Biology subjects

Ionta, S.

Publications and source records attributed to Ionta, S..

5 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

Early changes in corticomotor excitability underlie proactive inhibitory control of error correction

Converging evidence indicates that response inhibition may arise from the interaction of effortful proactive and reflexive reactive mechanisms. However, the distinction between the neural basis sustaining proactive and reactive inhibitory processes is still unclear. To identify reliable neural markers of proactive inhibition, we examined the behavioral and electrophysiological correlates elicited by manipulating the degree of inhibitory control in a task that involved the detection and amendment of errors. Restraining or encouraging the correction of errors did not affect the time course of the behavioral and neural correlates associated to reactive inhibition. We rather found that a bilateral and sustained decrease of corticomotor excitability was required for an effective proactive inhibitory control, whereas selective strategies were associated with defective response suppression. Our results provide behavioral and electrophysiological conclusive evidence of a comprehensive proactive inhibitory mechanism, with a distinctive underlying neural basis, governing the commission and amendment of errors. Together, these findings hint at a decisive role for changes in corticomotor excitability in determining whether an action will be successfully suppressed. SIGNIFICANCE STATEMENTResponse inhibition is a fundamental brain function that must be flexible enough to incorporate volitional goal-directed demands, along with rapid, automatic and well consolidated behaviors. Previous studies reflect a lack of consensus regarding the neural correlates subserving these two -proactive and reactive- distinct modes of inhibitory control. We combined electrophysiological recordings with behavioral measures within a paradigm of detection and correction of errors under two degrees of inhibitory control to identify genuine neural markers of proactive inhibitory control. We found evidence supporting a sustained and global -not selective- reduction of corticomotor excitability subserving successful proactive inhibition of motor responses. Our findings favor a distinctive mechanism of comprehensive inhibitory control to amend errors under a high degree of response competition.

neuroscience↗

bioRxiv2020

3-Dimensional Magnetic Resonance Imaging of the Freely Moving Human Eye

Abstract Eye motion is a major confound for magnetic resonance imaging (MRI) in neuroscience or ophthalmology. Currently, solutions toward eye stabilisation include participants fixating or administration of paralytics/anaesthetics. We developed a novel MRI protocol for acquiring 3-dimensional images while the eye freely moves. Eye motion serves as the basis for image reconstruction, rather than an impediment. We fully reconstruct videos of the moving eye and head. We quantitatively validate data quality with millimetre resolution in two ways for individual participants. First, eye position based on reconstructed images correlated with simultaneous eye-tracking. Second, the reconstructed images preserve anatomical properties; the eye’s axial length measured from MRI images matched that obtained with ocular biometry. The technique operates on a standard clinical setup, without necessitating specialized hardware, facilitating wide deployment. In clinical practice, we anticipate that this may help reduce burden on both patients and infrastructure, by integrating multiple varieties of assessments into a single comprehensive session. More generally, our protocol is a harbinger for removing the necessity of fixation, thereby opening new opportunities for ethologically-valid, naturalistic paradigms, the inclusion of populations typically unable to stably fixate, and increased translational research such as in awake animals whose eye movements constitute an accessible behavioural readout.Author contributions B.F., L.D.S., M.S., and M.M.M. conceptualised the problem. B.F. and L.D.S. developed, implemented, and tested the protocol. A.M. provided optometry assessments and assisted with eye movement analysis. S.I., D.Z., and M.P.N. assisted with installation of the eye-tracking system within the MRI scanner. J.A.M.B., J.J. and J.Y. contributed with the MRI sequences and compressed sensing framework. B.F., L.D.S. and M.M.M. drafted the manuscript, and all authors contributed to internal review.Competing interests B.F., L.D.S., J.A.M.B., J.Y., M.S., and M.M.M. declare the following competing financial interest: a patent application for the protocol described in this manuscript has been filed (patent application: EP19160832). A.M., S.I., D.Z., M.P.N. and J.J. declare no competing financial interests.Competing Interest StatementB.F., L.D.S., J.A.M.B., J.Y., M.S., and M.M.M. declare the following competing financial interest: a patent application for the protocol described in this manuscript has been filed (patent application: EP19160832). A.M., S.I., D.Z., M.P.N. and J.J. declare no competing financial interests.View Full Text

bioengineering↗