Román Francisco J, Iturria-Medina Yasser, Martínez Kenia, Karama Sherif, Burgaleta Miguel, Evans Alan C, Jaeggi Susanne M, Colom Roberto
Universidad Autónoma de Madrid, Madrid, Spain; Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, USA.
Montreal Neurological Institute (MNI), McGill University, Montreal, Canada.
Neurobiol Learn Mem. 2017 May;141:33-43. doi: 10.1016/j.nlm.2017.03.010. Epub 2017 Mar 18.
The structural connectome provides relevant information about experience and training-related changes in the brain. Here, we used network-based statistics (NBS) and graph theoretical analyses to study structural changes in the brain as a function of cognitive training. Fifty-six young women were divided in two groups (experimental and control). We assessed their cognitive function before and after completing a working memory intervention using a comprehensive battery that included fluid and crystallized abilities, working memory and attention control, and we also obtained structural MRI images. We acquired and analyzed diffusion-weighted images to reconstruct the anatomical connectome and we computed standardized changes in connectivity as well as group differences across time using NBS. We also compared group differences relying on a variety of graph-theory indices (clustering, characteristic path length, global and local efficiency and strength) for the whole network as well as for the sub-network derived from NBS analyses. Finally, we calculated correlations between these graph indices and training performance as well as the behavioral changes in cognitive function. Our results revealed enhanced connectivity for the training group within one specific network comprised of nodes/regions supporting cognitive processes required by the training (working memory, interference resolution, inhibition, and task engagement). Significant group differences were also observed for strength and global efficiency indices in the sub-network detected by NBS. Therefore, the connectome approach is a valuable method for tracking the effects of cognitive training interventions across specific sub-networks. Moreover, this approach allowsfor the computation of graph theoretical network metricstoquantifythetopological architecture of the brain networkdetected. The observed structural brain changes support the behavioral results reported earlier (see Colom, Román, et al., 2013).
结构连接组提供了有关大脑中与经验和训练相关变化的相关信息。在此,我们使用基于网络的统计方法(NBS)和图论分析来研究大脑结构变化与认知训练的关系。56名年轻女性被分为两组(实验组和对照组)。我们使用包括流体智力和晶体智力、工作记忆和注意力控制的综合测试电池,在她们完成工作记忆干预前后评估其认知功能,并且我们还获取了结构MRI图像。我们采集并分析扩散加权图像以重建解剖连接组,并使用NBS计算连接性的标准化变化以及不同时间点的组间差异。我们还根据各种图论指标(聚类系数、特征路径长度、全局和局部效率以及强度)比较了整个网络以及从NBS分析得出的子网络的组间差异。最后,我们计算了这些图指标与训练表现以及认知功能行为变化之间的相关性。我们的结果显示,训练组在一个特定网络内的连接性增强,该网络由支持训练所需认知过程(工作记忆、干扰解决、抑制和任务参与)的节点/区域组成。在NBS检测到的子网络中,强度和全局效率指标也观察到了显著的组间差异。因此,连接组方法是一种追踪特定子网络上认知训练干预效果的有价值方法。此外,这种方法允许计算图论网络指标以量化检测到的大脑网络的拓扑结构。观察到的大脑结构变化支持了先前报道的行为结果(见科洛姆、罗曼等人,2013年)。