Individualized prediction of dispositional worry using white matter connectivity.

Abstract:

BACKGROUND:Excessive worry is a defining feature of generalized anxiety disorder and is present in a wide range of other psychiatric conditions. Therefore, individualized predictions of worry propensity could be highly relevant in clinical practice, with respect to the assessment of worry symptom severity at the individual level. METHODS:We applied a multivariate machine learning approach to predict dispositional worry based on microstructural integrity of white matter (WM) tracts. RESULTS:We demonstrated that the machine learning model was able to decode individual dispositional worry scores from microstructural properties in widely distributed WM tracts (mean absolute error = 10.46, p < 0.001; root mean squared error = 12.82, p < 0.001; prediction R2 = 0.17, p < 0.001). WM tracts that contributed to worry prediction included the posterior limb of internal capsule, anterior corona radiate, and cerebral peduncle, as well as the corticolimbic pathways (e.g. uncinate fasciculus, cingulum, and fornix) already known to be critical for emotion processing and regulation. CONCLUSIONS:The current work thus elucidates potential neuromarkers for clinical assessment of worry symptoms across a wide range of psychiatric disorders. In addition, the identification of widely distributed pathways underlying worry propensity serves to better improve the understanding of the neurobiological mechanisms associated with worry.

journal_name

Psychol Med

journal_title

Psychological medicine

authors

Feng C,Cui Z,Cheng D,Xu R,Gu R

doi

10.1017/S0033291718002763

subject

Has Abstract

pub_date

2019-09-01 00:00:00

pages

1999-2008

issue

12

eissn

0033-2917

issn

1469-8978

pii

S0033291718002763

journal_volume

49

pub_type

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