Learning Objective #1: Identify the electrophysiological patterns associated with dementia of the Alzheimer type

Learning Objective #2: Understand how a clinical electophysiological evaluation contributes to better evidence-based practice by improving the assessment of dementia

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A clinical diagnosis of Alzheimer's disease (AD) emerges at the mild-moderate stage of AD progression from a clinical history. Depressive symptoms, the most common co-morbidity of AD, can also be present. With quantitative electroencephalogram (qEEG), researchers have identified abnormal electrophysiological patterns in persons with AD. Recent qEEG research has described a specific abnormal electrophysiological pattern as an increase in theta frequency, occurring sequentially throughout the stages of AD. However, little is known about the abnormal electrophysiological patterns in persons diagnosed with AD and depressive co-morbidity.

The purpose of this study is to characterize the electrophysiological patterns of patients diagnosed with dementia and depressive co-morbidity and to discuss how these electrophysiological patterns will affect evidence-based practice.

This retrospective study has a descriptive, cross-sectional design. Sample include 21 community residents (aged 50-89 years) diagnosed with dementia of the Alzheimer type and manifested depressive symptoms. Electrophysiological data were extracted from the clinical EEG laboratory in a university-based medical center. The electrophysiological data were quantified with NeuroGuide.

The 21 subjects (females = 12 (57%), males = 9 (43%)) had a mean age of 70.7 (SD 15). Nineteen (91%) were diagnosed with dementia and all manifested depressive symptoms. Clinical EEG recordings were analyzed, log transformed, quantified, and compared to a normative qEEG database to identify abnormal electrophysiological patterns indicative of dementia. Topographic brain images were used to visually display the topographic distribution of electrophysiological patterns. Statistical analyses will characterize the electrophysiological data. Descriptive analysis will be used to characterize the electrophysiological patterns. An ANOVA will be used to determine the group difference between gender and the effect of age.

An evaluation of electrophysiological patterns may contribute to a more comprehensive assessment and accurate diagnosis of AD that will improve the evidence-based practice of health care for persons with AD and their caregivers.