Aerobic exercise improves cognitive and motor function by inducing neural changes detected using molecular, cellular, and systems level neuroscience techniques. This review unifies the knowledge gained across various neuroscience techniques to provide a comprehensive profile of the neural mechanisms that mediate exercise-induced neuroplasticity. Using a model of exercise-induced neuroplasticity, this review emphasizes the sequence of neural events that accompany exercise, and ultimately promote changes in human performance. This is achieved by differentiating between neuroplasticity induced by acute versus chronic aerobic exercise. Furthermore, this review emphasizes experimental considerations that influence the opportunity to observe exercise-induced neuroplasticity in humans. These include modifiable factors associated with the exercise intervention and nonmodifiable factors such as biological sex, ovarian hormones, genetic variations, and fitness level. To maximize the beneficial effects of exercise in health, disease, and following injury, future research should continue to explore the mechanisms that mediate exercise-induced neuroplasticity. This review identifies some fundamental gaps in knowledge that may serve to guide future research in this area.

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Transcranial magnetic studies typically rely on measures of active and resting motor threshold (i.e. AMT, RMT). Previous work has demonstrated that adaptive threshold hunting approaches are efficient for estimating RMT. To date, no study has compared motor threshold estimation approaches for measures of AMT, yet this measure is fundamental in transcranial magnetic stimulation (TMS) studies that probe intracortical circuits. The present study compared two methods for acquiring AMT and RMT: the Rossini-Rothwell (R-R) relative-frequency estimation method and an adaptive threshold-hunting method based on maximum-likelihood parameter estimation by sequential testing (ML-PEST). AMT and RMT were quantified via the R-R and ML-PEST methods in 15 healthy right-handed participants in an experimenter-blinded within-subject study design. AMT and RMT estimations obtained with both the R-R and ML-PEST approaches were not different, with strong intraclass correlation and good limits of agreement. However, ML-PEST required 17 and 15 fewer stimuli than the R-R method for the AMT and RMT estimation, respectively. ML-PEST is effective in reducing the number of TMS pulses required to estimate AMT and RMT without compromising the accuracy of these estimates. Using ML-PEST to estimate AMT and RMT increases the efficiency of the TMS experiment as it reduces the number of pulses to acquire these measures without compromising accuracy. The benefits of using the ML-PEST approach are amplified when multiple target muscles are tested within a session.

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