Modulation of brain connectivity by memory load in a working memory network
Abstract
Cognition is the product of activation of billions of neurons and their timely interactions. While the activity of individual neurons is essential for proper functioning of the brain, the communication among them is arguably more vital. Previous studies of brain connectivity have largely focused on investigating causality across the brain in order to reveal the existing communication channels that form its internal networks. However, little is known about how these neuronal pathways respond to task demands with varying degrees of complexity. Towards understanding the pathways of information flow, we investigated the effect of memory load on network connectivity of brain. Independent component analysis (ICA) was used to identify brain areas, active during a working memory task, whose activations co-varied with memory load. An information theoretic metric called transfer entropy was adopted to examine the directed links across these areas. Empirical results suggest that the information flow rate across a primary working memory network is modulated by memory load. Furthermore, it was observed that the information flow is affected in pathways with opposite direction during encoding and maintenance stages of working memory operation.
Publication Title
IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CCMB 2014: 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Proceedings
Recommended Citation
Bashivan, P., Yeasin, M., & Bidelman, G. (2014). Modulation of brain connectivity by memory load in a working memory network. IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CCMB 2014: 2014 IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain, Proceedings, 127-133. https://doi.org/10.1109/CCMB.2014.7020705
