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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.
RESUMO
INTRODUCTION: In recent years, brain functional connectivity studies are extended using the advanced statistical methods. Functional connectivity is identified by synchronous activation in a spatially distinct region of the brain in resting-state functional Magnetic Resonance Imaging (MRI) data. For this purpose there are several methods such as seed-based correlation analysis based on temporal correlation between different Regions of Interests (ROIs) or between brain's voxels of prior seed. METHODS: In the current study, test-retest Resting State functional MRI (rs-fMRI) data of 21 healthy subjects were analyzed to predict second replication connectivity map using first replication data. A potential estimator is "raw estimator" that uses the first replication data from each subject to predict the second replication connectivity map of the same subject. The second estimator, "mean estimator" uses the average of all sample subjects' connectivity to estimate the correlation map. Shrinkage estimator is made by shrinking raw estimator towards the average connectivity map of all subjects' first replicate. Prediction performance of the second replication correlation map is evaluated by Mean Squared Error (MSE) criteria. RESULTS: By the employment of seed-based correlation analysis and choosing precentral gyrus as the ROI over 21 subjects in the study, on average MSE for raw, mean and shrinkage estimator were 0.2169, 0.1118, and 0.1103, respectively. Also, percent reduction of MSE for shrinkage and mean estimator in comparison with raw estimator is 49.14 and 48.45, respectively. CONCLUSION: Shrinkage approach has the positive effect on the prediction of functional connectivity. When data has a large between session variability, prediction of connectivity map can be improved by shrinking towards population mean.
