MRI Characterizes the Progressive Course of AD and Predicts Conversion to Alzheimer's Dementia 24 Months Before Probable Diagnosis

Abstract

There is no disease-modifying treatment currently available for AD, one of the more impacting neurodegenerative diseases affecting more than 47.5 million people worldwide. The definition of new approaches for the design of proper clinical trials is highly demanded in order to achieve non-confounding results and assess more effective treatment. In this study, a cohort of 200 subjects was obtained from the Alzheimer's Disease Neuroimaging Initiative. Subjects were followed-up for 24 months, and classified as AD (50), progressive-MCI to AD (50), stable-MCI (50), and cognitively normal (50). Structural T1-weighted MRI brain studies and neuropsychological measures of these subjects were used to train and optimize an artificial-intelligence classifier to distinguish mild-AD patients who need treatment (AD + pMCI) from subjects who do not need treatment (sMCI + CN). The classifier was able to distinguish between the two groups 24 months before AD definite diagnosis using a combination of MRI brain studies and specific neuropsychological measures, with 85% accuracy, 83% sensitivity, and 87% specificity. The combined-approach model outperformed the classification using MRI data alone (72% classification accuracy, 69% sensitivity, and 75% specificity). The patterns of morphological abnormalities localized in the temporal pole and medial-temporal cortex might be considered as biomarkers of clinical progression and evolution. These regions can be already observed 24 months before AD definite diagnosis. The best neuropsychological predictors mainly included measures of functional abilities, memory and learning, working memory, language, visuoconstructional reasoning, and complex attention, with a particular focus on some of the sub-scores of the FAQ and AVLT tests.

Keywords: Alzheimer’s disease; artificial intelligence; biomarkers; clinical trials; magnetic resonance imaging; neuropsychological tests; predictors.

FIGURE 1

Voxel-based pattern distribution of MRI differences between CN + sMCI and pMCI + AD at the time point 24 months before stable diagnosis. The pattern is shown according to the color scale with a threshold of 35%, and superimposed on a standard stereotactic brain.

FIGURE 2

Voxel-based pattern distribution of MRI differences between CN + sMCI and pMCI + AD at the time point 18 months before stable diagnosis. The pattern is shown according to the color scale with a threshold of 35%, and superimposed on a standard stereotactic brain.

FIGURE 3

Voxel-based pattern distribution of MRI differences between CN + sMCI and pMCI + AD at the time point 12 months before stable diagnosis. The pattern is shown according to the color scale with a threshold of 35%, and superimposed on a standard stereotactic brain.

FIGURE 4

Voxel-based pattern distribution of MRI differences between CN + sMCI and pMCI + AD at the time-zero point of stable diagnosis. The pattern is shown according to the color scale with a threshold of 35%, and superimposed on a standard stereotactic brain.