Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, China, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China and University of Chinese Academy of Sciences, China.
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, China and Chinese Institute for Brain Research, China.
Br J Psychiatry. 2022 Dec;221(6):732-739. doi: 10.1192/bjp.2022.22.
Previous analyses of grey and white matter volumes have reported that schizophrenia is associated with structural changes. Deep learning is a data-driven approach that can capture highly compact hierarchical non-linear relationships among high-dimensional features, and therefore can facilitate the development of clinical tools for making a more accurate and earlier diagnosis of schizophrenia.
To identify consistent grey matter abnormalities in patients with schizophrenia, 662 people with schizophrenia and 613 healthy controls were recruited from eight centres across China, and the data from these independent sites were used to validate deep-learning classifiers.
We used a prospective image-based meta-analysis of whole-brain voxel-based morphometry. We also automatically differentiated patients with schizophrenia from healthy controls using combined grey matter, white matter and cerebrospinal fluid volumetric features, incorporated a deep neural network approach on an individual basis, and tested the generalisability of the classification models using independent validation sites.
We found that statistically reliable schizophrenia-related grey matter abnormalities primarily occurred in regions that included the superior temporal gyrus extending to the temporal pole, insular cortex, orbital and middle frontal cortices, middle cingulum and thalamus. Evaluated using leave-one-site-out cross-validation, the performance of the classification of schizophrenia achieved by our findings from eight independent research sites were: accuracy, 77.19-85.74%; sensitivity, 75.31-89.29% and area under the receiver operating characteristic curve, 0.797-0.909.
These results suggest that, by using deep-learning techniques, multidimensional neuroanatomical changes in schizophrenia are capable of robustly discriminating patients with schizophrenia from healthy controls, findings which could facilitate clinical diagnosis and treatment in schizophrenia.
先前的灰质和白质体积分析报告表明，精神分裂症与结构变化有关。深度学习是一种数据驱动的方法，可以捕捉到高维特征之间高度紧凑的分层非线性关系，因此可以促进开发更准确和更早诊断精神分裂症的临床工具。
在精神分裂症患者中识别一致的灰质异常，从中国八个中心招募了 662 名精神分裂症患者和 613 名健康对照者，并使用这些独立站点的数据验证深度学习分类器。
我们使用基于图像的全脑体素形态计量学的前瞻性图像荟萃分析。我们还使用综合灰质、白质和脑脊液容积特征，基于个体自动区分精神分裂症患者和健康对照者，结合深度神经网络方法，并使用独立验证站点测试分类模型的泛化能力。
我们发现，与统计学上可靠的精神分裂症相关的灰质异常主要发生在包括颞极延伸至颞叶上部、岛叶、眶额和额中回、中扣带回和丘脑的区域。通过对 8 个独立研究站点的逐一留一交叉验证评估，我们的研究结果对精神分裂症的分类表现为：准确率为 77.19-85.74%；灵敏度为 75.31-89.29%；接收器工作特征曲线下面积为 0.797-0.909。
这些结果表明，通过使用深度学习技术，可以从多维神经解剖学变化中准确地区分精神分裂症患者和健康对照者，这有助于精神分裂症的临床诊断和治疗。
