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cM1 beta values in all eleven Participants (P1… P11). Individual cM1 beta values during baseline imagery, the three neurofeedback runs (NF run1, run2, run3) and the average across runs (Avg NF runs).
Source publication
Motor imagery (MI) has shown effectiveness in enhancing motor performance. This may be due to the common neural mechanisms underlying MI and motor execution (ME). The main region of the ME network, the primary motor cortex (M1), has been consistently linked to motor performance. However, the activation of M1 during motor imagery is controversial, w...
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Significance statement:
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Objectives:
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Citations
... Operant investigated for decades [2,3], primarily at the level of the brain [4]. In this manner, an individual learns to self-modulate a neural circuit, with the implication of neuroplastic changes [4][5][6][7]. LaCroix [8] hypothesized that in operant conditioning there are both implicit automatic learning processes occurring in parallel with explicit ones. However, these processes combined with physiology of a complex brain circuit is a poorly understood process [4]. ...
Operant conditioning of neural activation has been researched for decades in humans and animals. Many theories suggest two parallel learning processes, implicit and explicit. The degree to which feedback affects these processes individually remains to be fully understood and may contribute to a large percentage of non-learners. Our goal is to determine the explicit decision-making processes in response to feedback representing an operant conditioning environment. We developed a simulated operant conditioning environment based on a feedback model of spinal reflex excitability, one of the simplest forms of neural operant conditioning. We isolated the perception of the feedback signal from self-regulation of an explicit unskilled visuomotor task, enabling us to quantitatively examine feedback strategy. Our hypothesis was that feedback type, biological variability, and reward threshold affect operant conditioning performance and operant strategy. Healthy individuals (N = 41) were instructed to play a web application game using keyboard inputs to rotate a virtual knob representative of an operant strategy. The goal was to align the knob with a hidden target. Participants were asked to “down-condition” the amplitude of the virtual feedback signal, which was achieved by placing the knob as close as possible to the hidden target. We varied feedback type (knowledge of performance, knowledge of results), biological variability (low, high), and reward threshold (easy, moderate, difficult) in a factorial design. Parameters were extracted from real operant conditioning data. Our main outcomes were the feedback signal amplitude (performance) and the mean change in dial position (operant strategy). We observed that performance was modulated by variability, while operant strategy was modulated by feedback type. These results show complex relations between fundamental feedback parameters and provide the principles for optimizing neural operant conditioning for non-responders.
... This approach has shown substantial signal modulation in both healthy individuals [53][54][55] and neurological patients [48,49,52,56]. In contrast, studies using fMRI neurofeedback to train primary motor cortex (M1) upregulation reported inconsistent results: while some studies found no significant modulation [57,58], one study reported activation only for a subset of participants [59], and other studies reported even deactivation of M1 [54,60]. The neurophysiological basis for these inconsistent findings, particularly the lack of BOLD activation in M1 during motor imagery, has been a matter of debate [43,54]. ...
Neurofeedback allows individuals to monitor and self-regulate their brain
activity, potentially improving human brain function. Beyond the traditional
electrophysiological approach using primarily electroencephalography (EEG),
brain hemodynamics measured with functional magnetic resonance imaging
(fMRI) and more recently functional near-infrared spectroscopy (fNIRS) have
been used (hemodynamic-based neurofeedback), particularly to improve the
spatial specificity of neurofeedback. Over the last years, especially fNIRS has
attracted great attention because it offers several advantages over fMRI such
as increased user accessibility, cost-effectiveness, and mobility - the latter being
the most distinct feature of fNIRS. The next logical step would be to transfer
hemodynamic-based neurofeedback protocols that have already been proven
and validated by fMRI to mobile fNIRS. However, this undertaking is not
always easy, especially since fNIRS novices may miss important fNIRS-specific
methodological challenges. This review is aimed at researchers from different
fields who seek to exploit the unique capabilities of fNIRS for neurofeedback. It
carefully addresses fNIRS-specific challenges and offers suggestions for possible
solutions. If the challenges raised are addressed and further developed, fNIRS
could emerge as a useful neurofeedback technique with its own unique application
potential - the targeted training of brain activity in real-world environments,
thereby significantly expanding the scope and scalability of hemodynamic-based
neurofeedback applications.
... MI-NFB has been reported to provide feedback on activity in the primary motor cortex (M1) [38,39] and supplementary motor area (SMA) [40,41] in many studies. These areas are activated by motor imagery and have been reported to be related to the accuracy of motor imagery [31]. ...
Neurofeedback (NFB) is a closed-loop technique in which the patient receives feedback on brain activity to encourage voluntary control of brain activity. NFB promotes neuroplasticity and changes the brain functionally and structurally. Motor imagery-based NFB (MI-NFB) can improve motor imagery ability by providing feedback on brain activity during motor imagery, thereby showing effectiveness in performance and motor learning. Furthermore, the effects of MI-NFB are further enhanced when it is combined with noninvasive brain stimulation and motor exercise. Therefore, MI-NFB is used in the physiotherapy of patients with neurological diseases, such as stroke and Parkinson disease, as well as children with attention deficit-hyperactivity disorder and elderly people. This chapter reviews MI-NFB in physiotherapy practice, thus contributing to the development of effective evidence-based physiotherapy.
... The selfregulation of neural activity through neurofeedback training has been found in rodents, nonhuman primates and humans (Schafer and Moore, 2011;Collinger et al., 2013;Clancy et al., 2014). The consequence of this self-regulation can be represented as changes in intracortical neuronal synchronization that facilitate the output of EEG-based BCI (Hanslmayr et al., 2005;Blefari et al., 2015). Moreover, neurofeedback training can also exert long-term changes in the intrinsic functional connectivity in the visuo-spatial-motor network, even more than 2 months after the training (Megumi et al., 2015). ...
The brain-computer interface (BCI)-mediated rehabilitation is emerging as a solution to restore motor skills in paretic patients after stroke. In the human brain, cortical motor neurons not only fire when actions are carried out but are also activated in a wired manner through many cognitive processes related to movement such as imagining, perceiving, and observing the actions. Moreover, the recruitment of motor cortexes can usually be regulated by environmental conditions, forming a closed-loop through neurofeedback. However, this cognitive-motor control loop is often interrupted by the impairment of stroke. The requirement to bridge the stroke-induced gap in the motor control loop is promoting the evolution of the BCI-based motor rehabilitation system and, notably posing many challenges regarding the disease-specific process of post stroke motor function recovery. This review aimed to map the current literature surrounding the new progress in BCI-mediated post stroke motor function recovery involved with cognitive aspect, particularly in how it refired and rewired the neural circuit of motor control through motor learning along with the BCI-centric closed-loop.
... Crucially, recording of the actual force trace during scan acquisition allowed the use of MVC-normalized task regressors. Brain motor-task studies have used recorded force data in fMRI models to account for timing differences by defining task and rest blocks (Shanahan et al., 2015), by creating a force nuisance regressor to remove effects related to the actual achieved force or unintended exertion (Blefari et al., 2015;Haller et al., 2009) ...
Upper extremity motor paradigms during spinal cord functional magnetic resonance imaging (fMRI) can provide insight into the functional organization of the cord. Hand-grasping is an important daily function with clinical significance, but previous studies of similar squeezing movements have not reported consistent areas of activity and are limited by sample size and simplistic analysis methods. Here, we study spinal cord fMRI activation using a unimanual isometric hand-grasping task that is calibrated to participant maximum voluntary contraction (MVC). Two task modeling methods were considered: (1) a task regressor derived from an idealized block design (Ideal) and (2) a task regressor based on the recorded force trace normalized to individual MVC (%MVC). Across these two methods, group motor activity was highly lateralized to the hemicord ipsilateral to the side of the task. Activation spanned C5-C8 and was primarily localized to the C7 spinal cord segment. Specific differences in spatial distribution are also observed, such as an increase in C8 and dorsal cord activity when using the %MVC regressor. Furthermore, we explored the impact of data quantity and spatial smoothing on sensitivity to hand-grasp motor task activation. This analysis shows a large increase in number of active voxels associated with the number of fMRI runs, sample size, and spatial smoothing, demonstrating the impact of experimental design choices on motor activation.
... MI comes in two flavors: visual imagination (VI) and kinaesthetic imagination (KI) [11]. MI has a significant function since it is often utilized in motor learning exercises [12]. It may be beneficial to athletes, skill development, and rehabilitation since it may be used to improve motor performance over periods [13]. ...
... When physical movement, such as clenching or tapping, was imagined by participants using MI, the cerebellum was activated [10]. According to a study by Blefari et al. [12], even though cerebellar activity was high during motor execution, it decreased by 30% during MI. Neurofeedback is using visual or auditory stimuli to assist in self-regulation in a person [12]. ...
... According to a study by Blefari et al. [12], even though cerebellar activity was high during motor execution, it decreased by 30% during MI. Neurofeedback is using visual or auditory stimuli to assist in self-regulation in a person [12]. Real-time fMRI (rt-fMRI) systems have been used for neurofeedback because of their speed and capacity to supply signal feedback from the brain activity of the subcortical brain framework. ...
Objective:
The potential of neurofeedback to alter the M1-cerebellum connectivity was explored using motor imagery-based rt-fMRI. These regions were chosen due to their importance in motor performance and motor rehabilitation.
Methods:
Four right-handed individuals were recruited to examine the potential to change the M1-cerebellum neurofeedback link. The University of Glasgow Cognitive Neuroimaging Centre used a 3T MRI scanner from January 2019 to January 2020 to conduct this prospective study. Everyone participated in each fMRI session, which included six NF training runs. Participants were instructed to imagine complicated hand motions during the NF training to raise a thermometer bar's height. To contrast the correlation coefficients between the initial and last NF runs, a t-test was performed post hoc.
Results:
The neurofeedback connection between M1 and the cerebellum was strengthened in each participant. Motor imagery strategy was a significant task in training M1-cerebellum connectivity as participants used it successfully to enhance the activation level between these regions during M1-cerebellum modulation using real-time fMRI. The t-test and linear regression, on the other hand, showed this increase to be insignificant.
Conclusion:
A novel technique to manipulate M1-cerebellum connectivity was discovered using real-time fMRI NF. This study showed that each participant's neurofeedback connectivity between M1 and cerebellum was enhanced. This increase, on the other hand, was insignificant statistically. The results showed that the connectivity between both areas increased positively. Through the integration of fMRI and neurofeedback, M1-cerebellum connectivity can be positively affected.
... More than half of the reviewed studies engaged healthy volunteers to probe the feasibility of NF training in FSC regions. These studies sought to modify behavioral aspects such as emotion regulation , motor performance (Hui et al., 2014;Blefari et al., 2015;Scharnowski et al., 2015;Al-Wasity et al., 2021), motivation , working memory Sherwood et al., 2016), speech processing (Rota et al., 2009) and social avoidance (Lisk et al., 2020) with their results underscoring the relevance of the functional organization of the FSC. The studies that aimed to improve motor performance focused on regions in the motor subsystem, such as the M1, the SMA, and the PMA. ...
Dysregulated frontostriatal circuitries are viewed as a common target for the treatment of aberrant behaviors in various psychiatric and neurological disorders. Accordingly, experimental neurofeedback paradigms have been applied to modify the frontostriatal circuitry. The human frontostriatal circuitry is topographically and functionally organized into the “limbic,” the “associative,” and the “motor” subsystems underlying a variety of affective, cognitive, and motor functions. We conducted a systematic review of the literature regarding functional magnetic resonance imaging-based neurofeedback studies that targeted brain activations within the frontostriatal circuitry. Seventy-nine published studies were included in our survey. We assessed the efficacy of these studies in terms of imaging findings of neurofeedback intervention as well as behavioral and clinical outcomes. Furthermore, we evaluated whether the neurofeedback targets of the studies could be assigned to the identifiable frontostriatal subsystems. The majority of studies that targeted frontostriatal circuitry functions focused on the anterior cingulate cortex, the dorsolateral prefrontal cortex, and the supplementary motor area. Only a few studies ( n = 14) targeted the connectivity of the frontostriatal regions. However, post-hoc analyses of connectivity changes were reported in more cases ( n = 32). Neurofeedback has been frequently used to modify brain activations within the frontostriatal circuitry. Given the regulatory mechanisms within the closed loop of the frontostriatal circuitry, the connectivity-based neurofeedback paradigms should be primarily considered for modifications of this system. The anatomical and functional organization of the frontostriatal system needs to be considered in decisions pertaining to the neurofeedback targets.
... The findings of this study might be useful to guide brain-targeted interventions. Areas such as the SMA [67], premotor cortex [68], and primary motor cortex [69] have been the target for upregulation in neurofeedback studies, consequently increasing their functional connectivity to other brain regions. Neurofeedback interventions also target connectivity between motor regions directly [70]. ...
Aim:
The aim of this study was to differentiate the effects of spinal cord injury (SCI) and central neuropathic pain (CNP) on effective connectivity during motor imagery of legs, where CNP is typically experienced.
Methods:
Multichannel EEG was recorded during motor imagery of the legs in 3 groups of people: able-bodied (N = 10), SCI with existing CNP (N = 10), and SCI with no CNP (N = 20). The last group was followed up for 6 months to check for the onset of CNP. Source reconstruction was performed to obtain cortical activity in 17 areas spanning sensorimotor regions and pain matrix. Effective connectivity was calculated using the directed transfer function in 4 frequency bands and compared between groups.
Results:
A total of 50% of the SCI group with no CNP developed CNP later. Statistically significant differences in effective connectivity were found between all groups. The differences between groups were not dependent on the frequency band. Outflows from the supplementary motor area were greater for the able-bodied group while the outflows from the secondary somatosensory cortex were greater for the SCI groups. The group with existing CNP showed the least differences from the able-bodied group, appearing to reverse the effects of SCI. The connectivities involving the pain matrix were different between able-bodied and SCI groups irrespective of CNP status, indicating their involvement in motor networks generally.
Significance:
The study findings might help guide therapeutic interventions targeted at the brain for CNP alleviation as well as motor recovery post SCI.
... NF refers to a procedure in which contingent feedback of motor-related neural activity is presented to the participants via visual or audio signals [12,13], haptic stimulation [14,15], or kinaesthetic representations assisted by robotic devices [16][17][18] to facilitate the regulation of neural patterns. As the most widely used paradigm for neurorehabilitation, motor imagery (MI)-based NF training has been proven useful in improving impaired motor cortical activity in patients [19][20][21]. These MI-based NF studies have typically focused on training the modulation of sensorimotor rhythms (SMRs) that are oscillations occurring in the alpha and beta bands recorded by electroencephalogram over the sensorimotor areas during MI [22][23][24][25]. ...
Objective. Investigating how to promote the functional activation of the central sensorimotor system is an important goal in the neurorehabilitation research domain. We aim to validate the effectiveness of facilitating cortical excitability using a closed-loop visuomotor task, in which the task difficulty is adaptively adjusted based on an individual’s sensorimotor cortical activation. Approach. We developed a novel visuomotor task, in which subjects moved a handle of a haptic device along a specific path while exerting a constant force against a virtual surface under visual feedback. The difficulty levels of the task were adapted with the aim of increasing the activation of sensorimotor areas, measured non-invasively by functional near-infrared spectroscopy. The changes in brain activation of the bilateral prefrontal cortex, sensorimotor cortex, and the occipital cortex obtained during the adaptive visuomotor task (adaptive group), were compared to the brain activation pattern elicited by the same duration of task with random difficulties in a control group. Main results. During one intervention session, the adaptive group showed significantly increased activation in the bilateral sensorimotor cortex, also enhanced effective connectivity between the prefrontal and sensorimotor areas compared to the control group. Significance. Our findings demonstrated that the fNIRS-based adaptive visuomotor task with high ecological validity can facilitate the neural activity in sensorimotor areas and thus has the potential to improve hand motor functions.
... It also served to assess motor performance of the participants. We used the raw data from the force sensors to locate individual tap onsets (force derivative, Ḟ , above 10% of maximum, as in (Blefari et al., 2015) and calculated reaction times for the externally-cued task as the time difference between cue and tap onset. To test for agreement in motor performance between the externally-cued and self-paced experiments, mean tapping frequencies and mean peak forces ( Fig. 1) were correlated, indicating good protocol adherence and comparable motor output (Bichsel et al., 2018). ...
Deep brain stimulation (DBS) electrodes provide an unparalleled window to record and investigate neuronal activity right at the core of pathological brain circuits. In Parkinson's disease (PD), basal ganglia beta-oscillatory activity (13–35 Hz) seems to play an outstanding role. Conventional DBS, which globally suppresses beta-activity, does not meet the requirements of a targeted treatment approach given the intricate interplay of physiological and pathological effects of beta-frequencies. Here, we wanted to characterise the local field potential (LFP) in the subthalamic nucleus (STN) in terms of beta-burst prevalence, amplitude and length between movement and rest as well as during self-paced as compared to goal-directed motor control. Our electrophysiological recordings from externalised DBS-electrodes in nine patients with PD showed a marked decrease in beta-burst durations and prevalence during movement as compared to rest as well as shorter and less frequent beta-bursts during cued as compared to self-paced movements. These results underline the importance of beta-burst modulation in movement generation and are in line with the clinical observation that cued motor control is better preserved than self-paced movements. Furthermore, our findings motivate the use of adaptive DBS based on beta-bursts, which selectively trim longer beta-bursts, as it is more suitable and efficient over a range of motor behaviours than conventional DBS.
