Volume 12, Issue 1 (January & February 2021)                   BCN 2021, 12(1): 95-104 | Back to browse issues page

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Naseri P, Alavi Majd H, Tabatabaei S M, Khadembashi N, Najibi S M, Nazari A. Functional Brain Response to Emotional Muical Stimuli in Depression, Using INLA Approach for Approximate Bayesian Inference. BCN. 2021; 12 (1) :95-104
URL: http://bcn.iums.ac.ir/article-1-1259-en.html
1- Department of Biostatistics, School of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
2- Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
3- Department of English Language, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
4- Department of Statistics, School of Sciences, Shiraz University, Shiraz, Iran.
Introduction: One of the vital skills which has an impact on emotional health and well-being is the regulation of emotions. In recent years, the neural basis of this process has been considered widely. One of the powerful tools for eliciting and regulating emotion is music. The 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 an 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 autoregressive (AR) (1) process was used for temporal correlation via pre-weighting residuals. Finally, the inference was conducted using the Integrated Nested Laplace Approximation (INLA) algorithm in the R-INLA package.
Results: The results revealed that positive music, as compared to negative music, elicits stronger activation within the ACC area in both healthy and depressed subjects. In comparing MDD and Never-Depressed (ND) individuals, a 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 subjects. Also, a significant decrease from baseline to positive stimuli was observed in MDD subjects, but there was no significant difference between baseline and negative stimuli.
Conclusion: Assessing the pattern of activations within ACC in a 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 | Published: 2021/01/1

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