Abstracts

Rationale: Rationale: Many commonly prescribed drugs cause cognitive side effects severe enough that a subset of patients discontinue therapy due to declines in quality of life. However, a full understanding of the factors that confer vulnerability to these impairments has yet to be reached. In this study, we aimed to determine whether resting state EEG (rsEEG) measures are sensitive to the administration of two drugs known to cause cognitive deficits: topiramate (TPM), an antiseizure drug (ASD) with additional indications for migraine and obesity, and lorazepam (LZP), a benzodiazepine used to treat anxiety and sleep disorders. If so, we demonstrate that rsEEG measures could be used to predict the severity of drug-related impairment.  Methods: Methods: We conducted a double-blind, randomized, placebo-controlled crossover study. After a baseline visit, subjects were randomly assigned to 1 of 6 possible treatment sequences consisting of TPM (100, 150, or 200 mg, randomly assigned), LZP(2 mg), and placebo (PBO) administered once each. At the next three visits, separated by two-week intervals, subjects received either a single dose of TPM, LZP, or PBO according to their assigned treatment sequence. 4 hours after drug administration and at baseline, subjects completed a verbal working memory (VWM) task with three memory loads after their 128-channel rsEEG was recorded. 27 subjects completed all visits. In order to assess the magnitude of drug-related VWM deficits and changes in EEG measures, we calculated 'relative change scores' using the formula ((treatment-PBO)/PBO); this approach enables us to control for individual differences in unimpaired performance during the PBO session. For the VWM task, we calculated average accuracy (ACC) and reaction time (RT), as well as ACC and RT for each memory load individually. For the rsEEG measures, we calculated theta (4-8Hz), alpha (8-13Hz), beta1 (13-20Hz), beta2 (20-30Hz), and gamma (30-80Hz) power. For each participant's two treatment observations (TPM and LZP), we then constructed linear mixed effects models of relative change scores for each VWM and EEG measure separately; these models allowed us to determine whether each treatment differed significantly from PBO while adjusting for covariates such as treatment order, session number, TPM dose group, gender, age, education, and estimated glomerular filtration rate. Step-down Bonferroni correction procedures were used to control for potential inflation of Type I error due to multiple testing and multiple comparisons. Last, we constructed regression models, to investigate the relationship between the severity of impairment observed on the VWM task after drug administration and baseline rsEEG measures. Results: Results: As reported elsewhere (Barkley et al., 2018), the administration of TPM and LZP led to severe performance deficits on the VWM task. The results of the linear mixed effects models comparing rsEEG measures during LZP/TPM sessions to PBO are shown in Table 1; only the TPM-related increase in theta power remained significant after multiple comparison correction. The results of the regression models showed a number of robust relationships between baseline rsEEG parameters and the severity of TPM, but not LZP-related, VWM impairment (Table 2).  Conclusions: Conclusions: We showed for the first time that rsEEG measures are predictive of the severity of TPM-induced VWM deficits. Unlike other expensive or computationally burdensome methods used to predict treatment response or disease progression, the rsEEG measures analyzed here are quick and easy to acquire and thus may be clinically useful. Funding: NIH R01NS07665