ArticleNPJ Parkinson's disease2024
Exploiting macro- and micro-structural brain changes for improved Parkinson's disease classification from MRI data.
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 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- A survey of deep learning techniques in detecting neurological disorders using MRI.Biomedical engineering online · 2026Pooled it
- A curriculum-guided unified framework for robust unsupervised domain adaptation on multi-cohort Parkinson's disease diagnosis.Quantitative imaging in medicine and surgery · 2026Article
- Morphometric Explanations for Deep Learning Based Neuroimaging Classifiers.Human brain mapping · 2026Article
- Proto-memformer: deformable memory transformer for Parkinson's MRI classification.Scientific reports · 2026Article
- Testing an inverse link between limbic alpha-synucleinopathy and myelin markers in mice and humans.NPJ Parkinson's disease · 2026Article
- Structural and Statistical Knowledge-Enhanced Attention Network for early Parkinson's disease diagnosis.Quantitative imaging in medicine and surgery · 2026Article
- Enhancing interpretability of AI with radiomics-based deep neural network: proof of concept in the classification of Parkinsonian syndromes withEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification.Frontiers in neuroscience · 2026Review
- Mapping motor cortex "hand knob" metrics to symptom hemidominance in Parkinson's disease: from width comparison to machine learning.Brain & spine · 2026Article
- Volumetric signatures of basal ganglia-thalamo-cortical and cerebello-thalamo-cortical networks in Parkinson's disease and its motor subtypes.Frontiers in aging neuroscience · 2026Article
- A quantum-classical dual-track deep learning network for explainable Parkinson's disease classification.Frontiers in artificial intelligence · 2026Article
- Longitudinal non-negative matrix factorization identifies the altered trajectory of motor symptoms in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Imaging biomarkers of cortical neurodegeneration underlying cognitive impairment in Parkinson's disease.European journal of nuclear medicine and molecular imaging · 2025Article
- Network biomarkers of Alzheimer's disease risk derived from joint volume and texture covariance patterns in mouse models.PloS one · 2025Article
- Deep learning-based prediction of cerebral white matter hyperintensity burden using carotid magnetic resonance angiography.Frontiers in neurology · 2025Article
- Analysis of mortality in Parkinson disease in China: Exploration of recent and future trends.Aging medicine (Milton (N.S.W)) · 2024Article
- Classification of Parkinson's disease by deep learning on midbrain MRI.Frontiers in aging neuroscience · 2024Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease. Accurate PD diagnosis is crucial for effective treatment and prognosis but can be challenging, especially at early disease stages. This study aimed to develop and evaluate an explainable deep learning model for PD classification from multimodal neuroimaging data. The model was trained using one of the largest collections of T1-weighted and diffusion-tensor magnetic resonance imaging (MRI) datasets. A total of 1264 datasets from eight different studies were collected, including 611 PD patients and 653 healthy controls (HC). These datasets were pre-processed and non-linearly registered to the MNI PD25 atlas. Six imaging maps describing the macro- and micro-structural integrity of brain tissues complemented with age and sex parameters were used to train a convolutional neural network (CNN) to classify PD/HC subjects. Explainability of the model's decision-making was achieved using SmoothGrad saliency maps, highlighting important brain regions. The CNN was trained using a 75%/10%/15% train/validation/test split stratified by diagnosis, sex, age, and study, achieving a ROC-AUC of 0.89, accuracy of 80.8%, specificity of 82.4%, and sensitivity of 79.1% on the test set. Saliency maps revealed that diffusion tensor imaging data, especially fractional anisotropy, was more important for the classification than T1-weighted data, highlighting subcortical regions such as the brainstem, thalamus, amygdala, hippocampus, and cortical areas. The proposed model, trained on a large multimodal MRI database, can classify PD patients and HC subjects with high accuracy and clinically reasonable explanations, suggesting that micro-structural brain changes play an essential role in the disease course.
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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.