Abstract Details

Title Participant-specific interrogation of population-based data to predict cognitive decline from neuropsychiatric symptoms and neuroimaging biomarkers: A machine learning approach

Topic Aging, Dementia, and Behavioral Neurology

Presentation(s) P12 - Poster Session 12 (5:30 PM-6:30 PM)

Poster/Presentation
Number 7-001

Objective Develop machine learning (ML) models to predict decline in global cognition and attention from neuroimaging biomarkers of Alzheimer’s disease (AD) and neuropsychiatric symptoms (NPS).

Background ML is increasingly being used in brain aging research.

Design/Methods

We developed four ML models (support vector machine, Gaussian process, random forest, Naïve Bayes) to predict z-scored global cognition and attention domain changes between first and last study visits of each participant. We used a stepwise approach to develop the prediction models by: 1) including AD biomarkers (PiB- & FDG-PET), education, and APOE ε4 status; followed by 2) adding measures of depression (Beck Depression Inventory-II; BDI-II), and anxiety (Beck Anxiety Inventory; BAI); then 3) adding 12 NPS including NPS severity as measured by Neuropsychiatric Inventory Questionnaire (NPI-Q).

Results Data from 777 cognitively unimpaired (CU) participants for the outcome of global cognition z-score, and 795 CU participants for the outcome of attention z-score was available. 80% of the sample was randomly used to train the model, which was blind tested on the remaining 20%. For global cognition, the best model was able to predict 78% of participants with decreasing z-score using AD biomarkers, education, and APOE ε4 status. Adding BDI-II and BAI improved the prediction to 87%; and adding 12 NPS further improved the prediction to 96% of participants with decreasing z-scores. Including NPS severity did not improve the model’s performance. For the attention domain, the best model predicted 89% of participants with decreasing z-score using AD biomarkers, education, and APOE ε4 status. After adding BDI-II and BAI measurements, the model was able to predict 97% of participants with decreasing z-scores. Adding 12 NPS, and NPI severity only marginally improved the model’s performance to 98%.

Conclusions ML has the potential to successfully predict global and domain-specific cognitive decline in community-dwelling persons, with NPS improving prediction compared to AD neuroimaging biomarkers alone.

Authors/Disclosures
Jay Shah, MD (Arizona State University) PRESENTER	Mr. Shah has nothing to disclose.
Jeremy Syrjanen	Jeremy Syrjanen has nothing to disclose.
Janina Krell-Roesch, PhD (Karlsruhe Institute of Technology)	Dr. Krell-Roesch has nothing to disclose.
Walter Kremers	The institution of Walter Kremers has received research support from NIH. The institution of an immediate family member of Walter Kremers has received research support from NIH.
Prashanthi Vemuri, PhD (Mayo Clinic)	The institution of Dr. Vemuri has received research support from NIH.
Maria Vassilaki, MD, PhD (Mayo Clinic, Department of Quantitative Health Sciences)	Dr. Vassilaki has stock in Abbott Laboratories. Dr. Vassilaki has stock in Johnson and Johnson. Dr. Vassilaki has stock in Medtronic. Dr. Vassilaki has stock in Amgen. Dr. Vassilaki has stock in AbbVie. Dr. Vassilaki has stock in Merck. The institution of Dr. Vassilaki has received research support from NIH. The institution of Dr. Vassilaki has received research support from European Union/ St. Anne's University Hospital Brno, Czech Republic. The institution of an immediate family member of Dr. Vassilaki has received research support from Avobis Bio, LLC.
Ronald C. Petersen, MD, PhD, FAAN (Mayo Clinic)	Dr. Petersen has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Roche. Dr. Petersen has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Genentech. Dr. Petersen has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Eli Lilly and Co.. Dr. Petersen has received personal compensation in the range of $0-$499 for serving as a Consultant for Eisai, Inc.. Dr. Petersen has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Novo Nordisk. Dr. Petersen has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Novartis. Dr. Petersen has received publishing royalties from a publication relating to health care. Dr. Petersen has received publishing royalties from a publication relating to health care. Dr. Petersen has received publishing royalties from a publication relating to health care. Dr. Petersen has a non-compensated relationship as a Board of Directors with American Brain Foundation that is relevant to AAN interests or activities.
	No disclosure on file
Teresa Wu (Arizona State University)	Teresa Wu has nothing to disclose.
Yonas E. Geda, MD (Barrow Neurological Institute)	The institution of Dr. Geda has received research support from NIH.

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