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Nat Commun. 2019 Feb 25;10(1):936. doi: 10.1038/s41467-019-08840-8.

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

Author information

1
The Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London, W12 0NN, UK. e.soreq14@imperial.ac.uk.
2
Centre for Neuroimaging Sciences, Institute of Psychiatry, Kings College London, London, SE5 8AF, UK.
3
The Computational, Cognitive and Clinical Neuroimaging Laboratory, Division of Brain Sciences, Imperial College London, London, W12 0NN, UK.

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.

PMID:
30804436
PMCID:
PMC6389921
DOI:
10.1038/s41467-019-08840-8
[Indexed for MEDLINE]
Free PMC Article

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