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.
Psychol Med. 2019 Sep;49(12):1999-2008. doi: 10.1017/S0033291718002763. Epub 2018 Oct 25.
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.
We applied a multivariate machine learning approach to predict dispositional worry based on microstructural integrity of white matter (WM) tracts.
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.
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.
过度担忧是广泛性焦虑障碍的一个显著特征，也存在于广泛的其他精神疾病中。因此，对担忧倾向进行个体化预测在临床实践中可能具有重要意义，可用于评估个体水平的担忧症状严重程度。
我们应用了一种多变量机器学习方法，基于白质（WM）束的微观结构完整性来预测特质性担忧。
我们证明，机器学习模型能够从广泛分布的 WM 束的微观结构特性中解码个体特质性担忧评分（均方误差= 10.46，p < 0.001；均方根误差= 12.82，p < 0.001；预测 R2 = 0.17，p < 0.001）。有助于担忧预测的 WM 束包括内囊后肢、前冠状辐射和大脑脚，以及已知对情绪处理和调节至关重要的皮质边缘通路（例如钩束、扣带回和穹窿）。
因此，目前的工作阐明了广泛的精神障碍中担忧症状临床评估的潜在神经标记物。此外，确定担忧倾向的广泛分布途径有助于更好地理解与担忧相关的神经生物学机制。
