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Moraes, V. H.

Publications and source records attributed to Moraes, V. H..

2 recordsLinked to original sources

Effects of predictability and error on corticospinal excitability during an electronic prediction game

Background: Detecting environmental regularities is a sophisticated cognitive skill, but its neural basis remains unclear. We tested whether corticospinal excitability tracks stimulus predictability, prediction errors, and task progression while the participants played the Goalkeeper Game. Methods: Sixteen right-handed males (24.4 +/- 6.8 years) saved penalty kicks using index, middle, or ring fingers (left, center, right). Kick sequences were generated by a context tree model, in which contexts represent the minimum recent history required to predict the next event. Participants completed 1,200 trials (6 blocks x 200 trials each). Transcranial magnetic stimulation (TMS) delivered 400 ms before the go signal elicited motor evoked potentials (MEPs) in the first dorsal interosseous (FDI) and flexor digitorum superficialis (FDS) muscles during blocks 2, 4, and 6. Contexts were classified as unpredictable (1, 10) or predictable (2, 20, 00); prediction errors were defined for non-deterministic transitions after context 1. Log-MEPs were analyzed using mixed-effects models. Results: In FDI, a Predictability x Previous-Error x Block interaction emerged (F(2,8997)=6.89, p=.001), confined to the final block. Predictability increased MEP amplitudes after successful transitions (+6.3%; p=0.006) but decreased MEP amplitudes after failed transitions (-7.0%; p=0.007). For FDS, unpredictable trials elicited larger MEPs (+7.2%; p=0.002), and MEPs increased from block 2 to 6 (+16.8%; p=0.003). Conclusions: FDS MEP responses were broadly associated with uncertainty and task progression, whereas FDI MEP responses showed context-specific, error-dependent modulation after extended exposure, suggesting fine-grained predictive coding in goal-executing effectors.

neuroscience↗

Tractography-based transcranial magnetic stimulation prediction using machine learning

BackgroundIdentifying the stimulation brain target is fundamental for transcranial magnetic stimulation (TMS). Currently, this process is time-consuming and heavily dependent on the operators expertise. ObjectiveThis study evaluated a deep learning-based approach to structural brain connectivity for improving stimulation site prediction and real-time cortical excitability mapping during neuronavigation. ResultsTractography-derived connectivity features and distal myographic responses were used to train four neural network models across five subjects. Neural network inputs were incrementally varied using either tractography alone, coil coordinates alone, or hybrid combinations of both using different concatenation regimes. The best performance was observed in two subjects out of 5, where hybrid models integrating coil coordinates and fiber information achieved higher F1 scores and accuracy. ConclusionDespite these promising results, substantial inter-subject variability was observed. This approach may also be applied to other brain regions to investigate connectivity, and the use of machine learning could be extended to functional domains beyond the motor cortex, including sensory and cognitive areas.

neuroscience↗