Association Between Gait, Cognition, and Gray Matter Volumes in Mild Cognitive Impairment and Healthy Controls.

AIMS: To assess the correlation between cognitive functioning and 3 gait parameters (gait speed, cadence, and stride length) in persons with mild cognitive impairment (MCI) and cognitively healthy controls and investigate linear correlations between gait and gray matter volumes. MATERIALS AND METHODS: Participants were recruited at IRCCS San Camillo Hospital, Venice, Italy (MCI=43; age-matched controls=43). Participants underwent comprehensive neuropsychological assessment. Gait speed, cadence, and stride length, were assessed with the BTS FREEMG 300 device. Three-dimensional (3D) T1-weighted MR images were acquired using a 1.5 T Philips Achieva MRI system with a Turbo Field Echo sequence. RESULTS: In MCI there was a positive correlation between gait speed and memory tests (P<0.05). In controls all 3 gait parameters correlated with executive functioning (P<0.01). Temporal and limbic areas (ie, superior temporal gyrus, thalamus and parahippocampal gyrus) were associated with gait parameters in MCI whereas in controls the associations were with frontal areas (ie, middle, inferior, and superior frontal gyrus) and in the cerebellum (anterior and posterior lobe). CONCLUSIONS: Our results highlight a distinct pattern of association between gray matter volume and gait parameters in MCI patients and controls (temporal areas in MCI and frontal areas in healthy elderly), suggesting a relationship between dementia-related pathology and gait dysfunction.

Screening for Cognitive Impairment by Model-Assisted Cerebral Blood Flow Estimation.

OBJECTIVE: Alzheimer's disease (AD) is a progressive and debilitating neurodegenerative disease; a major health concern in the ageing population with an estimated prevalence of 46 million dementia cases worldwide. Early diagnosis is therefore crucial so mitigating treatments can be initiated at an early stage. Cerebral hypoperfusion has been linked with blood-brain barrier dysfunction in the early stages of AD, and screening for chronic cerebral hypoperfusion in individuals has been proposed for improving the early diagnosis of AD. However, ambulatory measurements of cerebral blood flow are not routinely carried out in the clinical setting. In this study, we combine physiological modeling with Holter blood pressure monitoring and carotid ultrasound imaging to predict 24-h cerebral blood flow (CBF) profiles in individuals. One hundred and three participants [53 with mild cognitive impairment (MCI) and 50 healthy controls] underwent model-assisted prediction of 24-h CBF. Model-predicted CBF and neuropsychological tests were features in lasso regression models for MCI diagnosis. RESULTS: A CBF-enhanced classifier for diagnosing MCI performed better, area-under-the-curve (AUC) = 0.889 (95%-CI: 0.800 to 0.978), than a classifier based only on the neuropsychological test scores, AUC = 0.818 (95%-CI: 0.643 to 0.992). An additional cohort of 25 participants (11 MCI and 14 healthy) was recruited to perform model validation by arterial spin-labeling magnetic resonance imaging, and to establish a link between measured CBF that predicted by the model. CONCLUSION: Ultrasound imaging and ambulatory blood pressure measurements enhanced with physiological modeling can improve MCI diagnosis accuracy.