Rotman Research Institute, Baycrest Health Sciences, Toronto, Ontario M6A 2E1, Canada.
University of Toronto, Toronto, Ontario M5S 1A1, Canada.
eNeuro. 2022 Feb 16;9(1). doi: 10.1523/ENEURO.0075-21.2022. Print 2022 Jan-Feb.
Following traumatic brain injury (TBI), cognitive impairments manifest through interactions between microscopic and macroscopic changes. On the microscale, a neurometabolic cascade alters neurotransmission, while on the macroscale diffuse axonal injury impacts the integrity of long-range connections. Large-scale brain network modeling allows us to make predictions across these spatial scales by integrating neuroimaging data with biophysically based models to investigate how microscale changes invisible to conventional neuroimaging influence large-scale brain dynamics. To this end, we analyzed structural and functional neuroimaging data from a well characterized sample of 44 adult TBI patients recruited from a regional trauma center, scanned at 1-2 weeks postinjury, and with follow-up behavioral outcome assessed 6 months later. Thirty-six age-matched healthy adults served as comparison participants. Using The Virtual Brain, we fit simulations of whole-brain resting-state functional MRI to the empirical static and dynamic functional connectivity of each participant. Multivariate partial least squares (PLS) analysis showed that patients with acute traumatic intracranial lesions had lower cortical regional inhibitory connection strengths than comparison participants, while patients without acute lesions did not differ from the comparison group. Further multivariate PLS analyses found correlations between lower semiacute regional inhibitory connection strengths and more symptoms and lower cognitive performance at a 6 month follow-up. Critically, patients without acute lesions drove this relationship, suggesting clinical relevance of regional inhibitory connection strengths even when traumatic intracranial lesions were not present. Our results suggest that large-scale connectome-based models may be sensitive to pathophysiological changes in semi-acute phase TBI patients and predictive of their chronic outcomes.
在创伤性脑损伤（TBI）后，认知障碍表现为微观和宏观变化之间的相互作用。在微观层面上，神经代谢级联改变神经传递，而在宏观层面上弥漫性轴索损伤影响长程连接的完整性。大规模脑网络建模允许我们通过将神经影像学数据与基于生物物理的模型相结合，在这些空间尺度上进行预测，从而研究微观变化如何影响大规模脑动力学，而这些微观变化是常规神经影像学无法察觉的。为此，我们分析了来自区域创伤中心招募的 44 名成年 TBI 患者的结构和功能神经影像学数据，这些患者在损伤后 1-2 周进行了扫描，并在 6 个月后进行了后续行为结果评估。36 名年龄匹配的健康成年人作为对照组。我们使用虚拟大脑，对每个参与者的经验性静态和动态功能连接拟合了全脑静息状态功能 MRI 的模拟。多元偏最小二乘法（PLS）分析显示，有急性创伤性颅内病变的患者皮质区域抑制性连接强度低于对照组参与者，而没有急性病变的患者与对照组没有差异。进一步的多元 PLS 分析发现，半急性区域抑制性连接强度较低与更多症状和 6 个月随访时认知表现较低之间存在相关性。关键是，没有急性病变的患者驱动了这种关系，这表明即使没有创伤性颅内病变，区域抑制性连接强度也具有临床相关性。我们的结果表明，基于连接组的大规模模型可能对亚急性 TBI 患者的病理生理变化敏感，并能预测其慢性结局。
