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Individualized prediction of dispositional worry using white matter connectivity

Published online by Cambridge University Press:  25 October 2018

Chunliang Feng ,
Zaixu Cui ,
Dazhi Cheng ,
Rui Xu  and
Ruolei Gu
Chunliang Feng
Affiliation:
Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China College of Information Science and Technology, Beijing Normal University, Beijing 100875, China
Zaixu Cui
Affiliation:
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104, USA
Dazhi Cheng
Affiliation:
Department of Pediatric Neurology, Capital Institute of Pediatrics, Beijing 100020, China
Rui Xu*
Affiliation:
Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
Ruolei Gu*
Affiliation:
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
*
Author for correspondence: Rui Xu, Ruolei Gu, E-mail: [email protected], [email protected]
Author for correspondence: Rui Xu, Ruolei Gu, E-mail: [email protected], [email protected]
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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.

Keywords

Cross-validationdiffusion tensor imagingmachine learningrelevance vector regressionworry
Type
Original Articles
Information
Psychological Medicine , Volume 49 , Issue 12 , September 2019 , pp. 1999 - 2008
Copyright
Copyright © Cambridge University Press 2018 

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Footnotes

*

These authors contributed equally to this work.

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