Motivation: Mild traumatic brain injury (mTBI) often involves prolonged symptoms, but traditional imaging lacks the sensitivity to detect underlying neural differences. Identifying biomarkers associated with specific symptom phenotypes is essential for improving diagnosis and treatment. Goal(s): To assess the feasibility of using resting-state fMRI time series entropy as a biomarker to differentiate between symptom-phenotype-specific mTBI subgroups. Approach: We analyzed entropy in time series data from 32 brain regions in mTBI patients and controls, focusing on symptom clusters from phenotype analysis. Results: Symptomatic mTBI groups showed significantly lower entropy in Rostral Prefrontal Cortex, Supramarginal Gyrus, and Occipital Cortex, supporting entropy as a symptom-sensitive diagnostic tool. Impact: This study highlights changes in brain temporal entropy as a promising biomarker for detecting symptom-specific neural changes in mTBI, paving the way for more precise diagnosis and tailored treatment approaches.
Chen et al. (Tue,) studied this question.