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

被引:20
作者
Egli, Tobias [1 ,2 ]
Coynel, David [2 ,3 ]
Spalek, Klara [2 ,3 ]
Fastenrath, Matthias [2 ,3 ]
Freytag, Virginie [1 ,2 ]
Heck, Angela [1 ,2 ,4 ]
Loos, Eva [2 ,3 ]
Auschra, Bianca [1 ,2 ]
Papassotiropoulos, Andreas [1 ,2 ,4 ,5 ]
de Quervain, Dominique J. -F. [2 ,3 ,4 ]
Milnik, Annette [1 ,2 ,4 ]
机构
[1] Univ Basel, Div Mol Neurosci, Dept Psychol, CH-4055 Basel, Switzerland
[2] Univ Basel, Transfac Res Platform Mol & Cognit Neurosci, CH-4055 Basel, Switzerland
[3] Univ Basel, Div Cognit Neurosci, Dept Psychol, CH-4055 Basel, Switzerland
[4] Univ Basel, Psychiat Univ Clin, CH-4055 Basel, Switzerland
[5] Univ Basel, Dept Biozentrum, Life Sci Training Facil, CH-4056 Basel, Switzerland
基金
瑞士国家科学基金会;
关键词
cognition; functional networks; ICA; n-back; working memory; WHITE-MATTER INTEGRITY; INDEPENDENT COMPONENT ANALYSIS; SHORT-TERM-MEMORY; POSTERIOR PARIETAL CORTEX; HUMAN CEREBRAL-CORTEX; FUNCTIONAL CONNECTIVITY; RESTING-STATE; EXECUTIVE FUNCTION; MICROARRAY DATA; FMRI RESPONSE;
D O I
10.1523/ENEURO.0222-17.2018
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
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.
引用
收藏
页数:19
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