Abstract
The concept of Mild Cognitive Impairment (MCI) is used to describe the early stages of Alzheimer’s disease (AD), and identification and treatment before further decline is an important clinical task. We selected longitudinal data from the ADNI database to investigate how well normal function (HC, n= 134) vs. conversion to MCI (cMCI, n= 134) and stable MCI (sMCI, n=333) vs. conversion to AD (cAD, n= 333) could be predicted from cognitive tests, and whether the predictions improve by adding information from magnetic resonance imaging (MRI) examinations. Features representing trajectories of change in the selected cognitive and MRI measures were derived from mixed effects models and used to train ensemble machine learning models to classify the pairs of subgroups based on a subset of the data set. Evaluation in an independent test set showed that the predictions for HC vs. cMCI improved substantially when MRI features were added, with an increase in F_1-score from 60 to 77%. The F_1-scores for sMCI vs. cAD were 77% without and 78% with inclusion of MRI features. The results are in-line with findings showing that cognitive changes tend to manifest themselves several years after the Alzheimer’s disease is well-established in the brain.
Highlights
The concept of Mild Cognitive Impairment (MCI) is used to describe the early stages of Alzheimer’s disease (AD), and identification and treatment before further decline is an important clinical task
A more precise prediction of AD is expected if information from results on cognitive tests are combined with information from magnetic resonance imaging (MRI) of the b rain[15,16]
We investigated the following experiments: 1. Classifying subjects with stable MCI vs. those who converted from MCI to AD
Summary
The concept of Mild Cognitive Impairment (MCI) is used to describe the early stages of Alzheimer’s disease (AD), and identification and treatment before further decline is an important clinical task. Impaired performance on psychometric tests of memory function[10,11] and on more global measures of cognitive function[9] have been recognized as early cognitive predictors of AD. This impairment tend not to be uncovered until years after the condition is well-established in the brain[12]. Based on data available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) we investigated how well a set of machine learning models could predict conversion from normal function through MCI to AD. Expecting more precise predictions by including information from MRI e xaminations[15,16], we investigated the add-on effect of including morphometric brain measures associated with memory function (entorhinal cortex and hippocampus14) and a global measure of cognitive function
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