Poster A137, Saturday, March 25, 5:00 – 7:00 pm, Pacific Concourse
Older adults at-risk for developing MCI show changes in brain signal complexity: A multiscale entropy analysis
Joshua W. Villafuerte1,2, Rachel N. Newsome1,2, Sarah M. Carpentier1,2, Morgan D. Barense1,2, Jennifer D. Ryan1,2, Cheryl L. Grady1,2; 1University of Toronto, 2Rotman Research Institute at Baycrest
Detecting Alzheimer’s disease (AD) early in its progression is critical to effectively treating the neurodegenerative disease. Recent work assessing brain signal complexity using entropy-based indices in AD patients suggests complexity measures may prove useful in permitting earlier diagnosis. In the current study, multiscale entropy (MSE) analysis was used to investigate signal complexity in EEG acquired during an auditory oddball task in older adults at-risk for developing Mild Cognitive Impairment (MCI), relative to healthy young and older adults. At-risk individuals were undiagnosed and presented as healthy members of the community, but were classified as at-risk based on the Montreal Cognitive Assessment (MoCA), a brief, standardized neuropsychological test. We found an overall effect showing higher MSE at fine scales for at-risk adults, compared to higher sample entropy at coarser timescales in younger adults (healthy older adults in between). We also found a condition effect in healthy young and older adults who showed a difference in MSE between standard and deviant trials; at-risk adults did not show this pattern. Together, these results suggest that signal complexity may be usefully applied toward diagnosing pre-clinical Alzheimer’s disease.
Topic Area: OTHER