ArticleBrain imaging and behavior2022
Classification of Parkinson's disease using a region-of-interest- and resting-state functional magnetic resonance imaging-based radiomics approach.
Article in Brain imaging and behavior, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- 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 differential diagnosis value of radiomics-based machine learning in Parkinson's disease: a systematic review and meta-analysis.Frontiers in aging neuroscience · 2023Pooled it
- Whole-brain functional activity and connectivity for the classification of subjective tinnitus: a machine learning study.Frontiers in neurology · 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
- Identify MRI negative temporal lobe epilepsy with resting fMRI indicators and machine learning techniques.Scientific reports · 2025Article
- AlzFormer: Video-based space-time attention model for early diagnosis of Alzheimer's disease.Neuroscience · 2025Article
- FCN-PD: An Advanced Deep Learning Framework for Parkinson's Disease Diagnosis Using MRI Data.Diagnostics (Basel, Switzerland) · 2025Article
- Causal Forest Machine Learning Analysis of Parkinson's Disease in Resting-State Functional Magnetic Resonance Imaging.Tomography (Ann Arbor, Mich.) · 2024Article
- Gray matter atrophy and white matter lesions burden in delayed cognitive decline following carbon monoxide poisoning.Human brain mapping · 2024Article
- An automated hybrid approach via deep learning and radiomics focused on the midbrain and substantia nigra to detect early-stage Parkinson's disease.Frontiers in aging neuroscience · 2024Article
- Radiomics for Parkinson's disease classification using advanced texture-based biomarkers.MethodsX · 2023Article
- Distinguishing Patients with MRI-Negative Temporal Lobe Epilepsy from Normal Controls Based on Individual Morphological Brain Network.Brain topography · 2023Article
- Using histogram analysis of the intrinsic brain activity mapping to identify essential tremor.Frontiers in neurology · 2023Article
- Combined brain network topological metrics with machine learning algorithms to identify essential tremor.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To investigate the value of combining amplitude of low-frequency fluctuations-based radiomics and the support vector machine classifier method in distinguishing patients with Parkinson's disease from healthy controls. A total of 123 patients with Parkinson's disease and 90 healthy controls from three centers with functional and structural MRI images were included in this study. We extracted radiomics features using the Brainnetome 246 atlas from the mean amplitude of low-frequency fluctuations maps. Two-sample t-tests and recursive feature elimination combined with support vector machine method were applied for feature selection and dimensionality reduction. We used support vector machine classifier to construct model and identify the discriminative features. The automated anatomical labeling 90 atlas and fivefold cross-validation were used to evaluate the robustness and generalization of the classifier. We found our model obtained a high classification performance with an accuracy of 78.07%, and AUC, sensitivity, and specificity of 0.8597, 78.80%, and 76.08%, respectively. We detected 7 discriminative brain subregions. The fivefold cross-validation and automated anatomical labeling 90 atlas also got high classification accuracy, and we found Brainnetome 246 atlas achieved a higher classification performance than the automated anatomical labeling 90 atlas both with tenfold and fivefold cross-validation. Our findings may help the early diagnosis of Parkinson's disease and provide support for research on Parkinson's disease mechanisms and clinical evaluation.
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