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.

中文翻译：

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预测 1 年随访时精神分裂症症状严重程度的个体改善：连接组学、结构和临床预测因素的比较。

在机器学习环境中，本研究旨在比较精神分裂症患者症状严重程度个体变化的连接组学、大脑结构和临床/人口统计学预测因子的预后效用。使用简要精神病学评定量表评估了 30 名患有精神分裂症谱系障碍的个体在基线和 1 年随访时的症状严重程度。所有个体在基线时都获得了结构和功能神经影像学。机器学习分类器被训练来预测个体在阳性、阴性和整体症状严重程度方面是改善还是恶化。分类器使用各种预测因子组合进行训练，包括区域皮质厚度和灰质体积、静态和动态静息状态连接性和/或基线临床和人口统计学变量。基线和 1 年随访之间总体症状严重程度的相对变化在个体之间显着不同（四分位距：55%）。在默认模式网络内测量的动态静息状态连接是随访时阳性（准确度：87%）、阴性（83%）和整体症状严重程度（77%）变化的最准确的单一预测因子​​。结合基于区域皮质厚度、灰质体积和基线临床变量的预测因子并没有显着提高预测准确性，这些预测因子单独的预后效用中等 (<70%)。在基线评估时，默认模式网络中的超连通性和低活力预测了 1 年随访时恶化的阴性症状，而低连通性和超活力预测了阳性症状的恶化。