Hippocampal sub-regional shape and physical activity in older adults.

Hippocampal atrophy is a hallmark of Alzheimer's disease pathology, and a target biomarker region for testing intervention efficacy. Over the last few decades, a growing body of evidence from animal and human models suggests that physical activity (PA) is associated with structural benefits to the hippocampus in older adults. Very few human studies, however have explored hippocampal sub-regional specificity of PA; this is significant considering that sub-regions of the hippocampus are associated with distinct cognitive tasks and are differentially affected by disease pathology. This study used objective and self-reported measures of daily walking activity and exercise, and surface-based regional shape analysis using high-field hippocampal sub-regional partitions to explore sub-region specific hippocampal associations in a sample of nondemented, community-dwelling older adults at elevated sociodemographic risk for cognitive decline. Vertex-wise surface areas, which may be more sensitive than global volume measures, were calculated using shape diffeomorphometry, and PA was assessed using step activity monitors and PA questionnaires. We found that daily walking activity in a participant's environment was associated in cross-section mainly with larger surface areas of the subiculum in women. Associations remained significant when controlling for self-reported exercise. Prior studies have found that PA related to exercise and aerobic fitness may be most closely associated with the anterior hippocampus, particularly the dentate gyrus of the hippocampus. These novel findings are the first, to our knowledge, in human models to suggest that PA related to navigation that may not reach the level of moderate-intensity exercise may be associated with specific sub-regions of the hippocampus. These findings underscore the importance of better understanding the independent and related biological mechanisms and pathways by which increasing exercise as well as non-exercise, lifestyle PA may influence structural brain health. © 2016 Wiley Periodicals, Inc.

An approach to determine myocardial ischemia by hidden Markov models.

A hidden Markov model (HMM) of electrocardiogram (ECG) signal is presented for detection of myocardial ischemia. The time domain signals that are recorded by the ECG before and during the episode of local ischemia were pre-processed to produce input sequences, which is needed for the model training. The model is also verified by test data, and the results show that the models have certain function for the detection of myocardial ischemia. The algorithm based on HMM provides a possible approach for the timely, rapid and automatic diagnosis of myocardial ischemia, and also can be used in portable medical diagnostic equipment in the future.