Behaviour consistency is a sensitive tool for distinguishing the effects of aging on physical activity

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

Older rats show slow modulation of hippocampal theta rhythm during voluntary running

Aging causes brain function degeneration and slows many motor and behavioural responses. The hippocampal theta rhythm (4-12 Hz) is related to cognition and locomotion. However, the findings on aging-related changes in the frequency and amplitude of hippocampal theta oscillations have been inconsistent. We hypothesized that older rats have slower responses in terms of hippocampal theta rhythm during voluntary wheel running than do young adult rats. By simultaneously recording electroencephalography and physical activity (PA), we evaluated theta oscillations in 8-week-old (young adult) and 60-week-old (middle-aged) rats before and during wheel running, which was conducted only during the rats' 12-h dark period. To test the alterations of hippocampal theta rhythm in voluntary wheel running, we analyzed the signals without (8-s) or with (2-s) chronological order. No significant difference was observed in total frequency (TP, 4-12 Hz), low-frequency (LT, 4-6.5 Hz), or high-frequency (9.5-12 Hz) theta activity between active waking and overall running in either group. The theta oscillations were slower in the middle-aged rats than in the young adult rats during wheel running but increased during running for both age groups. During wheel running, the middle-aged rats exhibited an increased LT, which was related to PA. On the basis of the chronological order of running, the young adult rats exhibited increased TP, and the middle-aged rats exhibited significant increases in middle-frequency (MT, 6.5-9.5 Hz) theta activity. The dominant modulations of MT in the middle-aged rats may have caused nonsignificant changes in total activity. These between-group differences in theta rhythm characteristics during voluntary running provide insights into age-related brain function decline.