Mary MacKillop Institute for Health Research, Australian Catholic University, 5/215 Spring Street, Melbourne, VIC, 3000, Australia.
Cognitive Neuroscience Unit, School of Psychology, Faculty of Health, Deakin University, 221 Burwood Highway, Burwood, VIC, 3125, Australia.
Brain Struct Funct. 2021 May;226(4):1281-1302. doi: 10.1007/s00429-021-02241-8. Epub 2021 Mar 11.
Processing speed on cognitive tasks relies upon efficient communication between widespread regions of the brain. Recently, novel methods of quantifying network communication like 'navigation efficiency' have emerged, which aim to be more biologically plausible compared to traditional shortest path length-based measures. However, it is still unclear whether there is a direct link between these communication measures and processing speed. We tested this relationship in forty-five healthy adults (27 females), where processing speed was defined as decision-making time and measured using drift rate from the hierarchical drift diffusion model. Communication measures were calculated from a graph theoretical analysis of the whole-brain structural connectome and of a task-relevant fronto-parietal structural subnetwork, using the large-scale Desikan-Killiany atlas. We found that faster processing speed on trials that require greater cognitive control are correlated with higher navigation efficiency (of both the whole-brain and the task-relevant subnetwork). In contrast, faster processing speed on trials that require more automatic processing are correlated with shorter path length within the task-relevant subnetwork. Our findings reveal that differences in the way communication is modelled between shortest path length and navigation may be sensitive to processing of automatic and controlled responses, respectively. Further, our findings suggest that there is a relationship between the speed of cognitive processing and the structural constraints of the human brain network.
在认知任务中，处理速度依赖于大脑广泛区域之间的有效沟通。最近，出现了一些新的量化网络通信的方法，如“导航效率”，与传统的最短路径长度测量方法相比，这些方法更具生物学意义。然而，目前尚不清楚这些通信测量方法与处理速度之间是否存在直接联系。我们在 45 名健康成年人（27 名女性）中测试了这种关系，其中处理速度定义为决策时间，使用分层漂移扩散模型中的漂移率来衡量。使用大规模的 Desikan-Killiany 图谱，我们从全脑结构连接组和任务相关的额顶叶结构子网络的图论分析中计算了通信测量值。我们发现，需要更大认知控制的任务中，处理速度较快的个体与较高的导航效率（包括全脑和任务相关子网）相关。相比之下，在需要更多自动处理的任务中，处理速度较快的个体与任务相关子网内的较短路径长度相关。我们的发现表明，在最短路径长度和导航之间建模通信方式的差异可能分别对自动和受控反应的处理敏感。此外，我们的研究结果表明，认知处理速度与人类大脑网络的结构约束之间存在关系。
