AI Article Synopsis
- This study investigates potential biomarkers for predicting the outcomes of cognitive behavioural therapy (CBT) in patients with depression.
- A machine learning algorithm was developed to assess how pre-therapy brain activity (specifically in the dorsolateral prefrontal cortex) could forecast changes in depression severity after therapy.
- Findings indicate that increased regional homogeneity in the left dorsolateral prefrontal cortex serves as a promising indicator of positive CBT effects in depression.
Article Abstract
Clear prognostic indicators of cognitive behavioural therapy (CBT) are lacking for depression. This study aims to identify a biomarker that predicts CBT outcomes in depression. We developed a machine learning algorithm to predict post-CBT Hamilton Depression Rating Scale (HAMD) using pre-CBT regional homogeneity (ReHo). We examined transcriptomic signatures of regions with CBT-related ReHo changes. Twenty-five patients completed CBT and had increased ReHo in the dorsolateral prefrontal cortex (DLPFC) following CBT. Pre-CBT ReHo in left DLPFC was shown to be a predictor of post-HAMD scores. We identified left DLPFC ReHo as a neuroimaging biomarker for therapeutic effects of CBT in depression.
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| http://dx.doi.org/10.1016/j.psychres.2023.115542 | DOI Listing |
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