Bidirectional synaptic plasticity rapidly modifies hippocampal representations

Learning requires neural adaptations thought to be mediated by activity-dependent synaptic plasticity. A relatively non-standard form of synaptic plasticity driven by dendritic calcium spikes, or plateau potentials, has been reported to underlie place field formation in rodent hippocampal CA1 neurons. Here we found that this behavioral timescale synaptic plasticity (BTSP) can also reshape existing place fields via bidirectional synaptic weight changes that depend on the temporal proximity of plateau potentials to pre-existing place fields. When evoked near an existing place field, plateau potentials induced less synaptic potentiation and more depression, suggesting BTSP might depend inversely on postsynaptic activation. However, manipulations of place cell membrane potential and computational modeling indicated that this anti-correlation actually results from a dependence on current synaptic weight such that weak inputs potentiate and strong inputs depress. A network model implementing this bidirectional synaptic learning rule suggested that BTSP enables population activity, rather than pairwise neuronal correlations, to drive neural adaptations to experience.

Maximal strength training-induced increase in efferent neural drive is not reflected in relative protein expression of SERCA

Introduction Maximal strength training (MST), performed with heavy loads (~ 90% of one repetition maximum; 1RM) and few repetitions, yields large improvements in efferent neural drive, skeletal muscle force production, and skeletal muscle efficiency. However, it is elusive whether neural adaptations following such high intensity strength training may be accompanied by alterations in energy-demanding muscular factors. Methods Sixteen healthy young males (24 ± 4 years) were randomized to MST 3 times per week for 8 weeks (n = 8), or a control group (CG; n = 8). Measurements included 1RM and rate of force development (RFD), and evoked potentials recordings (V-wave and H-reflex normalized to M-wave (M) in the soleus muscle) applied to assess efferent neural drive to maximally contracting skeletal muscle. Biopsies were obtained from vastus lateralis and analyzed by western blots and real-time PCR to investigate the relative protein expression and mRNA expression of Sarcoplasmic Reticulum Ca2+ ATPase (SERCA) 1 and SERCA2. Results Significant improvements in 1RM (17 ± 9%; p < 0.001) and early (0–100 ms), late (0–200 ms) and maximal RFD (31–53%; p < 0.01) were observed after MST, accompanied by increased maximal Vmax/Msup-ratio (9 ± 14%; p = 0.046), with no change in H-reflex to M-wave ratio. No changes were observed in the CG. No pre- to post-training differences were found in mRNA or protein expressions of SERCA1 and SERCA2 in either group. Conclusion MST increased efferent neural drive to maximally contracting skeletal muscle, causing improved force production. No change was observed in SERCA expression, indicating that responses to high intensity strength training may predominantly be governed by neural adaptations.

Should We Trust Perceived Effort for Loading Control and Resistance Exercise Prescription After ACL Reconstruction?

Context: The rating of perceived effort (RPE) is a common method used in clinical practice for monitoring, loading control, and resistance training prescription during rehabilitation after rupture and anterior cruciate ligament reconstruction (ACLR). It is suggested that the RPE results from the integration of the afferent feedback and corollary discharge in the motor and somatosensory cortex, and from the activation of brain areas related to emotions, affect, memory, and pain (eg, posterior cingulate cortex, precuneus, and prefrontal cortex). Recent studies have shown that rupture and ACLR induce neural adaptations in the brain commonly associated with the RPE. Therefore, we hypothesize that RPE could be affected because of neural adaptations induced by rupture and ACLR. Study Design: Clinical review. Level of Evidence: Level 5. Results: RPE could be directly altered by changes in the activation of motor cortex, posterior cingulate cortex, and prefrontal cortex. These neural adaptations may be induced by indirect mechanisms, such as the afferent feedback deficit, pain, and fear of movement (kinesiophobia) that patients may feel after rupture and ACLR. Conclusion: Using only RPE for monitoring, loading control, and resistance training prescription in patients who had undergone ACLR could lead to under- or overdosing resistance exercise, and therefore, impair the rehabilitation process. Strength-of-Recommendation Taxonomy: 3C.

Multifactorial Etiology of Adolescent Nicotine Addiction: A Review of the Neurobiology of Nicotine Addiction and Its Implications for Smoking Cessation Pharmacotherapy

Nicotine is the primary pharmacologic component of tobacco, and its highly addictive nature is responsible for its widespread use and significant withdrawal effects that result in challenges to smoking cessation therapeutics. Nicotine addiction often begins in adolescence and this is at least partially attributed to the fact that adolescent brain is most susceptible to the neuro-inflammatory effects of nicotine. There is increasing evidence for the involvement of microglial cells, which are the brain's primary homeostatic sensor, in drug dependence and its associated behavioral manifestations particularly in the adolescent brain. A hallmark of neuro-inflammation is microglial activation and activation of microglia by nicotine during adolescent development, which may result in long-term addiction to nicotine. This non-systematic review examines multifactorial etiology of adolescent nicotine addiction, neurobiology of nicotine addiction and the potential mechanisms that underlie the effects of nicotine on inflammatory signaling in the microglia, understanding how nicotine affects the adolescent brain. We speculate, that modulating homeostatic balance in microglia, could have promising therapeutic potential in withdrawal, tolerance, and abstinence-related neural adaptations in nicotine addiction, in the adolescent brain. Further, we discuss nicotine addiction in the context of the sensitization-homeostasis model which provides a theoretical framework for addressing the potential role of microglial homeostasis in neural adaptations underlying nicotine abuse.

The knowns and unknowns of neural adaptations to resistance training

The initial increases in force production with resistance training are thought to be primarily underpinned by neural adaptations. This notion is firmly supported by evidence displaying motor unit adaptations following resistance training; however, the precise locus of neural adaptation remains elusive. The purpose of this review is to clarify and critically discuss the literature concerning the site(s) of putative neural adaptations to short-term resistance training. The proliferation of studies employing non-invasive stimulation techniques to investigate evoked responses have yielded variable results, but generally support the notion that resistance training alters intracortical inhibition. Nevertheless, methodological inconsistencies and the limitations of techniques, e.g. limited relation to behavioural outcomes and the inability to measure volitional muscle activity, preclude firm conclusions. Much of the literature has focused on the corticospinal tract; however, preliminary research in non-human primates suggests reticulospinal tract is a potential substrate for neural adaptations to resistance training, though human data is lacking due to methodological constraints. Recent advances in technology have provided substantial evidence of adaptations within a large motor unit population following resistance training. However, their activity represents the transformation of afferent and efferent inputs, making it challenging to establish the source of adaptation. Whilst much has been learned about the nature of neural adaptations to resistance training, the puzzle remains to be solved. Additional analyses of motoneuron firing during different training regimes or coupling with other methodologies (e.g., electroencephalography) may facilitate the estimation of the site(s) of neural adaptations to resistance training in the future.