ArticleEuropean radiology2018
Extraction of large-scale structural covariance networks from grey matter volume for Parkinson's disease classification.
Article in European radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 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
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- Alterations of gray matter volume and structural covariance network in unilateral frontal lobe low-grade gliomas.BMC medical imaging · 2025Article
- Large-scale structural covariance networks changes relate to executive function deficit in betel quid-dependent chewers.Brain imaging and behavior · 2025Article
- Unlocking the potential: T1-weighed MRI as a powerful predictor of levodopa response in Parkinson's disease.Insights into imaging · 2024Article
- Acceleration of brain aging after small-volume infarcts.Frontiers in aging neuroscience · 2024Article
- A multicentre study on grey matter morphometric biomarkers for classifying early schizophrenia and parkinson's disease psychosis.NPJ Parkinson's disease · 2023Article
- Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease.Heliyon · 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
- Regional rather than global brain age mediates cognitive function in cerebral small vessel disease.Brain communications · 2022Article
- Structural Covariance of the Ipsilesional Primary Motor Cortex in Subcortical Stroke Patients with Motor Deficits.Neural plasticity · 2022Article
- Altered gray matter structural covariance networks at both acute and chronic stages of mild traumatic brain injury.Brain imaging and behavior · 2021Article
- Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework.Frontiers in psychiatry · 2021Article
- Detraining Effect on Pulmonary and Cardiovascular Autonomic Function and Functional Outcomes in Patients With Parkinson's Disease After Respiratory Muscle Training: An 18-Month Follow-Up Study.Frontiers in neurology · 2021Article
- Simultaneously Improved Pulmonary and Cardiovascular Autonomic Function and Short-Term Functional Outcomes in Patients with Parkinson's Disease after Respiratory Muscle Training.Journal of clinical medicine · 2020Article
- Structural Covariance Network Disruption and Functional Compensation in Parkinson's Disease.Frontiers in aging neuroscience · 2020Article
- Structural covariance in subcortical stroke patients measured by automated MRI-based volumetry.NeuroImage. Clinical · 2019Article
- Altered Global Synchronizations in Patients With Parkinson's Disease: A Resting-State fMRI Study.Frontiers in aging neuroscience · 2019Article
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Authors and funding
9 authors at 6 institutions in 1 country.
Funding
Abstract
objectivesTo identify disease-related spatial covariance patterns of grey matter volume as an aid in the classification of Parkinson's disease (PD).
methodsSeventy structural covariance networks (SCNs) based on grey matter volume covariance patterns were defined using independent component analysis with T1-weighted structural MRI scans (discovery sample, 70 PD patients and 70 healthy controls). An image-based classifier was constructed from SCNs using a multiple logistic regression analysis with a leave-one-out cross-validation-based feature selection scheme. A validation sample (26 PD patients and 26 healthy controls) was further collected to evaluate the generalization ability of the constructed classifier.
resultsIn the discovery sample, 13 SCNs, including the cerebellum, anterior temporal poles, parahippocampal gyrus, parietal operculum, occipital lobes, supramarginal gyri, superior parietal lobes, paracingulate gyri and precentral gyri, had higher classification performance for PD. In the validation sample, the classifier had moderate generalization ability, with a mean sensitivity of 81%, specificity of 69% and overall accuracy of 75%. Furthermore, certain individual SCNs were also associated with disease severity.
conclusionsAlthough not applicable for routine care at present, our results provide empirical evidence that disease-specific, large-scale structural networks can provide a foundation for the further improvement of diagnostic MRI in movement disorders. KEY POINTS: • Disease-specific, large-scale SCNs can be identified from structural MRI. • A new network-based framework for PD classification is proposed. • An SCN-based classifier had moderate generalization ability in PD classification. • The selected SCNs provide valuable functional information regarding PD patients.
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