TY - JOUR
T1 - Dynamic network coding of working-memory domains and working-memory processes
AU - Soreq, Eyal
AU - Leech, Robert
AU - Hampshire, Adam
PY - 2019/2/25
Y1 - 2019/2/25
N2 - 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 machine-learning 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 connectivity that 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 to brain 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-coding mechanism, where the same brain regions support diverse WM demands by adopting different connectivity states.
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 machine-learning 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 connectivity that 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 to brain 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-coding mechanism, where the same brain regions support diverse WM demands by adopting different connectivity states.
UR - http://www.scopus.com/inward/record.url?scp=85062101483&partnerID=8YFLogxK
U2 - 10.1038/s41467-019-08840-8
DO - 10.1038/s41467-019-08840-8
M3 - Article
C2 - 30804436
AN - SCOPUS:85062101483
SN - 2041-1723
VL - 10
JO - Nature Communications
JF - Nature Communications
IS - 1
M1 - 936
ER -