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IC‐P‐158: Developing an Electrophysiological Indicator of Contextual Orientation
Author(s) -
Hajra Sujoy Ghosh,
Chang Liu Careesa,
Song Xiaowei,
Fickling Shaun,
Cheung Teresa,
D'Arcy Ryan C.N.
Publication year - 2016
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.2016.06.189
Subject(s) - statistical parametric mapping , electroencephalography , psychology , audiology , orientation (vector space) , electrophysiology , dementia , salience (neuroscience) , cognitive psychology , pattern recognition (psychology) , neuroscience , mathematics , medicine , geometry , disease , pathology , magnetic resonance imaging , radiology
or generalized linear regression analysis. Here, we introduce a novel statistical software written in Matlab which can perform longitudinal generalized regression analysis with multiple imaging covariates.Methods:The biggest challenge encountered to incorporate complex statistical models and multiple imaging covariates is the required time and memory complexity. This has been dealt by utilizing data parallelism techniques through the Matlab parallel computing toolbox and the Matlab distributed computing server. The function that performs generalized linear regression supports binomial, normal, poisson, gamma and inverse gaussian response variable distributions and can accommodate any number of imaging variables in the regression model and repeated measurements for longitudinal study designs. To illustrate the voxel-wise generalized linear regression functionality, neuroimaging data ([F]FDG PET, T1-MRI) were acquired for 219 individuals from the ADNI database. Demographic and MMSE scores were also obtained for the same individuals to be included in the regression models. T1 data were processed using the CIVET image processing pipeline while the PET data were processed with an established image processing pipeline. The statistical model included a logistic regression analysis to evaluate the contribution from the interaction of grey matter density and glucose metabolism for developing Alzheimer’s dementia in a cohort of MCI patients. Results: Figure 1. shows the brain regions with highest statistical significance to increase the odds of developing Alzheimer’s dementia within 24 months from MCI. Reduced glucose metabolism in the temporal brain structures and the precuneus show significant contribution toward increasing the odds of developing AD, while the interaction of reducing glucose metabolism and reducing grey matter density in the superior gyrus of the temporal lobe increases the odds of developing AD. Conclusions:This novel software enables rapid prototyping and testing of sophisticated image based hypotheses particularly involving longitudinal data with multispectral neuroimaging resources expanding the existing methods for neuroimage analysis. IC-P-158 DEVELOPING AN ELECTROPHYSIOLOGICAL INDICATOR OF CONTEXTUAL ORIENTATION Sujoy Ghosh Hajra, Careesa Chang Liu, Xiaowei Song, Shaun Fickling, Teresa Cheung, Ryan C. N. D’Arcy, Simon Fraser University, Surrey, BC, Canada; 2 Fraser Health Authority, Surrey, BC, Canada; 3 Simon Fraser University, Burnaby, BC, Canada. Contact e-mail: ssujoy@sfu.ca