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Identification of Two Distinct Working Memory-Related Brain Networks in Healthy Young Adults

Working memory (WM) is an important cognitive domain for everyday life functioning and is often disturbed in neuropsychiatric disorders. Functional magnetic resonance imaging (fMRI) studies in humans show that distributed brain areas typically described as fronto-parietal regions are implicated in W...

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Detalles Bibliográficos
Autores principales: Egli, Tobias, Coynel, David, Spalek, Klara, Fastenrath, Matthias, Freytag, Virginie, Heck, Angela, Loos, Eva, Auschra, Bianca, Papassotiropoulos, Andreas, de Quervain, Dominique J.-F., Milnik, Annette
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society for Neuroscience 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5815845/
https://www.ncbi.nlm.nih.gov/pubmed/29464194
http://dx.doi.org/10.1523/ENEURO.0222-17.2018
Descripción
Sumario:Working memory (WM) is an important cognitive domain for everyday life functioning and is often disturbed in neuropsychiatric disorders. Functional magnetic resonance imaging (fMRI) studies in humans show that distributed brain areas typically described as fronto-parietal regions are implicated in WM tasks. Based on data from a large sample of healthy young adults (N = 1369), we applied independent component analysis (ICA) to the WM-fMRI signal and identified two distinct networks that were relevant for differences in individual WM task performance. A parietally-centered network was particularly relevant for individual differences in task measures related to WM performance (“WM dependent”) and a frontally-centered network was relevant for differences in attention-dependent task performance. Importantly, frontal areas that are typically considered as key regions for WM were either involved in both WM-dependent and attention-dependent performance, or in attention-dependent performance only. The networks identified here are provided as publicly available datasets. These networks can be applied in future studies to derive a low-dimensional representation of the overall WM brain activation.