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Poster B21

Resting rhythmic activity and anxiety

Poster Session B - Sunday, April 14, 2024, 8:00 – 10:00 am EDT, Sheraton Hall ABC

Tamari Shalamberidze1 (, Jeremy Caplan1,2, Kyle Nash1,2; 1Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta Canada, 2Department of Psychology, University of Alberta, Edmonton, Alberta, Canada

The need to find an objective, physiological marker for anxiety processes and disorders is paramount to both Neuroscience and Psychiatry. We examined whether resting state rhythmic electrophysiological (EEG) activity could predict anxiety and serve as a biomarker. EEG was recorded during rest (two cycles alternating 1-minute eyes open and 1-minute eyes closed). The EEG signal was analyzed for oscillations using the oscillation detection method, BOSC (Better OSCillation detection; Whitten et al., 2011). Anxiety was assessed according to the State/Trait Anxiety Inventory, Ten-Item Personality Inventory, and BIS/BAS scale. Linear regression was conducted to ask if anxiety scores are predicted by theta (4-8 Hz), alpha (8-13 Hz) or beta (12-27 Hz) oscillations recorded at the mid-frontal (Fz) electrode. The model predicting the BIS anxiety scale and all three frequency bands during both eyes open and eyes closed states was significant and driven by theta (t(45)= 2.409, p=0.021) and alpha oscillations (t(45)= -3.202, p=0.003) during the eyes-open condition. With Bayesian analysis, there is substantial evidence that theta and alpha oscillations during eyes open and only alpha oscillations during eyes closed should be included in the model as reliable predictors of anxiety. To our knowledge, this is the first report of frontal midline resting alpha and theta oscillations predicting personality measures of anxiety, and the results suggest avenues for biomarker development.

Topic Area: EMOTION & SOCIAL: Other


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