ArticleNeuroradiology2021
Parkinson's disease: deep learning with a parameter-weighted structural connectome matrix for diagnosis and neural circuit disorder investigation.
Article in Neuroradiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 45 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
- Artificial intelligence role in advancement of human brain connectome studies.Frontiers in neuroinformatics · 2024Pooled it
- Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.Neural regeneration research · 2026Article
- A pilot study on protocol consistency and graph metric reproducibility in microstructure-weighted connectomes.Scientific reports · 2026Article
- MRI-based diffusion weighted imaging and diffusion kurtosis imaging grading of clear cell renal cell carcinoma using a deep learning classifier.Oncology letters · 2025Article
- Classification of Interventional Radiology Reports into Technique Categories with a Fine-Tuned Large Language Model.Journal of imaging informatics in medicine · 2025Article
- An Automated Diagnosis of Parkinson's Disease from MRI Scans Based on Enhanced Residual Dense Network with Attention Mechanism.Journal of imaging informatics in medicine · 2025Article
- AI-Driven Advances in Parkinson's Disease Neurosurgery: Enhancing Patient Selection, Trial Efficiency, and Therapeutic Outcomes.Brain sciences · 2025Review
- Exploiting macro- and micro-structural brain changes for improved Parkinson's disease classification from MRI data.NPJ Parkinson's disease · 2024Article
- A survey of detection of Parkinson's disease using artificial intelligence models with multiple modalities and various data preprocessing techniques.Journal of education and health promotion · 2024Review
- Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics.Investigative radiology · 2023Review
- Deep learning distinguishes connectomes from focal epilepsy patients and controls: feasibility and clinical implications.Brain communications · 2023Article
- Deep Learning Classifies Low- and High-Grade Glioma Patients with High Accuracy, Sensitivity, and Specificity Based on Their Brain White Matter Networks Derived from Diffusion Tensor Imaging.Diagnostics (Basel, Switzerland) · 2022Article
- A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives.Diagnostics (Basel, Switzerland) · 2022Review
- Diffusion Tensor Imaging Radiomics for Diagnosis of Parkinson's Disease.Brain sciences · 2022Article
- Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021).Sensors (Basel, Switzerland) · 2021Review
- Application of Artificial Intelligence in the MRI Classification Task of Human Brain Neurological and Psychiatric Diseases: A Scoping Review.Diagnostics (Basel, Switzerland) · 2021Article
- Diffusion Magnetic Resonance Imaging-Based Biomarkers for Neurodegenerative Diseases.International journal of molecular sciences · 2021Review
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
Abstract
purposeTo investigate whether Parkinson's disease (PD) can be differentiated from healthy controls and to identify neural circuit disorders in PD by applying a deep learning technique to parameter-weighted and number of streamlines (NOS)-based structural connectome matrices calculated from diffusion-weighted MRI.
methodsIn this prospective study, 115 PD patients and 115 healthy controls were enrolled. NOS-based and parameter-weighted connectome matrices were calculated from MRI images obtained with a 3-T MRI unit. With 5-fold cross-validation, diagnostic performance of convolutional neural network (CNN) models using those connectome matrices in differentiating patients with PD from healthy controls was evaluated. To identify the important brain connections for diagnosing PD, gradient-weighted class activation mapping (Grad-CAM) was applied to the trained CNN models.
resultsCNN models based on some parameter-weighted structural matrices (diffusion kurtosis imaging (DKI)-weighted, neurite orientation dispersion and density imaging (NODDI)-weighted, and g-ratio-weighted connectome matrices) showed moderate performance (areas under the receiver operating characteristic curve (AUCs) = 0.895, 0.801, and 0.836, respectively) in discriminating PD patients from healthy controls. The DKI-weighted connectome matrix performed significantly better than the conventional NOS-based matrix (AUC = 0.761) (DeLong's test, p < 0.0001). Alterations of neural connections between the basal ganglia and cerebellum were indicated by applying Grad-CAM to the NODDI- and g-ratio-weighted matrices.
conclusionPatients with PD can be differentiated from healthy controls by applying the deep learning technique to the parameter-weighted connectome matrices, and neural circuit disorders including those between the basal ganglia on one side and the cerebellum on the contralateral side were visualized.
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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.