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Abstract:  
Background: One of the vital skills which has impact on emotional health and well-being is regulation of emotions. In recent years, the neural basis of this process has been considered widely. One of the powerful tool for eliciting and regulating emotion is music. Anterior cingulate cortex (ACC) is part of the emotional neural circuitry involved in major depressive disorder (MDD). The current study uses functional magnetic resonance imaging (fMRI) to examine how neural processing of emotional musical auditory stimuli is changed within the ACC in depression. Statistical inference is conducted using a Bayesian Generalized Linear Model (GLM) approach with Integrated Nested Laplace Approximation (INLA) algorithm.
Methods: A new proposed Bayesian approach was applied for assessing functional response to emotional musical auditory stimuli in a block design fMRI data with 105 scans of two healthy and depressed women. In this Bayesian approach, unweighted graph-Laplacian (UGL) prior was chosen for spatial dependency and AR (1) process was used for temporal correlation via pre-weighting residuals. Finally, inference was conducted using INLA algorithm in the R-INLA package.
Results: The results revealed that positive music as compared to negative music elicits stronger activation within ACC area in both healthy and depressed subjects. In comparison of MDD and never-depressed (ND) individuals, significant difference was found between MDD and ND groups in response to positive music vs negative music stimuli. The activations increase from baseline to positive stimuli and decrease from baseline to negative stimuli in ND subject, whereas participant with depression showed no difference from baseline to negative stimuli and a significant decrease to positive stimuli.
Conclusion: Assessing the pattern of activations within ACC in depressed individual may be useful in retraining the ACC and improving its function, and lead to more effective therapeutic interventions.
 
Type of Study: Original | Subject: Computational Neuroscience
Received: 2018/06/24 | Accepted: 2019/03/10

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