In this section
@article{Soreq:2019:10.1038/s41467-019-08840-8,
author = {Soreq, E and Leech, R and Hampshire, A},
doi = {10.1038/s41467-019-08840-8},
journal = {Nature Communications},
title = {Dynamic network coding of working-memory domains and working-memory processes},
url = {http://dx.doi.org/10.1038/s41467-019-08840-8},
volume = {10},
year = {2019}
}
TY - JOUR
AB - The classic mapping of distinct aspects of working memory (WM) to mutually exclusive brain areas is at odds with the distributed processing mechanisms proposed by contemporary network science theory. Here, we use machinelearning to determine how aspects of WM are dynamically coded in the human brain. Using cross-validation across independent fMRI studies, we demonstrate that stimulus domains (spatial, number and fractal) and WM processes(encode, maintain, probe) are classifiable with high accuracy from the patterns of network activity and connectivitythat they evoke. This is the case even when focusing on ‘multiple demands’ brain regions, which are active across all WM conditions. Contrary to early neuropsychological perspectives, these aspects of WM do not map exclusively tobrain areas or processing streams; however, the mappings from that literature form salient features within the corresponding multivariate connectivity patterns. Furthermore, connectivity patterns provide the most precise basis for classification and become fine-tuned as maintenance load increases. These results accord with a network-codingmechanism, where the same brain regions support diverse WM demands by adopting different connectivity states.
AU - Soreq,E
AU - Leech,R
AU - Hampshire,A
DO - 10.1038/s41467-019-08840-8
PY - 2019///
SN - 2041-1723
TI - Dynamic network coding of working-memory domains and working-memory processes
T2 - Nature Communications
UR - http://dx.doi.org/10.1038/s41467-019-08840-8
UR - http://hdl.handle.net/10044/1/66354
VL - 10
ER -
