Transcranial magnetic stimulation (TMS) studies have shown that cortico-cortical paired associative stimulation (ccPAS) can strengthen connectivity between the ventral premotor cortex (PMv) and the primary motor cortex (M1) by modulating convergent input over M1 via Hebbian spike-timing-dependent plasticity (STDP). However, whether ccPAS locally affects M1 activity remains unclear. We tested 60 right-handed young healthy humans in two studies, using a combination of dual coil TMS and ccPAS over the left PMv and M1 to probe and manipulate PMv-to-M1 connectivity, and single- and paired-pulse TMS to assess neural activity within M1. We provide convergent evidence that ccPAS, relying on repeated activations of excitatory PMv-to-M1 connections, acts locally over M1. During ccPAS, motor-evoked potentials (MEPs) induced by paired PMv-M1 stimulation gradually increased. Following ccPAS, the threshold for inducing MEPs of different amplitudes decreased, and the input-output curve (IO) slope increased, highlighting increased M1 corticospinal excitability. Moreover, ccPAS reduced the magnitude of short-interval intracortical inhibition (SICI), reflecting suppression of GABA-ergic interneuronal mechanisms within M1, without affecting intracortical facilitation (ICF). These changes were specific to ccPAS Hebbian strengthening of PMv-to-M1 connectivity, as no modulations were observed when reversing the order of the PMv-M1 stimulation during a control ccPAS protocol. These findings expand prior ccPAS research that focused on the malleability of cortico-cortical connectivity at the network-level, and highlight local changes in the area of convergent activation (i.e., M1) during plasticity induction. These findings provide new mechanistic insights into the physiological basis of ccPAS that are relevant for protocol optimization.

Introduction: The association of total arsenic exposure with impaired glucose tolerance and gestational diabetes has been shown; however, evidence regarding urinary inorganic arsenic in pregnant women is still limited. Our aim was to evaluate the association between urinary inorganic arsenic concentration and gestational diabetes among pregnant women living in Arica, Chile. Methods: Cross-sectional study of pregnant women receiving care at primary health centers in urban Arica. The exposure was urinary inorganic arsenic concentration, while gestational diabetes was the outcome. The association was evaluated using multiple logistic regression models adjusted by age, education level, ethnicity, and pre-pregnancy body mass index. Results: 244 pregnant women were surveyed. The median urinary inorganic arsenic was 14.95 μg/L, and the prevalence of gestational diabetes was 8.6%. After adjusting, we did not find a significant association between gestational diabetes and inorganic arsenic exposure tertiles (Odds ratio (OR) 2.98, 95% CI = 0.87–10.18), (OR 1.07, 95% CI = 0.26–4.33). Conclusion: This study did not provide evidence on the relationship between urinary inorganic arsenic concentration and gestational diabetes. Further research is needed to elucidate the factors underlying this association.

Two event-related brain potential (ERP) components, the frontocentral feedback-related negativity (FRN) and the posterior P300, are key in feedback processing. The FRN typically exhibits greater amplitude in response to negative and unexpected outcomes, whereas the P300 is generally more pronounced for positive outcomes. In an influential ERP study, Hajcak et al., (2005) manipulated outcome valence and expectancy in a guessing task. They found the FRN was larger for negative outcomes regardless of expectancy, and the P300 larger for unexpected outcomes regardless of valence. These findings challenged the dominant Reinforcement Learning Theory of the ERN. We aimed to replicate these results within the #EEGManyLabs project (Pavlov et al., 2021) across thirteen labs. Our replication, including robustness tests, a PCA and Bayesian models, found that both FRN and P300 were significantly modulated by outcome valence and expectancy: FRN amplitudes (no-reward - reward) were largest for unexpected outcomes, and P300 amplitudes were largest for reward outcomes. These results were consistent across different methods and analyses. Although our findings only partially replicate the original study, they underscore the complexity of feedback processing and demonstrate how aspects of Reinforcement Learning Theory may apply to the P300 component, reinforcing the need for rigorous ERP research methodologies.