The DOI matches its Crossref record; 99 of the 99 references it cites were found in a registry or an index.
Working memory revived in older adults by synchronizing rhythmic brain circuits
Open accessAbstract
Understanding normal brain aging and developing methods to maintain or improve cognition in older adults are major goals of fundamental and translational neuroscience. Here we show a core feature of cognitive decline—working-memory deficits—emerges from disconnected local and long-range circuits instantiated by theta–gamma phase–amplitude coupling in temporal cortex and theta phase synchronization across frontotemporal cortex. We developed a noninvasive stimulation procedure for modulating long-range theta interactions in adults aged 60–76 years. After 25 min of stimulation, frequency-tuned to individual brain network dynamics, we observed a preferential increase in neural synchronization patterns and the return of sender–receiver relationships of information flow within and between frontotemporal regions. The end result was rapid improvement in working-memory performance that outlasted a 50 min post-stimulation period. The results provide insight into the physiological foundations of age-related cognitive impairment and contribute to groundwork for future non-pharmacological interventions targeting aspects of cognitive decline. The authors develop a noninvasive stimulation protocol to restore neural synchronization patterns and improve working memory in older humans, contributing to groundwork for future drug-free therapeutics targeting age-related cognitive decline.
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Metadata
- Authors
- Reinhart, R. M. G., & Nguyen, J. A.
- Venue
- Nature Neuroscience
- Published
- 2019-04-08
- Volume
- 22
- Issue
- 5
- Pages
- 820-827
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2927777239
- Cited by
- 639
- References
- 99
- FWCI
- 41.24
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
