Department of Radiology and Medical Informatics, University of Geneva; 1211 Geneva, Switzerland; Medical Image Processing Laboratory, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland; Department of Physical Medicine and Rehabilitation, University of Pittsburgh; Pittsburgh, PA, USA; Rehabilitation Neural Engineering Laboratories, University of Pittsburgh; Pittsburgh, PA, USA; Department of BioEngineering, University of Pittsburgh; Pittsburgh, PA, USA.
Department of Radiology and Medical Informatics, University of Geneva; 1211 Geneva, Switzerland; Medical Image Processing Laboratory, Center for Neuroprosthetics, Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland; Bertarelli Foundation Chair in Translational Neuroengineering, Center for Neuroprosthetics, Institute of Bioengineerin, Ecole Polytechnique Fédérale de Lausanne (EPFL); 1202 Geneva, Switzerland.
Neuroimage. 2022 Jul 15;255:119201. doi: 10.1016/j.neuroimage.2022.119201. Epub 2022 Apr 9.
Functional magnetic resonance imaging (fMRI) has been widely employed to study stroke pathophysiology. In particular, analyses of fMRI signals at rest were directed at quantifying the impact of stroke on spatial features of brain networks. However, brain networks have intrinsic time features that were, so far, disregarded in these analyses. In consequence, standard fMRI analysis failed to capture temporal imbalance resulting from stroke lesions, hence restricting their ability to reveal the interdependent pathological changes in structural and temporal network features following stroke. Here, we longitudinally analyzed hemodynamic-informed transient activity in a large cohort of stroke patients (n = 103) to assess spatial and temporal changes of brain networks after stroke. Metrics extracted from the hemodynamic-informed transient activity were replicable within- and between-individuals in healthy participants, hence supporting their robustness and their clinical applicability. While large-scale spatial patterns of brain networks were preserved after stroke, their durations were altered, with stroke subjects exhibiting a varied pattern of longer and shorter network activations compared to healthy individuals. Specifically, patients showed a longer duration in the lateral precentral gyrus and anterior cingulum, and a shorter duration in the occipital lobe and in the cerebellum. These temporal alterations were associated with white matter damage in projection and association pathways. Furthermore, they were tied to deficits in specific behavioral domains as restoration of healthy brain dynamics paralleled recovery of cognitive functions (attention, language and spatial memory), but was not significantly correlated to motor recovery. These findings underscore the critical importance of network temporal properties in dissecting the pathophysiology of brain changes after stroke, thus shedding new light on the clinical potential of time-resolved methods for fMRI analysis.
功能磁共振成像(fMRI)已被广泛用于研究中风的病理生理学。特别是,静息状态下 fMRI 信号的分析旨在量化中风对脑网络空间特征的影响。然而,脑网络具有内在的时间特征,到目前为止,这些分析都忽略了这些特征。因此,标准的 fMRI 分析未能捕捉到中风病灶引起的时间不平衡,从而限制了它们揭示中风后结构和时间网络特征相互依赖的病理变化的能力。在这里,我们对一大组中风患者(n=103)进行了血流动力学瞬时活动的纵向分析,以评估中风后脑网络的空间和时间变化。在健康参与者中,从血流动力学瞬时活动中提取的指标在个体内和个体间具有可重复性,因此支持其稳健性及其临床适用性。虽然中风后大脑网络的大规模空间模式得以保留,但它们的持续时间发生了改变,与健康个体相比,中风患者的网络激活持续时间更长或更短。具体来说,患者在外侧中央前回和前扣带的持续时间较长,而在枕叶和小脑的持续时间较短。这些时间上的改变与投射和关联通路中的白质损伤有关。此外,它们与特定行为领域的缺陷有关,因为健康大脑动态的恢复与认知功能(注意力、语言和空间记忆)的恢复平行,但与运动功能的恢复没有显著相关性。这些发现强调了网络时间特性在剖析中风后大脑变化的病理生理学中的关键重要性,从而为 fMRI 分析的时间分辨方法的临床潜力提供了新的认识。