RESTING STATE ELECTROENCEPHALOGRAPHY AND INHIBITORY CONTROL IN COGNITIVELY UNIMPAIRED OLDER ADULTS
Abstract Resting state electroencephalography (EEG) activity captures spontaneous neural activity while at rest. Mounting evidence suggests that resting state EEG activity is linked to cognitive performance and event-related potentials in young adults. However, little is known on the relationship between resting state EEG activity and event-related spectral perturbations (ERSPs) and/or behavioral performance linked to inhibitory control in cognitively unimpaired older adults (OA). This study examined the relationship between resting state alpha power and 1) alpha-band ERSPs and 2) behavioral measures during a Go/NoGo inhibitory control task in 22 OA (Age: M = 64.04, SD = 6.18 years; Montreal Cognitive Assessment score: M = 28.43, SD = 0.99). Participants’ EEG data were collected during eyes-open resting state and a Go/NoGo task. Fourier transformation-based spectral analysis was used to calculate power in the alpha frequency band (8-12 Hz), with absolute power spectral density for the resting state condition and event-related desynchronization (ERD) for the Go/NoGo task. Behavioral performance was assessed using reaction time on Go trials (M = 340.27 ms, SD = 33.71) and accuracy on Go (M = 0.92, SD = 0.13) and NoGo trials (M = 0.92, SD = 0.06). Based on preliminary analyses, lower absolute alpha power in resting state was correlated with better accuracy (r(4) = -0.995, p =.005) and lower alpha ERD (r(4) = -0.96, p =.005) on Go trials. These preliminary results suggest that resting state brain oscillations may predict brain dynamics and performance linked to inhibitory control in OA.
Authors
- Shraddha A. Shende (ORCID: https://orcid.org/0000-0001-9844-7078)
- Raksha Anand Mudar (ORCID: https://orcid.org/0000-0003-0170-0554)
- Elizabeth Lydon (ORCID: https://orcid.org/0000-0002-4969-2269)
- Teresa Warren
- Jaya Ashrafi
Institutions
- University of Illinois Urbana-Champaign (US)
- Illinois State University (US)
Publication Details
- Journal
- Innovation in Aging
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1093/geroni/igae098.2307
- Citations
- 1
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.34