Activation Likelihood Estimation Meta-Analysis of the Effects of Cognitive Behavioral Therapy on Brain Activation in the Treatment of Depression and Anxiety Disorders

认知行为疗法对抑郁症和焦虑症治疗中脑激活影响的激活可能性估计荟萃分析

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Abstract

Background: Cognitive behavioral therapy (CBT) stands as a highly efficacious psychological treatment for both anxiety and depressive disorders. Nonetheless, scholarly debates persist regarding the specificities of brain area activation during CBT treatment for these disorders. Methodology: Utilizing activation likelihood estimation (ALE) analysis, this study aims to discern the neurobiological similarities and disparities between CBT's effects on anxiety and depressive disorders by examining functional brain areas. Results: A total of 22 articles, encompassing 443 patients, were included in the meta-analysis. Our results show that in the resting state, patients with depression treated with CBT resulted in increased activation of the right and left ventral anterior cingulate cortex (vACC), left parahippocampal gyrus (PG), right subgyral, left inferior temporal gyrus (ITG), and right inferior occipital gyrus (IOG), whereas patients with anxiety disorders had increased activation of the right and left superior frontal gyrus (SFG) and decreased activation of the caudate after treatment. In the task state, increased activation of the right PG, right orbital frontal lobe, and right dorsal anterior cingulate cortex (dACC) was mainly observed after treatment in patients with anxiety disorders, and the left lentiform nucleus (LN), left dorsal entorhinal cortex, and right caudate activation were decreased. For depressive disorders, no consistent activation patterns emerged in the task state, likely due to limited studies or heterogeneity in task paradigms across included studies. Conclusion: CBT's efficacy relies on both shared (e.g., vACC-mediated emotion regulation and cognitive control) and distinct neural mechanisms (fear-circuit modulation in anxiety vs. memory-network enhancement in depression), informing biomarker-driven treatment personalization.

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