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IC‐P‐160: Developing Brain Vital Signs: Initial Assessments Across The Adult Life Span
Author(s) -
Hajra Sujoy Ghosh,
Chang Liu Careesa,
Song Xiaowei,
Fickling Shaun,
Pawlowski Gabriela,
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.2016.06.191
Subject(s) - psychology , cognition , dementia , montreal cognitive assessment , audiology , n400 , cognitive impairment , neuroscience , physical medicine and rehabilitation , disease , event related potential , medicine , pathology
permit enrich populations for disease modifying clinical trials. Recent developments in machine learning techniques have enabled us to achieve highly accurate predictions in multiple clinical applications. Here we demonstrate a novel data driven method to improve the accuracy of a Random Forest based classifier to predict the development of dementia. Methods: [F]Florbetapir images were acquired for 275 MCI individuals from the ADNI cohort and the images were processed using an established PET image processing pipeline. Regional SUVr values were extracted from brain regions such as the Angular Gyrus, Supramarginal Gyrus, Posterior Cingulate Cortex and Precuneus which were identified to have a significant amyloid accumulation based on existing literature. 70%(192) and 30% of the population were labeled as the training set and the testing set, respectively. Data driven method included a Voxel-wise logistic regression analysis using the training population to identify anatomically significant brain regions with highest odds-ratios (ORs) to develop dementia. Two Random Forest based predictors were trained with the regional SUVr values based on literature and the data driven method respectively. Their performances were measured against the testing population. Results: Voxel-wise logistic regression analysis indicated that brain regions including Orbitofrontal Cortex, Mid Frontal Sulci, Mid Temporal Sulci, Temporal Occipital junction, PCC, Angular Gyrus, Precuneus, Putamen and the Nucleus Accumbens have the highest OR values for a unit SD increase of [F]Florbetapir [Figure 1]. The Random Forest predictor trained using regions based on literature achieved 79% and 78% as validation and testing accuracy (0.89 AUC) while the predictor based on the data driven method achieved 84% for both validation and testing accuracy (0.91 AUC). The Temporal Occipital junction, Mid Temporal Sulci andMid Frontal Sulci regions indicated the highest contribution in predicting the development of dementia [Figure 2]. Conclusions:The data driven method using Voxel-wise logistic regression analysis have increased the accuracy of the Random Forest predictor and have outperforms the methods developed in previously published literature and can be a utilized as a valuable clinical framework.