Article ; Online: Predicting Working Memory Capacity Based on Glutamatergic Concentration and its Modulation of Functional Connectivity.
2020 Volume 457, Page(s) 12–19
Abstract: Working memory (WM) capacity, the amount of information one can hold online in mind, has a central role in cognition. Previous electrophysiological and imaging studies revealed the pivotal role of persistent activity within parietal and frontal regions ... ...
Abstract | Working memory (WM) capacity, the amount of information one can hold online in mind, has a central role in cognition. Previous electrophysiological and imaging studies revealed the pivotal role of persistent activity within parietal and frontal regions as the neural foundations underpinning WM capacity. The best candidate molecules determining persistent activity are the brain's major excitatory and inhibitory neurotransmitters, glutamate and gamma-aminobutyric acid (GABA), respectively. However, our knowledge of these neurophysiological determinants in forming WM capacity is still poor. Using magnetic resonance spectroscopy (MRS), we examined the contribution of glutamate and GABA within the left intraparietal sulcus (IPS) and the left inferior/middle frontal gyrus (FG) in tracking WM capacity. A positive association was found between glutamate within the left IPS and WM capacity. By utilising resting-state functional MRI, we identified a negative association between parieto-cingulate connectivity and WM capacity. Individual variation in parieto-cingulate connectivity was explained by glutamatergic concentration in the IPS. Moreover, we found that parieto-cingulate connectivity mediated the relationship between interparietal sulcus glutamate and WM capacity. This set of findings reveals a novel mechanistic insight by which glutamatergic concentration within the IPS shapes WM capacity via parieto-cingulate connectivity. |
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MeSH term(s) | Brain Mapping ; Magnetic Resonance Imaging ; Magnetic Resonance Spectroscopy ; Memory, Short-Term ; Parietal Lobe/diagnostic imaging ; Prefrontal Cortex |
Language | English |
Publishing date | 2020-11-17 |
Publishing country | United States |
Document type | Journal Article ; Research Support, Non-U.S. Gov't |
ZDB-ID | 196739-3 |
ISSN | 1873-7544 ; 0306-4522 |
ISSN (online) | 1873-7544 |
ISSN | 0306-4522 |
DOI | 10.1016/j.neuroscience.2020.10.037 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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