Background: We propose a completely automated methodology to investigate the relationship between magnetic resonance image (MRI) features and changes in cognitive estimates, applied to the study of Mini-Mental State Examination (MMSE) changes in mild cognitive impairment (MCI). Subjects: A reference group composed of 75 patients with clinically probable Alzheimer's Disease (AD) and 75 age-matched controls; and a study group composed of 49 MCI, 20 having progressed to clinically probable AD and 29 having remained stable after a 48 month follow-up. Methods: We created a pathology-specific reference space using principal component analysis of MRI-based features (intensity, local volume changes) within the medial temporal lobe of T1-weighted baseline images for the reference group. We projected similar data from the study group and identified a restricted set of image features highly correlated with one-year change in MMSE, using a bootstrap sampling estimation. We used robust linear regression models to predict one-year MMSE changes from baseline MRI, baseline MMSE, age, gender, and years of education. Results: All experiments were performed using a leave-one-out paradigm. We found multiple image-based features highly correlated with one-year MMSE changes (|r| > 0.425). The model for all N = 49 MCI subjects had a correlation of r = 0.31 between actual and predicted one-year MMSE change values. A second model only for MCI subjects with MMSE loss larger than 1 U had a pairwise correlation r = 0.80 with an adjusted coefficient of determination r2 = 0.61. Findings: Our automated MRI-based technique revealed a strong relationship between baseline MRI features and one-year cognitive changes in a sub-group of MCI subjects. This technique should be generalized to other aspects of cognitive evaluation and to a wider scope of dementias.
- High-dimensional analysis
- Mild cognitive impairment
- Mini-Mental Score Examination
ASJC Scopus subject areas
- Cognitive Neuroscience