Causal mapping of the functional organisation of the human brain requires evidence of \textit{necessity} available at adequate scale only from pathological lesions of natural origin. This demands inferential models with sufficient flexibility to capture both the observable distribution of pathological damage and the unobserved distribution of the neural substrate. Current model frameworks -- both mass-univariate and multivariate -- either ignore distributed lesion-deficit relations or do not model them explicitly, relying on featurization incidental to a predictive task. Here we initiate the application of deep generative neural network architectures to the task of lesion-deficit inference, formulating it as the estimation of an expressive hierarchical model of the joint lesion and deficit distributions conditioned on a latent neural substrate. We implement such deep lesion deficit inference with variational convolutional volumetric auto-encoders. We introduce a comprehensive framework for lesion-deficit model comparison, incorporating diverse candidate substrates, forms of substrate interactions, sample sizes, noise corruption, and population heterogeneity. Drawing on 5500 volume images of ischaemic stroke, we show that our model outperforms established methods by a substantial margin across all simulation scenarios, including comparatively small-scale and noisy data regimes. Our analysis justifies the widespread adoption of this approach, for which we provide an open source implementation: https://github.com/guilherme-pombo/vae_lesion_deficit

翻译：人类大脑功能组织的因果映射需要从自然病理损伤中获取具有充分规模的\textit{必要性}证据，这要求推断模型具备足够的灵活性，既能捕获病理损伤的可观测分布，又能估计神经基底的不可观测分布。当前模型框架（包括单变量和多变量方法）要么忽略分布式损伤-缺陷关系，要么未对其进行显式建模，仅依赖预测任务中附带的特征化处理。本研究首次将深度生成式神经网络架构应用于损伤-缺陷推断任务，将其表述为对以潜在神经基底为条件的联合损伤与缺陷分布的层级模型进行估计。我们通过变分卷积体积自编码器实现这一深度损伤-缺陷推断，并引入全面的损伤-缺陷模型比较框架，整合了多种候选基底、基底交互形式、样本规模、噪声污染及群体异质性。基于5500幅缺血性卒中体积影像数据，我们证明所提模型在所有模拟场景（包括较小规模和噪声数据条件）下均显著优于既有方法。本分析验证了该方法的广泛适用性，并提供开源实现：https://github.com/guilherme-pombo/vae_lesion_deficit