Chunliang Feng1,2,3, Zaixu Cui2,4, Dazhi Cheng5, Rui Xu1, Ruolei Gu6,7. 1. Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China. 2. State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China. 3. College of Information Science and Technology, Beijing Normal University, Beijing 100875, China. 4. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA19104, USA. 5. Department of Pediatric Neurology, Capital Institute of Pediatrics, Beijing 100020, China. 6. CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing 100101, China. 7. Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China.
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.
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.
Authors: Hai-Di Shan; Yong-Ming Wang; Hui-Xin Hu; Shu-Yao Jiang; Min-Yi Chu; Yi Wang; Simon S Y Lui; Eric F C Cheung; Zhen Wang; Raymond C K Chan Journal: Eur Arch Psychiatry Clin Neurosci Date: 2021-01-03 Impact factor: 5.270