B – 13 ASMs may differently impact attention, working memory, and speed based on seizure network

Abstract Objective Although anti-seizure medications (ASMs) are an integral pillar of treating patients with epilepsy, they can have potential adverse effects on cognition. This study hypothesizes that the higher the ASM#, the lower the scores on measures of attention, working memory, and processing speed. Method The sample comprised 123 patients (62.6% female, M age = 39.3) who completed an abbreviated neuropsychological battery and underwent electroencephalogram (EEG) monitoring. Four groups were established using the foci of their electrographic correlates: Temporal, Extratemporal, Generalized, and Unspecified. Attention, working memory, and processing speed were measured through the following subtests: WAIS-IV Digit Span (DS), RBANS Coding (CD), and Trail Making Test Part A (TMT-A). Results Among all patients, Spearman’s correlations demonstrated a significant relationship between ASM# and CD (r = -0.21), DS Forward (r = -0.26), and TMT-A (r = -0.25), respectively. At the group-level, the Extratemporal group had a significant relationship between ASM# and CD (r = -0.67), DS Sequencing (r = -0.60), and TMT-A (r = -0.58), respectively. The Unspecified group had a significant relationship between ASM# and DS Sequencing (r = -0.29) and TMT-A (r = -0.27), respectively. No significant correlations were found within the Temporal or Generalized groups. Conclusion Results indicate ASM polypharmacy may negatively impact attention and processing speed, particularly among the Extratemporal group. These findings are likely explained by increased vulnerability in patients whose seizure networks predominantly involve frontal regions.

Authors

Publication Details

Journal
Archives of Clinical Neuropsychology
Published
2025-09-26
DOI
https://doi.org/10.1093/arclin/acaf084.165
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
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article

B – 13 ASMs may differently impact attention, working memory, and speed based on seizure network

Zachary T. Resch, Nicole L Florentino
Archives of Clinical Neuropsychology
EEG and Brain-Computer Interfaces
article

B – 13 ASMs may differently impact attention, working memory, and speed based on seizure network

Zachary T. Resch, Nicole L Florentino
article en

Abstract

Abstract Objective Although anti-seizure medications (ASMs) are an integral pillar of treating patients with epilepsy, they can have potential adverse effects on cognition. This study hypothesizes that the higher the ASM#, the lower the scores on measures of attention, working memory, and processing speed. Method The sample comprised 123 patients (62.6% female, M age = 39.3) who completed an abbreviated neuropsychological battery and underwent electroencephalogram (EEG) monitoring. Four groups were established using the foci of their electrographic correlates: Temporal, Extratemporal, Generalized, and Unspecified. Attention, working memory, and processing speed were measured through the following subtests: WAIS-IV Digit Span (DS), RBANS Coding (CD), and Trail Making Test Part A (TMT-A). Results Among all patients, Spearman’s correlations demonstrated a significant relationship between ASM# and CD (r = -0.21), DS Forward (r = -0.26), and TMT-A (r = -0.25), respectively. At the group-level, the Extratemporal group had a significant relationship between ASM# and CD (r = -0.67), DS Sequencing (r = -0.60), and TMT-A (r = -0.58), respectively. The Unspecified group had a significant relationship between ASM# and DS Sequencing (r = -0.29) and TMT-A (r = -0.27), respectively. No significant correlations were found within the Temporal or Generalized groups. Conclusion Results indicate ASM polypharmacy may negatively impact attention and processing speed, particularly among the Extratemporal group. These findings are likely explained by increased vulnerability in patients whose seizure networks predominantly involve frontal regions.

Archives of Clinical NeuropsychologyVol. 40(Supplement_2)
Openalex Percentile: Top 74%
EEG and Brain-Computer Interfaces
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