Do quantified sleep architecture abnormalities underlie cognitive disturbances in amnestic mild cognitive impairment?

The study was designed to gauge association between occult sleep-related breathing disturbances and sleep architecture changes on cognitive trajectories in subjects with amnestic mild cognitive impairment (aMCI) relative to cognitively normal healthy controls, phenotyped by neuroimaging. Subjects with aMCI and normal cognition were prospectively recruited. Following standardized neuropsychological and sleep questionnaire assessment they underwent a single overnight polysomnography (PSG); multimodality MRI was used to ascertain age-corrected radiological differences between the 2 groups. The aMCI cohort was followed up longitudinally with serial cognitive assessments for the next 3 years. Thirty seven subjects with aMCI and 24 control subjects consented for evaluation. Although occult moderate to severe obstructive sleep apnea (OSA) was more prevalent in aMCI (43.6%) as opposed to controls (22.7%); higher median apnea-hypopnea index (AHI = 11.5) and total apnea-hypopnea time (26.6 min) were also noted in aMCI relative to controls (6.6 and 11.4 min respectively), the differences were not statistically significant. In the aMCI group, better sleep efficiency, longer duration of REM sleep correlated with higher associative learning, free-recall/recognition memory performance. Higher AHI had negative correlation with visual memory scores. However longitudinal cognitive trends in the aMCI group over 3 years reflected relative stability (only 5% progressed to AD) notwithstanding imaging differences from controls and appeared to be independent of sleep parameters. The study concluded that despite associations between sleep efficiency, REM sleep and sleep-related breathing variables with neuropsychological test-scores in aMCI, these appear to be comorbidities rather than causative factors for the degree of cognitive impairment or its longitudinal trajectory.

Multimodality Neuroimaging in Mild Cognitive Impairment: A Cross-sectional Comparison Study.

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.