ArticleFrontiers in psychiatry2018
Weighted Random Support Vector Machine Clusters Analysis of Resting-State fMRI in Mild Cognitive Impairment.
Article in Frontiers in psychiatry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Functional MRI-Specific Alterations in Salience Network in Mild Cognitive Impairment: An ALE Meta-Analysis.Frontiers in aging neuroscience · 2021Pooled it
- A cross-sectional and longitudinal study of changes in complex networks related to brain function in temporal lobe epilepsy patients treated with levetiracetam intervention therapy.Brain imaging and behavior · 2026Article
- Mutations in sugar metabolism-related genes driving global drug-resistant Mycobacterium tuberculosis transmission revealed by whole-genome sequencing.BMC infectious diseases · 2026Article
- Differences in fractional amplitude of low-frequency fluctuations (fALFF) and cognitive function between untreated major depressive disorder and schizophrenia with depressive mood patients.BMC psychiatry · 2024Article
- Toxin-antitoxin system gene mutations drivingFrontiers in microbiology · 2024Article
- Association between two-component systems gene mutation and Mycobacterium tuberculosis transmission revealed by whole genome sequencing.BMC genomics · 2023Article
- The alterations of spontaneous neural activities and white matter microstructures in anti-N-methyl-D-aspartate receptor encephalitis: a resting-state fMRI and DTI study.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2023Article
- Deep Learning-Based Ensembling Technique to Classify Alzheimer's Disease Stages Using Functional MRI.Journal of healthcare engineering · 2023Article
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5 authors.
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Abstract
The identification of abnormal cognitive decline at an early stage becomes an increasingly significant conundrum to physicians and is of major interest in the studies of mild cognitive impairment (MCI). Support vector machine (SVM) as a high-dimensional pattern classification technique is widely employed in neuroimaging research. However, the application of a single SVM classifier may be difficult to achieve the excellent classification performance because of the small-sample size and noise of imaging data. To address this issue, we propose a novel method of the weighted random support vector machine cluster (WRSVMC) in which multiple SVMs were built and different weights were given to corresponding SVMs with different classification performances. We evaluated our algorithm on resting state functional magnetic resonance imaging (RS-fMRI) data of 93 MCI patients and 105 healthy controls (HC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The maximum accuracy given by the WRSVMC is 87.67%, demonstrating excellent diagnostic power. Furthermore, the most discriminative brain areas have been found out as follows: gyrus rectus (REC.L), precentral gyrus (PreCG.R), olfactory cortex (OLF.L), and middle occipital gyrus (MOG.R). These findings of the paper provide a new perspective for the clinical diagnosis of MCI.
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