ABSTRACT
PURPOSE: To assess visual associative learning and famous face recognition ability among subjects with stable amnestic mild cognitive impairment (MCI) relative to early stage dementia due to Alzheimer's disease (AD) and cognitively normal healthy controls (NC) and to correlate these differences with volumetric changes on MRI. METHODS: A hospital-based cross-sectional observational study was conducted on 61 participants. The subjects underwent neuropsychological evaluation, including validated newly designed tests for novel face-name paired association learning recall and famous face recognition. MRI volumetry was done on a subset of patients to ascertain the topographical patterns of volume loss. RESULTS: There were significant differences in performance on free recall for face-name paired associate learning in MCI (n = 22) compared to NC (n = 20) (p < 0.001) and MCI compared to AD (n = 19; p < 0.001). Significant differences were also noted in scores on the famous personalities test between MCI and NC (p = 0.007), and MCI and AD (p = 0.032). The free recall component of face-name pair associative learning significantly correlated with left cuneus (p = 0.005; r = 0.833) and right cuneus (p = 0.003; r = 0.861) volume in AD with no significant correlation among MCI and NC cohorts. CONCLUSIONS: Novel and semantically familiar face-name associative recalls are significantly impaired in MCI, and these potentially predate the MRI volumetric changes in MCI. Our findings expand the spectrum of recall deficits in MCI.
ABSTRACT
BACKGROUND AND PURPOSE: Mild cognitive impairment (MCI) is a focus of considerable research. The present study aimed to test the utility of a logistic regression-derived classifier, combining specific quantitative multimodal magnetic resonance imaging (MRI) data for the early objective phenotyping of MCI in the clinic, over structural MRI data. METHODS: Thirty-three participants with cognitively stable amnestic MCI; 15 MCI converters to early Alzheimer's disease (AD; diseased controls) and 20 healthy controls underwent high-resolution T1-weighted volumetric MRI, diffusion tensor imaging (DTI), and proton magnetic resonance spectroscopy (1H MR spectroscopy). The regional volumes were obtained from T1-weighted MRI. The fractional anisotropy and mean diffusivity maps were derived from DTI over multiple white matter regions. The 1H MRS voxels were placed over posterior cingulate gyri, and N-acetyl aspartate (NAA)/creatine (Cr), choline (Cho)/Cr, myoinositol (mI/Cr), and NAA/mI ratios were obtained. A multimodal classifier comprising MR volumetry, DTI, and MRS was prepared. A cutoff point was arrived based on receiver operator characteristics analysis. Results were considered significant, if P < 0.05. RESULTS: The most sensitive individual marker to discriminate MCI from controls was DTI (90.9%), with a specificity of 50%. For classifying MCI from AD, the best individual modality was DTI (72.7%), with a high specificity of 87.9%. The multimodal classifier approach for MCI control classification achieved an area under curve (AUC) (AUC = 0.89; P < 0.001), with 93.9% sensitivity and 70% specificity. The combined classifier for MCI-AD achieved a highest AUC (AUC = 0.93; P < 0.001), with 93% sensitivity and 85.6% specificity. CONCLUSIONS: The combined method of gray matter atrophy, white matter tract changes, and metabolite variation achieved a better performance at classifying MCI compared to the application of individual MRI biomarkers.
