Aglinskas Aidas, Bergeron Alicia, Anzellotti Stefano
Department of Psychology and Neuroscience, Boston College, Chestnut Hill, Massachusetts, USA.
Psychiatry Clin Neurosci. 2025 Jul;79(7):406-414. doi: 10.1111/pcn.13829. Epub 2025 Apr 30.
Most psychiatric and neurodevelopmental disorders are heterogeneous. Neural abnormalities in patients might differ in magnitude and kind, giving rise to distinct subtypes that can be partly overlapping (comorbidity). Identifying disorder-related individual differences is challenging due to the overwhelming presence of disorder-unrelated variation shared with healthy controls. Recently, Contrastive Variational Autoencoders (CVAEs) have been shown to separate disorder-related individual variation from disorder-unrelated variation. However, it is not known if CVAEs can also satisfy the other key desiderata for psychiatric research: capturing disease subtypes and disentangling comorbidity. In this paper, we compare CVAEs to other methods as a function of hyperparameters, such as model size and training data availability. We also introduce a new architecture for modeling comorbid disorders and test a novel training procedure for CVAEs that improves their reproducibility.
We use synthetic neuroanatomical MRI data with known ground truth for shared and disorder-specific effects and study the performance of the CVAE and non-contrastive baseline models at detecting disorder-subtypes and disentangling comorbidity in brain images varying along shared and disorder-specific dimensions.
CVAE models consistently outperformed non-contrastive alternatives as measured by correlation with disorder-specific ground truth effects and accuracy of subtype discovery. The CVAE also successfully disentangled neuroanatomical loci of comorbid disorders, due to its novel architecture. Improved training procedure reduced variability in the results by up to 5.5×.
The results showcase how the CVAE can be used as an overall framework in precision psychiatry studies, enabling reliable detection of interpretable neuromarkers, discovering disorder subtypes and disentangling comorbidity.
大多数精神疾病和神经发育障碍具有异质性。患者的神经异常在程度和种类上可能有所不同，从而产生部分重叠的不同亚型（共病）。由于与健康对照共享的大量与疾病无关的变异的存在，识别与疾病相关的个体差异具有挑战性。最近，对比变分自编码器（CVAE）已被证明能够将与疾病相关的个体变异与与疾病无关的变异区分开来。然而，尚不清楚CVAE是否也能满足精神病学研究的其他关键要求：捕捉疾病亚型和厘清共病情况。在本文中，我们将CVAE与其他方法作为超参数的函数进行比较，如模型大小和训练数据可用性。我们还引入了一种用于对共病进行建模的新架构，并测试了一种新的CVAE训练程序，以提高其可重复性。
我们使用具有已知真实情况的合成神经解剖MRI数据，用于共享效应和疾病特异性效应，并研究CVAE和非对比基线模型在检测沿共享维度和疾病特异性维度变化的脑图像中的疾病亚型和厘清共病情况方面的性能。
通过与疾病特异性真实效应的相关性和亚型发现的准确性来衡量，CVAE模型始终优于非对比替代模型。由于其新颖的架构，CVAE还成功地厘清了共病的神经解剖学位点。改进的训练程序将结果的变异性降低了多达5.5倍。
结果展示了CVAE如何用作精准精神病学研究的整体框架，从而可靠地检测可解释的神经标记物，发现疾病亚型并厘清共病情况。
