Predicting the future development of mild cognitive impairment in the cognitively healthy elderly

Abstract

Identifying measures that predict future cognitive impairment in healthy individuals is necessary to inform treatment strategies for candidate dementia-preventative and modifying interventions. Here, we derive such measures by studying converters who transitioned from cognitively normal at baseline to mild-cognitive impairment (MCI) in a longitudinal study of 1213 elderly participants. We first establish reduced grey matter density (GMD) in left entorhinal cortex (EC) as a biomarker for impending cognitive decline in healthy individuals, employing a matched sampling control for several dementia risk-factors, thereby mitigating the potential effects of bias on our statistical tests. Next, we determine the predictive performance of baseline demographic, genetic, neuropsychological and MRI measures by entering these variables into an elastic net-regularized classifier. Our trained statistical model classified converters and controls with validation Area-Under-the-Curve>0.9, identifying only delayed verbal memory and left EC GMD as relevant predictors for classification. This performance was maintained on test classification of out-of-sample converters and controls. Our results suggest a parsimonious but powerful predictive model for MCI development in the cognitively healthy elderly.

Towards in vivo MRI-based diagnostics and tracking of dementia

“…Reduced cortical thickness in the entorhinal cortex has also been identified in MCI and AD, standing out as a good predictor of conversion from one to the other [92]. Beyond assessing disease progression, the EC also shows detectable changes in pre-symptomatic stages, showing a good predictive potential of MCI in healthy individuals [93]. The relevance of these MTL structures is also denoted by changes in brain connectivity, such as those revealed by resting-state functional MRI (fMRI).…”