Xia Jing, Chan Yi Hao, Girish Deepank, Chew Qian Hui, Sim Kang, Rajapakse Jagath C
College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore.
Research Division, Institute of Mental Health (IMH), Singapore, Singapore.
Commun Biol. 2025 Aug 13;8(1):1215. doi: 10.1038/s42003-025-08637-0.
Individuals with schizophrenia experience significant cognitive impairments and alterations in brain function. However, the shared and unique brain functional patterns underlying cognition deficits and symptom severity in schizophrenia remain poorly understood. We design an interpretable graph-based multi-task deep learning framework to enhance the simultaneous prediction of schizophrenia illness severity and cognitive functioning measurements by using functional connectivity, and identify both shared and unique brain patterns associated with these phenotypes on 378 subjects from three datasets. Our framework outperforms both single-task and state-of-the-art multi-task learning methods in predicting four Positive and Negative Syndrome Scale (PANSS) subscales and four cognitive domain scores. The performance is replicable across three datasets, and the shared and unique functional changes are confirmed by meta-analysis at both regional and modular levels. Our study provides insights into the neural correlates of illness severity and cognitive implications, offering potential targets for further evaluations of treatment effects and longitudinal follow-up.
精神分裂症患者存在显著的认知障碍和脑功能改变。然而，精神分裂症中认知缺陷和症状严重程度背后的共同和独特脑功能模式仍知之甚少。我们设计了一个基于可解释图的多任务深度学习框架，通过使用功能连接来增强对精神分裂症疾病严重程度和认知功能测量的同时预测，并在来自三个数据集的378名受试者中识别与这些表型相关的共同和独特脑模式。我们的框架在预测四个阳性和阴性症状量表（PANSS）子量表和四个认知领域得分方面优于单任务和最先进的多任务学习方法。该性能在三个数据集上均可重复，并且通过区域和模块水平的荟萃分析证实了共同和独特的功能变化。我们的研究为疾病严重程度的神经相关性和认知影响提供了见解，为进一步评估治疗效果和纵向随访提供了潜在靶点。
