Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China.
Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
Nat Commun. 2024 Jul 17;15(1):5996. doi: 10.1038/s41467-024-50267-3.
Machine learning can be used to define subtypes of psychiatric conditions based on shared biological foundations of mental disorders. Here we analyzed cross-sectional brain images from 4,222 individuals with schizophrenia and 7038 healthy subjects pooled across 41 international cohorts from the ENIGMA, non-ENIGMA cohorts and public datasets. Using the Subtype and Stage Inference (SuStaIn) algorithm, we identify two distinct neurostructural subgroups by mapping the spatial and temporal 'trajectory' of gray matter change in schizophrenia. Subgroup 1 was characterized by an early cortical-predominant loss with enlarged striatum, whereas subgroup 2 displayed an early subcortical-predominant loss in the hippocampus, striatum and other subcortical regions. We confirmed the reproducibility of the two neurostructural subtypes across various sample sites, including Europe, North America and East Asia. This imaging-based taxonomy holds the potential to identify individuals with shared neurobiological attributes, thereby suggesting the viability of redefining existing disorder constructs based on biological factors.
机器学习可用于根据精神障碍的共同生物学基础来定义精神疾病的亚型。在这里，我们分析了来自 41 个国际队列的 4222 名精神分裂症患者和 7038 名健康对照者的横断面脑图像，这些队列来自 ENIGMA、非 ENIGMA 队列和公共数据集。我们使用亚型和阶段推断 (SuStaIn) 算法，通过映射精神分裂症灰质变化的空间和时间“轨迹”，识别出两个不同的神经结构亚组。亚组 1 的特征是早期皮质为主的损失伴纹状体增大，而亚组 2 则表现为早期皮质下为主的损失，涉及海马体、纹状体和其他皮质下区域。我们在包括欧洲、北美和东亚在内的不同样本地点证实了这两种神经结构亚型的可重复性。这种基于影像学的分类法有可能识别具有共同神经生物学特征的个体，从而表明基于生物学因素重新定义现有疾病结构的可行性。
