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P4‐257: Changes in fMRI resting state functional connectivity in rats after acute and repeat dosing with Pioglitazone
Author(s) -
Asin Karen,
Crenshaw Donna,
Gottschalk Kirby,
Zhang Nanyin,
Liang Zifeng,
Roses Allen
Publication year - 2012
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.2013.08.038
Subject(s) - pioglitazone , thiazolidinedione , dosing , medicine , in vivo , region of interest , pharmacology , endocrinology , type 2 diabetes , diabetes mellitus , biology , microbiology and biotechnology , radiology
mean MMSE1⁄427.2 62.2) we developed automated unimodal and multimodal classifiers based on support vector machines. The pool of features provided to the classifiers included MMSE, Trails B, AVLT delayed recall, plasma levels of ApoE, BDNF, TNF alpha, IL13, IL6 and clusterin, and hippocampal volume. All classifiers included age and sex. Three separate classifiers were built to predict mean cortical, lateral parietal and precuneal SUVR as the outcome variable. Results: All classifiers consistently ranked ApoE genotype as the most powerful predictor of brain amyloidosis. ApoE genotype alone predicted brain amyloidosis with 75-77% accuracy (AUC1⁄40.67-0.75). When cognitive, imaging and peripheral blood protein variables were added, classifier performance improved. Greatest classifier accuracy in predicting PIB+ vs. PIBsubjects was seen for the lateral parietal (accuracy1⁄485%, AUC1⁄40.87) and precuneal regions (accuracy1⁄485%, AUC1⁄40.89). The variables selected by these classifiers included ApoE genotype, MMSE, Trails B, AVLT delayed recall, clusterin, TNF alpha and BDNF. The precuneal classifier also included IL13 and ApoE protein. The average cortical classifier used ApoE genotype, MMSE, Trails B, hippocampal volume, clusterin, BDNF and IL6 and achieved 80% accuracy (AUC1⁄40.81). Conclusions: Automated classifiers based on ApoE4 genotype, cognitive and peripheral blood protein variables can identify the presence of brain amyloidosis with high accuracy. Such methods could have implications for clinical trial design and enrollment in the near future.