ArticleFrontiers in neuroimaging2023
Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies.
Article in Frontiers in neuroimaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Wakeful targeted memory reactivation during short rest periods modulates early motor learning.NPJ science of learning · 2026Article
- Integrated Blood Biomarker and Neurobehavioural Signatures of Latent Neuroinjury in Experienced Military Breachers Exposed to Repetitive Low-Intensity Blast.International journal of molecular sciences · 2026Article
- A novel strategy for detecting multiple mediators in high-dimensional mediation models.Frontiers in psychiatry · 2025Article
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4 authors.
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Abstract
In this article, we developed a Bayesian multimodal model to detect biomarkers (or neuromarkers) using resting-state functional and structural data while comparing a late-life depression group with a healthy control group. Biomarker detection helps determine a target for treatment intervention to get the optimal therapeutic benefit for treatment-resistant patients. The borrowing strength of the structural connectivity has been quantified for functional activity while detecting the biomarker. In the biomarker searching process, thousands of hypotheses are generated and tested simultaneously using our novel method to control the false discovery rate for small samples. Several existing statistical approaches, frequently used in analyzing neuroimaging data have been investigated and compared via simulation with the proposed approach to show its excellent performance. Results are illustrated with a live data set generated in a late-life depression study. The role of detected biomarkers in terms of cognitive function has been explored.
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