ArticleScientific reports2021
Multitask fMRI and machine learning approach improve prediction of differential brain activity pattern in patients with insomnia disorder.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- The altered hypothalamic network functional connectivity in chronic insomnia disorder and regulation effect of acupuncture: a randomized controlled neuroimaging study.BMC complementary medicine and therapies · 2024Trial
- A Review on Automated Sleep Study.Annals of biomedical engineering · 2024Review
- Multimodal transformer graph convolution attention isomorphism network (MTCGAIN): a novel deep network for detection of insomnia disorder.Quantitative imaging in medicine and surgery · 2024Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Discriminating Paradoxical and Psychophysiological Insomnia Based on Structural and Functional Brain Images: A Preliminary Machine Learning Study.Brain sciences · 2023Article
- Differences in Gray Matter Volume in Cerebral Small Vessel Disease Patients with and without Sleep Disturbance.Brain sciences · 2023Article
- Orbitofrontal Cortex Functional Connectivity-Based Classification for Chronic Insomnia Disorder Patients With Depression Symptoms.Frontiers in psychiatry · 2022Article
- Level up the brain! Novel PCA method reveals key neuroplastic refinements in action video gamers.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We investigated the differential spatial covariance pattern of blood oxygen level-dependent (BOLD) responses to single-task and multitask functional magnetic resonance imaging (fMRI) between patients with psychophysiological insomnia (PI) and healthy controls (HCs), and evaluated features generated by principal component analysis (PCA) for discrimination of PI from HC, compared to features generated from BOLD responses to single-task fMRI using machine learning methods. In 19 patients with PI and 21 HCs, the mean beta value for each region of interest (ROIbval) was calculated with three contrast images (i.e., sleep-related picture, sleep-related sound, and Stroop stimuli). We performed discrimination analysis and compared with features generated from BOLD responses to single-task fMRI. We applied support vector machine analysis with a least absolute shrinkage and selection operator to evaluate five performance metrics: accuracy, recall, precision, specificity, and F2. Principal component features showed the best classification performance in all aspects of metrics compared to BOLD response to single-task fMRI. Bilateral inferior frontal gyrus (orbital), right calcarine cortex, right lingual gyrus, left inferior occipital gyrus, and left inferior temporal gyrus were identified as the most salient areas by feature selection. Our approach showed better performance in discriminating patients with PI from HCs, compared to single-task fMRI.
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