ArticleThe British journal of radiology2019
Machine-learning identifies Parkinson's disease patients based on resting-state between-network functional connectivity.
Article in The British journal of radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 3 syntheses or guidelines pooled it, 60 citations in OpenAlex.
- Performance of machine learning algorithms in diffusion tensor imaging of movement disorders: an exploratory meta-analysis.Biomedical engineering online · 2026Pooled 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
- Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.Frontiers in aging neuroscience · 2021Pooled it
- Effects of four-week intranasal oxytocin administration on large-scale brain networks in older adults.Neuropharmacology · 2024Trial
- Hierarchical reorganization of cortical and cerebellar functional gradients in Parkinson's disease.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
- Machine-learning models for Alzheimer's disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation.Brain informatics · 2025Review
- Causal Forest Machine Learning Analysis of Parkinson's Disease in Resting-State Functional Magnetic Resonance Imaging.Tomography (Ann Arbor, Mich.) · 2024Article
- Parkinson's Disease Recognition Using Decorrelated Convolutional Neural Networks: Addressing Imbalance and Scanner Bias in rs-fMRI Data.Biosensors · 2024Article
- Simulated brain networks reflecting progression of Parkinson's disease.Network neuroscience (Cambridge, Mass.) · 2024Article
- Radiomics for Parkinson's disease classification using advanced texture-based biomarkers.MethodsX · 2023Article
- Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease.Heliyon · 2023Article
- Whole-brain dynamical modelling for classification of Parkinson's disease.Brain communications · 2023Article
- Classification of Parkinson's disease using a region-of-interest- and resting-state functional magnetic resonance imaging-based radiomics approach.Brain imaging and behavior · 2022Article
- Parameters from site classification to harmonize MRI clinical studies: Application to a multi-site Parkinson's disease dataset.Human brain mapping · 2022Article
- Feeding the machine: Challenges to reproducible predictive modeling in resting-state connectomics.Network neuroscience (Cambridge, Mass.) · 2022Article
- A brainnetome atlas-based methamphetamine dependence identification using neighborhood component analysis and machine learning on functional MRI data.Frontiers in cellular neuroscience · 2022Article
- Machine Learning for Detecting Parkinson's Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis.Frontiers in aging neuroscience · 2022Article
- Within- and across-network alterations of the sensorimotor network in Parkinson's disease.Neuroradiology · 2021Article
- Brain functional connectivity analysis based on multi-graph fusion.Medical image analysis · 2021Article
Corrections and comments
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Authors and funding
14 authors at 5 institutions in 1 country.
Funding
Abstract
objectiveEvaluation of a data-driven, model-based classification approach to discriminate idiopathic Parkinson's disease (PD) patients from healthy controls (HC) based on between-network connectivity in whole-brain resting-state functional MRI (rs-fMRI).
methodsWhole-brain rs-fMRI (EPI, TR = 2.2 s, TE = 30 ms, flip angle = 90°. resolution = 3.1 × 3.1 × 3.1 mm, acquisition time ≈ 11 min) was assessed in 42 PD patients (medical OFF) and 47 HC matched for age and gender. Between-network connectivity based on full and L2-regularized partial correlation measures were computed for each subject based on canonical functional network architectures of two cohorts at different levels of granularity (Human Connectome Project: 15/25/50/100/200 networks; 1000BRAINS: 15/25/50/70 networks). A Boosted Logistic Regression model was trained on the correlation matrices using a nested cross-validation (CV) with 10 outer and 10 inner folds for an unbiased performance estimate, treating the canonical functional network architecture and the type of correlation as hyperparameters. The number of boosting iterations was fixed at 100. The model with the highest mean accuracy over the inner folds was trained using an non-nested 10-fold 20-repeats CV over the whole dataset to determine feature importance.
resultsOver the outer folds the mean accuracy was found to be 76.2% (median 77.8%, SD 18.2, IQR 69.4 - 87.1%). Mean sensitivity was 81% (median 80%, SD 21.1, IQR 75 - 100%) and mean specificity was 72.7% (median 75%, SD 20.4, IQR 66.7 - 80%). The 1000BRAINS 50-network-parcellation, using full correlations, performed best over the inner folds. The top features predominantly included sensorimotor as well as sensory networks.
conclusionA rs-fMRI whole-brain-connectivity, data-driven, model-based approach to discriminate PD patients from healthy controls shows a very good accuracy and a high sensitivity. Given the high sensitivity of the approach, it may be of use in a screening setting. ADVANCES IN KNOWLEDGE: Resting-state functional MRI could prove to be a valuable, non-invasive neuroimaging biomarker for neurodegenerative diseases. The current model-based, data-driven approach on whole-brain between-network connectivity to discriminate Parkinson's disease patients from healthy controls shows promising results with a very good accuracy and a very high sensitivity.
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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.