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P2‐087: EARLY IMPACT INDUCED BY CEREBRAL MICROBLEED IN A MODEL OF TRANSGENIC FEMALE MICE OF ALZHEIMER'S DISEASE
Author(s) -
Chen Yaohua Sophie,
Barus Romain,
Bergeron Sandrine,
Deguil Julie,
Auger Florent,
Durieux Nicolas,
Skrobala Emilie,
Bordet Régis,
Pasquier Florence,
Gautier Sophie
Publication year - 2019
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.2019.06.2494
Subject(s) - medicine , genetically modified mouse , hippocampus , anxiety , amyloid beta , endocrinology , psychology , pathology , neuroscience , disease , transgene , biology , psychiatry , biochemistry , gene
testing. The mean squared error (MSE) was calculated for each ROI in each test case matrix and averaged across all the test cases to evaluate the overall model performance. Results: The average MSE for FDG was .001 (+/.01), indicating the model performed well over all ROIs. Figure 1 shows the actual versus predicted values over all ROIs for the test data for a 5-year prediction (R 1⁄4 .98). The average MSE for volumetrics was .002 (+/.02). Figure 2 plots the actual versus predicted values for the test data for a 5 year prediction (R 1⁄4 .91). Figure 3 depicts the error histogram for the test data for 1-5 year predictions. Most errors were clustered around zero, indicating little to no difference in the actual and predicted values. Conclusions: Machine learning algorithms can provide decision support and predictive analytics in medicine. Our model is highly accurate and capable of reliably forecasting neurodegenerative changes (structural and metabolic) due to ADAD, and illustrates the feasibility of applying machine learning algorithms for targeted patient care in people with AD. P2-086 ESTABLISHMENTAND VALIDATION OF AN Ab MODEL FOR ALZHEIMER’S DISEASE USING ZEBRAFISH FOR HIGH THROUGHPUT DRUG SCREENING