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 build a predictive model of individual differences in trait anxiety from brain morphometric features, by taking advantage of 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 hypothesis that trait anxiety was associated with specific cognitive emotion regulation strategies, and whether anxiety may decrease 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, supervised machine learning (decision tree) and backward regression were used to extract and test the generalizability of a predictive model of trait anxiety. 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 network 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. Moreover, trait anxiety was negatively associated 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.

Anxious Brains: A Combined Data Fusion Machine Learning Approach to Predict Trait Anxiety from Morphometric Features / Baggio, Teresa; Grecucci, Alessandro; Meconi, Federica; Messina, Irene. - In: SENSORS. - ISSN 1424-8220. - ELETTRONICO. - 23:2(2023), p. 610. [10.3390/s23020610]

Anxious Brains: A Combined Data Fusion Machine Learning Approach to Predict Trait Anxiety from Morphometric Features

Baggio, Teresa
Co-primo
;
Grecucci, Alessandro
Co-primo
;
Meconi, Federica;Messina, Irene
2023-01-01

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 build a predictive model of individual differences in trait anxiety from brain morphometric features, by taking advantage of 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 hypothesis that trait anxiety was associated with specific cognitive emotion regulation strategies, and whether anxiety may decrease 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, supervised machine learning (decision tree) and backward regression were used to extract and test the generalizability of a predictive model of trait anxiety. 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 network 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. Moreover, trait anxiety was negatively associated 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.
2023
2
Baggio, Teresa; Grecucci, Alessandro; Meconi, Federica; Messina, Irene
Anxious Brains: A Combined Data Fusion Machine Learning Approach to Predict Trait Anxiety from Morphometric Features / Baggio, Teresa; Grecucci, Alessandro; Meconi, Federica; Messina, Irene. - In: SENSORS. - ISSN 1424-8220. - ELETTRONICO. - 23:2(2023), p. 610. [10.3390/s23020610]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/364069
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