Department of Anatomy, University of Rostock, Germany.
Department of Neurological Surgery, UCSF, San Francisco, CA 94158, USA.
Sci Rep. 2017 Apr 13;7:46316. doi: 10.1038/srep46316.
Recent advances in functional connectivity suggest that shared neuronal activation patterns define brain networks linking anatomically separate brain regions. We sought to investigate how cortical stroke disrupts multiple brain regions in processing spatial information. We conducted a connectome investigation at the mesoscale-level using the neuroVIISAS-framework, enabling the analysis of directed and weighted connectivity in bilateral hemispheres of cortical and subcortical brain regions. We found that spatial-exploration induced brain activation mapped by Fos, a proxy of neuronal activity, was differentially affected by stroke in a region-specific manner. The extent of hypoactivation following spatial exploration is inversely correlated with the spatial distance between the region of interest and region damaged by stroke, in particular within the parietal association and the primary somatosensory cortex, suggesting that the closer a region is to a stroke lesion, the more it would be affected during functional activation. Connectome modelling with 43 network parameters failed to reliably predict regions of hypoactivation in stroke rats exploring a novel environment, despite a modest correlation found for the centrality and hubness parameters in the home-caged animals. Further investigation in the inhibitory versus excitatory neuronal networks and microcircuit connectivity is warranted to improve the accuracy of predictability in post-stroke functional impairment.
最近的功能连接研究进展表明，共享的神经元激活模式定义了将解剖上分离的大脑区域连接起来的大脑网络。我们试图研究皮质卒中如何破坏处理空间信息的多个大脑区域。我们使用 neuroVIISAS 框架进行了中尺度水平的连接组学研究，从而能够分析皮质和皮质下脑区双侧半球的有向和加权连接。我们发现，由 Fos （神经元活动的替代物）映射的空间探索诱导的脑激活在特定区域受到卒中的影响程度不同。空间探索后激活不足的程度与感兴趣区域和卒中损伤区域之间的空间距离呈负相关，特别是在顶叶联合区和初级体感皮层，这表明距离卒中病变越近，在功能激活期间受影响越大。尽管在笼养动物中发现了中心性和枢纽性参数的适度相关性，但使用 43 个网络参数的连接组学模型无法可靠地预测在新环境中探索的卒中大鼠的低激活区域。有必要进一步研究抑制性和兴奋性神经元网络以及微电路连接，以提高卒中后功能障碍预测的准确性。
