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Altham C, Zhang H, Pereira E. Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review. PLoS One 2024; 19:e0303644. [PMID: 38753740 PMCID: PMC11098383 DOI: 10.1371/journal.pone.0303644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 04/29/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Parkinson's Disease is the second most common neurological disease in over 60s. Cognitive impairment is a major clinical symptom, with risk of severe dysfunction up to 20 years post-diagnosis. Processes for detection and diagnosis of cognitive impairments are not sufficient to predict decline at an early stage for significant impact. Ageing populations, neurologist shortages and subjective interpretations reduce the effectiveness of decisions and diagnoses. Researchers are now utilising machine learning for detection and diagnosis of cognitive impairment based on symptom presentation and clinical investigation. This work aims to provide an overview of published studies applying machine learning to detecting and diagnosing cognitive impairment, evaluate the feasibility of implemented methods, their impacts, and provide suitable recommendations for methods, modalities and outcomes. METHODS To provide an overview of the machine learning techniques, data sources and modalities used for detection and diagnosis of cognitive impairment in Parkinson's Disease, we conducted a review of studies published on the PubMed, IEEE Xplore, Scopus and ScienceDirect databases. 70 studies were included in this review, with the most relevant information extracted from each. From each study, strategy, modalities, sources, methods and outcomes were extracted. RESULTS Literatures demonstrate that machine learning techniques have potential to provide considerable insight into investigation of cognitive impairment in Parkinson's Disease. Our review demonstrates the versatility of machine learning in analysing a wide range of different modalities for the detection and diagnosis of cognitive impairment in Parkinson's Disease, including imaging, EEG, speech and more, yielding notable diagnostic accuracy. CONCLUSIONS Machine learning based interventions have the potential to glean meaningful insight from data, and may offer non-invasive means of enhancing cognitive impairment assessment, providing clear and formidable potential for implementation of machine learning into clinical practice.
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Affiliation(s)
- Callum Altham
- Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, United Kingdom
| | - Huaizhong Zhang
- Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, United Kingdom
| | - Ella Pereira
- Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, United Kingdom
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Castelli MB, Alonso-Recio L, Carvajal F, Serrano JM. Does the Montreal Cognitive Assessment (MoCA) identify cognitive impairment profiles in Parkinson's disease? An exploratory study. APPLIED NEUROPSYCHOLOGY. ADULT 2024; 31:238-247. [PMID: 34894908 DOI: 10.1080/23279095.2021.2011727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
An important proportion of patients with Parkinson's Disease (PD) present signs of cognitive impairment, although this is heterogeneous. In an attempt to classify this, the dual syndrome hypothesis distinguishes between two profiles: one defined by attentional and executive problems with damage in anterior cerebral regions, and another with mnesic and visuospatial alterations, with damage in posterior cerebral regions. The Montreal Cognitive Assessment (MoCA) is one of the recommended screening tools, and one of the most used, to assess cognitive impairment in PD. However, its ability to specifically identify these two profiles of cognitive impairment has not been studied. The aim of this study was, therefore, to analyze the capacity of the MoCA to detect cognitive impairment, and also to identify anterior and posterior profiles defined by the dual syndrome hypothesis. For this purpose, 59 patients with idiopathic PD were studied with the MoCA and a neuropsychological battery of tests covering all cognitive domains. Results of logistic regression analysis with ROC (Receiver Operating Characteristic) curves showed that MoCA detected cognitive impairment and identified patients with a profile of anterior/attentional and executive deficit, with acceptable sensibility and specificity. However, it did not identify patients with a posterior/mnesic-visuospatial impairment. We discuss the reasons for the lack of sensitivity of MoCA in this profile, and other possible implications of these results with regards the usefulness of this tool to assess cognitive impairment in PD.
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Affiliation(s)
- María Belén Castelli
- Departamento de Psicología Biológica y de la Salud, Facultad de Psicología, Universidad Autónoma de Madrid, Madrid, Spain
| | - Laura Alonso-Recio
- Departamento de Psicología y Salud, Facultad de Ciencias de la Salud y la Educación, Universidad a Distancia de Madrid, Madrid, Spain
| | - Fernando Carvajal
- Departamento de Psicología Biológica y de la Salud, Facultad de Psicología, Universidad Autónoma de Madrid, Madrid, Spain
| | - Juan Manuel Serrano
- Departamento de Psicología Biológica y de la Salud, Facultad de Psicología, Universidad Autónoma de Madrid, Madrid, Spain
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Burgio F, Menardi A, Benavides-Varela S, Danesin L, Giustiniani A, Van den Stock J, De Mitri R, Biundo R, Meneghello F, Antonini A, Vallesi A, de Gelder B, Semenza C. Facial emotion recognition in individuals with mild cognitive impairment: An exploratory study. COGNITIVE, AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2024:10.3758/s13415-024-01160-5. [PMID: 38316707 DOI: 10.3758/s13415-024-01160-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/10/2024] [Indexed: 02/07/2024]
Abstract
Understanding facial emotions is fundamental to interact in social environments and modify behavior accordingly. Neurodegenerative processes can progressively transform affective responses and affect social competence. This exploratory study examined the neurocognitive correlates of face recognition, in individuals with two mild cognitive impairment (MCI) etiologies (prodromal to dementia - MCI, or consequent to Parkinson's disease - PD-MCI). Performance on the identification and memorization of neutral and emotional facial expressions was assessed in 31 individuals with MCI, 26 with PD-MCI, and 30 healthy controls (HC). Individuals with MCI exhibited selective impairment in recognizing faces expressing fear, along with difficulties in remembering both neutral and emotional faces. Conversely, individuals with PD-MCI showed no differences compared with the HC in either emotion recognition or memory. In MCI, no significant association emerged between the memory for facial expressions and cognitive difficulties. In PD-MCI, regression analyses showed significant associations with higher-level cognitive functions in the emotional memory task, suggesting the presence of compensatory mechanisms. In a subset of participants, voxel-based morphometry revealed that the performance on emotional tasks correlated with regional changes in gray matter volume. The performance in the matching of negative expressions was predicted by volumetric changes in brain areas engaged in face and emotional processing, in particular increased volume in thalamic nuclei and atrophy in the right parietal cortex. Future studies should leverage on neuroimaging data to determine whether differences in emotional recognition are mediated by pathology-specific atrophic patterns.
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Affiliation(s)
| | - Arianna Menardi
- Department of Neuroscience, University of Padova, 35128, Padova, Italy
- Padova Neuroscience Center, University of Padova, 35129, Padova, Italy
| | - Silvia Benavides-Varela
- Padova Neuroscience Center, University of Padova, 35129, Padova, Italy
- Department of Developmental and Social Psychology, University of Padova, Padova, Italy
| | | | | | - Jan Van den Stock
- Department of Neuroscience, Leuven Brain Institute, KU Leuven, 3000, Leuven, Belgium
- Geriatric Psychiatry, University Psychiatric Center KU Leuven, 3000, Leuven, Belgium
| | | | - Roberta Biundo
- Department of General Psychology (DPG), University of Padua, 35131, Padua, Italy
- Study Center for Neurodegeneration (CESNE), University of Padua, 35131, Padua, Italy
| | - Francesca Meneghello
- Unità Operativa Complessa Cure Primarie Distretto 3 Mirano-Dolo, Aulss 3, Serenissima, Italy
| | - Angelo Antonini
- Parkinson's Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, Padova, Italy
| | - Antonino Vallesi
- Department of Neuroscience, University of Padova, 35128, Padova, Italy
- Padova Neuroscience Center, University of Padova, 35129, Padova, Italy
| | - Beatrice de Gelder
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200, MD, Maastricht, the Netherlands
| | - Carlo Semenza
- Department of Neuroscience, University of Padova, 35128, Padova, Italy
- Padova Neuroscience Center, University of Padova, 35129, Padova, Italy
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Shu Z, Wang J, Cheng Y, Lu J, Lin J, Wang Y, Zhang X, Yu Y, Zhu Z, Han J, Wu J, Yu N. fNIRS-based graph frequency analysis to identify mild cognitive impairment in Parkinson's disease. J Neurosci Methods 2024; 402:110031. [PMID: 38040127 DOI: 10.1016/j.jneumeth.2023.110031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 11/23/2023] [Accepted: 11/28/2023] [Indexed: 12/03/2023]
Abstract
BACKGROUND Early identification of mild cognitive impairment (MCI) is essential for its treatment and the prevention of dementia in Parkinson's disease (PD). Existing approaches are mostly based on neuropsychological assessments, while brain activation and connection have not been well considered. NEW METHOD This paper presents a neuroimaging-based graph frequency analysis method and the generated features to quantify the brain functional neurodegeneration and distinguish between PD-MCI patients and healthy controls. The Stroop color-word experiment was conducted with 20 PD-MCI patients and 34 healthy controls, and the brain activation was recorded with functional near-infrared spectroscopy (fNIRS). Then, the functional brain network was constructed based on Pearson's correlation coefficient calculation between every two fNIRS channels. Next, the functional brain network was represented as a graph and decomposed in the graph frequency domain through the graph Fourier transform (GFT) to obtain the eigenvector matrix. Total variation and weighted zero crossings of eigenvectors were defined and integrated to quantify functional interaction between brain regions and the spatial variability of the brain network in specific graph frequency ranges, respectively. After that, the features were employed in training a support vector machine (SVM) classifier. RESULTS The presented method achieved a classification accuracy of 0.833 and an F1 score of 0.877, significantly outperforming existing methods and features. COMPARISON WITH EXISTING METHODS Our method provided improved classification performance in the identification of PD-MCI. CONCLUSION The results suggest that the presented graph frequency analysis method well identify PD-MCI patients and the generated features promise functional brain biomarkers for PD-MCI diagnosis.
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Affiliation(s)
- Zhilin Shu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China
| | - Jin Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin 300350, China; Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin 300370, China
| | - Yuanyuan Cheng
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin 300370, China; Department of Rehabilitation Medicine, Tianjin Huanhu Hospital, Tianjin 300350, China
| | - Jiewei Lu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China
| | - Jianeng Lin
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China
| | - Yue Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin 300350, China
| | - Xinyuan Zhang
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin 300370, China
| | - Yang Yu
- Department of Rehabilitation Medicine, Tianjin Huanhu Hospital, Tianjin 300350, China
| | - Zhizhong Zhu
- Clinical College of Neurology, Neurosurgery and Neurorehabilitation, Tianjin Medical University, Tianjin 300370, China; Department of Rehabilitation Medicine, Tianjin Huanhu Hospital, Tianjin 300350, China
| | - Jianda Han
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China; Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China.
| | - Jialing Wu
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin 300350, China; Tianjin Key Laboratory of Cerebral Vascular and Neurodegenerative Diseases, Tianjin Neurosurgical Institute, Tianjin 300350, China.
| | - Ningbo Yu
- College of Artificial Intelligence, Nankai University, Tianjin 300350, China; Engineering Research Center of Trusted Behavior Intelligence, Ministry of Education, Nankai University, Tianjin 300350, China; Institute of Intelligence Technology and Robotic Systems, Shenzhen Research Institute of Nankai University, Shenzhen 518083, China.
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5
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Wang J, He Y. Toward individualized connectomes of brain morphology. Trends Neurosci 2024; 47:106-119. [PMID: 38142204 DOI: 10.1016/j.tins.2023.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/16/2023] [Accepted: 11/30/2023] [Indexed: 12/25/2023]
Abstract
The morphological brain connectome (MBC) delineates the coordinated patterns of local morphological features (such as cortical thickness) across brain regions. While classically constructed using population-based approaches, there is a growing trend toward individualized modeling. Currently, the methods for individualized MBCs are varied, posing challenges for method selection and cross-study comparisons. Here, we summarize how individualized MBCs are modeled through low-order methods (correlation-, divergence-, distance-, and deviation-based methods) describing relations in brain morphology, as well as high-order methods capturing similarities in these low-order relations. We discuss the merits and limitations of different methods, examining them in the context of robustness, reproducibility, and reliability. We highlight the importance of elucidating the cellular and molecular mechanisms underlying the individualized connectome, and establishing normative benchmarks to assess individual variation in development, aging, and neuropsychiatric disorders.
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Affiliation(s)
- Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China; Center for Studies of Psychological Application, South China Normal University, Guangzhou 510631, China.
| | - Yong He
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China; National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China.
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6
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Jellinger KA. Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks. Int J Mol Sci 2023; 25:498. [PMID: 38203667 PMCID: PMC10778722 DOI: 10.3390/ijms25010498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 12/11/2023] [Accepted: 12/27/2023] [Indexed: 01/12/2024] Open
Abstract
Cognitive impairment (CI) is a characteristic non-motor feature of Parkinson disease (PD) that poses a severe burden on the patients and caregivers, yet relatively little is known about its pathobiology. Cognitive deficits are evident throughout the course of PD, with around 25% of subtle cognitive decline and mild CI (MCI) at the time of diagnosis and up to 83% of patients developing dementia after 20 years. The heterogeneity of cognitive phenotypes suggests that a common neuropathological process, characterized by progressive degeneration of the dopaminergic striatonigral system and of many other neuronal systems, results not only in structural deficits but also extensive changes of functional neuronal network activities and neurotransmitter dysfunctions. Modern neuroimaging studies revealed multilocular cortical and subcortical atrophies and alterations in intrinsic neuronal connectivities. The decreased functional connectivity (FC) of the default mode network (DMN) in the bilateral prefrontal cortex is affected already before the development of clinical CI and in the absence of structural changes. Longitudinal cognitive decline is associated with frontostriatal and limbic affections, white matter microlesions and changes between multiple functional neuronal networks, including thalamo-insular, frontoparietal and attention networks, the cholinergic forebrain and the noradrenergic system. Superimposed Alzheimer-related (and other concomitant) pathologies due to interactions between α-synuclein, tau-protein and β-amyloid contribute to dementia pathogenesis in both PD and dementia with Lewy bodies (DLB). To further elucidate the interaction of the pathomechanisms responsible for CI in PD, well-designed longitudinal clinico-pathological studies are warranted that are supported by fluid and sophisticated imaging biomarkers as a basis for better early diagnosis and future disease-modifying therapies.
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Affiliation(s)
- Kurt A Jellinger
- Institute of Clinical Neurobiology, Alberichgasse 5/13, A-1150 Vienna, Austria
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Su S, Sha R, Qiu H, Chu J, Lin L, Qian L, Hu M, Wu C, Cheung GL, Yang Z, Chen Y, Zhao J. Altered large-scale individual-based morphological brain network in spinocerebellar ataxia type 3. CNS Neurosci Ther 2023; 29:4102-4112. [PMID: 37392035 PMCID: PMC10651944 DOI: 10.1111/cns.14332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 06/19/2023] [Accepted: 06/20/2023] [Indexed: 07/02/2023] Open
Abstract
BACKGROUND Accumulating evidences indicate regional gray matter (GM) morphology atrophy in spinocerebellar ataxia type 3 (SCA3); however, whether large-scale morphological brain networks (MBNs) undergo widespread reorganization in these patients remains unclear. OBJECTIVE To investigate the topological organization of large-scale individual-based MBNs in SCA3 patients. METHODS The individual-based MBNs were constructed based on the inter-regional morphological similarity of GM regions. Graph theoretical analysis was taken to assess GM structural connectivity in 76 symptomatic SCA3, 24 pre-symptomatic SCA3, and 54 healthy normal controls (NCs). Topological parameters of the resulting graphs and network-based statistics analysis were compared among symptomatic SCA3, pre-symptomatic SCA3, and NCs groups. The inner association between network properties and clinical variables was further analyzed. RESULTS Compared to NCs and pre-symptomatic SCA3 patients, symptomatic SCA3 indicated significantly decreased integration and segregation, a shift to "weaker small-worldness", characterized by decreased Cp , lower Eloc, and Eglob (all p < 0.005). Regarding nodal properties, symptomatic SCA3 exhibited significantly decreased nodal profiles in the central executive network (CEN)-related left inferior frontal gyrus, limbic regions involving the bilateral amygdala, left hippocampus, and bilateral pallidum, thalamus; and increased nodal degree, efficiency in bilateral caudate (all pFDR <0.05). Meanwhile, clinical variables were correlated with altered nodal profiles (pFDR ≤0.029). SCA3-related subnetwork was closely interrelated with dorsolateral cortico-striatal circuitry extending to orbitofrontal-striatal circuits and dorsal visual systems (lingual gyrus-striatal). CONCLUSION Symptomatic SCA3 patients undergo an extensive and significant reorganization in large-scale individual-based MBNs, probably due to disrupted prefrontal cortico-striato-thalamo-cortical loops, limbic-striatum circuitry, and enhanced connectivity in the neostriatum. This study highlights the crucial role of abnormal morphological connectivity alterations beyond the pattern of brain atrophy, which might pave the way for therapeutic development in the future.
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Affiliation(s)
- Shu Su
- Department of Radiology, The First Affiliated HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Runhua Sha
- Department of Radiology, The First Affiliated HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Haishan Qiu
- Department of Radiology, The First Affiliated HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Jianping Chu
- Department of Radiology, The First Affiliated HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Liping Lin
- Department of Radiology, The First Affiliated HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Long Qian
- Department of Biomedical Engineering, College of EngineeringPeking UniversityBeijingChina
| | - Manshi Hu
- Department of Radiology, The First Affiliated HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Chao Wu
- Department of Neurology, The First Affiliated HospitalSun Yat‐Sen UniversityGuangzhouChina
| | | | - Zhiyun Yang
- Department of Radiology, The First Affiliated HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Yingqian Chen
- Department of Radiology, The First Affiliated HospitalSun Yat‐sen UniversityGuangzhouChina
| | - Jing Zhao
- Department of Radiology, The First Affiliated HospitalSun Yat‐sen UniversityGuangzhouChina
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Cai M, Ma J, Wang Z, Zhao Y, Zhang Y, Wang H, Xue H, Chen Y, Zhang Y, Wang C, Zhao Q, Xue K, Liu F. Individual-level brain morphological similarity networks: Current methodologies and applications. CNS Neurosci Ther 2023; 29:3713-3724. [PMID: 37519018 PMCID: PMC10651978 DOI: 10.1111/cns.14384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 07/12/2023] [Accepted: 07/18/2023] [Indexed: 08/01/2023] Open
Abstract
AIMS The human brain is an extremely complex system in which neurons, clusters of neurons, or regions are connected to form a complex network. With the development of neuroimaging techniques, magnetic resonance imaging (MRI)-based brain networks play a key role in our understanding of the intricate architecture of human brain. Among them, the structural MRI-based brain morphological network approach has attracted increasing attention due to the advantages in data acquisition, image quality, and in revealing the structural organizing principles intrinsic to the brain. This review is to summarize the methodology and related applications of individual-level morphological networks. BACKGROUND There have been a growing number of studies related to brain morphological similarity networks. Conventional morphological networks are intersubject covariance networks constructed using a certain morphological indicator of a group of subjects; individual-level morphological networks, on the other hand, measure the morphological similarity between brain regions for individual brains and can reflect the morphological information of single subjects. In recent years, individual morphological networks have demonstrated significant worth in exploring the topological changes of the human brain under both normal and disease conditions. Such studies provided novel perspectives for understanding human brain development and exploring the pathological mechanisms of neuropsychiatric disorders. CONCLUSION This paper mainly focuses on the studies of brain morphological networks at the individual level, introduces several ways for network construction, reviews representative work in this field, and finally points out current problems and future directions.
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Affiliation(s)
- Mengjing Cai
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Juanwei Ma
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Zirui Wang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Yao Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Yijing Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - He Wang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Hui Xue
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Yayuan Chen
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Yujie Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Chunyang Wang
- Department of Scientific ResearchTianjin Medical University General HospitalTianjinChina
| | - Qiyu Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Kaizhong Xue
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional ImagingTianjin Medical University General HospitalTianjinChina
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Zuo C, Suo X, Lan H, Pan N, Wang S, Kemp GJ, Gong Q. Global Alterations of Whole Brain Structural Connectome in Parkinson's Disease: A Meta-analysis. Neuropsychol Rev 2023; 33:783-802. [PMID: 36125651 PMCID: PMC10770271 DOI: 10.1007/s11065-022-09559-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 06/14/2022] [Indexed: 10/14/2022]
Abstract
Recent graph-theoretical studies of Parkinson's disease (PD) have examined alterations in the global properties of the brain structural connectome; however, reported alterations are not consistent. The present study aimed to identify the most robust global metric alterations in PD via a meta-analysis. A comprehensive literature search was conducted for all available diffusion MRI structural connectome studies that compared global graph metrics between PD patients and healthy controls (HC). Hedges' g effect sizes were calculated for each study and then pooled using a random-effects model in Comprehensive Meta-Analysis software, and the effects of potential moderator variables were tested. A total of 22 studies met the inclusion criteria for review. Of these, 16 studies reporting 10 global graph metrics (916 PD patients; 560 HC) were included in the meta-analysis. In the structural connectome of PD patients compared with HC, we found a significant decrease in clustering coefficient (g = -0.357, P = 0.005) and global efficiency (g = -0.359, P < 0.001), and a significant increase in characteristic path length (g = 0.250, P = 0.006). Dopaminergic medication, sex and age of patients were potential moderators of global brain network changes in PD. These findings provide evidence of decreased global segregation and integration of the structural connectome in PD, indicating a shift from a balanced small-world network to 'weaker small-worldization', which may provide useful markers of the pathophysiological mechanisms underlying PD.
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Affiliation(s)
- Chao Zuo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Xueling Suo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Huan Lan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Nanfang Pan
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China
| | - Song Wang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
- Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan, China.
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan, China.
- Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian, China.
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10
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Li T, Le W, Jankovic J. Linking the cerebellum to Parkinson disease: an update. Nat Rev Neurol 2023; 19:645-654. [PMID: 37752351 DOI: 10.1038/s41582-023-00874-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/01/2023] [Indexed: 09/28/2023]
Abstract
Parkinson disease (PD) is characterized by heterogeneous motor and non-motor symptoms, resulting from neurodegeneration involving various parts of the central nervous system. Although PD pathology predominantly involves the nigral-striatal system, growing evidence suggests that pathological changes extend beyond the basal ganglia into other parts of the brain, including the cerebellum. In addition to a primary involvement in motor control, the cerebellum is now known to also have an important role in cognitive, sleep and affective processes. Over the past decade, an accumulating body of research has provided clinical, pathological, neurophysiological, structural and functional neuroimaging findings that clearly establish a link between the cerebellum and PD. This Review presents an overview and update on the involvement of the cerebellum in the clinical features and pathogenesis of PD, which could provide a novel framework for a better understanding the heterogeneity of the disease.
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Affiliation(s)
- Tianbai Li
- Liaoning Provincial Key Laboratory for Research on the Pathogenic Mechanisms of Neurological Diseases, the First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Weidong Le
- Liaoning Provincial Key Laboratory for Research on the Pathogenic Mechanisms of Neurological Diseases, the First Affiliated Hospital, Dalian Medical University, Dalian, China.
- Institute of Neurology, Sichuan Academy of Medical Sciences, Sichuan Provincial Hospital, Chengdu, China.
| | - Joseph Jankovic
- Parkinson's Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, TX, USA.
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Jin C, Qi S, Yang L, Teng Y, Li C, Yao Y, Ruan X, Wei X. Abnormal functional connectivity density involvement in freezing of gait and its application for subtyping Parkinson's disease. Brain Imaging Behav 2023; 17:375-385. [PMID: 37243751 DOI: 10.1007/s11682-023-00765-7] [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: 03/19/2023] [Indexed: 05/29/2023]
Abstract
The pathophysiological mechanisms at work in Parkinson's disease (PD) patients with freezing of gait (FOG) remain poorly understood. Functional connectivity density (FCD) could provide an unbiased way to analyse connectivity across the brain. In this study, a total of 23 PD patients with FOG (PD FOG + patients), 26 PD patients without FOG (PD FOG- patients), and 22 healthy controls (HCs) were recruited, and their resting-state functional magnetic resonance imaging (rs-fMRI) images were collected. FCD mapping was first performed to identify differences between groups. Pearson correlation analysis was used to explore relationships between FCD values and the severity of FOG. Then, a machine learning model was employed to classify each pair of groups. PD FOG + patients showed significantly increased short-range FCD in the precuneus, cingulate gyrus, and fusiform gyrus and decreased long-range FCD in the frontal gyrus, temporal gyrus, and cingulate gyrus. Short-range FCD values in the middle temporal gyrus and inferior temporal gyrus were positively correlated with FOG questionnaire (FOGQ) scores, and long-range FCD values in the middle frontal gyrus were negatively correlated with FOGQ scores. Using FCD in abnormal regions as input, a support vector machine (SVM) classifier can achieve classification with good performance. The mean accuracy values were 0.895 (PD FOG + vs. HC), 0.966 (PD FOG- vs. HC), and 0.897 (PD FOG + vs. PD FOG-). This study demonstrates that PD FOG + patients showed altered short- and long-range FCD in several brain regions involved in action planning and control, motion processing, emotion, cognition, and object recognition.
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Affiliation(s)
- Chaoyang Jin
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Lei Yang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Yueyang Teng
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Chen Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Yudong Yao
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, USA
| | - Xiuhang Ruan
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Xinhua Wei
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China
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Disrupted topological organization of functional brain networks is associated with cognitive impairment in hypertension patients: a resting-state fMRI study. Neuroradiology 2023; 65:323-336. [PMID: 36219250 DOI: 10.1007/s00234-022-03061-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 09/24/2022] [Indexed: 01/25/2023]
Abstract
PURPOSE To investigate the alterations of topological organization of the whole brain functional networks in hypertension patients with cognitive impairment (HTN-CI) and characterize its relationship with cognitive scores. METHODS Fifty-seven hypertension patients with cognitive impairment and 59 hypertension patients with normal cognition (HTN-NC), and 49 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. Graph theoretical analysis was used to investigate the altered topological organization of the functional brain networks. The global topological properties and nodal metrics were compared among the three groups. Network-based statistic (NBS) analysis was used to determine the connected subnetwork. The relationships between network metrics and cognitive scores were also characterized. RESULTS HTN-CI patients exhibited significantly decreased global efficiency, lambda, and increased shortest path length when compared with HCs. In addition, both HTN-CI and HTN-NC groups exhibited altered nodal degree centrality and nodal efficiency in the right precentral gyrus. The disruptions of global network metrics (lambda, Lp) and the nodal metrics (degree centrality and nodal efficiency) in the right precentral gyrus were positively correlated with the MoCA scores in HTN-CI. NBS analysis demonstrated that decreased subnetwork connectivity was present both in the HTN-CI and HTN-NC groups, which were mainly involved in the default mode network, frontoparietal network, and cingulo-opercular network. CONCLUSION This study demonstrated the alterations of topographical organization and subnetwork connectivity of functional brain networks in HTN-CI. In addition, the global and nodal network properties were correlated with cognitive scores, which may provide useful insights for the understanding of neuropsychological mechanisms underlying HTN-CI.
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Xie Y, Gao C, Wu B, Peng L, Wu J, Lang L. Morphologic brain network predicts levodopa responsiveness in Parkinson disease. Front Aging Neurosci 2023; 14:990913. [PMID: 36688150 PMCID: PMC9849367 DOI: 10.3389/fnagi.2022.990913] [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: 07/11/2022] [Accepted: 10/18/2022] [Indexed: 01/06/2023] Open
Abstract
Background The levodopa challenge test (LCT) has been routinely used in Parkinson disease (PD) evaluation and predicts the outcome of deep brain stimulation (DBS). Guidelines recommend that patients with an improvement in Unified Parkinson's Disease Rating Scale (UPDRS)-III score > 33% in the LCT receive DBS treatment. However, LCT results are affected by many factors, and only provide information on the immediate effectiveness of dopamine. The aim of the present study was to investigate the relationship between LCT outcome and brain imaging features of PD patients to determine whether the latter can be used to identify candidates for DBS. Methods A total of 38 PD patients were enrolled in the study. Based on improvement in UPDRS-III score in the LCT, patients were divided into low improvement (PD-LCT-L) and high improvement (PD-LCT-H) groups. Each patient's neural network was reconstructed based on T1-weighted magnetic resonance imaging data using the Jensen-Shannon divergence similarity estimation method. The network was established with the multiple kernel support vector machine technique. We analyzed differences in individual morphologic brain networks and their global and local metrics to determine whether there were differences in the connectomes of PD-LCT-L and PD-LCT-H groups. Results The 2 groups were similar in terms of demographic and clinical characteristics. Mean ± SD levodopa responsiveness was 26.52% ± 3.47% in the PD-LCT-L group (N = 13) and 58.66% ± 4.09% in the PD-LCT-H group (N = 25). There were no significant differences between groups in global and local metrics. There were 43 consensus connections that were affected in both groups; in PD-LCT-L patients, most of these connections were decreased whereas those related to the dorsolateral superior frontal gyrus and left cuneus were significantly increased. Conclusion Morphologic brain network assessment is a valuable method for predicting levodopa responsiveness in PD patients, which can facilitate the selection of candidates for DBS.
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Affiliation(s)
- Yongsheng Xie
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China,National Center for Neurological Disorders, Shanghai, China,Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai, China
| | - Chunyan Gao
- Department of Nursing, Huashan Hospital, Fudan University, Shanghai, China
| | - Bin Wu
- Department of Neurology and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Liling Peng
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, China
| | - Jianjun Wu
- Department of Neurology and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Liqin Lang
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China,National Center for Neurological Disorders, Shanghai, China,Shanghai Key Laboratory of Brain Function Restoration and Neural Regeneration, Shanghai, China,State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, School of Basic Medical Sciences and Institutes of Brain Science, Fudan University, Shanghai, China,*Correspondence: Liqin Lang,
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14
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Shang S, Zhu S, Wu J, Xu Y, Chen L, Dou W, Yin X, Chen Y, Shen D, Ye J. Topological disruption of high-order functional networks in cognitively preserved Parkinson's disease. CNS Neurosci Ther 2022; 29:566-576. [PMID: 36468414 PMCID: PMC9873517 DOI: 10.1111/cns.14037] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 11/02/2022] [Accepted: 11/17/2022] [Indexed: 12/07/2022] Open
Abstract
AIMS This study aimed to characterize the topological alterations and classification performance of high-order functional connectivity (HOFC) networks in cognitively preserved patients with Parkinson's disease (PD), relative to low-order FC (LOFC) networks. METHODS The topological metrics of the constructed networks (LOFC and HOFC) obtained from fifty-one cognitively normal patients with PD and 60 matched healthy control subjects were analyzed. The discriminative abilities were evaluated using machine learning approach. RESULTS The HOFC networks in the PD group showed decreased segregation and integration. The normalized clustering coefficient and small-worldness in the HOFC networks were correlated to motor performance. The altered nodal centralities (distributed in the precuneus, putamen, lingual gyrus, supramarginal gyrus, motor area, postcentral gyrus and inferior occipital gyrus) and intermodular FC (frontoparietal and visual networks, sensorimotor and subcortical networks) were specific to HOFC networks. Several highly connected nodes (thalamus, paracentral lobule, calcarine fissure and precuneus) and improved classification performance were found based on HOFC profiles. CONCLUSION This study identified disrupted topology of functional interactions at a high level with extensive alterations in topological properties and improved differentiation ability in patients with PD prior to clinical symptoms of cognitive impairment, providing complementary insights into complex neurodegeneration in PD.
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Affiliation(s)
- Song'an Shang
- Department of Medical imaging centerClinical Medical College, Yangzhou UniversityYangzhouChina
| | - Siying Zhu
- Department of Medical imaging centerClinical Medical College, Yangzhou UniversityYangzhouChina
| | - Jingtao Wu
- Department of Medical imaging centerClinical Medical College, Yangzhou UniversityYangzhouChina
| | - Yao Xu
- Department of NeurologyClinical Medical College, Yangzhou UniversityYangzhouChina
| | - Lanlan Chen
- Department of NeurologyClinical Medical College, Yangzhou UniversityYangzhouChina
| | | | - Xindao Yin
- Department of RadiologyNanjing First Hospital, Nanjing Medical UniversityNanjingChina
| | - Yu‐Chen Chen
- Department of RadiologyNanjing First Hospital, Nanjing Medical UniversityNanjingChina
| | - Dejuan Shen
- Department of Medical imaging centerClinical Medical College, Yangzhou UniversityYangzhouChina
| | - Jing Ye
- Department of Medical imaging centerClinical Medical College, Yangzhou UniversityYangzhouChina
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15
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Li Q, Tao L, Xiao P, Gui H, Xu B, Zhang X, Zhang X, Chen H, Wang H, He W, Lv F, Cheng O, Luo J, Man Y, Xiao Z, Fang W. Combined brain network topological metrics with machine learning algorithms to identify essential tremor. Front Neurosci 2022; 16:1035153. [PMID: 36408403 PMCID: PMC9667093 DOI: 10.3389/fnins.2022.1035153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 10/17/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND AND OBJECTIVE Essential tremor (ET) is a common movement syndrome, and the pathogenesis mechanisms, especially the brain network topological changes in ET are still unclear. The combination of graph theory (GT) analysis with machine learning (ML) algorithms provides a promising way to identify ET from healthy controls (HCs) at the individual level, and further help to reveal the topological pathogenesis in ET. METHODS Resting-state functional magnetic resonance imaging (fMRI) data were obtained from 101 ET and 105 HCs. The topological properties were analyzed by using GT analysis, and the topological metrics under every single threshold and the area under the curve (AUC) of all thresholds were used as features. Then a Mann-Whitney U-test and least absolute shrinkage and selection operator (LASSO) were conducted to feature dimensionality reduction. Four ML algorithms were adopted to identify ET from HCs. The mean accuracy, mean balanced accuracy, mean sensitivity, mean specificity, and mean AUC were used to evaluate the classification performance. In addition, correlation analysis was carried out between selected topological features and clinical tremor characteristics. RESULTS All classifiers achieved good classification performance. The mean accuracy of Support vector machine (SVM), logistic regression (LR), random forest (RF), and naïve bayes (NB) was 84.65, 85.03, 84.85, and 76.31%, respectively. LR classifier achieved the best classification performance with 85.03% mean accuracy, 83.97% sensitivity, and an AUC of 0.924. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with tremor severity. CONCLUSION These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET from HCs but also help us to reveal the potential topological pathogenesis of ET.
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Affiliation(s)
- Qin Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Li Tao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Pan Xiao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Honge Gui
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Bintao Xu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xueyan Zhang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaoyu Zhang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Huiyue Chen
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hansheng Wang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Wanlin He
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Oumei Cheng
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jing Luo
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yun Man
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zheng Xiao
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Weidong Fang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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16
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Suo X, Lei D, Li N, Peng J, Chen C, Li W, Qin K, Kemp GJ, Peng R, Gong Q. Brain functional network abnormalities in parkinson's disease with mild cognitive impairment. Cereb Cortex 2022; 32:4857-4868. [PMID: 35078209 PMCID: PMC9923713 DOI: 10.1093/cercor/bhab520] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Revised: 12/18/2021] [Accepted: 12/19/2021] [Indexed: 11/13/2022] Open
Abstract
Mild cognitive impairment in Parkinson's disease (PD-M) is related to a high risk of dementia. This study explored the whole-brain functional networks in early-stage PD-M. Forty-one patients with PD classified as cognitively normal (PD-N, n = 17) and PD-M (n = 24) and 24 demographically matched healthy controls (HC) underwent clinical and neuropsychological evaluations and resting-state functional magnetic resonance imaging. The global, regional, and modular topological characteristics were assessed in the brain functional networks, and their relationships to cognitive scores were tested. At the global level, PD-M and PD-N exhibited higher characteristic path length and lower clustering coefficient, local and global efficiency relative to HC. At the regional level, PD-M and PD-N showed lower nodal centrality in sensorimotor regions relative to HC. At the modular level, PD-M showed lower intramodular connectivity in default mode and cerebellum modules, and lower intermodular connectivity between default mode and frontoparietal modules than PD-N, correlated with Montreal Cognitive Assessment scores. Early-stage PD patients showed weaker small-worldization of brain networks. Modular connectivity alterations were mainly observed in patients with PD-M. These findings highlight the shared and distinct brain functional network dysfunctions in PD-M and PD-N, and yield insight into the neurobiology of cognitive decline in PD.
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Affiliation(s)
- Xueling Suo
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, Sichuan 610041, China.,Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Du Lei
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH 45227, USA
| | - Nannan Li
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Jiaxin Peng
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Chaolan Chen
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Wenbin Li
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Kun Qin
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Graham J Kemp
- Liverpool Magnetic Resonance Imaging Centre (LiMRIC) and Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool L69 3GE, UK
| | - Rong Peng
- Department of Neurology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, Sichuan 610041, China.,Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, Fujian 361022, China
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17
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Gazzina S, Grassi M, Premi E, Alberici A, Benussi A, Archetti S, Gasparotti R, Bocchetta M, Cash DM, Todd EG, Peakman G, Convery RS, van Swieten JC, Jiskoot LC, Seelaar H, Sanchez-Valle R, Moreno F, Laforce R, Graff C, Synofzik M, Galimberti D, Rowe JB, Masellis M, Tartaglia MC, Finger E, Vandenberghe R, de Mendonça A, Tagliavini F, Butler CR, Santana I, Gerhard A, Ber IL, Pasquier F, Ducharme S, Levin J, Danek A, Sorbi S, Otto M, Rohrer JD, Borroni B. Structural brain splitting is a hallmark of Granulin-related frontotemporal dementia. Neurobiol Aging 2022; 114:94-104. [PMID: 35339292 DOI: 10.1016/j.neurobiolaging.2022.02.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 02/17/2022] [Accepted: 02/19/2022] [Indexed: 10/19/2022]
Abstract
Frontotemporal dementia associated with granulin (GRN) mutations presents asymmetric brain atrophy. We applied a Minimum Spanning Tree plus an Efficiency Cost Optimization approach to cortical thickness data in order to test whether graph theory measures could identify global or local impairment of connectivity in the presymptomatic phase of pathology, where other techniques failed in demonstrating changes. We included 52 symptomatic GRN mutation carriers (SC), 161 presymptomatic GRN mutation carriers (PSC) and 341 non-carriers relatives from the Genetic Frontotemporal dementia research Initiative cohort. Group differences of global, nodal and edge connectivity in (Minimum Spanning Tree plus an Efficiency Cost Optimization) graph were tested via Structural Equation Models. Global graph perturbation was selectively impaired in SC compared to non-carriers, with no changes in PSC. At the local level, only SC exhibited perturbation of frontotemporal nodes, but edge connectivity revealed a characteristic pattern of interhemispheric disconnection, involving homologous parietal regions, in PSC. Our results suggest that GRN-related frontotemporal dementia resembles a disconnection syndrome, with interhemispheric disconnection between parietal regions in presymptomatic phases that progresses to frontotemporal areas as symptoms emerge.
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Affiliation(s)
- Stefano Gazzina
- Neurophysiology Unit, ASST Spedali Civili Hospital, Brescia, Italy
| | - Mario Grassi
- Department of Brain and Behavioral Science, Medical and Genomic Statistics Unit, University of Pavia, Pavia, Italy
| | - Enrico Premi
- Stroke Unit, Neurology Unit, ASST Spedali Civili Hospital, Brescia, Italy
| | | | - Alberto Benussi
- Neurology Unit, ASST Spedali Civili Hospital, Brescia, Italy; Centre for Neurodegenerative Disorders, Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Silvana Archetti
- Biotechnology Laboratory, Department of Diagnostics, Spedali Civili Hospital, Brescia, Italy
| | | | - Martina Bocchetta
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - David M Cash
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - Emily G Todd
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - Georgia Peakman
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
| | - Rhian S Convery
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, London, UK
| | | | - Lize C Jiskoot
- Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands
| | - Harro Seelaar
- Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands
| | - Raquel Sanchez-Valle
- Alzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Fermin Moreno
- Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Gipuzkoa, Spain
| | - Robert Laforce
- Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, and Facultéde Médecine, Université Laval, Quebec City, Québec, Canada
| | - Caroline Graff
- Center for Alzheimer Research, Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Bioclinicum, Karolinska Institutet, Solna, Sweden
| | - Matthis Synofzik
- Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tubingen, Tubingen, Germany
| | - Daniela Galimberti
- Fondazione Ca' Granda, IRCCS Ospedale Policlinico, Milan, Italy; University of Milan, Centro Dino Ferrari, Milan, Italy
| | - James B Rowe
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, UK
| | - Mario Masellis
- Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Maria Carmela Tartaglia
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, Ontario, Canada
| | - Elizabeth Finger
- Department of Clinical Neurological Sciences, University of Western Ontario, London, Ontario, Canada
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium; Neurology Service, University Hospitals Leuven, Leuven, Belgium; Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | | | | | - Chris R Butler
- Nueld Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Isabel Santana
- University Hospital of Coimbra (HUC), Neurology Service, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Alexander Gerhard
- Division of Neuroscience & Experimental Psychology, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK; Departments of Geriatric Medicine and Nuclear Medicine, Essen University Hospital, Essen, Germany
| | - Isabelle Le Ber
- Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France; Centre de référence des démences rares ou précoces, Département de Neurologie, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France; Département de Neurologie, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France; Reference Network for Rare Neurological Diseases (ERN-RND), Paris, France
| | | | - Simon Ducharme
- Department of Psychiatry, McGill University Health Centre, McGill University, Montreal, Quebec, Canada
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-University, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Adrian Danek
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Sandro Sorbi
- Department of Neurofarba, University of Florence, Florence, Italy; IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Markus Otto
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Jonathan D Rohrer
- Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands
| | - Barbara Borroni
- Neurology Unit, ASST Spedali Civili Hospital, Brescia, Italy.
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18
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Peng J, Yang J, Li N, Lei D, Li J, Duan L, Chen C, Zeng Y, Xi J, Jiang Y, Gong Q, Peng R. Topologically Disrupted Gray Matter Networks in Drug-Naïve Essential Tremor Patients With Poor Sleep Quality. Front Neurol 2022; 13:834277. [PMID: 35557617 PMCID: PMC9086904 DOI: 10.3389/fneur.2022.834277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 03/14/2022] [Indexed: 11/16/2022] Open
Abstract
Background Sleep disturbances are widespread among patients with essential tremor (ET) and may have adverse effects on patients' quality of life. However, the pathophysiology underlying poor quality of sleep (QoS) in patients with ET remains unclear. Our study aimed to identify gray matter (GM) network alterations in the topological properties of structural MRI related to QoS in patients with ET. Method We enrolled 45 ET patients with poor QoS (SleET), 59 ET patients with normal QoS (NorET), and 66 healthy controls (HC), and they all underwent a three-dimensional T1-weighted MRI scan. We used a graph-theoretical approach to investigate the topological organization of GM morphological networks, and individual morphological brain networks were constructed according to the interregional similarity of GM volume distributions. Furthermore, we performed network-based statistics, and partial correlation analyses between topographic features and clinical characteristics were conducted. Results Global network organization was disrupted in patients with ET. Compared with the NorET group, the SleET group exhibited disrupted topological GM network organization with a shift toward randomization. Moreover, they showed altered nodal centralities in mainly the frontal, temporal, parietal, and cerebellar lobes. Morphological connection alterations within the default mode network (DMN), salience, and basal ganglia networks were observed in the SleET group and were generally more extensive than those in the NorET and HC groups. Alterations within the cerebello-thalamo-(cortical) network were only detected in the SleET group. The nodal degree of the left thalamus was negatively correlated with the Fahn-Tolosa-Marin Tremor Rating Scale score (r = −0.354, p =0.027). Conclusion Our findings suggest that potential complex interactions underlie tremor and sleep disruptions in patients with ET. Disruptions within the DMN and the cerebello-thalamo-(cortical) network may have a broader impact on sleep quality in patients with ET. Our results offer valuable insight into the neural mechanisms underlying poor QoS in patients with ET.
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Affiliation(s)
- Jiaxin Peng
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Yang
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China
| | - Nannan Li
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Du Lei
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China
| | - Junying Li
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Liren Duan
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Chaolan Chen
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Yan Zeng
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Xi
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Yi Jiang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Qiyong Gong
- Department of Radiology, Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China
| | - Rong Peng
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
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19
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Wang L, Zhou C, Cheng W, Rolls ET, Huang P, Ma N, Liu Y, Zhang Y, Guan X, Guo T, Wu J, Gao T, Xuan M, Gu Q, Xu X, Zhang B, Gong W, Du J, Zhang W, Feng J, Zhang M. Dopamine depletion and subcortical dysfunction disrupt cortical synchronization and metastability affecting cognitive function in Parkinson's disease. Hum Brain Mapp 2021; 43:1598-1610. [PMID: 34904766 PMCID: PMC8886656 DOI: 10.1002/hbm.25745] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 09/28/2021] [Accepted: 11/29/2021] [Indexed: 12/14/2022] Open
Abstract
Parkinson's disease (PD) is primarily characterized by the loss of dopaminergic cells and atrophy in subcortical regions. However, the impact of these pathological changes on large-scale dynamic integration and segregation of the cortex are not well understood. In this study, we investigated the effect of subcortical dysfunction on cortical dynamics and cognition in PD. Spatiotemporal dynamics of the phase interactions of resting-state blood-oxygen-level-dependent signals in 159 PD patients and 152 normal control (NC) individuals were estimated. The relationships between subcortical atrophy, subcortical-cortical fiber connectivity impairment, cortical synchronization/metastability, and cognitive performance were then assessed. We found that cortical synchronization and metastability in PD patients were significantly decreased. To examine whether this is an effect of dopamine depletion, we investigated 45 PD patients both ON and OFF dopamine replacement therapy, and found that cortical synchronization and metastability are significantly increased in the ON state. The extent of cortical synchronization and metastability in the OFF state reflected cognitive performance and mediates the difference in cognitive performance between the PD and NC groups. Furthermore, both the thalamic volume and thalamocortical fiber connectivity had positive relationships with cortical synchronization and metastability in the dopaminergic OFF state, and mediate the difference in cortical synchronization between the PD and NC groups. In addition, thalamic volume also reflected cognitive performance, and cortical synchronization/metastability mediated the relationship between thalamic volume and cognitive performance in PD patients. Together, these results highlight that subcortical dysfunction and reduced dopamine levels are responsible for decreased cortical synchronization and metastability, further affecting cognitive performance in PD. This might lead to biomarkers being identified that can predict if a patient is at risk of developing dementia.
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Affiliation(s)
- Linbo Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China.,Department of Computer Science, University of Warwick, Coventry, UK
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China.,Department of Computer Science, University of Warwick, Coventry, UK
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ningning Ma
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Yuchen Liu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Yajuan Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ting Gao
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Min Xuan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Quanquan Gu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Weikang Gong
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China
| | - Jingnan Du
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, China.,MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.,Zhangjiang Fudan International Innovation Center, Shanghai, China.,Department of Computer Science, University of Warwick, Coventry, UK
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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