Department of Statistics, Texas A&M University, 3143 TAMU, College Station, 77843, TX, USA.
Statistics Online Computational Resource, University of Michigan, 426 N. Ingalls St., Ann Arbor, 48109, MI, USA.
Neuroinformatics. 2024 Oct;22(4):457-472. doi: 10.1007/s12021-024-09670-w. Epub 2024 Jun 11.
This article seeks to investigate the impact of aging on functional connectivity across different cognitive control scenarios, particularly emphasizing the identification of brain regions significantly associated with early aging. By conceptualizing functional connectivity within each cognitive control scenario as a graph, with brain regions as nodes, the statistical challenge revolves around devising a regression framework to predict a binary scalar outcome (aging or normal) using multiple graph predictors. Popular regression methods utilizing multiplex graph predictors often face limitations in effectively harnessing information within and across graph layers, leading to potentially less accurate inference and predictive accuracy, especially for smaller sample sizes. To address this challenge, we propose the Bayesian Multiplex Graph Classifier (BMGC). Accounting for multiplex graph topology, our method models edge coefficients at each graph layer using bilinear interactions between the latent effects associated with the two nodes connected by the edge. This approach also employs a variable selection framework on node-specific latent effects from all graph layers to identify influential nodes linked to observed outcomes. Crucially, the proposed framework is computationally efficient and quantifies the uncertainty in node identification, coefficient estimation, and binary outcome prediction. BMGC outperforms alternative methods in terms of the aforementioned metrics in simulation studies. An additional BMGC validation was completed using an fMRI study of brain networks in adults. The proposed BMGC technique identified that sensory motor brain network obeys certain lateral symmetries, whereas the default mode network exhibits significant brain asymmetries associated with early aging.
本文旨在研究衰老对不同认知控制情境下功能连接的影响，特别强调识别与早期衰老显著相关的脑区。通过将每个认知控制情境中的功能连接概念化为一个图，其中脑区为节点，统计挑战在于设计一个回归框架，使用多个图预测因子来预测二元标量结果（衰老或正常）。利用多重图预测因子的常用回归方法在有效地利用图内和图间信息方面往往存在局限性，从而导致潜在的推断和预测准确性降低，尤其是在较小的样本量下。为了解决这个挑战，我们提出了贝叶斯多重图分类器（BMGC）。考虑到多重图拓扑结构，我们的方法使用连接两个节点的边缘上的潜在效应之间的双线性交互作用，对每个图层的边缘系数进行建模。该方法还在来自所有图层的节点特定潜在效应上使用变量选择框架，以识别与观察结果相关的有影响力的节点。至关重要的是，所提出的框架在计算上是高效的，并量化了节点识别、系数估计和二元结果预测的不确定性。在模拟研究中，BMGC 在上述指标方面优于其他方法。使用成人脑网络的 fMRI 研究完成了对 BMGC 的额外验证。所提出的 BMGC 技术确定了感觉运动脑网络遵循某些侧面对称性，而默认模式网络表现出与早期衰老相关的显著大脑不对称性。
