Multivariate classification of social anxiety disorder using whole brain functional connectivity

Abstract

Recent research has shown that social anxiety disorder (SAD) is accompanied by abnormalities in brain functional connections. However, these findings are based on group comparisons, and, therefore, little is known about whether functional connections could be used in the diagnosis of an individual patient with SAD. Here, we explored the potential of the functional connectivity to be used for SAD diagnosis. Twenty patients with SAD and 20 healthy controls were scanned using resting-state functional magnetic resonance imaging. The whole brain was divided into 116 regions based on automated anatomical labeling atlas. The functional connectivity between each pair of regions was computed using Pearson's correlation coefficient and used as classification feature. Multivariate pattern analysis was then used to classify patients from healthy controls. The pattern classifier was designed using linear support vector machine. Experimental results showed a correct classification rate of 82.5 % (p < 0.001) with sensitivity of 85.0 % and specificity of 80.0 %, using a leave-one-out cross-validation method. It was found that the consensus connections used to distinguish SAD were largely located within or across the default mode network, visual network, sensory-motor network, affective network, and cerebellar regions. Specifically, the right orbitofrontal region exhibited the highest weight in classification. The current study demonstrated that functional connectivity had good diagnostic potential for SAD, thus providing evidence for the possible use of whole brain functional connectivity as a complementary tool in clinical diagnosis. In addition, this study confirmed previous work and described novel pathophysiological mechanisms of SAD.

Emotion Representation in Whole-brain Functional Connectivity Patterns: An fMRI Study

“…Using multivariate pattern analysis and machine learning, we found that emotion information could be successfully decoded from the whole-brain FC patterns. Our results added to the recent FC-based decoding studies which found functional connectivity patterns could be used to discriminate different populations [14], task or mental states and different object categories [13], and further highlighted the effects of the whole-brain functional connectivity patterns in the emotion perception. Overall, our results provide new evidence that large-scale functional connectivity patterns also contain rich emotion information and effectively contribute to the recognition of emotions.…”

“…In accordance with the previous study [13], in this part, we only used positive FCs as inputs, which had values significantly higher than zero with one-sample t-test across participants and corrected with multiple comparisons with false discovery rate (FDR) q=0.01. We performed emotion-pairwise classification (joy VS anger, joy VS fear, and anger VS fear) with a leave one subject out cross-validation (LOOCV) scheme [13,14]. In each iteration, data of one subject was used as testing data while the data of the remaining subjects were used for training.…”

Application of Autoencoder in Depression Diagnosis

“…This method was called a base method in this study. Besides, functional connectivity (FC) and effective connectivity (EC) based on a prior knowledge were used as features for classification tasks.Functional connectivity analysis was performed using Automated Anatomical Labeling (AAL) template according to the workflow implemented in [9]. This resulted in 116 × 115 ÷ 2 = 6670 dimensions features.…”

“…∑ ‫ݎ‬ሺܽ||ܽ ො ሻ ୀଵ (9) where݈ is the number of neurons in the hidden layer. Another constraint was added to the cost function to reduce overfitting.…”

Amygdala-Prefrontal Connectivity Analysis of Decoding Human Emotion

“…The typical emotional illness includes major depressive disorder (MDD), social anxiety disorder (SAD), autism spectrum disorder (ASD) and so on [1][2][3]. For diagnoses of emotional illness, doctors make out a medical certificate according to the Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) [4] after asking for the patient's clinical signs and symptoms.…”