Distinct patterns of structural damage underlie working memory and reasoning deficits after traumatic brain injury
Distinct patterns of structural damage underlie working memory and reasoning deficits after traumatic brain injury
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DOI：
10.1093/brain/awaa067
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发表时间：
2020-04-01
期刊：
影响因子：
14.5
通讯作者：
Hampshire, Adam H.
中科院分区：
文献类型：
--
作者：
Jolly, Amy E.;Scott, Gregory T.;Hampshire, Adam H.
众所周知，创伤性脑损伤后的慢性认知问题与弥漫性轴索损伤以及随之而来的脑连接的广泛中断有关。然而，弥漫性轴索损伤的模式在患者之间变化，并且他们具有相应的认知缺陷的异质性。这种异质性是知之甚少，提出了一个不平凡的挑战，说明和治疗。认知问题中突出的是工作记忆和推理的缺陷。以前的功能性MRI在对照组中将这些认知方面与不同但部分重叠的大脑区域网络联系起来。基于此，一个合乎逻辑的预测是，连接这些网络的白色物质束的完整性的差异应该预测创伤性脑损伤后认知缺陷的类型和严重程度的变化。我们使用弥散加权成像，认知测试和网络分析来测试这一预测。我们通过将之前发布的在我们的工作记忆和推理任务期间激活的大脑区域的功能性MRI图与连接它们的白色物质束库交叉，来定义结构连接体的功能不同的子网络。我们研究了这些子网络中的图论测量与92名中重度创伤性脑损伤患者队列中相同任务的表现之间的关系。最后，我们使用机器学习来确定是否可以使用每个子网络的图论测量来预测患者的认知表现。行为得分的主成分分析证实，推理和工作记忆形成认知能力的不同组成部分，这两者都容易受到创伤性脑损伤的影响。重要的是，创伤性脑损伤后这些能力的损伤以一种不可分离的方式与它们所关联的子网络的信息处理结构相关。当使用典型相关分析检查子网络的度中心性度量时，证实了这种分离。值得注意的是，解离是普遍的一些节点为中心的措施，是不对称的：中断工作记忆子网络涉及工作记忆和推理性能，而中断推理子网络涉及推理性能选择性。机器学习分析进一步支持了这一发现，证明网络测量以同样的不对称方式预测患者的认知表现。这些结果与工作记忆的层次模型雅阁，即推理依赖于首先在工作记忆中保持任务相关信息的能力。我们认为，这种更细粒度的信息可能对未来试图预测长期结果或开发定制疗法的应用程序有用。
It is well established that chronic cognitive problems after traumatic brain injury relate to diffuse axonal injury and the consequent widespread disruption of brain connectivity. However, the pattern of diffuse axonal injury varies between patients and they have a correspondingly heterogeneous profile of cognitive deficits. This heterogeneity is poorly understood, presenting a non-trivial challenge for prognostication and treatment. Prominent amongst cognitive problems are deficits in working memory and reasoning. Previous functional MRI in controls has associated these aspects of cognition with distinct, but partially overlapping, networks of brain regions. Based on this, a logical prediction is that differences in the integrity of the white matter tracts that connect these networks should predict variability in the type and severity of cognitive deficits after traumatic brain injury. We use diffusion-weighted imaging, cognitive testing and network analyses to test this prediction. We define functionally distinct subnetworks of the structural connectome by intersecting previously published functional MRI maps of the brain regions that are activated during our working memory and reasoning tasks, with a library of the white matter tracts that connect them. We examine how graph theoretic measures within these subnetworks relate to the performance of the same tasks in a cohort of 92 moderate-severe traumatic brain injury patients. Finally, we use machine learning to determine whether cognitive performance in patients can be predicted using graph theoretic measures from each subnetwork. Principal component analysis of behavioural scores confirm that reasoning and working memory form distinct components of cognitive ability, both of which are vulnerable to traumatic brain injury. Critically, impairments in these abilities after traumatic brain injury correlate in a dissociable manner with the information-processing architecture of the subnetworks that they are associated with. This dissociation is confirmed when examining degree centrality measures of the subnetworks using a canonical correlation analysis. Notably, the dissociation is prevalent across a number of node-centric measures and is asymmetrical: disruption to the working memory subnetwork relates to both working memory and reasoning performance whereas disruption to the reasoning subnetwork relates to reasoning performance selectively. Machine learning analysis further supports this finding by demonstrating that network measures predict cognitive performance in patients in the same asymmetrical manner. These results accord with hierarchical models of working memory, where reasoning is dependent on the ability to first hold task-relevant information in working memory. We propose that this finer grained information may be useful for future applications that attempt to predict long-term outcomes or develop tailored therapies.