Motor task-to-task transfer learning for motor imagery brain-computer interfaces.

Motor imagery (MI) is one of the popular control paradigms in the non-invasive brain-computer interface (BCI) field. MI-BCI generally requires users to conduct the imagination of movement (e.g., left or right hand) to collect training data for generating a classification model during the calibration phase. However, this calibration phase is generally time-consuming and tedious, as users conduct the imagination of hand movement several times without being given feedback for an extended period. This obstacle makes MI-BCI non user-friendly and hinders its use. On the other hand, motor execution (ME) and motor observation (MO) are relatively easier tasks, yield lower fatigue than MI, and share similar neural mechanisms to MI. However, few studies have integrated these three tasks into BCIs. In this study, we propose a new task-to-task transfer learning approach of 3-motor tasks (ME, MO, and MI) for building a better user-friendly MI-BCI. For this study, 28 subjects participated in 3-motor tasks experiment, and electroencephalography (EEG) was acquired. User opinions regarding the 3-motor tasks were also collected through questionnaire survey. The 3-motor tasks showed a power decrease in the alpha rhythm, known as event-related desynchronization, but with slight differences in the temporal patterns. In the classification analysis, the cross-validated accuracy (within-task) was 67.05% for ME, 65.93% for MI, and 73.16% for MO on average. Consistently with the results, the subjects scored MI (3.16) as the most difficult task compared with MO (1.42) and ME (1.41), with p < 0.05. In the analysis of task-to-task transfer learning, where training and testing are performed using different task datasets, the ME-trained model yielded an accuracy of 65.93% (MI test), which is statistically similar to the within-task accuracy (p > 0.05). The MO-trained model achieved an accuracy of 60.82% (MI test). On the other hand, combining two datasets yielded interesting results. ME and 50% of the MI-trained model (50-shot) classified MI with a 69.21% accuracy, which outperformed the within-task accuracy (p < 0.05), and MO and 50% of the MI-trained model showed an accuracy of 66.75%. Of the low performers with an within-task accuracy of 70% or less, 90% (n = 21) of the subjects improved in training with ME, and 76.2% (n = 16) improved in training with MO on the MI test at 50-shot. These results demonstrate that task-to-task transfer learning is possible and could be a promising approach to building a user-friendly training protocol in MI-BCI.

Spectral specificity of gamma-frequency transcranial alternating current stimulation over motor cortex during sequential movements.

Motor control requires the coordination of spatiotemporally precise neural oscillations in the beta and gamma range within the primary motor cortex (M1). Recent studies have shown that motor performance can be differentially modulated based on the spectral target of noninvasive transcranial alternating current stimulation (tACS), with gamma-frequency tACS improving motor performance. However, the spectral specificity for eliciting such improvements remains unknown. Herein, we derived the peak movement-related gamma frequency in 25 healthy adults using magnetoencephalography and a motor control paradigm. These individualized peak gamma frequencies were then used for personalized sessions of tACS. All participants completed 4 sessions of high-definition (HD)-tACS (sham, low-, peak-, and high-gamma frequency) over M1 for 20 min during the performance of sequential movements of varying complexity (e.g. tapping adjacent fingers or nonadjacent fingers). Our primary findings demonstrated that individualized tACS dosing over M1 leads to enhanced motor performance/learning (i.e. greatest reduction in time to complete motor sequences) compared to nonspecific gamma-tACS in humans, which suggests that personalized neuromodulation may be advantageous to optimize behavioral outcomes.

fNIRS Study of Brain Activation during Multiple Motor Control Conditions in Younger and Older Adults.

BACKGROUND: Evidence suggests that aging contributes to decreased cerebral blood flow and brain oxyhemoglobin (HbO2) in the association cortices during rest. However, the influence of aging on functional brain activation is still controversial. The objective of this study was to investigate the age-related dependence of HbO2 across distinct motor control conditions in both primary and association cortices. METHODS: Using functional near-infrared spectroscopy (fNIRS), this study assessed HbO2 level changes within the primary somatosensory cortex (PSC), primary motor cortex (PMC), supplementary motor cortex (SMC), prefrontal cortex (PFC) and dorsolateral prefrontal cortex (DLPFC) under various motor control conditions. Analysis examined changes in the concentration of HbO2 measured by fNIRS during rest, motor execution (ME), motor passivity (MP) and motor imagery (MI) with elbow flexion in 30 younger (21.5 ± 1.17 years old) and 30 older (60.9 ± 0.79 years old) adults. RESULTS: During motor execution HbO2 was higher in younger adults than older adults in bilateral PMC, bilateral PFC, left PSC, left SMC and left DLPFC (p < 0.05). During motor passivity, HbO2 was higher in younger adults than older adults in bilateral PMC, left PSC and left SMC (p < 0.05). During motor imagery, HbO2 was higher in younger adults than older adults in bilateral PFC and bilateral DLPFC (p < 0.05). CONCLUSION: This study provided evidence that HbO2 levels are different in the primary and association cortices during different motor control conditions in young and old adults and that HbO2 levels in different brain regions under different motor control conditions can be influenced by age.

Time synchronization between parietal-frontocentral connectivity with MRCP and gait in post-stroke bipedal tasks.

BACKGROUND: In post-stroke rehabilitation, functional connectivity (FC), motor-related cortical potential (MRCP), and gait activities are common measures related to recovery outcomes. However, the interrelationship between FC, MRCP, gait activities, and bipedal distinguishability have yet to be investigated. METHODS: Ten participants were equipped with EEG devices and inertial measurement units (IMUs) while performing lower limb motor preparation (MP) and motor execution (ME) tasks. MRCP, FCs, and bipedal distinguishability were extracted from the EEG signals, while the change in knee degree during the ME phase was calculated from the gait data. FCs were analyzed with pairwise Pearson's correlation, and the brain-wide FC was fed into support vector machine (SVM) for bipedal classification. RESULTS: Parietal-frontocentral connectivity (PFCC) dysconnection and MRCP desynchronization were related to the MP and ME phases, respectively. Hemiplegic limb movement exhibited higher PFCC strength than nonhemiplegic limb movement. Bipedal classification had a short-lived peak of 75.1% in the pre-movement phase. These results contribute to a better understanding of the neurophysiological functions during motor tasks, with respect to localized MRCP and nonlocalized FC activities. The difference in PFCCs between both limbs could be a marker to understand the motor function of the brain of post-stroke patients. CONCLUSIONS: In this study, we discovered that PFCCs are temporally dependent on lower limb gait movement and MRCP. The PFCCs are also related to the lower limb motor performance of post-stroke patients. The detection of motor intentions allows the development of bipedal brain-controlled exoskeletons for lower limb active rehabilitation.

Temporo-Parietal cortex activation during motor imagery in older adults: A case study of Baduanjin.

Age-associated cognitive and motor decline is related to central nervous system injury in older adults. Motor imagery training (MIT), as an emerging rehabilitative intervention, can activate neural basis similar to that in actual exercise, so as to promote motor function in older adults. The complex motor skills rely on the functional integration of the cerebral cortex. Understanding the neural mechanisms underlying motor imagery in older adults would support its application in motor rehabilitation and slowing cognitive decline. Based on this, the present study used functional near infrared spectroscopy (fNIRS) to record the changes in oxygen saturation in older adults (20 participants; mean age, 64.8 ± 4.5 years) during Baduanjin motor execution (ME) and motor imagery (MI). ME significantly activated the left postcentral gyrus, while the oxy-hemoglobin concentration in the right middle temporal gyrus increased significantly during motor imagery. These results indicate that advanced ME activates brain regions related to sensorimotor function, and MI increases the activation of the frontal-parietal cortex related to vision. In older adults, MI overactivated the temporo-parietal region associated with vision, and tend to be activated in the right brain.

Decoding Multi-Class Motor Imagery and Motor Execution Tasks Using Riemannian Geometry Algorithms on Large EEG Datasets.

The use of Riemannian geometry decoding algorithms in classifying electroencephalography-based motor-imagery brain-computer interfaces (BCIs) trials is relatively new and promises to outperform the current state-of-the-art methods by overcoming the noise and nonstationarity of electroencephalography signals. However, the related literature shows high classification accuracy on only relatively small BCI datasets. The aim of this paper is to provide a study of the performance of a novel implementation of the Riemannian geometry decoding algorithm using large BCI datasets. In this study, we apply several Riemannian geometry decoding algorithms on a large offline dataset using four adaptation strategies: baseline, rebias, supervised, and unsupervised. Each of these adaptation strategies is applied in motor execution and motor imagery for both scenarios 64 electrodes and 29 electrodes. The dataset is composed of four-class bilateral and unilateral motor imagery and motor execution of 109 subjects. We run several classification experiments and the results show that the best classification accuracy is obtained for the scenario where the baseline minimum distance to Riemannian mean has been used. The mean accuracy values up to 81.5% for motor execution, and up to 76.4% for motor imagery. The accurate classification of EEG trials helps to realize successful BCI applications that allow effective control of devices.

Channel selection from source localization: A review of four EEG-based brain-computer interfaces paradigms.

Channel selection is a critical part of the classification procedure for multichannel electroencephalogram (EEG)-based brain-computer interfaces (BCI). An optimized subset of electrodes reduces computational complexity and optimizes accuracy. Different tasks activate different sources in the brain and are characterized by distinctive channels. The goal of the current review is to define a subset of electrodes for each of four popular BCI paradigms: motor imagery, motor execution, steady-state visual evoked potentials and P300. Twenty-one studies have been reviewed to identify the most significant activations of cortical sources. The relevant EEG sensors are determined from the reported 3D Talairach coordinates. They are scored by their weighted mean Cohen's d and its confidence interval, providing the magnitude of the corresponding effect size and its statistical significance. Our goal is to create a knowledge-based channel selection framework with a sufficient statistical power. The core channel selection (CCS) could be used as a reference by EEG researchers and would have the advantages of practicality and rapidity, allowing for an easy implementation of semiparametric algorithms.

Neural Code of Motor Planning and Execution during Goal-Directed Movements in Crows.

The planning and execution of head-beak movements are vital components of bird behavior. They require integration of sensory input and internal processes with goal-directed motor output. Despite its relevance, the neurophysiological mechanisms underlying action planning and execution outside of the song system are largely unknown. We recorded single-neuron activity from the associative endbrain area nidopallium caudolaterale (NCL) of two male carrion crows (Corvus corone) trained to plan and execute head-beak movements in a spatial delayed response task. The crows were instructed to plan an impending movement toward one of eight possible targets on the left or right side of a touchscreen. In a fraction of trials, the crows were prompted to plan a movement toward a self-chosen target. NCL neurons signaled the impending motion direction in instructed trials. Tuned neuronal activity during motor planning categorically represented the target side, but also specific target locations. As a marker of intentional movement preparation, neuronal activity reliably predicted both target side and specific target location when the crows were free to select a target. In addition, NCL neurons were tuned to specific target locations during movement execution. A subset of neurons was tuned during both planning and execution period; these neurons experienced a sharpening of spatial tuning with the transition from planning to execution. These results show that the avian NCL not only represents high-level sensory and cognitive task components, but also transforms behaviorally-relevant information into dynamic action plans and motor execution during the volitional perception-action cycle of birds.SIGNIFICANCE STATEMENT Corvid songbirds have become exciting new models for understanding complex cognitive behavior. As a key neural underpinning, the endbrain area nidopallium caudolaterale (NCL) represents sensory and memory-related task components. How such representations are converted into goal-directed motor output remained unknown. In crows, we report that NCL neurons are involved in the planning and execution of goal-directed movements. NCL neurons prospectively signaled motion directions in instructed trials, but also when the crows were free to choose a target. NCL neurons showed a target-specific sharpening of tuning with the transition from the planning to the execution period. Thus, the avian NCL not only represents high-level sensory and cognitive task components, but also transforms relevant information into action plans and motor execution.

Handedness impacts the neural correlates of kinesthetic motor imagery and execution: A FMRI study.

The human brain functional lateralization has been widely studied over the past decades, and neuroimaging studies have shown how activation of motor areas during hand movement execution (ME) is different according to hand dominance. Nevertheless, there is no research directly investigating the effects of the participant's handedness in a motor imagery (MI) and ME task in both right and left-handed individuals at the cortical and subcortical level. Twenty-six right-handed and 25 left-handed participants were studied using functional magnetic resonance imaging during the imagination and execution of repetitive self-paced movements of squeezing a ball with their dominant, non-dominant, and both hands. Results revealed significant statistical difference (p < 0.05) between groups during both the execution and the imagery task with the dominant, non-dominant, and both hands both at cortical and subcortical level. During ME, left-handers recruited a spread bilateral network, while in right-handers, activity was more lateralized. At the critical level, MI between-group analysis revealed a similar pattern in right and left-handers showing a bilateral activation for the dominant hand. Differentially at the subcortical level, during MI, only right-handers showed the involvement of the posterior cerebellum. No significant activity was found for left-handers. Overall, we showed a partial spatial overlap of neural correlates of MI and ME in motor, premotor, sensory cortices, and cerebellum. Our results highlight differences in the functional organization of motor areas in right and left-handed people, supporting the hypothesis that MI is influenced by the way people habitually perform motor actions.

Cerebellar Transcranial Direct Current Stimulation Reconfigures Brain Networks Involved in Motor Execution and Mental Imagery.

Transcranial direct current stimulation (tDCS) is growingly applied to the cerebellum to modulate the activity of cerebellar circuitry, affecting both motor and cognitive performances in a polarity-specific manner. The remote effects of tDCS are mediated in particular via the dentato-thalamo-cortical pathway. We showed recently that tDCS of the cerebellum exerts dynamic effects on resting state networks. We tested the neural hypothesis that tDCS reconfigurates brain networks involved in motor execution (ME) and motor mental imagery (MMI). We combined tDCS applied over the right cerebellum and fMRI to investigate tDCS-induced reconfiguration of ME- and MMI-related networks using a randomized, sham-controlled design in 21 right-handed healthy volunteers. Subjects were instructed to draw circles at comfortable speed and to imagine drawing circles with their right hand. fMRI data were recorded after real anodal stimulation (1.5 mA, 20 min) or sham tDCS. Real tDCS compared with SHAM specifically reconfigurated the functional links between the main intrinsic connected networks, especially the central executive network, in relation with lobule VII, and the salience network. The right cerebellum mainly influenced prefrontal and anterior cingulate areas in both tasks, and improved the overt motor performance. During MMI, the cerebellum also modulated the default-mode network and associative visual areas. These results demonstrate that tDCS of the cerebellum represents a novel tool to modulate cognitive brain networks controlling motor execution and mental imagery, tuning the activity of remote cortical regions. This approach opens novel doors for the non-invasive neuromodulation of disorders involving cerebello-thalamo-cortical paths.

Functional near-infrared spectroscopy during motor imagery and motor execution in healthy adults. / 健康成人运动想象与运动执行期间的近红外脑功能成像.

OBJECTIVES: Studies on the influence of motor imagery (MI) on brain structure and function are limited to traditional imaging techniques and the mechanism for MI therapy is not clear. By observing the brain activation mode during MI and motor execution (ME) in healthy adults, this study aims to use near-infrared brain imaging technology to provide theoretical basis for the treatment of MI. METHODS: A total of 30 healthy adults recruited to the public from June 2021 to August 2021. The MI and ME of the right knee movement served as the task mode. Block design was repeated 5 times alternately in a 20 s task period and a 30 s resting period. The activation patterns of brain regions were compared between the 2 tasks, and the regression coefficient was calculated to reflect the activation intensity of each brain region by Nirspark and SPSS 23.0 softwares. RESULTS: Lane 2, 3, 4, 5, 7, 9, 19, 20, 21, 24, 25, 26, 27, 32, 33, and 34 were significantly activated during the ME task (P<0.05, corrected by FDR) and lane 2, 5, 9, 16, 27, 29, 33, 34, and 35 were significantly activated during the MI task (P<0.05, corrected by FDR). According to the channel brain region registration information, the brain region activation pattern was similar during both MI and ME tasks in healthy adults, including left primary motor cortex (LM1), left primary sensory cortex (LS1), prefrontal pole, Broca area, and right supramarginal gyrus. Both LM1 and left pre-motor cortex (LPMC) were activated during MI in healthy adults, whereas dorsolateral prefrontal cortex (DLPFC) and only LM1 of the motor region were activated during ME. Compared to MI, the activation intensity of left sensory and left motor cortex was significantly enhanced in ME, and that of left and right prefrontal cortex especially left and right pars triangularis Broca's area (P<0.001, corrected by FDR) were significantly enhanced. CONCLUSIONS: The rationality of MI therapy is proved by functional near-infrared spectroscopy. The involvement of DLPFC in motor decision-making may regulate the two-way feedback of premoter cortex-M1 during ME; and Broca area, closely related to the motor program understanding, participates in MI and ME.

Stimulus transformation into motor action: Dynamic graph analysis reveals a posterior-to-anterior shift in brain network communication of older subjects.

Cognitive performance slows down with increasing age. This includes cognitive processes that are essential for the performance of a motor act, such as the slowing down in response to an external stimulus. The objective of this study was to identify aging-associated functional changes in the brain networks that are involved in the transformation of external stimuli into motor action. To investigate this topic, we employed dynamic graphs based on phase-locking of Electroencephalography signals recorded from healthy younger and older subjects while performing a simple visually-cued finger-tapping task. The network analysis yielded specific age-related network structures varying in time in the low frequencies (2-7 Hz), which are closely connected to stimulus processing, movement initiation and execution in both age groups. The networks in older subjects, however, contained several additional, particularly interhemispheric, connections and showed an overall increased coupling density. Cluster analyses revealed reduced variability of the subnetworks in older subjects, particularly during movement preparation. In younger subjects, occipital, parietal, sensorimotor and central regions were-temporally arranged in this order-heavily involved in hub nodes. Whereas in older subjects, a hub in frontal regions preceded the noticeably delayed occurrence of sensorimotor hubs, indicating different neural information processing in older subjects. All observed changes in brain network organization, which are based on neural synchronization in the low frequencies, provide a possible neural mechanism underlying previous fMRI data, which report an overactivation, especially in the prefrontal and pre-motor areas, associated with a loss of hemispheric lateralization in older subjects.

Precise force controls enhance loudness discrimination of self-generated sound.

Motor executions alter sensory processes. Studies have shown that loudness perception changes when a sound is generated by active movement. However, it is still unknown where and how the motor-related changes in loudness perception depend on the task demand of motor execution. We examined whether different levels of precision demands in motor control affects loudness perception. We carried out a loudness discrimination test, in which the sound stimulus was produced in conjunction with the force generation task. We tested three target force amplitude levels. The force target was presented on a monitor as a fixed visual target. The generated force was also presented on the same monitor as a movement of the visual cursor. Participants adjusted their force amplitude in a predetermined range without overshooting using these visual targets and moving cursor. In the control condition, the sound and visual stimuli were generated externally (without a force generation task). We found that the discrimination performance was significantly improved when the sound was produced by the force generation task compared to the control condition, in which the sound was produced externally, although we did not find that this improvement in discrimination performance changed depending on the different target force amplitude levels. The results suggest that the demand for precise control to produce a fixed amount of force may be key to obtaining the facilitatory effect of motor execution in auditory processes.

Single-Trial Kernel-Based Functional Connectivity for Enhanced Feature Extraction in Motor-Related Tasks.

Motor learning is associated with functional brain plasticity, involving specific functional connectivity changes in the neural networks. However, the degree of learning new motor skills varies among individuals, which is mainly due to the between-subject variability in brain structure and function captured by electroencephalographic (EEG) recordings. Here, we propose a kernel-based functional connectivity measure to deal with inter/intra-subject variability in motor-related tasks. To this end, from spatio-temporal-frequency patterns, we extract the functional connectivity between EEG channels through their Gaussian kernel cross-spectral distribution. Further, we optimize the spectral combination weights within a sparse-based ℓ2-norm feature selection framework matching the motor-related labels that perform the dimensionality reduction of the extracted connectivity features. From the validation results in three databases with motor imagery and motor execution tasks, we conclude that the single-trial Gaussian functional connectivity measure provides very competitive classifier performance values, being less affected by feature extraction parameters, like the sliding time window, and avoiding the use of prior linear spatial filtering. We also provide interpretability for the clustered functional connectivity patterns and hypothesize that the proposed kernel-based metric is promising for evaluating motor skills.

Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level.

Sensor-level human brain activity is studied during real and imaginary motor execution using functional near-infrared spectroscopy (fNIRS). Blood oxygenation and deoxygenation spatial dynamics exhibit pronounced hemispheric lateralization when performing motor tasks with the left and right hands. This fact allowed us to reveal biomarkers of hemodynamical response of the motor cortex on the motor execution, and use them for designing a sensing method for classification of the type of movement. The recognition accuracy of real movements is close to 100%, while the classification accuracy of imaginary movements is lower but quite high (at the level of 90%). The advantage of the proposed method is its ability to classify real and imaginary movements with sufficiently high efficiency without the need for recalculating parameters. The proposed system can serve as a sensor of motor activity to be used for neurorehabilitation after severe brain injuries, including traumas and strokes.

Instantaneous Midbrain Control of Saccade Velocity.

The ability to interact with our environment requires the brain to transform spatially represented sensory signals into temporally encoded motor commands for appropriate control of the relevant effectors. For visually guided eye movements, or saccades, the superior colliculus (SC) is assumed to be the final stage of spatial representation, and instantaneous control of the movement is achieved through a rate code representation in the lower brain stem. We investigated whether SC activity in nonhuman primates (Macaca mulatta, 2 male and 1 female) also uses a dynamic rate code, in addition to the spatial representation. Noting that the kinematics of amplitude-matched movements exhibit trial-to-trial variability, we regressed instantaneous SC activity with instantaneous eye velocity and found a robust correlation throughout saccade duration. Peak correlation was tightly linked to time of peak velocity, the optimal efferent delay between SC activity and eye velocity was constant at ∼12 ms both at onset and during the saccade, and SC neurons with higher firing rates exhibited stronger correlations. Moreover, the strong correlative relationship and constant efferent delay observation were preserved when eye movement profiles were substantially altered by a blink-induced perturbation. These results indicate that the rate code of individual SC neurons can control instantaneous eye velocity and argue against a serial process of spatial-to-temporal transformation. They also motivated us to consider a new framework of saccade control that does not incorporate traditionally accepted elements, such as the comparator and resettable integrator, whose neural correlates have remained elusive.SIGNIFICANCE STATEMENT All movements exhibit time-varying features that are under instantaneous control of the innervating neural command. At what stage in the brain is dynamical control present? It is well known that, in the skeletomotor system, neurons in the motor cortex use dynamical control. In the oculomotor system, in contrast, instantaneous velocity control of saccadic eye movements is not thought to be enforced until the lower brainstem. Using correlations between residual signals across trials, we show that instantaneous control of saccade velocity is present earlier in the visuo-oculomotor neuraxis, at the level of superior colliculus. The results require us to consider alternate frameworks of the neural control of saccades.

Neurofeedback learning for mental practice rather than repetitive practice improves neural pattern consistency and functional network efficiency in the subsequent mental motor execution.

During brain modulation, repeated mental practice may not always result in efficient learning. Particularly, the effectiveness of mental motor practice depends on how well one induces neural activity in a desired state consistently across mental trials, which calls for feedbacks to adjust one's performance. We hypothesized that even a brief experience of neurofeedback learning enhances trial-by-trial neural pattern consistency during subsequent mental motor execution and that this experience would change recruitment of functional connectivity in the motor imagery and default mode networks. To test this hypothesis, we conducted an experiment with two sessions of mental motor practice before and after a neurofeedback training session, in which participants conducted four types of first-person mental motor execution tasks (walking forward, turning left, turning right, and touching a tree). During the neurofeedback training session, in which participants conducted a virtual navigation game, 10 experimental participants received real-time fMRI neuro-feedbacks, while 10 control participants simply repeated the same mental task according to given cues without feedbacks. The experimental group showed significantly higher effects of neuro-feedback training on trial-by-trial consistencies and classification accuracies of activated neural patterns than the control group. Task-performing global node strength and network efficiency were increased in the motor imagery network but decreased in the default mode network only in the experimental group. These results demonstrate that even a brief experience of feedback learning is more effective than simple practice repetitions without evaluation, which was reflected in increased neural pattern consistency and task-dependent functional connectivity during a mental motor execution task.

Effects of Motor Imagery and Visual Neurofeedback on Activation in the Swallowing Network: A Real-Time fMRI Study.

Motor imagery of movements is used as mental strategy in neurofeedback applications to gain voluntary control over activity in motor areas of the brain. In the present functional magnetic resonance imaging (fMRI) study, we first addressed the question whether motor imagery and execution of swallowing activate comparable brain areas, which has been already proven for hand and foot movements. Prior near-infrared spectroscopy (NIRS) studies provide evidence that this is the case in the outer layer of the cortex. With the present fMRI study, we want to expand these prior NIRS findings to the whole brain. Second, we used motor imagery of swallowing as mental strategy during visual neurofeedback to investigate whether one can learn to modulate voluntarily activity in brain regions, which are associated with active swallowing, using real-time fMRI. Eleven healthy adults performed one offline session, in which they executed swallowing movements and imagined swallowing on command during fMRI scanning. Based on this functional localizer task, we identified brain areas active during both tasks and defined individually regions for feedback. During the second session, participants performed two real-time fMRI neurofeedback runs (each run comprised 10 motor imagery trials), in which they should increase voluntarily the activity in the left precentral gyrus by means of motor imagery of swallowing while receiving visual feedback (the visual feedback depicted one's own fMRI signal changes in real-time). Motor execution and imagery of swallowing activated a comparable network of brain areas including the bilateral pre- and postcentral gyrus, inferior frontal gyrus, basal ganglia, insula, SMA, and the cerebellum compared to a resting condition. During neurofeedback training, participants were able to increase the activity in the feedback region (left lateral precentral gyrus) but also in other brain regions, which are generally active during swallowing, compared to the motor imagery offline task. Our results indicate that motor imagery of swallowing is an adequate mental strategy to activate the swallowing network of the whole brain, which might be useful for future treatments of swallowing disorders.

Hand motor learning in a musical context and prefrontal cortex hemodynamic response: a functional near-infrared spectroscopy (fNIRS) study.

Due to movement automatization, the engagement of high-order cognitive processing during the motor execution of a task is expected to decrease over repetitions and practice. In this study, we assessed single session changes in the prefrontal hemodynamic signals in response to training a piano chord progression in an ecological experimental setting. We acquired functional near-infrared spectroscopy signals from 15 subjects without any previous experience on playing keyboard instruments. Our findings were that oxygenated hemoglobin changes at orbitofrontal cortex followed an inverted U-shaped curve over task execution, while the subjects' performance presented a steady slope. These results suggest an initial executive function engagement followed by facilitation of motor execution over time.

Neurophysiological and cognitive impairment following repeated sports concussion injuries in retired professional rugby league players.

BACKGROUND: Concussion is regarded as a common injury in rugby league, however no studies have explored the long-term neurophysiological and cognitive effects of repeated concussion injuries in this sport. METHODS: Former professional rugby athletes (n = 25) were compared to 25 age-matched participants with no history of a concussion. All participants completed standardised motor dexterity, reaction time, and cognitive tasks for working memory, associative learning and rule acquisition and reversal. Single-pulse transcranial magnetic stimulation (TMS) acquired motor evoked potentials and cortical silent period (cSP), as well as paired-pulse TMS for short latency intracortical inhibition and long intracortical inhibition (LICI). RESULTS: Compared to controls, dexterity and visuomotor reaction time was slower in the rugby group compared to controls (p = 0.02, p < 0.01, respectively). The rugby group also demonstrated poorer cognitive performance than controls (p range 0.02 to < 0.01). TMS revealed significantly reduced cSP at suprathreshold stimulation intensities (p range 0.02 to <0.01), and increased LICI (p = 0.03) in the rugby group. DISCUSSION: These findings of motor and cognitive changes, along with neurophysiological alterations, particularly with intracortical inhibition, nearly two decades post-concussion provides evidence for long-term sequelae for athletes with a history of repeated head trauma in contact sports.