Biology subjects

Pamplona, G. S. P.

Publications and source records attributed to Pamplona, G. S. P..

4 recordsLinked to original sources

bioRxiv2024

Neural correlates of kinematic features of passive finger movement revealed by univariate and multivariate fMRI analyses

Finger movements are associated with a relatively large neural representation. Passive finger movement - which involves refraining from actively performing or resisting movement - is a robust approach to investigate the neural representation of kinesthesia and proprioception in the brain. While some studies have characterized the neural correlates of passive finger movement, they have relied solely on mass univariate analysis, potentially affecting result sensitivity. Additionally, limited consideration has been given to stimulus duration, a factor closely tied to some kinematic features (amplitude and velocity), which recently proposed modeling approaches now take into account. Here, we reanalyzed previously published data using univariate and multivariate analysis to understand how kinesthesia is neurally encoded in neurotypical subjects in two separate experiments. Systematic passive stimulation of the fingers was provided using an MR-compatible robot while functional magnetic resonance imaging data was recorded. Our analyses consisted of univariate and multivariate approaches, conducted separately for each kinematic feature and adjusted for stimulus duration, regardless of whether brain activation scales with it. We provide a detailed mapping of brain areas related to amplitude, velocity, and direction of passive finger movement, including sensorimotor, subcortical, and cerebellar areas. In general, multivariate pattern analysis was more sensitive than the univariate approach in identifying brain regions associated with passive finger movement. Our univariate analysis demonstrated that activity in sensorimotor and subcortical areas was higher for larger amplitudes and slower velocities, which opposes to the original studys results, likely due to our treatment of stimulus duration as a confounder specified as a parametric modulator. A novel result, we also demonstrated that brain activity in sensorimotor areas was higher for extension compared to flexion of passive finger movement. In terms of kinematic features, a larger neural representation was found for amplitude and direction compared to velocity of passive finger movement. This indicates that kinesthesia and proprioception may be more reliant on displacement than kinematic aspects of passive finger movement. While univariate analyses are limited in addressing spatial heterogeneity and subject-level variability, our multivariate analyses showed increased sensitivity in identifying brain regions encoding passive movement. Our findings may extend the knowledge of how the brain encodes physical movements and may help design neurorehabilitation strategies.

neuroscience↗

bioRxiv2024

Neural Mechanisms of Feedback Processing and Behavioral Adaptation during Neurofeedback Training

The acquisition of new skills is facilitated by providing individuals with feedback that reflects their performance. This process creates a closed loop that involves feedback processing and regulation recalibration to promote effective training. Functional magnetic resonance imaging (fMRI)-based neurofeedback is unique in applying this principle by delivering direct feedback on the self-regulation of brain activity. Understanding how feedback-driven learning occurs requires examining how feedback is evaluated and how regulation adjusts in response to feedback signals. In this pre-registered mega-analysis, we re-analyzed data from eight intermittent fMRI neurofeedback studies (N = 153 individuals) to investigate brain regions where activity and connectivity are linked to feedback processing and regulation recalibration (i.e., regulation after feedback) during training. We harmonized feedback scores presented during training in these studies and computed their linear associations with brain activity and connectivity using parametric general linear model analyses. We observed that, during feedback processing, feedback scores were positively associated with (1) activity in the reward system, dorsal attention network, default mode network, and cerebellum; and with (2) reward system-related connectivity within the salience network. During regulation recalibration, no significant associations were observed between feedback scores and either activity or associative learning-related connectivity. Our results suggest that neurofeedback is processed in the reward system, supporting the theory that reinforcement learning shapes this form of brain training. In addition, the involvement of large-scale networks in feedback processing, continuously transitioning between evaluating external feedback and internally assessing the adopted cognitive state, suggests that higher-level processing is integral to this type of learning. Our findings highlight the pivotal role of performance-related feedback as a driving force in learning, potentially extending beyond neurofeedback training to other feedback-based processes. Key PointsWe conducted a pre-registered mega-analysis integrating data from eight fMRI neurofeedback studies to examine feedback processing and regulation recalibration during neurofeedback training. During feedback processing, feedback was associated with activity in the reward system, dorsal attention network, default mode network, and cerebellum; as well as with reward system-related connectivity within the salience network. We found no positive results during regulation blocks; however, additional analyses suggest that recalibration may have already occurred during feedback presentation.

neuroscience↗

bioRxiv2021

Long-term effects of network-based fMRI neurofeedback training for sustained attention

Neurofeedback allows for learning voluntary control over ones own brain activity, aiming to enhance cognition and clinical symptoms. A recent study improved sustained attention temporarily by training healthy participants to up-regulate the differential activity of the sustained attention network (SAN) minus the default mode network (DMN). However, long-term learning effects of functional magnetic resonance imaging (fMRI) neurofeedback training remain under-explored. Here, we evaluate the effects of network-based fMRI neurofeedback training for sustained attention by assessing behavioral and brain measures before, one day after, and two months after training. The behavioral measures include task as well as questionnaire scores, and the brain measures include activity and connectivity during self-regulation runs without feedback (i.e., transfer runs) and during resting-state runs. Neurally, we found that participants maintained their ability to control the differential activity during follow-up sessions. Further, exploratory analyses showed that the training-induced increase in FC between the DMN and occipital gyrus was maintained during follow-up transfer runs, but not during follow-up resting-state runs. Behaviorally, we found that enhanced sustained attention right after training returned to baseline level during follow-up. The discrepancy between lasting regulation-related brain changes but transient behavioral and resting-state effects raises the question of how neural changes induced by neurofeedback training translate to potential behavioral improvements. Since neurofeedback directly targets brain measures to indirectly improve behavior long-term, a better understanding of the brain-behavior associations during and after neurofeedback training is needed to develop its full potential as a promising scientific and clinical tool. Key pointsO_LIParticipants were still able to self-regulate the differential activity between large-scale networks two months after the end of neurofeedback training and this during transfer runs without feedback. C_LIO_LILasting brain changes were also observed in the functional connectivity of trained regions in runs during which participants engaged in active self-regulation as well as during resting-state runs without concomitant self-regulation. C_LIO_LIThe increased sustained attention we observed right after the end of neurofeedback training did not persist two months later. C_LI

neuroscience↗

bioRxiv2021

On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting

Human intelligence is one of the main objects of study in cognitive neuroscience. Reviews and meta-analyses have proved to be fundamental to establish and cement neuroscientific theories on intelligence. The prediction of intelligence using in vivo neuroimaging data and machine learning has become a widely accepted and replicated result. Here, we present a systematic review of this growing area of research, based on studies that employ structural, functional, and/or diffusion MRI to predict human intelligence in cognitively normal subjects using machine-learning. We performed a systematic assessment of methodological and reporting quality, using the PROBAST and TRIPOD assessment forms and 30 studies identified through a systematic search. We observed that fMRI is the most employed modality, resting-state functional connectivity (RSFC) is the most studied predictor, and the Human Connectome Project is the most employed dataset. A meta-analysis revealed a significant difference between the performance obtained in the prediction of general and fluid intelligence from fMRI data, confirming that the quality of measurement moderates this association. The expected performance of studies predicting general intelligence from fMRI was estimated to be r = 0.42 (CI95% = [0.35, 0.50]) while for studies predicting fluid intelligence obtained from a single test, expected performance was estimated as r = 0.15 (CI95% = [0.13, 0.17]). We further enumerate some virtues and pitfalls we identified in the methods for the assessment of intelligence and machine learning. The lack of treatment of confounder variables, including kinship, and small sample sizes were two common occurrences in the literature which increased risk of bias. Reporting quality was fair across studies, although reporting of results and discussion could be vastly improved. We conclude that the current literature on the prediction of intelligence from neuroimaging data is reaching maturity. Performance has been reliably demonstrated, although extending findings to new populations is imperative. Current results could be used by future works to foment new theories on the biological basis of intelligence differences.

neuroscience↗