Targeted stimulation of the brain has the potential to treat mental illnesses. We propose an approach to help design the stimulation protocol by identifying electrical dynamics across many brain regions that relate to illness states. We model multi-region electrical activity as a superposition of activity from latent networks, where the weights on the latent networks relate to an outcome of interest. In order to improve on drawbacks of latent factor modeling in this context, we focus on supervised autoencoders (SAEs), which can improve predictive performance while maintaining a generative model. We explain why SAEs yield improved predictions, describe the distributional assumptions under which SAEs are an appropriate modeling choice, and provide modeling constraints to ensure biological relevance of the learned network. We use the analysis strategy to find a network associated with stress that characterizes a genotype associated with bipolar disorder. This discovered network aligns with a previously used stimulation technique, providing experimental validation of our approach.

翻译：靶向脑刺激具有治疗精神疾病的潜力。我们提出一种方法，通过识别与疾病状态相关的跨脑区电动力学特征来辅助设计刺激方案。将多脑区电活动建模为潜在网络活动的叠加，其中潜在网络的权重与感兴趣的结果变量相关。为改善该语境下潜在因子建模的局限性，我们聚焦于受监督自编码器（SAEs），该方法能在保持生成模型的同时提升预测性能。我们阐释了SAEs为何能改进预测效果，描述了SAEs作为建模选择所适用的分布假设，并提供建模约束条件以确保学习到的网络具有生物学相关性。采用该分析策略，我们发现了与压力相关且表征双相情感障碍相关基因型的脑网络。该发现网络与既往使用的刺激技术相一致，为我们的方法提供了实验验证。