Directed functional connectivity analysis of bimanual motor skill learning under acute stress

Abstract

Stress is a pervasive factor in high-stakes emergency medicine environments. Understanding its impact on the learning and retention of bimanual motor skills is critical for developing effective training protocols. This study investigates how acute stress modulates directed functional connectivity (dFC) between brain regions during the acquisition and retention of endotracheal intubation (ETI), a vital emergency procedure. Functional near-infrared spectroscopy (fNIRS) provides a non-invasive method for monitoring task-evoked cortical activity, offering valuable insights into the neural processes associated with learning. We apply causal connectivity analysis to fNIRS signals to investigate how stress alters learning-related brain network dynamics, and whether these changes negatively affect behavioral performance.dFC was computed from fNIRS signal acquired during a ETI training procedure where two groups performed ETI trials under non-stress and stress-simulated conditions. Each participant completed ten repetitions of the ETI procedure on an airway manikin each day for three consecutive training days, followed by three retention trials after an interval of at least eight weeks (on average). Each trial lasted up to three minutes in the non-stressor condition and up to two minutes in the stressor condition, with a two-minute rest period between trials. We adopted a non-linear Granger causality framework to estimate the dFCs between five brain regions involved in psychomotor tasks. This model replaces the vector autoregressive (VAR) component of linear Granger causality with an attention-based Long Short-Term Memory (LSTM) recurrent neural network, enabling it to capture long-term temporal dependencies between brain regions. Granger causality was computed by ablating fNIRS signals from each brain region and evaluating the resulting effect on signal prediction, yielding twenty-five directional connectivity scores for each repetition of the ETI task. The dFC measures from the non-stressor trials were used as biomarkers to identify learning-related connectivity changes across training and retention. These patterns were then compared with the stress condition dFCs to assess how stress modulates learning, providing insight into adaptive strategies under stress. Multivariate comparisons were performed using a support vector machine (SVM) classifier with five-fold cross-validation. The dFC-based analysis showed that participants in the non-stressor group achieved higher success rates across training days and during retention. The non-stress learning was associated with early stabilization of top-down prefrontal-to-motor connectivity. Their learning stage-wise discrimination was primarily driven by reorganization of dFCs within motor circuits (e.g., SMA→RPMC connectivity early in training and SMA↔LPMC connectivity across learning stages). The dFC features yielded near-perfect discrimination between the stress and non-stress conditions. Distinct asymmetric inter-PFC patterns emerged between the cohorts. Non-stressor learners relied on a higher LPFC→RPFC “strategic bandwidth” compared to the learners in stress-induced cognitive constraints. Conversely, RPFC→LPFC “vigilance” signature was higher in stressor learners. As a compensatory strategy, stress learners relied on a consistently high SMA→LPMC motor driven signal with a down-weighted RPMC→LPFC feedback to maintain speed. Moreover, a higher RPMC→LPFC signal during the first half of training suggested a probable strategic refinement endeavor in the early stages of learning. Notably, both SMA→LPMC and RPMC→LPFC connectivity down-regulated at retention consistent with automatization. Stressor group non-learners failed to up-regulate RPMC→LPFC connectivity while the non-learners in non-stressor cohort showed volatile LPMC→RPFC connectivity. These results identify connectivity motifs that distinguish success from failure under acute stress, offering targets for stress-aware training and neurofeedback.