Daily-Life Physical Activity of Healthy Young Adults Associates With Function and Structure of the Hippocampus

Hippocampal Subfield Volumes in Amateur Marathon Runners

PURPOSE

Numerous studies have implicated the involvement of structure and function of the hippocampus in physical exercise, and the larger hippocampal volume is one of the relevant benefits reported in exercise. It remains to be determined how the different subfields of hippocampus respond to physical exercise.

METHODS

A 3D T1-weighted magnetic resonance imaging was acquired in 73 amateur marathon runners (AMR) and 52 healthy controls (HC) matched with age, sex, and education. The Montreal Cognitive Assessment, the Pittsburgh Sleep Quality Index (PSQI), and the Fatigue Severity Scale were assessed in all participants. We obtained hippocampal subfield volumes using FreeSurfer 6.0. We compared the volumes of the hippocampal subfield between the two groups and ascertained correlation between the significant subfield metrics and the significant behavioral measure in AMR group.

RESULTS

The AMR had significantly better sleep than HC, manifested as with lower score of PSQI. Sleep duration in AMR and HC was not significantly different from each other. In the AMR group, the left and right hippocampus, cornu ammonis 1 (CA1), CA4, granule cell and molecular layers of the dentate gyrus, molecular layer, left CA2-3, and left hippocampal-amygdaloid transition area volumes were significantly larger compared with those in the HC group. In AMR group, the correlations between the PSQI and the hippocampal subfield volumes were not significant. No correlations were found between hippocampal subfield volumes and sleep duration in AMR group.

CONCLUSIONS

We reported larger volumes of specific hippocampal subfields in AMR, which may provide a hippocampal volumetric reserve that protects against age-related hippocampal deterioration. These findings should be further investigated in longitudinal studies.

Hippocampal blood flow rapidly and preferentially increases after a bout of moderate-intensity exercise in older adults with poor cerebrovascular health

Over the course of aging, there is an early degradation of cerebrovascular health, which may be attenuated with aerobic exercise training. Yet, the acute cerebrovascular response to a single bout of exercise remains elusive, particularly within key brain regions most affected by age-related disease processes. We investigated the acute global and region-specific cerebral blood flow (CBF) response to 15 minutes of moderate-intensity aerobic exercise in older adults (≥65 years; n = 60) using arterial spin labeling magnetic resonance imaging. Within 0-6 min post-exercise, CBF decreased across all regions, an effect that was attenuated in the hippocampus. The exercise-induced CBF drop was followed by a rebound effect over the 24-minute postexercise assessment period, an effect that was most robust in the hippocampus. Individuals with low baseline perfusion demonstrated the greatest hippocampal-specific CBF effect post-exercise, showing no immediate drop and a rapid increase in CBF that exceeded baseline levels within 6-12 minutes postexercise. Gains in domain-specific cognitive performance postexercise were not associated with changes in regional CBF, suggesting dissociable effects of exercise on acute neural and vascular plasticity. Together, the present findings support a precision-medicine framework for the use of exercise to target brain health that carefully considers age-related changes in the cerebrovascular system.

Difference analysis of multidimensional electroencephalogram characteristics between young and old patients with generalized anxiety disorder

Growing evidences indicate that age plays an important role in the development of mental disorders, but few studies focus on the neuro mechanisms of generalized anxiety disorder (GAD) in different age groups. Therefore, this study attempts to reveal the neurodynamics of Young_GAD (patients with GAD under the age of 50) and Old_GAD (patients with GAD over 50 years old) through statistical analysis of multidimensional electroencephalogram (EEG) features and machine learning models. In this study, 10-min resting-state EEG data were collected from 45 Old_GAD and 33 Young_GAD. And multidimensional EEG features were extracted, including absolute power (AP), fuzzy entropy (FE), and phase-lag-index (PLI), on which comparison and analyses were performed later. The results showed that Old_GAD exhibited higher power spectral density (PSD) value and FE value in beta rhythm compared to theta, alpha1, and alpha2 rhythms, and functional connectivity (FC) also demonstrated significant reorganization of brain function in beta rhythm. In addition, the accuracy of machine learning classification between Old_GAD and Young_GAD was 99.67%, further proving the feasibility of classifying GAD patients by age. The above findings provide an objective basis in the field of EEG for the age-specific diagnosis and treatment of GAD.