Anxious brains: A combined data fusion machine learning approach to predict trait anxiety from gray and white matter features

Author:

Baggio Teresa1,Grecucci Alessandro1,Meconi Federica1,Messina Irene2

Affiliation:

1. University of Trento

2. Universitas Mercatorum

Abstract

Abstract Trait anxiety relates to the steady propensity to experience and report negative emotions and thoughts such as fear and worries across different situations, along with a stable perception of the environment as characterized by threatening stimuli. Previous studies have tried to investigate neuroanatomical features related to anxiety mostly using univariate analyses and, thus, giving rise to contrasting results. The aim of this study is to find a predictive model of individual differences in trait anxiety from brain structural features, with a combined data fusion machine learning approach to allow generalization to new cases. Additionally, we aimed to perform a Network analysis to test the hypothesis that anxiety-related networks have a central role in modulating other networks not strictly associated with anxiety. Finally, we wanted to test the hypotheses that trait anxiety is associated with specific cognitive emotion regulation strategies, and that it decreases with ageing. Structural brain images of 158 participants were first decomposed into independent covarying gray and white matter networks with a data fusion unsupervised machine learning approach (Parallel ICA). Then, trait anxiety was predicted from these networks via supervised machine learning (Decision Trees) and backward regression. Two covarying gray and white matter independent networks successfully predicted trait anxiety. The first network included mainly parietal and temporal regions, such as the postcentral gyrus, the precuneus, and the middle and superior temporal gyrus, while the second included frontal and parietal regions such as the superior and middle temporal gyrus, the anterior cingulate and the precuneus. We also found that trait anxiety was positively associated with catastrophizing, rumination, other- and self-blame, and negatively associated with positive refocusing and reappraisal, and that it decreased with age. This paper provides new insights regarding the prediction of individual differences in trait anxiety from brain and psychological features and can pave the way for future diagnostic predictive models of anxiety.

Publisher

Research Square Platform LLC

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