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P1‐068: ERP as a biomarker for Alzheimer's disease: The cognision system
Author(s) -
Casey David Allan,
Cecchi Marco,
Jicha Gregory,
Wolk D.A.,
Doraiswamy Murali,
Fadem K.C.,
Smith Charles,
Kulkarni Mauktik
Publication year - 2011
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2011.05.348
Subject(s) - biomarker , dementia , cognition , medicine , disease , cognitive decline , neuroimaging , cognitive test , psychology , oncology , psychiatry , biochemistry , chemistry
Background: Biomarker research has led to development of promising markers (e.g., amyloid imaging, cerebrospinal fluid analysis). However, the causal link between the pathologic process and cognitive decline remains unclear, contributing to uncertainty in early AD diagnosis as well as monitoring progression. Recent consensus statements suggest that AD diagnosis should involve evidence of pathological biochemical process as well as cognitive malfunction, with progressive cognitive decline as the core criterion. The FDA has stated that a treatment must not only show the desired pharmacodynamic effect (as demonstrated by biomarkers) but also prove efficacy in a cognitive domain. This requires the development of a reliable cognitive biomarker. The limitations of psychometric testing are well known. Event-related potentials (ERP) have potential as a cognitive biomarker for early AD as well as evaluating drug efficacy. Methods: ERP studies demonstrate its utility in diagnosis. Our portable, easy-to-use ERP system enables standardized data collection in outpatient settings. This device is being used in a multi-center trial (100 well-characterized AD and 100 age-matched controls to validate ERP as a cognitive biomarker). Results: ERP and other biomarker data will be analyzed to address following specific aims: 1) To train an ensemble-of-classifiers neural network system with time and frequency based features of ERP and to determine whether the sensitivity, specificity, and positive likelihood ratio (PLR) in detecting early AD can meet the performance of community clinic physicians. 2) To test, within the dementia cohort, how well ERP biomarkers correlate with CSF biomarkers and test whether combining ERP data with MRI data in a decision-fusion classifier boosts the classification accuracy of differential diagnosis (AD vs. non-AD dementia). 3) To compare statistically, within the dementia cohort, ERP biomarkers with ADAS-Cog scores to assess the utility of ERP biomarkers in monitoring efficacy of AD drugs. Conclusions: Clinical trials are currently underway and preliminary results will be presented.