ArticleHuman brain mapping2023
Functional connectome automatically differentiates multiple system atrophy (parkinsonian type) from idiopathic Parkinson's disease at early stages.
Article in Human brain mapping, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Detection of parkinson's disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Pooled it
- The role of neuro-imaging in multiple system atrophy.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- Functional MRI in Multiple System Atrophy: A Promising Biomarker for Clinical Applications.Neuropsychiatric disease and treatment · 2026Review
- Regional homogeneity differences in resting-state fMRI between Parkinson's disease and multiple system atrophy-parkinsonian type.Journal of neural transmission (Vienna, Austria : 1996) · 2026Article
- Early classification of functional connectomes in Parkinson's disease: a comparison of machine learning classifiers using multi-scale topological features.BMC medical informatics and decision making · 2025Article
- Improvement in Insulin Sensitivity Prevents Decline in Glucose Uptake, Functional Connectivity, and Volume in the Insulin Resistant Human Brain.Research square · 2025Article
- Brain structural features with functional priori to classify Parkinson's disease and multiple system atrophy using diagnostic MRI.Scientific reports · 2025Article
- Investigating glymphatic function and bed nucleus of the stria terminalis-based functional connectivity in Parkinson's disease with and without depression.NPJ Parkinson's disease · 2025Article
- Sex-specific signatures of GLP-1 and amylin on resting state brain activity and functional connectivity in awake rats.Neuropharmacology · 2025Article
- Hot Topics and Frontiers of Resting-State fMRI in Parkinson's Disease: Research Trends and Paradigm Shifts From a Bibliometric Perspective.Parkinson's disease · 2025Review
- Resting-State Functional MRI Approaches to Parkinsonisms and Related Dementia.Current neurology and neuroscience reports · 2024Review
- Corneal confocal microscopy may help to distinguish Multiple System Atrophy from Parkinson's disease.NPJ Parkinson's disease · 2024Article
- 3D-CAM: a novel context-aware feature extraction framework for neurological disease classification.Frontiers in neuroscience · 2024Article
- The reconfiguration pattern of individual brain metabolic connectome for Parkinson's disease identification.MedComm · 2023Article
- Functional connectome automatically differentiates multiple system atrophy (parkinsonian type) from idiopathic Parkinson's disease at early stages.Human brain mapping · 2023Article
- Brain disease research based on functional magnetic resonance imaging data and machine learning: a review.Frontiers in neuroscience · 2023Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Differentiating the parkinsonian variant of multiple system atrophy (MSA-P) from idiopathic Parkinson's disease (IPD) is challenging, especially in the early stages. This study aimed to investigate differences and similarities in the brain functional connectomes of IPD and MSA-P patients and use machine learning methods to explore the diagnostic utility of these features. Resting-state fMRI data were acquired from 88 healthy controls, 76 MSA-P patients, and 53 IPD patients using a 3.0 T scanner. The whole-brain functional connectome was constructed by thresholding the Pearson correlation matrices of 116 regions, and topological properties were evaluated through graph theory approaches. Connectome measurements were used as features in machine learning models (random forest [RF]/logistic regression [LR]/support vector machine) to distinguish IPD and MSA-P patients. Regarding graph metrics, early IPD and MSA-P patients shared network topological properties. Both patient groups showed functional connectivity disruptions within the cerebellum-basal ganglia-cortical network, but these disconnections were mainly in the cortico-thalamo-cerebellar circuits in MSA-P patients and the basal ganglia-thalamo-cortical circuits in IPD patients. Among the connectome parameters, t tests combined with the RF method identified 15 features, from which the LR classifier achieved the best diagnostic performance on the validation set (accuracy = 92.31%, sensitivity = 90.91%, specificity = 93.33%, area under the receiver operating characteristic curve = 0.89). MSA-P and IPD patients show similar whole-brain network topological alterations. MSA-P primarily affects cerebellar nodes, and IPD primarily affects basal ganglia nodes; both conditions disrupt the cerebellum-basal ganglia-cortical network. Moreover, functional connectome parameters showed outstanding value in the differential diagnosis of early MSA-P and IPD.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.