detected network features of PD and DBS, few studies have considered their dynamic characteristics. Objective: We tested two hypotheses. (1) Reduced brain dynamics, as evidenced by slowed microstate dynamic change, is a characteristic of PD and is related to the movement disorders of patients with PD. (2) Therapeutic acute DBS can partially reverse slow brain dynamics in PD to healthy levels. Methods: We used electroencephalography (EEG) microstate analysis based on high density (256-channel) EEG to detect the effects of PD and DBS on brain dynamic changes on a sub-second timescale. We compared 21 healthy controls (HCs) with 20 patients with PD who were in either DBS-OFF or DBS-ON states. Assessment of movement disorder using the Unified Parkinson's Disease Rating Scale III was correlated with microstate parameters. Results: Compared with HCs, patients with PD displayed a longer mean microstate duration with reduced occurrence per second, which were significantly associated with movement disorders. In patients with PD, some parameters of microstate analysis were restored toward healthy levels after DBS. Conclusions: Resting-state EEG microstate analysis is an important tool for investigating brain dynamic changes in PD and DBS. PD can slow down brain dynamic change, and therapeutic acute DBS can partially reverse this change toward a healthy level.
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Affiliation(s)
- Zhibao Li
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Guoping Ren
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Chong Liu
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Qiao Wang
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Kun Liang
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Chunlei Han
- Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neurostimulation, Beijing Neurosurgical Institute, Beijing, China
| | - Hui Qiao
- Neuroelectrophysiology Room, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China
| | - Jianguo Zhang
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neurostimulation, Beijing Neurosurgical Institute, Beijing, China
| | - Qun Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China
| | - Fangang Meng
- Department of Functional Neurosurgery, Beijing Neurosurgical Institute, Capital Medical University, Beijing, China.,Department of Functional Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neurostimulation, Beijing Neurosurgical Institute, Beijing, China.,Chinese Institute for Brain Research Beijing (CIBR), Beijing, China
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40
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Bagattini C, Brignani D, Bonnì S, Quattrini G, Gasparotti R, Pievani M. Functional Imaging to Guide Network-Based TMS Treatments: Toward a Tailored Medicine Approach in Alzheimer's Disease. Front Neurosci 2021; 15:687493. [PMID: 34290585 PMCID: PMC8287201 DOI: 10.3389/fnins.2021.687493] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Accepted: 06/09/2021] [Indexed: 12/15/2022] Open
Abstract
A growing number of studies is using fMRI-based connectivity to guide transcranial magnetic stimulation (TMS) target identification in both normal and clinical populations. TMS has gained increasing attention as a potential therapeutic strategy also in Alzheimer's disease (AD), but an endorsed target localization strategy in this population is still lacking. In this proof of concept study, we prove the feasibility of a tailored TMS targeting approach for AD, which stems from a network-based perspective. Based on functional imaging, the procedure allows to extract individual optimal targets meanwhile accounting for functional variability. Single-subject resting-state fMRI was used to extract individual target coordinates of two networks primarily affected in AD, the default mode and the fronto-parietal network. The localization of these targets was compared to that of traditional group-level approaches and tested against varying degrees of TMS focality. The distance between individual fMRI-derived coordinates and traditionally defined targets was significant for a supposed TMS focality of 12 mm and in some cases up to 20 mm. Comparison with anatomical labels confirmed a lack of 1:1 correspondence between anatomical and functional targets. The proposed network-based fMRI-guided TMS approach, while accounting for inter-individual functional variability, allows to target core AD networks, and might thus represent a step toward tailored TMS interventions for AD.
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Affiliation(s)
- Chiara Bagattini
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Debora Brignani
- Neurophysiology Lab, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Sonia Bonnì
- Non Invasive Brain Stimulation Unit/Department of Behavioral and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy
| | - Giulia Quattrini
- Laboratory Alzheimer’s Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Roberto Gasparotti
- Neuroradiology Unit, ASST Spedali Civili Hospital, University of Brescia, Brescia, Italy
| | - Michela Pievani
- Laboratory Alzheimer’s Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
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Luo W, Greene AS, Constable RT. Within node connectivity changes, not simply edge changes, influence graph theory measures in functional connectivity studies of the brain. Neuroimage 2021; 240:118332. [PMID: 34224851 PMCID: PMC8493952 DOI: 10.1016/j.neuroimage.2021.118332] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 05/31/2021] [Accepted: 07/01/2021] [Indexed: 01/24/2023] Open
Abstract
Interest in understanding the organization of the brain has led to the application of graph theory methods across a wide array of functional connectivity studies. The fundamental basis of a graph is the node. Recent work has shown that functional nodes reconfigure with brain state. To date, all graph theory studies of functional connectivity in the brain have used fixed nodes. Here, using fixed-, group-, state-specific, and individualized- parcellations for defining nodes, we demonstrate that functional connectivity changes within the nodes significantly influence the findings at the network level. In some cases, state- or group-dependent changes of the sort typically reported do not persist, while in others, changes are only observed when node reconfigurations are considered. The findings suggest that graph theory investigations into connectivity contrasts between brain states and/or groups should consider the influence of voxel-level changes that lead to node reconfigurations; the fundamental building block of a graph.
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Affiliation(s)
- Wenjing Luo
- Biomedical Engineering, Yale University School of Medicine, United States
| | - Abigail S Greene
- Interdepartmental Neuroscience Program, Yale University School of Medicine, United States; MD/PhD program, Yale University School of Medicine, United States
| | - R Todd Constable
- Biomedical Engineering, Yale University School of Medicine, United States; Radiology and Biomedical Imaging, Yale University School of Medicine, United States; Interdepartmental Neuroscience Program, Yale University School of Medicine, United States.
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42
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Hyder R, Jensen M, Højlund A, Kimppa L, Bailey CJ, Schaldemose JL, Kinnerup MB, Østergaard K, Shtyrov Y. Functional connectivity of spoken language processing in early-stage Parkinson's disease: An MEG study. NEUROIMAGE-CLINICAL 2021; 32:102718. [PMID: 34455187 PMCID: PMC8403765 DOI: 10.1016/j.nicl.2021.102718] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 04/01/2021] [Accepted: 06/02/2021] [Indexed: 11/24/2022]
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder, well-known for its motor symptoms; however, it also adversely affects cognitive functions, including language, a highly important human ability. PD pathology is associated, even in the early stage of the disease, with alterations in the functional connectivity within cortico-subcortical circuitry of the basal ganglia as well as within cortical networks. Here, we investigated functional cortical connectivity related to spoken language processing in early-stage PD patients. We employed a patient-friendly passive attention-free paradigm to probe neurophysiological correlates of language processing in PD patients without confounds related to active attention and overt motor responses. MEG data were recorded from a group of newly diagnosed PD patients and age-matched healthy controls who were passively presented with spoken word stimuli (action and abstract verbs, as well as grammatically correct and incorrect inflectional forms) while focussing on watching a silent movie. For each of the examined linguistic aspects, a logistic regression classifier was used to classify participants as either PD patients or healthy controls based on functional connectivity within the temporo-fronto-parietal cortical language networks. Classification was successful for action verbs (accuracy = 0.781, p-value = 0.003) and, with lower accuracy, for abstract verbs (accuracy = 0.688, p-value = 0.041) and incorrectly inflected forms (accuracy = 0.648, p-value = 0.021), but not for correctly inflected forms (accuracy = 0.523, p-value = 0.384). Our findings point to quantifiable differences in functional connectivity within the cortical systems underpinning language processing in newly diagnosed PD patients compared to healthy controls, which arise early, in the absence of clinical evidence of deficits in cognitive or general language functions. The techniques presented here may aid future work on establishing neurolinguistic markers to objectively and noninvasively identify functional changes in the brain's language networks even before clinical symptoms emerge.
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Affiliation(s)
- Rasha Hyder
- Center of Functionally Integrative Neuroscience (CFIN), Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany.
| | - Mads Jensen
- Center of Functionally Integrative Neuroscience (CFIN), Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Research Unit for Robophilosophy and Integrative Social Robotics, Aarhus University, Denmark
| | - Andreas Højlund
- Center of Functionally Integrative Neuroscience (CFIN), Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Lilli Kimppa
- Center of Functionally Integrative Neuroscience (CFIN), Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Cognitive Brain Research Unit, Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Finland
| | - Christopher J Bailey
- Center of Functionally Integrative Neuroscience (CFIN), Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Jeppe L Schaldemose
- Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, Aarhus, Denmark
| | - Martin B Kinnerup
- Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, Aarhus, Denmark
| | - Karen Østergaard
- Sano Private Hospital, Denmark; Department of Neurology, Aarhus University Hospital (AUH), Denmark
| | - Yury Shtyrov
- Center of Functionally Integrative Neuroscience (CFIN), Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, HSE University, Moscow, Russian Federation.
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Deng X, Liu Z, Kang Q, Lu L, Zhu Y, Xu R. Cortical Structural Connectivity Alterations and Potential Pathogenesis in Mid-Stage Sporadic Parkinson's Disease. Front Aging Neurosci 2021; 13:650371. [PMID: 34135748 PMCID: PMC8200851 DOI: 10.3389/fnagi.2021.650371] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 03/08/2021] [Indexed: 11/13/2022] Open
Abstract
Many clinical symptoms of sporadic Parkinson's disease (sPD) cannot be completely explained by a lesion of the simple typical extrapyramidal circuit between the striatum and substantia nigra. Therefore, this study aimed to explore the new potential damaged pathogenesis of other brain regions associated with the multiple and complex clinical symptoms of sPD through magnetic resonance imaging (MRI). A total of 65 patients with mid-stage sPD and 35 healthy controls were recruited in this study. Cortical structural connectivity was assessed by seed-based analysis using the vertex-based morphology of MRI. Seven different clusters in the brain regions of cortical thickness thinning derived from the regression analysis using brain size as covariates between sPD and control were selected as seeds. Results showed that the significant alteration of cortical structural connectivity mainly occurred in the bilateral frontal orbital, opercular, triangular, precentral, rectus, supplementary-motor, temporal pole, angular, Heschl, parietal, supramarginal, postcentral, precuneus, occipital, lingual, cuneus, Rolandic-opercular, cingulum, parahippocampal, calcarine, olfactory, insula, paracentral-lobule, and fusiform regions at the mid-stage of sPD. These findings suggested that the extensive alteration of cortical structural connectivity is one of possible pathogenesis resulting in the multiple and complex clinical symptoms in sPD.
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Affiliation(s)
- Xia Deng
- Department of Neurology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Zheng Liu
- Department of Neurology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Qin Kang
- Department of Neurology, Jiangxi Provincial People’s Hospital, The Affiliated People’s Hospital of Nanchang University, Nanchang, China
| | - Lin Lu
- Department of Neurology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yu Zhu
- Department of Neurology, Jiangxi Provincial People’s Hospital, The Affiliated People’s Hospital of Nanchang University, Nanchang, China
| | - Renshi Xu
- Department of Neurology, The First Affiliated Hospital of Nanchang University, Nanchang, China
- Department of Neurology, Jiangxi Provincial People’s Hospital, The Affiliated People’s Hospital of Nanchang University, Nanchang, China
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Within- and across-network alterations of the sensorimotor network in Parkinson's disease. Neuroradiology 2021; 63:2073-2085. [PMID: 34019112 PMCID: PMC8589810 DOI: 10.1007/s00234-021-02731-w] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 05/02/2021] [Indexed: 12/03/2022]
Abstract
Purpose Parkinson’s disease (PD) is primarily defined by motor symptoms and is associated with alterations of sensorimotor areas. Evidence for network changes of the sensorimotor network (SMN) in PD is inconsistent and a systematic evaluation of SMN in PD yet missing. We investigate functional connectivity changes of the SMN in PD, both, within the network, and to other large-scale connectivity networks. Methods Resting-state fMRI was assessed in 38 PD patients under long-term dopaminergic treatment and 43 matched healthy controls (HC). Independent component analysis (ICA) into 20 components was conducted and the SMN was identified within the resulting networks. Functional connectivity within the SMN was analyzed using a dual regression approach. Connectivity between the SMN and the other networks from group ICA was investigated with FSLNets. We investigated for functional connectivity changes between patients and controls as well as between medication states (OFF vs. ON) in PD and for correlations with clinical parameters. Results There was decreased functional connectivity within the SMN in left inferior parietal and primary somatosensory cortex in PD OFF. Across networks, connectivity between SMN and two motor networks as well as two visual networks was diminished in PD OFF. All connectivity decreases partially normalized in PD ON. Conclusion PD is accompanied by functional connectivity losses of the SMN, both, within the network and in interaction to other networks. The connectivity changes in short- and long-range connections are probably related to impaired sensory integration for motor function in PD. SMN decoupling can be partially compensated by dopaminergic therapy. Supplementary Information The online version contains supplementary material available at 10.1007/s00234-021-02731-w.
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Ghasemi M, Foroutannia A, Babajani‐Feremi A. Characterizing resting-state networks in Parkinson's disease: A multi-aspect functional connectivity study. Brain Behav 2021; 11:e02101. [PMID: 33784022 PMCID: PMC8119826 DOI: 10.1002/brb3.2101] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 01/03/2021] [Accepted: 02/21/2021] [Indexed: 01/04/2023] Open
Abstract
PURPOSE Resting-state functional magnetic resonance imaging (Rs-fMRI) can be used to investigate the alteration of resting-state brain networks (RSNs) in patients with Parkinson's disease (PD) when compared with healthy controls (HCs). The aim of this study was to identify the differences between individual RSNs and reveal the most important discriminatory characteristic of RSNs between the HCs and PDs. METHODS This study used Rs-fMRI data of 23 patients with PD and 18 HCs. Group independent component analysis (ICA) was performed, and 23 components were extracted by spatially overlapping the components with a template RSN. The extracted components were used in the following three methods to compare RSNs of PD patients and HCs: (1) a subject-specific score based on group RSNs and a dual-regression approach (namely RSN scores); (2) voxel-wise comparison of the RSNs in the PD patient and HC groups using a nonparametric permutation test; and (3) a hierarchical clustering analysis of RSNs in the PD patient and HC groups. RESULTS The results of RSN scores showed a significant decrease in connectivity in seven ICs in patients with PD compared with HCs, and this decrease was particularly striking on the lateral and medial posterior occipital cortices. The results of hierarchical clustering of the RSNs revealed that the cluster of the default mode network breaks down into the three other clusters in PD patients. CONCLUSION We found various characteristics of the alteration of the RSNs in PD patients compared with HCs. Our results suggest that different characteristics of RSNs provide insights into the biological mechanism of PD.
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Affiliation(s)
- Mahdieh Ghasemi
- Neural Engineering LaboratoryDepartment of Biomedical EngineeringUniversity of NeyshaburNeyshaburIran
| | - Ali Foroutannia
- Neural Engineering LaboratoryDepartment of Biomedical EngineeringUniversity of NeyshaburNeyshaburIran
| | - Abbas Babajani‐Feremi
- Department of NeurologyDell Medical SchoolThe University of Texas at AustinAustinTXUSA
- Magnetoencephalography LabDell Children's Medical CenterAustinTXUSA
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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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Disrupted Brain Network Topology in Drug-naïve Essential Tremor Patients with and Without Depression : A Resting State Functional Magnetic Resonance Imaging Study. Clin Neuroradiol 2021; 31:981-992. [PMID: 33687483 DOI: 10.1007/s00062-021-01002-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2020] [Accepted: 02/08/2021] [Indexed: 02/05/2023]
Abstract
PURPOSE This study was carried out to investigate brain functional connectome and its potential relationships with the disease severity and emotion function in patients with essential tremor with and without depressive symptoms by using resting-state functional magnetic resonance imaging and graph theory approaches. METHODS In this study 33 essential tremor patients with depression, 45 essential tremor patients without depression and 79 age and gender-matched healthy controls were recruited to undergo a 3.0‑T imaging scan. The whole brain functional connectome was constructed by thresholding the partial correlation matrices of 116 brain regions, and the topologic properties were analyzed by using graph theory approaches and network-based statistic approaches. Nonparametric permutation test was also used for group comparisons of topological metrics. Correlation analyses between topographic features and the clinical characteristics were performed. RESULTS The functional connectome in both essential tremor patients with and without depression showed abnormalities at the global level (decrease in clustering coefficient, global efficiency, and local efficiency but increase in characteristic path length) and at the nodal level (decrease nodal centralities in the cerebellum, motor cortex, prefrontal-limbic regions, default mode network) (p < 0.05, false discovery rate corrected). Moreover, essential tremor patients with depression showed higher node efficiency in superior frontal gyrus and posterior cingulate gyrus compared to essential tremor without depression. CONCLUSION Our results may provide insights into the underlying pathophysiology of essential tremor patients with and without depression and aid the development of some potential biomarkers of the depressive symptoms in patients with essential tremor.
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Wang B, Liu X, Xu S, Liu Z, Zhu Y, Zhang X, Xu R. Sporadic Parkinson's Disease Potential Risk Loci Identified in Han Ancestry of Chinese Mainland. Front Aging Neurosci 2021; 12:603793. [PMID: 33510632 PMCID: PMC7835639 DOI: 10.3389/fnagi.2020.603793] [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: 09/07/2020] [Accepted: 11/30/2020] [Indexed: 11/13/2022] Open
Abstract
Recent investigations demonstrated that genetic factors might play an important role in sporadic Parkinson's disease (sPD). To clarify the specific loci susceptibility to sPD, we analyze the relationship between 30 candidate single nucleotide polymorphisms (SNPs) and sPD in the population of Han ancestry from Chinese mainland (HACM) by using genome-wide association study, sequenom massARRAY, DNA sequence, and biological information analysis. Results showed that the subjects carrying the T allele of rs863108 and rs28499371 exhibited a decreased risk for sPD. The subjects carrying the T allele of rs80315856 exhibited an increased risk for sPD. The A/T genotype of rs863108 and the C/T genotype of rs28499371 were a potential increased risk for sPD, and the G/T genotype of rs80315856 and T/T genotype of rs2270568 were a potential decreased risk for sPD. The minor allele frequency (MAF) of rs80315856 and rs2270568 was higher in sPD. The T allele of rs80315856 and rs2270568 might be a risk locus for sPD. Our data suggested that the alteration of these SNPs might play some roles through changing/affecting LINC01524/LOC105372666, DMRT2/SMARCA2, PLEKHN1, and FLJ23172/FNDC3B genes in the pathogenesis of sPD.
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Affiliation(s)
- Bo Wang
- Department of Neurology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xin Liu
- Department of Neurology, Jiangxi Provincial People's Hospital, The Affiliated People's Hospital of Nanchang University, Nanchang, China
| | - Shengyuan Xu
- Department of Neurology, Jiangxi Provincial People's Hospital, The Affiliated People's Hospital of Nanchang University, Nanchang, China
| | - Zheng Liu
- Department of Neurology, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Yu Zhu
- Department of Neurology, Jiangxi Provincial People's Hospital, The Affiliated People's Hospital of Nanchang University, Nanchang, China
| | - Xiong Zhang
- Department of Neurology, Maoming People's Hospital, Maoming, China
| | - Renshi Xu
- Department of Neurology, The First Affiliated Hospital of Nanchang University, Nanchang, China.,Department of Neurology, Jiangxi Provincial People's Hospital, The Affiliated People's Hospital of Nanchang University, Nanchang, China
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Mihaescu AS, Kim J, Masellis M, Graff-Guerrero A, Cho SS, Christopher L, Valli M, Díez-Cirarda M, Koshimori Y, Strafella AP. Graph theory analysis of the dopamine D2 receptor network in Parkinson's disease patients with cognitive decline. J Neurosci Res 2020; 99:947-965. [PMID: 33271630 DOI: 10.1002/jnr.24760] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 11/14/2020] [Indexed: 12/30/2022]
Abstract
Cognitive decline in Parkinson's disease (PD) is a common sequela of the disorder that has a large impact on patient well-being. Its physiological etiology, however, remains elusive. Our study used graph theory analysis to investigate the large-scale topological patterns of the extrastriatal dopamine D2 receptor network. We used positron emission tomography with [11 C]FLB-457 to measure the binding potential of cortical dopamine D2 receptors in two networks: the meso-cortical dopamine network and the meso-limbic dopamine network. We also investigated the application of partial volume effect correction (PVEC) in conjunction with graph theory analysis. Three groups were investigated in this study divided according to their cognitive status as measured by the Montreal Cognitive Assessment score, with a score ≤25 considered cognitively impaired: (a) healthy controls (n = 13, 11 female), (b) cognitively unimpaired PD patients (PD-CU, n = 13, 5 female), and (c) PD patients with mild cognitive impairment (PD-MCI, n = 17, 4 female). In the meso-cortical network, we observed increased small-worldness, normalized clustering, and local efficiency in the PD-CU group compared to the PD-MCI group, as well as a hub shift in the PD-MCI group. Compensatory reorganization of the meso-cortical dopamine D2 receptor network may be responsible for some of the cognitive preservation observed in PD-CU. These results were found without PVEC applied and PVEC proved detrimental to the graph theory analysis. Overall, our findings demonstrate how graph theory analysis can be used to detect subtle changes in the brain that would otherwise be missed by regional comparisons of receptor density.
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Affiliation(s)
- Alexander S Mihaescu
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada.,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, ON, Canada
| | - Jinhee Kim
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
| | - Mario Masellis
- Institute of Medical Science, University of Toronto, ON, Canada.,LC Campbell Cognitive Neurology Research Unit, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.,Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada
| | - Ariel Graff-Guerrero
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, ON, Canada
| | - Sang Soo Cho
- Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, ON, Canada
| | - Leigh Christopher
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
| | - Mikaeel Valli
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada.,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, ON, Canada
| | - María Díez-Cirarda
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada.,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, ON, Canada.,Neurodegenerative Diseases Group, Biocruces Bizkaia Health Research Institute, Barakaldo, Spain
| | - Yuko Koshimori
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada
| | - Antonio P Strafella
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, University of Toronto, Toronto, ON, Canada.,Division of Brain, Imaging and Behaviour - Systems Neuroscience, Krembil Research Institute, University Health Network, University of Toronto, Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, ON, Canada.,Morton and Gloria Shulman Movement Disorder Unit & E.J. Safra Program in Parkinson Disease, Neurology Division, Department of Medicine, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, ON, Canada
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Spetsieris PG, Eidelberg D. Spectral guided sparse inverse covariance estimation of metabolic networks in Parkinson's disease. Neuroimage 2020; 226:117568. [PMID: 33246128 PMCID: PMC8409106 DOI: 10.1016/j.neuroimage.2020.117568] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 10/23/2020] [Accepted: 11/12/2020] [Indexed: 01/21/2023] Open
Abstract
In neurodegenerative disorders, a clearer understanding of the underlying aberrant networks facilitates the search for effective therapeutic targets and potential cures. [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging data of brain metabolism reflects the distribution of glucose consumption known to be directly related to neural activity. In FDG PET resting-state metabolic data, characteristic disease-related patterns have been identified in group analysis of various neurodegenerative conditions using principal component analysis of multivariate spatial covariance. Notably, among several parkinsonian syndromes, the identified Parkinson’s disease-related pattern (PDRP) has been repeatedly validated as an imaging biomarker of PD in independent groups worldwide. Although the primary nodal associations of this network are known, its connectivity is not fully understood. Here, we describe a novel approach to elucidate functional principal component (PC) network connections by performing graph theoretical sparse network derivation directly within the disease relevant PC partition layer of the whole brain data rather than by searching for associations retrospectively in whole brain sparse representations. Using sparse inverse covariance estimation of each overlapping PC partition layer separately, a single coherent network is detected for each layer in contrast to more spatially modular segmentation in whole brain data analysis. Using this approach, the major nodal hubs of the PD disease network are identified and their characteristic functional pathways are clearly distinguished within the basal ganglia, midbrain and parietal areas. Network associations are further clarified using Laplacian spectral analysis of the adjacency matrices. In addition, the innate discriminative capacity of the eigenvector centrality of the graph derived networks in differentiating PD versus healthy external data provides evidence of their validity.
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Affiliation(s)
- Phoebe G Spetsieris
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA.
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