Premium
P4‐296: DIFFERENTIAL NEUROCOGNITIVE NETWORK PERTURBATION IN TYPICAL AND ATYPICAL ALZHEIMER DISEASE
Author(s) -
Martersteck Adam,
Sridhar Jaiashre,
Rader Benjamin,
Parrish Todd,
Mesulam Marsel,
Rogalski Emily J.
Publication year - 2018
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.2018.07.118
Subject(s) - temporal lobe , neuroscience , psychology , episodic memory , resting state fmri , lingual gyrus , dementia with lewy bodies , frontal lobe , white matter , parahippocampal gyrus , neurocognitive , dementia , superior frontal gyrus , primary progressive aphasia , semantic dementia , medial frontal gyrus , audiology , medicine , frontotemporal dementia , cognition , disease , pathology , magnetic resonance imaging , radiology , epilepsy
construct latent measures of dementia severity. However, our approach can derive the cognitive correlates of ANY variable. Here, we derive the cognitive correlates of the apolipoprotein E (APOE) ε4 allele in data from the Alzheimer’s Neuroimaging Initiative (ADNI) (N@1,750). Methods:First, we regressed APOE ε4 onto an ad hoc selection of cognitive measures. Six with associations > r 1⁄4 0.25 were retained (Figure 1). We fixed the surviving regression weights. Next, we introduced a latent variable i.e., “g’ ”, representing the fraction of Spearman’s general intelligence factor “g” that is residual (i.e., unrelated) to APOE ε4. g’ ‘s parameters were fixed and the regression paths removed. Next, we introduced a second latent variable, i.e., “d” representing the fraction of g that IS related to APOE. Next, we used APOE ε4 burden as the “target” indicator of a bifactor d “paralog” i.e., “dAPOE”. In genetics, a paralog is a gene descended from an ancestral gene but often having a novel function. dAPOE’s residual in d (i.e., the fraction of d that is NOT related to APOE ε4 burden) was labeled “g” ”. dAPOE’s parameters were fixed. Next, observedAPOE ε4 burden was removed, leaving only the cognitive indicators. We confirmed this construct’s association with APOE ε4 burden by correlation. dAPOE’s association with APOE ε4 burden was compared to ADAS-Cog’s and CDR-SB’s by multivariate regression. Results: dAPOE’s final model had excellent fit. [i.e., CHI SQ 1⁄4 104.04 (15), p < 0.001; CFI 1⁄4 0.987; RMSEA 1⁄4 0.052]. dAPOE correlated significantly with APOE ε4 burden (r 1⁄4 0.97, p <0.001) (Figure 1). dAPOE’s association with APOE ε4 burden was independent of both CDRSB and ADAS-Cog, stronger than both effects and largely attenuated both. Conclusions: This analysis provides proof of concept for our ability to accurately predict APOE ε4 burden from cognitive performance alone. P4-296 DIFFERENTIAL NEUROCOGNITIVE NETWORK PERTURBATION IN TYPICAL AND ATYPICAL ALZHEIMER DISEASE