Bennett, W. | Search bioRxiv

bioRxiv2021

Low intensity repetitive transcranial magnetic stimulation drives structural synaptic plasticity in the young and aged motor cortex.

BackgroundRepetitive transcranial magnetic stimulation (rTMS) is a non-invasive tool commonly used to drive neural plasticity in the young adult and aged brain. Recent data from mouse models have shown that even at subthreshold intensities (0.12 Tesla), rTMS can drive neuronal and glial plasticity in the motor cortex. However, the physiological mechanisms underlying subthreshold rTMS induced plasticity and whether these are altered with normal ageing are unclear. ObjectiveTo assess the effect of subthreshold rTMS, using the intermittent theta burst stimulation (iTBS) protocol on structural synaptic plasticity in the mouse motor cortex of young and aged mice. MethodsLongitudinal in vivo 2-photon microscopy was used to measure changes to the structural plasticity of pyramidal neuron dendritic spines in the motor cortex following a single train of subthreshold rTMS (in young adult and aged animals) or the same rTMS train administered on 4 consecutive days (in young adult animals only). Data were analysed with Bayesian hierarchical generalized linear regression models and interpreted with the aid of Bayes Factors (BF). ResultsWe found strong evidence (BF>10) that subthreshold rTMS altered the rate of dendritic spine losses and gains, dependent on the number of stimulation sessions and that a single session of subthreshold rTMS was effective in driving structural synaptic plasticity in both young adult and aged mice. ConclusionThese findings provide further evidence that rTMS drives synaptic plasticity in the brain and uncovers structural synaptic plasticity as a key mechanism of subthreshold rTMS induced plasticity.

neuroscience↗

bioRxiv2021

Machine Learning to Summarize and Provide Context for Sleep and Eating Schedules.

The relative timing of sleep and of eating within the circadian day is important for human health. Despite much data on sleep, and a growing data set for eating, there remains a need for an interpretative framework for the understanding of this data for health decisions. This study provides a new statistical and machine learning analysis of more than 500 participants in the Daily24 project. From their data, and the analysis, we propose a framework for determining the classification of participants into different chronotypes and with that the ability to realize the potential impact of daily circadian habits on health. We propose that our resulting distribution curves could be used, similar to NHANES (National Health and Nutrition Examination Survey) data for pediatric growth, as a measure for circadian misalignment and used to help guide re-entrainment schedules. Author summaryDaily habits can be positive, negative or neutral for human health. Generally sleep and eating schedules are assumed without thought for their potential to help or interfere with health. In this study we propose a framework, based on data from more than 500 participants, for evaluating the relative timing of meals and sleep schedules. This evaluation, similar to pediatric growth charts, can guide clinical suggestions for those at the extremes, while helping others to realize that they are unusual relative to the population average

bioinformatics↗