Dynamic network coding of working-memory domains and working-memory processes.

Nature Communications
Eyal SoreqAdam Hampshire

Abstract

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.

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Citations

Feb 12, 2020·Network Neuroscience·Daniel J LurieVince D Calhoun
Aug 11, 2020·Cortex; a Journal Devoted to the Study of the Nervous System and Behavior·Moataz AssemEvelina Fedorenko
Dec 1, 2020·Journal of Neural Engineering·Jaakko Johannes SyrjäläVittorio Pizzella
Feb 25, 2021·Journal of Neural Engineering·Jaakko SyrjäläVittorio Pizzella
Mar 17, 2021·Brain : a Journal of Neurology·Romy LorenzFatemeh Geranmayeh
Aug 26, 2021·Cell Reports·Fahimeh MamashliJyrki Ahveninen

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