Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.
Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia.
Hum Brain Mapp. 2020 Aug 15;41(12):3342-3357. doi: 10.1002/hbm.25020. Epub 2020 May 29.
In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at baseline and 1-year follow-up was assessed in 30 individuals with a schizophrenia-spectrum disorder using the Brief Psychiatric Rating Scale. Structural and functional neuroimaging was acquired in all individuals at baseline. Machine learning classifiers were trained to predict whether individuals improved or worsened with respect to positive, negative, and overall symptom severity. Classifiers were trained using various combinations of predictors, including regional cortical thickness and gray matter volume, static and dynamic resting-state connectivity, and/or baseline clinical and demographic variables. Relative change in overall symptom severity between baseline and 1-year follow-up varied markedly among individuals (interquartile range: 55%). Dynamic resting-state connectivity measured within the default-mode network was the most accurate single predictor of change in positive (accuracy: 87%), negative (83%), and overall symptom severity (77%) at follow-up. Incorporating predictors based on regional cortical thickness, gray matter volume, and baseline clinical variables did not markedly improve prediction accuracy and the prognostic utility of these predictors in isolation was moderate (<70%). Worsening negative symptoms at 1-year follow-up were predicted by hyper-connectivity and hypo-dynamism within the default-mode network at baseline assessment, while hypo-connectivity and hyper-dynamism predicted worsening positive symptoms. Given the modest sample size investigated, we recommend giving precedence to the relative ranking of the predictors investigated in this study, rather than the prediction accuracy estimates.
在机器学习环境中，本研究旨在比较连接组学、大脑结构和临床/人口统计学预测因子在个体精神分裂症患者症状严重程度个体变化中的预后效用。使用简明精神病评定量表评估了 30 名精神分裂症谱系障碍个体的基线和 1 年随访时的症状严重程度。所有个体均在基线时采集结构和功能神经影像学数据。使用各种预测因子的组合（包括区域皮质厚度和灰质体积、静息态和动态连接）训练机器学习分类器，以预测个体在阳性、阴性和总体症状严重程度方面是改善还是恶化。分类器使用基于区域皮质厚度、灰质体积和基线临床变量的预测因子进行训练，并未显著提高预测精度，这些预测因子的单独预后效用也适中（<70%）。默认模式网络内的动态静息态连接是预测随访时阳性（准确性：87%）、阴性（83%）和总体症状严重程度（77%）变化的最准确单一预测因子。在基线评估时，默认模式网络内的超连接和低动态性预测了 1 年随访时阴性症状的恶化，而连接不足和高动态性则预测了阳性症状的恶化。考虑到所研究的样本量较小，我们建议优先考虑本研究中研究的预测因子的相对排名，而不是预测精度估计。
