Ahmed Ishfaque, Reeves William D, Laballe Morgan H, Taber Moira F, Sneed Sydney E, Kaiser Erin E, West Franklin D, Zhao Qun
Department of Physics and Astronomy, University of Georgia, Athens, United States; Bio-Imaging Research Center, University of Georgia, Athens, United States; Institute of Physics, University of Sindh, Jamshoro, Pakistan.
Department of Physics and Astronomy, University of Georgia, Athens, United States; Bio-Imaging Research Center, University of Georgia, Athens, United States.
J Neurosci Methods. 2025 Jul;419:110459. doi: 10.1016/j.jneumeth.2025.110459. Epub 2025 Apr 22.
The ability of the brain to perform multiple complex tasks with fixed structures has yet to be fully elucidated. Structural connectivity (SC) and functional connectivity (FC) have been increasingly used to understand the structure and function of the brain respectively. However, a limited number of studies have explored the relationship between both entities especially in translational animal models.
We proposed an integration of both SC and FC can improve understanding of brain's structure, function, their interplay, and brain's response to neurological conditions such as traumatic brain injury (TBI). We investigated structure-function correlation at multiple scales (small: cortical regions, medium: resting state networks, and large: hemispheric and whole brain), and adapted a Bayesian framework to incorporate SC for constructing structurally-informed FC (siFC) using a translational porcine model.
There is a significantly strong correlation r = 0.277 ± 0.011 between SC and FC in healthy pigs which is consistent across different scales. Further, siFC stability is measured as a Pearson correlation (r = 0.72 ± 0.07) between time-resolved FCs. Subsequent differential degree test analysis using siFC provided more explicit profiling of perturbations caused by TBI.
The siFC is more immune to large, dynamic variability than FC alone. A more accurate profiling of significantly altered connections and affected hubs by TBI is achieved which is consistent with TBI induced structural deformations.
Our findings demonstrated that SC-FC integration model improved detection of significant differences in brain connectivity and pinpoints hub regions that had been directly impacted by TBI.
大脑利用固定结构执行多种复杂任务的能力尚未完全阐明。结构连接性（SC）和功能连接性（FC）已越来越多地分别用于理解大脑的结构和功能。然而，仅有少数研究探讨了这两者之间的关系，尤其是在转化动物模型中。
我们提出，将SC和FC整合起来可以增进对大脑结构、功能、它们之间的相互作用以及大脑对诸如创伤性脑损伤（TBI）等神经疾病的反应的理解。我们在多个尺度上（小尺度：皮质区域，中尺度：静息态网络，大尺度：半球和全脑）研究了结构 - 功能相关性，并采用贝叶斯框架，利用转化猪模型纳入SC以构建结构信息功能连接性（siFC）。
在健康猪中，SC和FC之间存在显著强相关性r = 0.277±0.011，且在不同尺度上一致。此外，siFC稳定性通过时间分辨FC之间的皮尔逊相关性（r = 0.72±0.07）来衡量。随后使用siFC进行的差异程度测试分析提供了TBI引起的扰动的更明确概况。
与单独的FC相比，siFC对大的动态变异性更具抗性。实现了对TBI引起的显著改变的连接和受影响枢纽的更准确概况分析，这与TBI诱导的结构变形一致。
我们的研究结果表明，SC - FC整合模型改善了对大脑连接性显著差异的检测，并精确指出了直接受TBI影响的枢纽区域。
