A novel biomarker selection method using multimodal neuroimaging data.
Yue WangPei-Shan YenOlusola A AjiloreDulal K BhaumikPublished in: PloS one (2024)
Identifying biomarkers is essential to obtain the optimal therapeutic benefit while treating patients with late-life depression (LLD). We compare LLD patients with healthy controls (HC) using resting-state functional magnetic resonance and diffusion tensor imaging data to identify neuroimaging biomarkers that may be potentially associated with the underlying pathophysiology of LLD. We implement a Bayesian multimodal local false discovery rate approach for functional connectivity, borrowing strength from structural connectivity to identify disrupted functional connectivity of LLD compared to HC. In the Bayesian framework, we develop an algorithm to control the overall false discovery rate of our findings. We compare our findings with the literature and show that our approach can better detect some regions never discovered before for LLD patients. The Hub of our discovery related to various neurobehavioral disorders can be used to develop behavioral interventions to treat LLD patients who do not respond to antidepressants.
Keyphrases
- resting state
- functional connectivity
- small molecule
- magnetic resonance
- high throughput
- end stage renal disease
- electronic health record
- ejection fraction
- newly diagnosed
- pain management
- big data
- chronic kidney disease
- physical activity
- machine learning
- depressive symptoms
- prognostic factors
- magnetic resonance imaging
- patient reported outcomes
- contrast enhanced
- patient reported
- artificial intelligence
- bipolar disorder
- drug induced