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Chen Y, Qi Y, Li T, Lin A, Ni Y, Pu R, Sun B. A more objective PD diagnostic model: integrating texture feature markers of cerebellar gray matter and white matter through machine learning. Front Aging Neurosci 2024; 16:1393841. [PMID: 38912523 PMCID: PMC11190310 DOI: 10.3389/fnagi.2024.1393841] [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: 02/29/2024] [Accepted: 05/27/2024] [Indexed: 06/25/2024] Open
Abstract
Objective The purpose of this study is to explore whether machine learning can be used to establish an effective model for the diagnosis of Parkinson's disease (PD) by using texture features extracted from cerebellar gray matter and white matter, so as to identify subtle changes that cannot be observed by the naked eye. Method This study involved a data collection period from June 2010 to March 2023, including 374 subjects from two cohorts. The Parkinson's Progression Markers Initiative (PPMI) served as the training set, with control group and PD patients (HC: 102 and PD: 102) from 24 global sites. Our institution's data was utilized as the test set (HC: 91 and PD: 79). Machine learning was employed to establish multiple models for PD diagnosis based on texture features of the cerebellum's gray and white matter. Results underwent evaluation through 5-fold cross-validation analysis, calculating the area under the receiver operating characteristic curve (AUC) for each model. The performance of each model was compared using the Delong test, and the interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP). Results The AUCs for all pipelines in the validation dataset were compared using FeAture Explorer (FAE) software. Among the models established by Kruskal-Wallis (KW) and logistic regression via Lasso (LRLasso), the AUC was highest using the "one-standard error" rule. 'WM_original_glrlm_GrayLevelNonUniformity' was considered the most stable and predictive feature. Conclusion The texture features of cerebellar gray matter and white matter combined with machine learning may have potential value in the diagnosis of Parkinson's disease, in which the heterogeneity of white matter may be a more valuable imaging marker.
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Affiliation(s)
- Yini Chen
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yiwei Qi
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Tianbai Li
- Liaoning Provincial Key Laboratory for Research on the Pathogenic Mechanisms of Neurological Diseases, The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Andong Lin
- Department of Neurology, Zhejiang Taizhou Municipal Hospital, Taizhou, Zhejiang, China
| | - Yang Ni
- Liaoning Provincial Key Laboratory for Research on the Pathogenic Mechanisms of Neurological Diseases, The First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Renwang Pu
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Bo Sun
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
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2
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Valderhaug VD, Ramstad OH, van de Wijdeven R, Heiney K, Nichele S, Sandvig A, Sandvig I. Micro-and mesoscale aspects of neurodegeneration in engineered human neural networks carrying the LRRK2 G2019S mutation. Front Cell Neurosci 2024; 18:1366098. [PMID: 38644975 PMCID: PMC11026646 DOI: 10.3389/fncel.2024.1366098] [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: 01/05/2024] [Accepted: 03/11/2024] [Indexed: 04/23/2024] Open
Abstract
Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene have been widely linked to Parkinson's disease, where the G2019S variant has been shown to contribute uniquely to both familial and sporadic forms of the disease. LRRK2-related mutations have been extensively studied, yet the wide variety of cellular and network events related to these mutations remain poorly understood. The advancement and availability of tools for neural engineering now enable modeling of selected pathological aspects of neurodegenerative disease in human neural networks in vitro. Our study revealed distinct pathology associated dynamics in engineered human cortical neural networks carrying the LRRK2 G2019S mutation compared to healthy isogenic control neural networks. The neurons carrying the LRRK2 G2019S mutation self-organized into networks with aberrant morphology and mitochondrial dynamics, affecting emerging structure-function relationships both at the micro-and mesoscale. Taken together, the findings of our study points toward an overall heightened metabolic demand in networks carrying the LRRK2 G2019S mutation, as well as a resilience to change in response to perturbation, compared to healthy isogenic controls.
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Affiliation(s)
- Vibeke Devold Valderhaug
- Department of Research and Innovation, Møre and Romsdal Hospital Trust, Ålesund, Norway
- Department of Neuromedicine and Movement Science, Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Ola Huse Ramstad
- Department of Neuromedicine and Movement Science, Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Rosanne van de Wijdeven
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, NTNU, Trondheim, Norway
| | - Kristine Heiney
- Department of Computer Science, Faculty of Technology, Art and Design, Oslo Metropolitan University (OsloMet), Oslo, Norway
- Department of Computer Science, Faculty of Information Technology and Electrical Engineering, NTNU, Trondheim, Norway
| | - Stefano Nichele
- Department of Computer Science, Faculty of Technology, Art and Design, Oslo Metropolitan University (OsloMet), Oslo, Norway
- Department of Computer Science and Communication, Østfold University College, Halden, Norway
| | - Axel Sandvig
- Department of Neuromedicine and Movement Science, Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
- Department of Clinical Neuroscience, Division of Neuro, Head and Neck, Umeå University Hospital, Umeå, Sweden
- Department of Community Medicine and Rehabilitation, Umeå University, Umeå, Sweden
- Department of Neurology and Clinical Neurophysiology, St Olav’s Hospital, Trondheim, Norway
| | - Ioanna Sandvig
- Department of Neuromedicine and Movement Science, Faculty of Medicine, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
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3
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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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4
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Barbero JA, Unadkat P, Choi YY, Eidelberg D. Functional Brain Networks to Evaluate Treatment Responses in Parkinson's Disease. Neurotherapeutics 2023; 20:1653-1668. [PMID: 37684533 PMCID: PMC10684458 DOI: 10.1007/s13311-023-01433-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
Network analysis of functional brain scans acquired with [18F]-fluorodeoxyglucose positron emission tomography (FDG PET, to map cerebral glucose metabolism), or resting-state functional magnetic resonance imaging (rs-fMRI, to map blood oxygen level-dependent brain activity) has increasingly been used to identify and validate reproducible circuit abnormalities associated with neurodegenerative disorders such as Parkinson's disease (PD). In addition to serving as imaging markers of the underlying disease process, these networks can be used singly or in combination as an adjunct to clinical diagnosis and as a screening tool for therapeutics trials. Disease networks can also be used to measure rates of progression in natural history studies and to assess treatment responses in individual subjects. Recent imaging studies in PD subjects scanned before and after treatment have revealed therapeutic effects beyond the modulation of established disease networks. Rather, other mechanisms of action may be at play, such as the induction of novel functional brain networks directly by treatment. To date, specific treatment-induced networks have been described in association with novel interventions for PD such as subthalamic adeno-associated virus glutamic acid decarboxylase (AAV2-GAD) gene therapy, as well as sham surgery or oral placebo under blinded conditions. Indeed, changes in the expression of these networks with treatment have been found to correlate consistently with clinical outcome. In aggregate, these attributes suggest a role for functional brain networks as biomarkers in future clinical trials.
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Affiliation(s)
- János A Barbero
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA
| | - Prashin Unadkat
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA
- Elmezzi Graduate School of Molecular Medicine, Manhasset, NY, 11030, USA
| | - Yoon Young Choi
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA.
- Molecular Medicine and Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA.
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5
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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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6
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Rus T, Mlakar J, Ležaić L, Vo A, Nguyen N, Tang C, Fiorini M, Prieto E, Marti-Andres G, Arbizu J, Eidelberg D, Trošt M. Sporadic Creutzfeldt-Jakob disease is associated with reorganization of metabolic connectivity in a pathological brain network. Eur J Neurol 2023; 30:1035-1047. [PMID: 36583625 DOI: 10.1111/ene.15669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 12/23/2022] [Accepted: 12/27/2022] [Indexed: 12/31/2022]
Abstract
BACKGROUND AND PURPOSE Although sporadic Creutzfeldt-Jakob disease (sCJD) is a rare cause of dementia, it is critical to understand its functional networks as the prion protein spread throughout the brain may share similar mechanisms with other more common neurodegenerative disorders. In this study, the metabolic brain network associated with sCJD was investigated and its internal network organization was explored. METHODS We explored 2-[18 F]fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) brain scans of 29 sCJD patients, 56 normal controls (NCs) and 46 other dementia patients from two independent centers. sCJD-related pattern (CJDRP) was identified in a cohort of 16 pathologically proven sCJD patients and 16 age-matched NCs using scaled subprofile modeling/principal component analysis and was prospectively validated in an independent cohort of 13 sCJD patients and 20 NCs. The pattern's specificity was tested on other dementia patients and its clinical relevance by clinical correlations. The pattern's internal organization was further studied using graph theory methods. RESULTS The CJDRP was characterized by relative hypometabolism in the bilateral caudate, thalami, middle and superior frontal gyri, parietal lobe and posterior cingulum in association with relative hypermetabolism in the hippocampi, parahippocampal gyri and cerebellum. The pattern's expression significantly discriminated sCJD from NCs and other dementia patients (p < 0.005; receiver operating characteristic analysis CJD vs. NCs area under the curve [AUC] 0.90-0.96, sCJD vs. Alzheimer's disease AUC 0.78, sCJD vs. behavioral variant of frontotemporal dementia AUC 0.84). The pattern's expression significantly correlated with cognitive, functional decline and disease duration. The metabolic connectivity analysis revealed inefficient information transfer with specific network reorganization. CONCLUSIONS The CJDRP is a robust metabolic biomarker of sCJD. Due to its excellent clinical correlations it has the potential to monitor disease in emerging disease-modifying trials.
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Affiliation(s)
- Tomaž Rus
- Department of Neurology, University Medical Centre, Ljubljana, Slovenia
- Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Jernej Mlakar
- Institute of Pathology, Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Luka Ležaić
- Department of Nuclear Medicine, University Medical Centre, Ljubljana, Slovenia
| | - An Vo
- Center for Neurosciences, Feinstein Institutes for Medical Research, Manhasset, New York City, USA
| | - Nha Nguyen
- Department of Genetics, Albert Einstein College of Medicine, Bronx, New York City, USA
| | - Chris Tang
- Center for Neurosciences, Feinstein Institutes for Medical Research, Manhasset, New York City, USA
| | - Michele Fiorini
- Section of Neuropathology, Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy
| | - Elena Prieto
- Department of Nuclear Medicine and Molecular Imaging, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - Gloria Marti-Andres
- Department of Neurology, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - Javier Arbizu
- Department of Nuclear Medicine and Molecular Imaging, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - David Eidelberg
- Center for Neurosciences, Feinstein Institutes for Medical Research, Manhasset, New York City, USA
| | - Maja Trošt
- Department of Neurology, University Medical Centre, Ljubljana, Slovenia
- Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
- Department of Nuclear Medicine, University Medical Centre, Ljubljana, Slovenia
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7
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Perovnik M, Vo A, Nguyen N, Jamšek J, Rus T, Tang CC, Trošt M, Eidelberg D. Automated differential diagnosis of dementia syndromes using FDG PET and machine learning. Front Aging Neurosci 2022; 14:1005731. [PMID: 36408106 PMCID: PMC9667048 DOI: 10.3389/fnagi.2022.1005731] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 10/10/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Metabolic brain imaging with 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography (FDG PET) is a supportive diagnostic and differential diagnostic tool for neurodegenerative dementias. In the clinic, scans are usually visually interpreted. However, computer-aided approaches can improve diagnostic accuracy. We aimed to build two machine learning classifiers, based on two sets of FDG PET-derived features, for differential diagnosis of common dementia syndromes. METHODS We analyzed FDG PET scans from three dementia cohorts [63 dementia due to Alzheimer's disease (AD), 79 dementia with Lewy bodies (DLB) and 23 frontotemporal dementia (FTD)], and 41 normal controls (NCs). Patients' clinical diagnosis at follow-up (25 ± 20 months after scanning) or cerebrospinal fluid biomarkers for Alzheimer's disease was considered a gold standard. FDG PET scans were first visually evaluated. Scans were pre-processed, and two sets of features extracted: (1) the expressions of previously identified metabolic brain patterns, and (2) the mean uptake value in 95 regions of interest (ROIs). Two multi-class support vector machine (SVM) classifiers were tested and their diagnostic performance assessed and compared to visual reading. Class-specific regional feature importance was assessed with Shapley Additive Explanations. RESULTS Pattern- and ROI-based classifier achieved higher overall accuracy than expert readers (78% and 80% respectively, vs. 71%). Both SVM classifiers performed similarly to one another and to expert readers in AD (F1 = 0.74, 0.78, and 0.78) and DLB (F1 = 0.81, 0.81, and 0.78). SVM classifiers outperformed expert readers in FTD (F1 = 0.87, 0.83, and 0.63), but not in NC (F1 = 0.71, 0.75, and 0.92). Visualization of the SVM model showed bilateral temporal cortices and cerebellum to be the most important features for AD; occipital cortices, hippocampi and parahippocampi, amygdala, and middle temporal lobes for DLB; bilateral frontal cortices, middle and anterior cingulum for FTD; and bilateral angular gyri, pons, and vermis for NC. CONCLUSION Multi-class SVM classifiers based on the expression of characteristic metabolic brain patterns or ROI glucose uptake, performed better than experts in the differential diagnosis of common dementias using FDG PET scans. Experts performed better in the recognition of normal scans and a combined approach may yield optimal results in the clinical setting.
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Affiliation(s)
- Matej Perovnik
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia,Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia,Center for Neurosciences, The Feinstein Institutes for Medical Research, New York, NY, United States,*Correspondence: Matej Perovnik,
| | - An Vo
- Center for Neurosciences, The Feinstein Institutes for Medical Research, New York, NY, United States
| | - Nha Nguyen
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, United States
| | - Jan Jamšek
- Department of Nuclear Medicine, University Medical Center Ljubljana, Ljubljana, Slovenia
| | - Tomaž Rus
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia
| | - Chris C. Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, New York, NY, United States
| | - Maja Trošt
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia,Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia,Department of Nuclear Medicine, University Medical Center Ljubljana, Ljubljana, Slovenia
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, New York, NY, United States
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Chen H, Dai L, Zhang Y, Feng L, Jiang Z, Wang X, Xie D, Guo J, Chen H, Wang J, Liu C. Network Reconfiguration Among Cerebellar Visual, and Motor Regions Affects Movement Function in Spinocerebellar Ataxia Type 3. Front Aging Neurosci 2022; 14:773119. [PMID: 35478700 PMCID: PMC9036064 DOI: 10.3389/fnagi.2022.773119] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 03/14/2022] [Indexed: 12/01/2022] Open
Abstract
Background Spinocerebellar ataxia type 3 (SCA3) is a rare movement disorder characterized with ataxia. Previous studies on movement disorders show that the whole-brain functional network tends to be more regular, and these reconfigurations correlate with genetic and clinical variables. Methods To test whether the brain network in patients with SCA3 follows a similar reconfiguration course to other movement disorders, we recruited 41 patients with SCA3 (mean age = 40.51 ± 12.13 years; 23 male) and 41 age and sex-matched healthy individuals (age = 40.10 ± 11.56 years; 24 male). In both groups, the whole-brain network topology of resting-state functional magnetic resonance imaging (rs-fMRI) was conducted using graph theory, and the relationships among network topologies, cytosine-adenine-guanine (CAG) repeats, clinical symptoms, and functional connectivity were explored in SCA3 patients using partial correlation analysis, controlling for age and sex. Results The brain networks tended to be more regular with a higher clustering coefficient, local efficiency, and modularity in patients with SCA3. Hubs in SCA3 patients were reorganized as the number of hubs increased in motor-related areas and decreased in cognitive areas. At the global level, small-worldness and normalized clustering coefficients were significantly positively correlated with clinical motor symptoms. At the nodal level, the clustering coefficient and local efficiency increased significantly in the visual (bilateral cuneus) and sensorimotor (right cerebellar lobules IV, V, VI) networks and decreased in the cognitive areas (right middle frontal gyrus). The clustering coefficient and local efficiency in the bilateral cuneus gyrus were negatively correlated with clinical motor symptoms. The functional connectivity between right caudate nucleus and bilateral calcarine gyrus were negatively correlated with disease duration, while connectivity between right posterior cingulum gyrus and left cerebellar lobule III, left inferior occipital gyrus and right cerebellar lobule IX was positively correlated. Conclusion Our results demonstrate that a more regular brain network occurred in SCA3 patients, with motor and visual-related regions, such as, cerebellar lobules and cuneus gyrus, both forayed neighbor nodes as “resource predators” to compensate for normal function, with motor and visual function having the higher priority comparing with other high-order functions. This study provides new information about the neurological mechanisms underlying SCA3 network topology impairments in the resting state, which give a potential guideline for future clinical treatments. Clinical Trial Registration [www.ClinicalTrials.gov], identifier [ChiCTR1800019901].
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Affiliation(s)
- Hui Chen
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Limeng Dai
- Department of Medical Genetics, Third Military Medical University (Army Medical University), Chongqing, China
| | - Yuhan Zhang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Liu Feng
- Department of Laboratory Medicine, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Zhenzhen Jiang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Xingang Wang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Dongjing Xie
- Department of Neurology, Xinqiao Hospital and The Second Affiliated Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Jing Guo
- Biomedical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Huafu Chen
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
- *Correspondence: Huafu Chen,
| | - Jian Wang
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
- Jian Wang,
| | - Chen Liu
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
- Chen Liu,
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9
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Wright, N, Patel, R, Chaulk, SJ, Alcolado, G, Podnar, D, Mota, N, Monson, CM, Girard, TA, Ko, JH. Novel Analysis Identifying Functional Connectivity Patterns Associated with Posttraumatic Stress Disorder. CHRONIC STRESS (THOUSAND OAKS, CALIF.) 2022; 6:24705470221092428. [PMID: 35465401 PMCID: PMC9019376 DOI: 10.1177/24705470221092428] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 03/17/2022] [Accepted: 03/21/2022] [Indexed: 06/14/2023]
Abstract
Posttraumatic stress disorder (PTSD) is a prevalent psychiatric disorder that can result from experiencing traumatic events. Accurate diagnosis and optimal treatment strategies can be difficult to achieve, due to the heterogeneous etiology and symptomology of PTSD, and overlap with other psychiatric disorders. Advancing our understanding of PTSD pathophysiology is therefore critical. While functional connectivity alterations have shown promise for elucidating the neurobiological mechanisms of PTSD, previous findings have been inconsistent. Eleven patients with PTSD in our first cohort (PTSD-A) and 11 trauma-exposed controls (TEC) underwent functional magnetic resonance imaging. First, we investigated the intrinsic connectivity within known resting state networks (eg, default mode, salience, and central executive networks) previously implicated in functional abnormalities with PTSD symptoms. Second, the overall topology of network structure was compared between PTSD-A and TEC using graph theory. Finally, we used a novel combination of graph theory analysis and scaled subprofile modeling (SSM) to identify a disease-related, covarying pattern of brain network organization. No significant group differences were found in intrinsic connectivity of known resting state networks and graph theory metrics (clustering coefficients, characteristic path length, smallworldness, global and local efficiencies, and degree centrality). The graph theory/SSM analysis revealed a topographical pattern of altered degree centrality differentiating PTSD-A from TEC. This PTSD-related network pattern expression was additionally investigated in a separate cohort of 33 subjects who were scanned with a different MRI scanner (22 patients with PTSD or PTSD-B, and 11 healthy trauma-naïve controls or TNC). Across all participant groups, pattern expression scores were significantly lower in the TEC group, while PTSD-A, PTSD-B, and TNC subject profiles did not differ from each other. Expression level of the pattern was correlated with symptom severity in the PTSD-B group. This method offers potential in developing objective biomarkers associated with PTSD. Possible interpretations and clinical implications will be discussed.
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Affiliation(s)
- Natalie Wright,
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
| | - Ronak Patel,
- Department of Clinical Health Psychology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Sarah J. Chaulk,
- Department of Clinical Health Psychology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Gillian Alcolado,
- Department of Clinical Health Psychology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - David Podnar,
- Department of Clinical Health Psychology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Natalie Mota,
- Department of Clinical Health Psychology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | | | - Todd A. Girard,
- Department of Psychology, Ryerson University, Toronto, ON, Canada
| | - Ji Hyun Ko,
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
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10
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Meles SK, Oertel WH, Leenders KL. Circuit imaging biomarkers in preclinical and prodromal Parkinson's disease. Mol Med 2021; 27:111. [PMID: 34530732 PMCID: PMC8447708 DOI: 10.1186/s10020-021-00327-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 06/02/2021] [Indexed: 11/10/2022] Open
Abstract
Parkinson's disease (PD) commences several years before the onset of motor features. Pathophysiological understanding of the pre-clinical or early prodromal stages of PD are essential for the development of new therapeutic strategies. Two categories of patients are ideal to study the early disease stages. Idiopathic rapid eye movement sleep behavior disorder (iRBD) represents a well-known prodromal stage of PD in which pathology is presumed to have reached the lower brainstem. The majority of patients with iRBD will develop manifest PD within years to decades. Another category encompasses non-manifest mutation carriers, i.e. subjects without symptoms, but with a known mutation or genetic variant which gives an increased risk of developing PD. The speed of progression from preclinical or prodromal to full clinical stages varies among patients and cannot be reliably predicted on the individual level. Clinical trials will require inclusion of patients with a predictable conversion within a limited time window. Biomarkers are necessary that can confirm pre-motor PD status and can provide information regarding lead time and speed of progression. Neuroimaging changes occur early in the disease process and may provide such a biomarker. Studies have focused on radiotracer imaging of the dopaminergic nigrostriatal system, which can be assessed with dopamine transporter (DAT) single photon emission computed tomography (SPECT). Loss of DAT binding represents an effect of irreversible structural damage to the nigrostriatal system. This marker can be used to monitor disease progression and identify individuals at specific risk for phenoconversion. However, it is known that changes in neuronal activity precede structural changes. Functional neuro-imaging techniques, such as 18F-2-fluoro-2-deoxy-D-glucose Positron Emission Tomography (18F-FDG PET) and functional magnetic resonance imaging (fMRI), can be used to model the effects of disease on brain networks when combined with advanced analytical methods. Because these changes occur early in the disease process, functional imaging studies are of particular interest in prodromal PD diagnosis. In addition, fMRI and 18F-FDG PET may be able to predict a specific future phenotype in prodromal cohorts, which is not possible with DAT SPECT. The goal of the current review is to discuss the network-level brain changes in pre-motor PD.
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Affiliation(s)
- Sanne K Meles
- Department of Neurology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, PO Box 30.001, 9700 RB, Groningen, The Netherlands.
| | - Wolfgang H Oertel
- Department of Neurology, Philipps-Universität Marburg, Marburg, Germany.,Institute for Neurogenomics, Helmholtz Center for Health and Environment, Munich, Germany
| | - Klaus L Leenders
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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11
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Shadrina M, Slominsky P. Modeling Parkinson's Disease: Not Only Rodents? Front Aging Neurosci 2021; 13:695718. [PMID: 34421573 PMCID: PMC8377290 DOI: 10.3389/fnagi.2021.695718] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 06/29/2021] [Indexed: 01/12/2023] Open
Abstract
Parkinson’s disease (PD) is a common chronic progressive multifactorial neurodegenerative disease. In most cases, PD develops as a sporadic idiopathic disease. However, in 10%–15% of all patients, Mendelian inheritance of the disease is observed in an autosomal dominant or autosomal recessive manner. To date, mutations in seven genes have been convincingly confirmed as causative in typical familial forms of PD, i.e., SNCA, LRRK2, VPS35, PRKN, PINK1, GBA, and DJ-1. Family and genome-wide association studies have also identified a number of candidate disease genes and a common genetic variability at 90 loci has been linked to risk for PD. The analysis of the biological function of both proven and candidate genes made it possible to conclude that mitochondrial dysfunction, lysosomal dysfunction, impaired exosomal transport, and immunological processes can play important roles in the development of the pathological process of PD. The mechanisms of initiation of the pathological process and its earliest stages remain unclear. The study of the early stages of the disease (before the first motor symptoms appear) is extremely complicated by the long preclinical period. In addition, at present, the possibility of performing complex biochemical and molecular biological studies familial forms of PD is limited. However, in this case, the analysis of the state of the central nervous system can only be assessed by indirect signs, such as the level of metabolites in the cerebrospinal fluid, peripheral blood, and other biological fluids. One of the potential solutions to this problem is the analysis of disease models, in which it is possible to conduct a detailed in-depth study of all aspects of the pathological process, starting from its earliest stages. Many modeling options are available currently. An analysis of studies published in the 2000s suggests that toxic models in rodents are used in the vast majority of cases. However, interesting and important data for understanding the pathogenesis of PD can be obtained from other in vivo models. Within the framework of this review, we will consider various models of PD that were created using various living organisms, from unicellular yeast (Saccharomyces cerevisiae) and invertebrate (Nematode and Drosophila) forms to various mammalian species.
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Affiliation(s)
- Maria Shadrina
- Laboratory of Molecular Genetics of Hereditary Diseases, Institute of Molecular Genetics of National Research Centre "Kurchatov Institute", Moscow, Russia
| | - Petr Slominsky
- Laboratory of Molecular Genetics of Hereditary Diseases, Institute of Molecular Genetics of National Research Centre "Kurchatov Institute", Moscow, Russia
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12
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Chen X, Necus J, Peraza LR, Mehraram R, Wang Y, O'Brien JT, Blamire A, Kaiser M, Taylor JP. The functional brain favours segregated modular connectivity at old age unless affected by neurodegeneration. Commun Biol 2021; 4:973. [PMID: 34400752 PMCID: PMC8367990 DOI: 10.1038/s42003-021-02497-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2018] [Accepted: 07/22/2021] [Indexed: 11/29/2022] Open
Abstract
Brain's modular connectivity gives this organ resilience and adaptability. The ageing process alters the organised modularity of the brain and these changes are further accentuated by neurodegeneration, leading to disorganisation. To understand this further, we analysed modular variability-heterogeneity of modules-and modular dissociation-detachment from segregated connectivity-in two ageing cohorts and a mixed cohort of neurodegenerative diseases. Our results revealed that the brain follows a universal pattern of high modular variability in metacognitive brain regions: the association cortices. The brain in ageing moves towards a segregated modular structure despite presenting with increased modular heterogeneity-modules in older adults are not only segregated, but their shape and size are more variable than in young adults. In the presence of neurodegeneration, the brain maintains its segregated connectivity globally but not locally, and this is particularly visible in dementia with Lewy bodies and Parkinson's disease dementia; overall, the modular brain shows patterns of differentiated pathology.
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Affiliation(s)
- Xue Chen
- College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China.
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.
| | - Joe Necus
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom.
- University of Nottingham, NIHR Nottingham Biomedical Research Centre, School of Medicine, Nottingham, UK.
| | - Luis R Peraza
- Translational and Clinical Research Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, United Kingdom
- IXICO Plc, London, UK
| | - Ramtin Mehraram
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- Translational and Clinical Research Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, United Kingdom
- Experimental Oto-rhino-laryngology (ExpORL) Research Group, Department of Neurosciences, KU Leuven, Leuven, Belgium
- NIHR Newcastle Biomedical Research Centre, Campus for Ageing and Vitality, Newcastle upon Tyne, UK
| | - Yanjiang Wang
- College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge School of Medicine, Cambridge, United Kingdom
| | - Andrew Blamire
- Translational and Clinical Research Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, United Kingdom
| | - Marcus Kaiser
- Interdisciplinary Computing and Complex BioSystems (ICOS) research group, School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
- University of Nottingham, NIHR Nottingham Biomedical Research Centre, School of Medicine, Nottingham, UK
- Translational and Clinical Research Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, United Kingdom
| | - John-Paul Taylor
- Translational and Clinical Research Institute, Newcastle University, Campus for Ageing and Vitality, Newcastle upon Tyne, United Kingdom
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13
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Xian WB, Shi XC, Luo GH, Yi C, Zhang XS, Pei Z. Co-registration Analysis of Fluorodopa and Fluorodeoxyglucose Positron Emission Tomography for Differentiating Multiple System Atrophy Parkinsonism Type From Parkinson's Disease. Front Aging Neurosci 2021; 13:648531. [PMID: 33958998 PMCID: PMC8093399 DOI: 10.3389/fnagi.2021.648531] [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: 12/31/2020] [Accepted: 02/22/2021] [Indexed: 11/13/2022] Open
Abstract
It is difficult to differentiate between Parkinson's disease and multiple system atrophy parkinsonian subtype (MSA-P) because of the overlap of their signs and symptoms. Enormous efforts have been made to develop positron emission tomography (PET) imaging to differentiate these diseases. This study aimed to investigate the co-registration analysis of 18F-fluorodopa and 18F-flurodeoxyglucose PET images to visualize the difference between Parkinson's disease and MSA-P. We enrolled 29 Parkinson's disease patients, 28 MSA-P patients, and 10 healthy controls, who underwent both 18F-fluorodopa and 18F-flurodeoxyglucose PET scans. Patients with Parkinson's disease and MSA-P exhibited reduced bilateral striatal 18F-fluorodopa uptake (p < 0.05, vs. healthy controls). Both regional specific uptake ratio analysis and statistical parametric mapping analysis of 18F-flurodeoxyglucose PET revealed hypometabolism in the bilateral putamen of MSA-P patients and hypermetabolism in the bilateral putamen of Parkinson's disease patients. There was a significant positive correlation between 18F-flurodeoxyglucose uptake and 18F-fluorodopa uptake in the contralateral posterior putamen of MSA-P patients (rs = 0.558, p = 0.002). Both 18F-flurodeoxyglucose and 18F-fluorodopa PET images showed that the striatum was rabbit-shaped in the healthy control group segmentation analysis. A defective rabbit-shaped striatum was observed in the 18F-fluorodopa PET image of patients with Parkinson's disease and MSA-P. In the segmentation analysis of 18F-flurodeoxyglucose PET image, an intact rabbit-shaped striatum was observed in Parkinson's disease patients, whereas a defective rabbit-shaped striatum was observed in MSA-P patients. These findings suggest that there were significant differences in the co-registration analysis of 18F-flurodeoxyglucose and 18F-fluorodopa PET images, which could be used in the individual analysis to differentiate Parkinson's disease from MSA-P.
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Affiliation(s)
- Wen-Biao Xian
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Guangzhou, China
| | - Xin-Chong Shi
- Department of Nuclear Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Gan-Hua Luo
- Department of Nuclear Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Chang Yi
- Department of Nuclear Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiang-Song Zhang
- Department of Nuclear Medicine, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhong Pei
- Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.,Guangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Guangzhou, China
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14
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Beheshti I, Ko JH. Modulating brain networks associated with cognitive deficits in Parkinson's disease. Mol Med 2021; 27:24. [PMID: 33691622 PMCID: PMC7945662 DOI: 10.1186/s10020-021-00284-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 03/02/2021] [Indexed: 12/12/2022] Open
Abstract
Parkinson's disease (PD) is a relatively well characterised neurological disorder that primarily affects motor and cognitive functions. This paper reviews on how transcranial direct current stimulation (tDCS) can be used to modulate brain networks associated with cognitive deficits in PD. We first provide an overview of brain network abnormalities in PD, by introducing the brain network modulation approaches such as pharmacological interventions and brain stimulation techniques. We then present the potential underlying mechanisms of tDCS technique, and specifically highlight how tDCS can be applied to modulate brain network abnormality associated with cognitive dysfunction among PD patients. More importantly, we address the limitations of existing studies and suggest possible future directions, with the aim of helping researchers to further develop the use of tDCS technique in clinical settings.
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Affiliation(s)
- Iman Beheshti
- Department of Human Anatomy and Cell Science, University of Manitoba, 130-745 Bannatyne Ave., Winnipeg, MB R3E 0J9 Canada
- Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB Canada
| | - Ji Hyun Ko
- Department of Human Anatomy and Cell Science, University of Manitoba, 130-745 Bannatyne Ave., Winnipeg, MB R3E 0J9 Canada
- Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB Canada
- Graduate Program in Biomedical Engineering, University of Manitoba, Winnipeg, MB Canada
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15
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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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16
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Ge J, Wang M, Lin W, Wu P, Guan Y, Zhang H, Huang Z, Yang L, Zuo C, Jiang J, Rominger A, Shi K. Metabolic network as an objective biomarker in monitoring deep brain stimulation for Parkinson's disease: a longitudinal study. EJNMMI Res 2020; 10:131. [PMID: 33119814 PMCID: PMC7596139 DOI: 10.1186/s13550-020-00722-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 10/21/2020] [Indexed: 12/28/2022] Open
Abstract
Background With the advance of subthalamic nucleus (STN) deep brain stimulation (DBS) in the treatment of Parkinson’s disease (PD), it is desired to identify objective criteria for the monitoring of the therapy outcome.
This paper explores the feasibility of metabolic network derived from positron emission tomography (PET) with 18F-fluorodeoxyglucose in monitoring the STN DBS treatment for PD.
Methods Age-matched 33 PD patients, 33 healthy controls (HCs), 9 PD patients with bilateral DBS surgery and 9 controls underwent 18F-FDG PET scans. The DBS patients were followed longitudinally to investigate the alternations of the PD-related metabolic covariance pattern (PDRP) expressions. Results The PDRP expression was abnormally elevated in PD patients compared with HCs (P < 0.001). For DBS patients, a significant decrease in the Unified Parkinson’s Disease Rating Scale (UPDRS, P = 0.001) and PDRP expression (P = 0.004) was observed 3 months after STN DBS treatment, while a rollback was observed in both UPDRS and PDRP expressions (both P < 0.01) 12 months after treatment. The changes in PDRP expression mediated by STN DBS were generally in line with UPDRS improvement. The graphical network analysis shows increased connections at 3 months and a return at 12 months confirmed by small-worldness coefficient. Conclusions The preliminary results demonstrate the potential of metabolic network expression as complimentary objective biomarker for the assessment and monitoring of STN DBS treatment in PD patients. Clinical Trial Registration ChiCTR-DOC-16008645. http://www.chictr.org.cn/showproj.aspx?proj=13865.
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Affiliation(s)
- Jingjie Ge
- PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235, China
| | - Min Wang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
| | - Wei Lin
- Department of Neurosurgery, 904 Hospital of PLA, Wuxi, China
| | - Ping Wu
- PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235, China
| | - Yihui Guan
- PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235, China
| | - Huiwei Zhang
- PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235, China
| | - Zhemin Huang
- PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235, China
| | - Likun Yang
- Department of Neurosurgery, 904 Hospital of PLA, Wuxi, China
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235, China. .,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.
| | - Jiehui Jiang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China. .,Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai, China.
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.,Department of Informatics, Technical University of Munich, Munich, Germany
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17
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Elevated caudate connectivity in cognitively normal Parkinson's disease patients. Sci Rep 2020; 10:17978. [PMID: 33087833 PMCID: PMC7578639 DOI: 10.1038/s41598-020-75008-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 09/29/2020] [Indexed: 01/29/2023] Open
Abstract
Mild cognitive impairment (MCI) is common in Parkinson’s disease patients. However, its underlying mechanism is not well understood, which has hindered new treatment discoveries specific to MCI. The aim of this study was to investigate functional connectivity changes of the caudate nucleus in cognitively impaired Parkinson’s patients. We recruited 18 Parkinson’s disease patients—10 PDNC [normal cognition Parkinson’s disease; Montreal Cognitive Assessment (MoCA) ≥ 26], 8 PDLC (low cognition Parkinson’s disease; MoCA < 26) —and 10 age-matched healthy controls. All subjects were scanned with resting-state functional magnetic resonance imaging (MRI) and perfusion MRI. We analyzed these data for graph theory metrics and Alzheimer’s disease-like pattern score, respectively. A strong positive correlation was found between the functional connectivity of the right caudate nucleus and MoCA scores in Parkinson’s patient groups, but not in healthy control subjects. Interestingly, PDNC’s functional connectivity of the right caudate was significantly higher than both PDLC and healthy controls, while PDLC and healthy controls were not significantly different from each other. We found that Alzheimer’s disease-like metabolic/perfusion pattern score correlated with MoCA scores in healthy controls, but not in Parkinson’s disease. Increased caudate connectivity may be related to a compensatory mechanism found in cognitively normal patients with Parkinson’s disease. Our findings support and complement the dual syndrome hypothesis.
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18
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Liu C, Jiang J, Zhou H, Zhang H, Wang M, Jiang J, Wu P, Ge J, Wang J, Ma Y, Zuo C. Brain Functional and Structural Signatures in Parkinson's Disease. Front Aging Neurosci 2020; 12:125. [PMID: 32528272 PMCID: PMC7264099 DOI: 10.3389/fnagi.2020.00125] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Accepted: 04/14/2020] [Indexed: 11/13/2022] Open
Abstract
The aim of this study is to explore functional and structural properties of abnormal brain networks associated with Parkinson’s disease (PD). 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) and T1-weighted magnetic resonance imaging from 20 patients with moderate-stage PD and 20 age-matched healthy controls were acquired to identify disease-related patterns in functional and structural networks. Dual-modal images from another prospective subject of 15 PD patients were used as the validation group. Scaled Subprofile Modeling based on principal component analysis method was applied to determine disease-related patterns in both modalities, and brain connectome analysis based on graph theory was applied to verify these patterns. The results showed that the expressions of the metabolic and structural patterns in PD patients were significantly higher than healthy controls (PD1-HC, p = 0.0039, p = 0.0058; PD2-HC, p < 0.001, p = 0.044). The metabolic pattern was characterized by relative increased metabolic activity in pallidothalamic, pons, putamen, and cerebellum, associated with metabolic decreased in parietal–occipital areas. The structural pattern was characterized by relative decreased gray matter (GM) volume in pons, transverse temporal gyrus, left cuneus, right superior occipital gyrus, and right superior parietal lobule, associated with preservation in GM volume in pallidum and putamen. In addition, both patterns were verified in the connectome analysis. The findings suggest that significant overlaps between metabolic and structural patterns provide new evidence for elucidating the neuropathological mechanisms of PD.
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Affiliation(s)
- Chunhua Liu
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Jiehui Jiang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.,Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai, China
| | - Hucheng Zhou
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Huiwei Zhang
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Min Wang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Juanjuan Jiang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Ping Wu
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jingjie Ge
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jian Wang
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yilong Ma
- Center for Neurosciences, Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, NY, United States
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.,Human Phenome Institute, Fudan University, Shanghai, China
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19
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Wang M, Jiang J, Yan Z, Alberts I, Ge J, Zhang H, Zuo C, Yu J, Rominger A, Shi K. Individual brain metabolic connectome indicator based on Kullback-Leibler Divergence Similarity Estimation predicts progression from mild cognitive impairment to Alzheimer's dementia. Eur J Nucl Med Mol Imaging 2020; 47:2753-2764. [PMID: 32318784 PMCID: PMC7567735 DOI: 10.1007/s00259-020-04814-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Accepted: 04/06/2020] [Indexed: 01/10/2023]
Abstract
Purpose Positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) reveals altered cerebral metabolism in individuals with mild cognitive impairment (MCI) and Alzheimer’s dementia (AD). Previous metabolic connectome analyses derive from groups of patients but do not support the prediction of an individual’s risk of conversion from present MCI to AD. We now present an individual metabolic connectome method, namely the Kullback-Leibler Divergence Similarity Estimation (KLSE), to characterize brain-wide metabolic networks that predict an individual’s risk of conversion from MCI to AD. Methods FDG-PET data consisting of 50 healthy controls, 332 patients with stable MCI, 178 MCI patients progressing to AD, and 50 AD patients were recruited from ADNI database. Each individual’s metabolic brain network was ascertained using the KLSE method. We compared intra- and intergroup similarity and difference between the KLSE matrix and group-level matrix, and then evaluated the network stability and inter-individual variation of KLSE. The multivariate Cox proportional hazards model and Harrell’s concordance index (C-index) were employed to assess the prediction performance of KLSE and other clinical characteristics. Results The KLSE method captures more pathological connectivity in the parietal and temporal lobes relative to the typical group-level method, and yields detailed individual information, while possessing greater stability of network organization (within-group similarity coefficient, 0.789 for sMCI and 0.731 for pMCI). Metabolic connectome expression was a superior predictor of conversion than were other clinical assessments (hazard ratio (HR) = 3.55; 95% CI, 2.77–4.55; P < 0.001). The predictive performance improved further upon combining clinical variables in the Cox model, i.e., C-indices 0.728 (clinical), 0.730 (group-level pattern model), 0.750 (imaging connectome), and 0.794 (the combined model). Conclusion The KLSE indicator identifies abnormal brain networks predicting an individual’s risk of conversion from MCI to AD, thus potentially constituting a clinically applicable imaging biomarker. Electronic supplementary material The online version of this article (10.1007/s00259-020-04814-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Min Wang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
| | - Jiehui Jiang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China. .,Key laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai, China.
| | - Zhuangzhi Yan
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
| | - Ian Alberts
- Department of Nuclear Medicine, Inselspital, University Hospital Bern, Bern, Switzerland
| | - Jingjie Ge
- Department of Nuclear Medicine, PET Center, Huashan Hospital, Fudan University, 518 Wuzhong Dong Road, Shanghai, 201103, China
| | - Huiwei Zhang
- Department of Nuclear Medicine, PET Center, Huashan Hospital, Fudan University, 518 Wuzhong Dong Road, Shanghai, 201103, China
| | - Chuantao Zuo
- Department of Nuclear Medicine, PET Center, Huashan Hospital, Fudan University, 518 Wuzhong Dong Road, Shanghai, 201103, China. .,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.
| | - Jintai Yu
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, University Hospital Bern, Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, University Hospital Bern, Bern, Switzerland.,Department of Informatics, Technische Universität München, Munich, Germany
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20
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Isaias IU, Brumberg J, Pozzi NG, Palmisano C, Canessa A, Marotta G, Volkmann J, Pezzoli G. Brain metabolic alterations herald falls in patients with Parkinson's disease. Ann Clin Transl Neurol 2020; 7:579-583. [PMID: 32162447 PMCID: PMC7187714 DOI: 10.1002/acn3.51013] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 02/12/2020] [Accepted: 02/19/2020] [Indexed: 01/16/2023] Open
Abstract
Pathophysiological understanding of gait and balance disorders in Parkinson’s disease is insufficient and late recognition of fall risk limits efficacious follow‐up to prevent or delay falls. We show a distinctive reduction of glucose metabolism in the left posterior parietal cortex, with increased metabolic activity in the cerebellum, in parkinsonian patients 6–8 months before their first fall episode. Falls in Parkinson’s disease may arise from altered cortical processing of body spatial orientation, possibly predicted by abnormal cortical metabolism.
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Affiliation(s)
- Ioannis U Isaias
- Department of Neurology, University Hospital and Julius Maximilian University of Würzburg, Würzburg, Germany
| | - Joachim Brumberg
- Department of Nuclear Medicine, University Hospital Würzburg, Würzburg, Germany
| | - Nicoló G Pozzi
- Department of Neurology, University Hospital and Julius Maximilian University of Würzburg, Würzburg, Germany
| | - Chiara Palmisano
- Department of Neurology, University Hospital and Julius Maximilian University of Würzburg, Würzburg, Germany
| | - Andrea Canessa
- Fondazione Europea di Ricerca Biomedica (FERB Onlus), Cernusco s/N (Milano), Italy
| | - Giorgio Marotta
- Department of Nuclear Medicine, Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico, Milano, Italy
| | - Jens Volkmann
- Department of Neurology, University Hospital and Julius Maximilian University of Würzburg, Würzburg, Germany
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21
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Schindlbeck KA, Vo A, Nguyen N, Tang CC, Niethammer M, Dhawan V, Brandt V, Saunders-Pullman R, Bressman SB, Eidelberg D. LRRK2 and GBA Variants Exert Distinct Influences on Parkinson's Disease-Specific Metabolic Networks. Cereb Cortex 2019; 30:2867-2878. [PMID: 31813991 DOI: 10.1093/cercor/bhz280] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 08/23/2019] [Accepted: 09/17/2019] [Indexed: 12/11/2022] Open
Abstract
The natural history of idiopathic Parkinson's disease (PD) varies considerably across patients. While PD is generally sporadic, there are known genetic influences: the two most common, mutations in the LRRK2 or GBA1 gene, are associated with slower and more aggressive progression, respectively. Here, we applied graph theory to metabolic brain imaging to understand the effects of genotype on the organization of previously established PD-specific networks. We found that closely matched PD patient groups with the LRRK2-G2019S mutation (PD-LRRK2) or GBA1 variants (PD-GBA) expressed the same disease networks as sporadic disease (sPD), but PD-LRRK2 and PD-GBA patients exhibited abnormal increases in network connectivity that were not present in sPD. Using a community detection strategy, we found that the location and modular distribution of these connections differed strikingly across genotypes. In PD-LRRK2, connections were gained within the network core, with the formation of distinct functional pathways linking the cerebellum and putamen. In PD-GBA, by contrast, the majority of functional connections were formed outside the core, involving corticocortical pathways at the network periphery. Strategically localized connections within the core in PD-LRRK2 may maintain PD network activity at lower levels than in PD-GBA, resulting in a less aggressive clinical course.
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Affiliation(s)
- Katharina A Schindlbeck
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - An Vo
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - Nha Nguyen
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Chris C Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - Martin Niethammer
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - Vijay Dhawan
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - Vicky Brandt
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - Rachel Saunders-Pullman
- Department of Neurology, Mount Sinai Beth Israel, Mount Sinai Hospital, New York, NY 10003, USA
| | - Susan B Bressman
- Department of Neurology, Mount Sinai Beth Israel, Mount Sinai Hospital, New York, NY 10003, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
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22
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Guan X, Guo T, Zeng Q, Wang J, Zhou C, Liu C, Wei H, Zhang Y, Xuan M, Gu Q, Xu X, Huang P, Pu J, Zhang B, Zhang MM. Oscillation-specific nodal alterations in early to middle stages Parkinson's disease. Transl Neurodegener 2019; 8:36. [PMID: 31807287 PMCID: PMC6857322 DOI: 10.1186/s40035-019-0177-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Accepted: 11/07/2019] [Indexed: 02/26/2023] Open
Abstract
BACKGROUND Different oscillations of brain networks could carry different dimensions of brain integration. We aimed to investigate oscillation-specific nodal alterations in patients with Parkinson's disease (PD) across early stage to middle stage by using graph theory-based analysis. METHODS Eighty-eight PD patients including 39 PD patients in the early stage (EPD) and 49 patients in the middle stage (MPD) and 36 controls were recruited in the present study. Graph theory-based network analyses from three oscillation frequencies (slow-5: 0.01-0.027 Hz; slow-4: 0.027-0.073 Hz; slow-3: 0.073-0.198 Hz) were analyzed. Nodal metrics (e.g. nodal degree centrality, betweenness centrality and nodal efficiency) were calculated. RESULTS Our results showed that (1) a divergent effect of oscillation frequencies on nodal metrics, especially on nodal degree centrality and nodal efficiency, that the anteroventral neocortex and subcortex had high nodal metrics within low oscillation frequencies while the posterolateral neocortex had high values within the relative high oscillation frequency was observed, which visually showed that network was perturbed in PD; (2) PD patients in early stage relatively preserved nodal properties while MPD patients showed widespread abnormalities, which was consistently detected within all three oscillation frequencies; (3) the involvement of basal ganglia could be specifically observed within slow-5 oscillation frequency in MPD patients; (4) logistic regression and receiver operating characteristic curve analyses demonstrated that some of those oscillation-specific nodal alterations had the ability to well discriminate PD patients from controls or MPD from EPD patients at the individual level; (5) occipital disruption within high frequency (slow-3) made a significant influence on motor impairment which was dominated by akinesia and rigidity. CONCLUSIONS Coupling various oscillations could provide potentially useful information for large-scale network and progressive oscillation-specific nodal alterations were observed in PD patients across early to middle stages.
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Affiliation(s)
- Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009 China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009 China
| | - Qiaoling Zeng
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009 China
| | - Jiaqiu Wang
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009 China
| | - Chunlei Liu
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA USA
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA USA
| | - Hongjiang Wei
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA USA
| | - Yuyao Zhang
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA USA
| | - Min Xuan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009 China
| | - Quanquan Gu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009 China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009 China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009 China
| | - Jiali Pu
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Min-Ming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, No.88 Jiefang Road, Shangcheng District, Hangzhou, 310009 China
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23
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Schmitt O, Eipert P, Schwanke S, Lessmann F, Meinhardt J, Beier J, Kadir K, Karnitzki A, Sellner L, Klünker AC, Ruß F, Jenssen J. Connectome verification: inter-rater and connection reliability of tract-tracing-based intrinsic hypothalamic connectivity. Brief Bioinform 2019; 20:1944-1955. [PMID: 29897426 DOI: 10.1093/bib/bby048] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 05/09/2018] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Structural connectomics supports understanding aspects of neuronal dynamics and brain functions. Conducting metastudies of tract-tracing publications is one option to generate connectome databases by collating neuronal connectivity data. Meanwhile, it is a common practice that the neuronal connections and their attributes of such retrospective data collations are extracted from tract-tracing publications manually by experts. As the description of tract-tracing results is often not clear-cut and the documentation of interregional connections is not standardized, the extraction of connectivity data from tract-tracing publications could be complex. This might entail that different experts interpret such non-standardized descriptions of neuronal connections from the same publication in variable ways. Hitherto, no investigation is available that determines the variability of extracted connectivity information from original tract-tracing publications. A relatively large variability of connectivity information could produce significant misconstructions of adjacency matrices with faults in network and graph analyzes. The objective of this study is to investigate the inter-rater and inter-observation variability of tract-tracing-based documentations of neuronal connections. To demonstrate the variability of neuronal connections, data of 16 publications which describe neuronal connections of subregions of the hypothalamus have been assessed by way of example. RESULTS A workflow is proposed that allows detecting variability of connectivity at different steps of data processing in connectome metastudies. Variability between three blinded experts was found by comparing the connection information in a sample of 16 publications that describe tract-tracing-based neuronal connections in the hypothalamus. Furthermore, observation scores, matrix visualizations of discrepant connections and weight variations in adjacency matrices are analyzed. AVAILABILITY The resulting data and software are available at http://neuroviisas.med.uni-rostock.de/neuroviisas.shtml.
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Affiliation(s)
- Oliver Schmitt
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Peter Eipert
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Sebastian Schwanke
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Felix Lessmann
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Jennifer Meinhardt
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Julia Beier
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Kanar Kadir
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Adrian Karnitzki
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Linda Sellner
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Ann-Christin Klünker
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Frauke Ruß
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
| | - Jörg Jenssen
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057 Rostock, Germany
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24
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Network imaging biomarkers: insights and clinical applications in Parkinson's disease. Lancet Neurol 2019; 17:629-640. [PMID: 29914708 DOI: 10.1016/s1474-4422(18)30169-8] [Citation(s) in RCA: 114] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 04/13/2018] [Accepted: 04/25/2018] [Indexed: 12/14/2022]
Abstract
Parkinson's disease presents several practical challenges: it can be difficult to distinguish from atypical parkinsonian syndromes, clinical ratings can be insensitive as markers of disease progression, and its non-motor manifestations are not readily assessed in animal models. These challenges, along with others, are beginning to be addressed by innovative imaging methods to characterise Parkinson's disease-specific functional networks across the whole brain and measure their expression in each patient. These signatures can help improve differential diagnosis, guide selection of patients for clinical trials, and quantify treatment responses and placebo effects in individual patients. The primary Parkinson's disease-related metabolic pattern has been replicated in multiple patient populations and used as an outcome measure in clinical trials. It can also be used as a predictor of near-term phenoconversion in prodromal syndromes, such as rapid eye movement sleep behaviour disorder. Functional network imaging holds great promise for future clinical use in the management of neurodegenerative disorders.
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25
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Niethammer M, Eidelberg D. Network Imaging in Parkinsonian and Other Movement Disorders: Network Dysfunction and Clinical Correlates. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2019; 144:143-184. [DOI: 10.1016/bs.irn.2018.10.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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Xu X, Guan X, Guo T, Zeng Q, Ye R, Wang J, Zhong J, Xuan M, Gu Q, Huang P, Pu J, Zhang B, Zhang M. Brain Atrophy and Reorganization of Structural Network in Parkinson's Disease With Hemiparkinsonism. Front Hum Neurosci 2018; 12:117. [PMID: 29636671 PMCID: PMC5881349 DOI: 10.3389/fnhum.2018.00117] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Accepted: 03/12/2018] [Indexed: 11/13/2022] Open
Abstract
Hemiparkinsonism duration in patients with Parkinson's disease (PD) is a key time window to study early pathology of PD. We aimed to comprehensively explore the alterations of deformation and structural network in PD patients with hemiparkinsonism, which could potentially disclose the early biomarker for PD. Thirty-one PD patients with hemiparkinsonism and 37 age- and gender- matched normal controls were included in the present study. First of all, we normalized the left hemisphere of structural images as the contralateral side to the affected limbs. Deformation-based morphometry (DBM) was conducted to evaluate the brain atrophy and/or enlargement. structural networks were constructed by thresholding gray matter volume correlation matrices of 116 regions and analyzed using graph theoretical approaches (e.g., small-worldness, global, and nodal measures). Significantly decreased deformation values were observed in the temporoparietal regions like bilateral middle temporal gyri, ipsilateral precuneus and contralateral Rolandic operculum extending to supramarginal and postcentral gyri. Lower deformation values in contralateral middle temporal gyrus were negatively correlated with higher motor impairment which was dominated by akinesia/rigidity. Moreover, nodal reorganization of structural network mainly located in frontal, temporal, subcortex and cerebellum was bilaterally explored in PD patients with hemiparkinsonism. Increased nodal properties could be commonly observed in frontal lobes. Disruption of subcortex including basal ganglia and amygdala was detected by nodal local efficiency and nodal clustering coefficient. Twelve hubs, mainly from paralimbic-limbic and heteromodal networks, were disrupted and, alternatively, 14 hubs, most of which were located in frontal lobes, were additionally detected in PD patients with hemiparkinsonism. In conclusion, during hemiparkinsonism period, mild brain atrophy in the temporoparietal regions and widespread reorganization of structural network, e.g., enhanced frontal function and disruption of basal ganglia nodes, occurred in both hemispheres. With our data, we can also argue that MTG contralateral to the affected limbs (expressing clinically verified brain atrophy) might be a potential living biomarker to monitor disease progression. Therefore, the combination of DBM and structural network analyses can provide a comprehensive and sensitive evaluation for potential pathogenesis of early PD patients with hemiparkinsonism.
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Affiliation(s)
- Xiaojun Xu
- Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Guan
- Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Guo
- Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Qiaoling Zeng
- Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Rong Ye
- Department of Neurology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Jiaqiu Wang
- Department of Neurology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Jianguo Zhong
- Department of Radiology, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Min Xuan
- Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Quanquan Gu
- Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Jiali Pu
- Department of Neurology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Minming Zhang
- Department of Radiology, Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
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