Large-scale neuroimaging studies often collect data from multiple scanners across different sites, where variations in scanners, scanning procedures, and other conditions across sites can introduce artificial site effects. These effects may bias brain connectivity measures, such as functional connectivity (FC), which quantify functional network organization derived from functional magnetic resonance imaging (fMRI). How to leverage high-dimensional network structures to effectively mitigate site effects has yet to be addressed. In this paper, we propose SLACC (Sparse LAtent Covariate-driven Connectome) factorization, a multivariate method that explicitly parameterizes covariate effects in latent subject scores corresponding to sparse rank-1 latent patterns derived from brain connectivity. The proposed method identifies localized site-driven variability within and across brain networks, enabling targeted correction. We develop a penalized Expectation-Maximization (EM) algorithm for parameter estimation, incorporating the Bayesian Information Criterion (BIC) to guide optimization. Extensive simulations validate SLACC's robustness in recovering the true parameters and underlying connectivity patterns. Applied to the Autism Brain Imaging Data Exchange (ABIDE) dataset, SLACC demonstrates its ability to reduce site effects.

翻译：大规模神经影像学研究常从不同站点的多个扫描仪收集数据，不同站点间扫描仪、扫描流程及其他条件的差异可能引入人为的站点效应。这些效应可能影响脑连接测量指标（如功能连接（FC）），后者是根据功能磁共振成像（fMRI）量化的功能网络组织特征。如何利用高维网络结构有效缓解站点效应仍是尚未解决的问题。本文提出SLACC（稀疏潜在协变量驱动连接组分解）因子分析方法，这是一种多变量方法，通过在源自脑连接稀疏秩1潜在模式的潜在受试者得分中显式参数化协变量效应。所提方法能够识别脑网络内部及跨网络的局部站点驱动变异，从而实现针对性校正。我们开发了一种惩罚期望最大化（EM）算法进行参数估计，并引入贝叶斯信息准则（BIC）指导优化过程。大量模拟实验验证了SLACC在恢复真实参数及潜在连接模式方面的鲁棒性。在自闭症脑影像数据交换（ABIDE）数据集上的应用表明，SLACC具有减少站点效应的能力。