ArticleHeliyon2023
Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 7 citations in OpenAlex.
- Spatial correspondence between altered brain function and normative neurotransmitter density maps in Type 1 diabetes mellitus.Journal of endocrinological investigation · 2026Article
- Editorial: Network neuroscience in neuropsychiatric disorders.Frontiers in human neuroscience · 2026Article
- Multimodal MRI changes associated with non-motor symptoms of rapid eye movement sleep behaviour disorder in Parkinson's disease patients.Neuroradiology · 2025Article
- Radiomics with structural magnetic resonance imaging, surface morphometry features, neurology scales, and clinical metrics to evaluate the neurodevelopment of preschool children with corrected tetralogy of Fallot.Translational pediatrics · 2024Article
- White matter biomarker for predicting de novo Parkinson's disease using tract-based spatial statistics: a machine learning-based model.Quantitative imaging in medicine and surgery · 2024Article
- A more objective PD diagnostic model: integrating texture feature markers of cerebellar gray matter and white matter through machine learning.Frontiers in aging neuroscience · 2024Article
- Volume-based structural connectome of epilepsy partialis continua in Rasmussen's encephalitis.Brain communications · 2024Article
- Functional brain activity in patients with amnestic mild cognitive impairment: an rs-fMRI study.Frontiers in neurology · 2023Article
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD) is a highly heterogeneous disorder that is difficult to diagnose. Therefore, reliable biomarkers are needed. We implemented a method constructing a regional radiomics similarity network (R2SN) based on the amplitude of low-frequency fluctuation (ALFF). We classified patients with PD and healthy individuals by using a machine learning approach in accordance with the R2SN connectome. The ALFF-based R2SN exhibited great reproducibility with different brain atlases and datasets. Great classification performances were achieved both in primary (AUC = 0.85 ± 0.02 and accuracy = 0.81 ± 0.03) and independent external validation (AUC = 0.77 and accuracy = 0.70) datasets. The discriminative R2SN edges correlated with the clinical evaluations of patients with PD. The nodes of discriminative R2SN edges were primarily located in the default mode, sensorimotor, executive control, visual and frontoparietal network, cerebellum and striatum. These findings demonstrate that ALFF-based R2SN is a robust potential neuroimaging biomarker for PD and could provide new insights into connectome reorganization in PD.
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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.