Biology subjects

Marins, T. F.

Publications and source records attributed to Marins, T. F..

3 recordsLinked to original sources

bioRxiv2025

A common neural architecture for encoding finger movements

Human behavior evokes cortical responses that are highly variable across individuals, challenging the existence of a shared informational architecture, previously reported in animal studies. Here, using 7-Tesla fMRI and Procrustean transformation, we show that high-dimensional alignment of neural information from finger-tapping sequences reveals a latent neural architecture for sequential finger movements that generalizes across brains. This shared representation is instantiated in both sides of the sensorimotor cortex and does not reflect trial-wise variability of motor behavior, as regional decoding differences persisted after controlling for movement-time fluctuations. This conserved sensorimotor encoding structure provides a neuroscientific foundation for scalable, calibration-free brain-computer interfaces and cross-subject models of motor rehabilitation.

neuroscience↗

bioRxiv2025

Distinct neural architectures underlie motor skill acquisition and transfer in human sensorimotor cortex

Motor learning typically emerges from repetitive practice but can also arise through transfer or generalization, when improvements extend to untrained actions. However, it remains unclear whether training-based learning (training effect) and transfer/generalization-based learning (transfer effect) rely on shared neural mechanisms or whether they represent distinct processes supported by different cortical architectures. Here, we employed between-subject multivariate decoding of hyperaligned functional magnetic resonance imaging data to characterize the universal neural codes underlying the training and transfer effects and their resultant motor engrams in the sensorimotor cortex. We found that both learning processes are encoded bilaterally but shifted to the right (ipsilateral) hemisphere, with the training effect showing higher decoding accuracy than the transfer effect. The bilateral central sulci showed a clear mechanism-specificity towards the training effect, in contrast to the rest of the sensorimotor cortex. Interestingly, these learning processes consolidate two distinct motor engrams. Remarkably, the information structure of these processes is shared among individual brains, and the between-subject classification provided limited results when performed with standard, anatomical alignment. Our findings support the notion that motor skill learning is hierarchically organized in the sensorimotor cortex, and extend current models by revealing that training and transfer encode distinct and mechanism-dependent high-dimensional representations embedded in a neural code that is shared across brains.

neuroscience↗

bioRxiv2020

Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity?

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large interindividual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pre-training functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pre-training activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

neuroscience↗