A major interest in longitudinal neuroimaging studies involves investigating voxel-level neuroplasticity due to treatment and other factors across visits. However, traditional voxel-wise methods are beset with several pitfalls, which can compromise the accuracy of these approaches. We propose a novel Bayesian tensor response regression approach for longitudinal imaging data, which pools information across spatially-distributed voxels to infer significant changes while adjusting for covariates. The proposed method, which is implemented using Markov chain Monte Carlo (MCMC) sampling, utilizes low-rank decomposition to reduce dimensionality and preserve spatial configurations of voxels when estimating coefficients. It also enables feature selection via joint credible regions which respect the shape of the posterior distributions for more accurate inference. In addition to group level inferences, the method is able to infer individual-level neuroplasticity, allowing for examination of personalized disease or recovery trajectories. The advantages of the proposed approach in terms of prediction and feature selection over voxel-wise regression are highlighted via extensive simulation studies. Subsequently, we apply the approach to a longitudinal Aphasia dataset consisting of task functional MRI images from a group of subjects who were administered either a control intervention or intention treatment at baseline and were followed up over subsequent visits. Our analysis revealed that while the control therapy showed long-term increases in brain activity, the intention treatment produced predominantly short-term changes, both of which were concentrated in distinct localized regions. In contrast, the voxel-wise regression failed to detect any significant neuroplasticity after multiplicity adjustments, which is biologically implausible and implies lack of power.

翻译：纵向神经影像学研究的一个核心兴趣在于探究干预及其他因素在多次随访中引起的体素级神经可塑性变化。然而，传统基于体素的方法存在若干缺陷，可能影响这些分析方法的准确性。我们提出了一种适用于纵向影像数据的新型贝叶斯张量响应回归方法，该方法通过整合空间分布体素的信息，在调整协变量的同时推断显著变化。该算法采用马尔可夫链蒙特卡洛（MCMC）采样实现，利用低秩分解降低维度，并在估计系数时保留体素的空间结构。此外，方法通过构建尊重后验分布形态的联合可信区间实现特征选择，从而提升推断精度。该方法不仅能进行群体水平推断，还能评估个体水平的神经可塑性，支持个性化疾病或康复轨迹的探究。通过大量模拟研究，我们证明了该方法在预测和特征选择方面相较于传统体素回归的优势。随后，我们将该方法应用于一个纵向失语症数据集，该数据集包含一组受试者的任务态功能磁共振成像，受试者在基线期接受控制干预或意图治疗，并在后续随访中进行追踪。分析揭示：控制治疗虽能长期增强脑活动，但意图治疗主要引发短期变化，且两种效应均集中于特定局部脑区。相比之下，基于体素的回归在进行多重比较校正后未能检测到任何显著神经可塑性，这一结果既不符合生物学机制，也表明其统计效能不足。