ArticleNPJ Parkinson's disease2024
Multimodal neuroimaging-based prediction of Parkinson's disease with mild cognitive impairment using machine learning technique.
Article in NPJ Parkinson's disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
What it found
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The trial behind it
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Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Performance of machine learning algorithms in diffusion tensor imaging of movement disorders: an exploratory meta-analysis.Biomedical engineering online · 2026Pooled it
- Potential protein blood-based biomarkers for cognitive dysfunction in Parkinson's disease: a systematic review and network meta-analysis.Frontiers in aging neuroscience · 2026Pooled it
- Machine learning methods for the detection and prediction of cognitive impairment in Parkinson's disease: a systematic review and meta-analysis.Frontiers in aging neuroscience · 2025Pooled it
- Neurofilament Light Chain (NfL) in Neurodegenerative Diseases: Biological and Clinical Significance, Multi-Omics Integration, and AI-Driven Biomarker Modeling for Precision Therapy.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Integrating retinal and brain imaging biomarkers for diagnosis of parkinson's disease: findings from a time-lagged cross-sectional persian cohort study.Scientific reports · 2026Article
- Advances in artificial intelligence for neuroimaging.Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2026Review
- A multimodal explainable artificial intelligence framework for interpretable Parkinson's disease prediction.Scientific reports · 2026Article
- Characteristics and mechanisms of cognitive impairment in Parkinson disease.Nature reviews. Neurology · 2026Review
- MultimodalCNN-PD: a Parkinson's disease diagnostics framework using multimodal convolutional neural network.Frontiers in aging neuroscience · 2026Article
- Pathway Anchored Multimodal Clustering Reveals Circuit Level Signatures in Parkinsons Disease.bioRxiv : the preprint server for biology · 2025Article
- Subitem-level multi-scale assessment and machine learning for three-class cognitive status classification in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Synuclein Proteoforms: Role in Health and Disease.Molecular neurobiology · 2025Review
- Construction of a mild cognitive impairment prediction model for Parkinson's disease patients on the basis of multimodal data.NPJ Parkinson's disease · 2025Article
- A critical review on neurodegenerative biomarker diagnostics: where is the field heading to?Analytical and bioanalytical chemistry · 2025Review
- Research progress of multimodal biomarkers in the early diagnosis of mild cognitive impairment in Parkinson's disease.Frontiers in neurology · 2025Review
- Brain structural MRI marker for predicting conversion to Parkinson's disease in individuals with prodromal symptoms.Frontiers in aging neuroscience · 2025Article
- A multimodal MRI framework employing machine learning for detecting beginning cognitive impairment in Parkinson's disease.Frontiers in neuroscience · 2025Article
- Integration of multimodal imaging data with machine learning for improved diagnosis and prognosis in neuroimaging.Frontiers in human neuroscience · 2025Article
- Diagnostic classification of mild cognitive impairment in Parkinson's disease using subject-level stratified machine-learning analysis.Frontiers in aging neuroscience · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
This study aimed to identify potential markers that can predict Parkinson's disease with mild cognitive impairment (PDMCI). We retrospectively collected general demographic data, clinically relevant scales, plasma samples, and neuroimaging data (T1-weighted magnetic resonance imaging (MRI) data as well as resting-state functional MRI [Rs-fMRI] data) from 173 individuals. Subsequently, based on the aforementioned multimodal indices, a support vector machine was employed to investigate the machine learning (ML) classification of PD patients with normal cognition (PDNC) and PDMCI. The performance of 29 classifiers was assessed based on various combinations of indicators. Results demonstrated that the optimal classifier in the validation set was composed by clinical + Rs-fMRI+ neurofilament light chain, exhibiting a mean Accuracy of 0.762, a mean area under curve of 0.840, a mean sensitivity of 0.745, along with a mean specificity of 0.783. The ML algorithm based on multimodal data demonstrated enhanced discriminative ability between PDNC and PDMCI patients.
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Registered trials
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.