Department of Neurology, University of California San Francisco, San Francisco, CA, USA; Neuroscape, University of California San Francisco, San Francisco, CA, USA.
Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA; Department of Psychology, University of California, Berkeley, Berkeley, CA, USA.
Trends Cogn Sci. 2019 Apr;23(4):293-304. doi: 10.1016/j.tics.2019.01.014. Epub 2019 Feb 28.
Interventions using methods such as cognitive training and aerobic exercise have shown potential to enhance cognitive abilities. However, there is often pronounced individual variability in the magnitude of these gains. Here, we propose that brain network modularity, a measure of brain subnetwork segregation, is a unifying biomarker of intervention-related plasticity. We present work from multiple independent studies demonstrating that individual differences in baseline brain modularity predict gains in cognitive control functions across several populations and interventions, spanning healthy adults to patients with clinical deficits and cognitive training to aerobic exercise. We believe that this predictive framework provides a foundation for developing targeted, personalized interventions to improve cognition.
采用认知训练和有氧运动等方法的干预措施已显示出增强认知能力的潜力。然而，这些收益的幅度在个体之间通常存在明显的差异。在这里，我们提出大脑网络模块性（衡量子网分离的指标）是干预相关可塑性的统一生物标志物。我们展示了多个独立研究的工作，这些研究表明，基线大脑模块性的个体差异可以预测多个群体和干预措施中的认知控制功能的提高，涵盖了健康成年人、有临床缺陷的患者以及认知训练和有氧运动。我们相信，这个预测框架为开发有针对性的个性化干预措施以改善认知提供了基础。
