Individualized prediction of dispositional worry using white matter connectivity. Issue 12 (25th October 2018)

• Record Type: Journal Article
• Authors: Feng, Chunliang; Cui, Zaixu; Cheng, Dazhi; Xu, Rui; Gu, Ruolei
• Is Part Of: Psychological medicine. Volume 49:Issue 12(2019)
• Page Start: 1999
• Page End: 2008
• Publication Date: 2018-10-25
• Subjects: Cross-validation, -- diffusion tensor imaging, -- machine learning, -- relevance vector regression, -- worry; Psychiatry -- Periodicals; Medicine and psychology -- Periodicals; Clinical psychology -- Periodicals; 616.89
• DOI: 10.1017/S0033291718002763
• Languages: English
• ISSNs: 0033-2917

Abstract

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 R 2 = 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.