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.

采用贝叶斯张量回归方法，在长期神经成像研究中，通过调整协变量并跨空间分布的体素进行信息汇集，实现对重要变化的推断，该方法具有降维和保留体素空间形状的优势，可应用于个体级神经可塑性研究和预测，在模拟研究和多周访问情景中验证了该方法相比体素回归在预测和特征选择方面的优越性。