Decoding fear of negative evaluation from brain morphology: A machine-learning study on structural neuroimaging data

Abstract

Background: Fear of negative evaluation (FNE), referring to negative expectation and feelings toward other people’s social evaluation, is closely associated with social anxiety that plays an important role in our social life. Exploring the neural markers of FNE may be of theoretical and practical significance to psychiatry research (e.g., studies on social anxiety).

Methods: To search for potentially relevant biomarkers of FNE in human brain, the current study applied multivariate relevance vector regression, a machine-learning and data-driven approach, on brain morphological features (e.g., cortical thickness) derived from structural imaging data; further, we used these features as indexes to predict self-reported FNE score in each participant.

Results: Our results confirm the predictive power of multiple brain regions, including those engaged in negative emotional experience (e.g., amygdala, insula), regulation and inhibition of emotional feeling (e.g., frontal gyrus, anterior cingulate gyrus), and encoding and retrieval of emotional memory (e.g., posterior cingulate cortex, parahippocampal gyrus).

Conclusions: The current findings suggest that anxiety represents a complicated construct that engages multiple brain systems, from primitive subcortical mechanisms to sophisticated cortical processes.

Graphical abstract

Keywords

fear of negative evaluation / social anxiety / structural magnetic resonance imaging / machine learning / relevance vector regression

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The Authors (2022). Published by Higher Education Press.