Towards neurally guided physical therapy

Fine motor skills such as individual finger movements are impaired after neurological injury (e.g. stroke). Conventional therapy, operating at the limb, has limited success in rehabilitating fine motor skills after stroke. This work lays the foundation for guiding physical therapy of the hand from the brain, rather than from the limb. This work aims to answer three fundamental questions: (1) how do people learn to control their own neural activity? (2) how can we best decode patterns of neural activity related to individual fingers? (3) can people learn to shift the patterns of neural activity associated with each of their fingers? We first investigated how people learn to control their own neural activity. Neurofeedback experiments in the MRI scanner are expensive, time-consuming, and rely on human participants to learn to control their own brain activity. Minor errors in data processing, feedback delivery, or instructions to participants can ruin an fMRI neurofeedback experiment, without any indication as to what was the problem. Here, we investigate how the properties of the fMRI signal, feedback timing, and self-regulation strategies affect this learning, using a simulated neurofeedback environment to compare how participants' strategies interact with feedback timing. In an experiment with human participants playing a simple neurofeedback game with a simulated brain, continuous feedback led to faster learning than an intermittent feedback signal. However, in a computer model of automatic reward-based learning, intermittent feedback was more reliable. These results provide critical guidelines to the design of fMRI neurofeedback experiments. Next, we developed techniques to most accurately decode individual finger presses in real-time from fMRI recordings. The neural correlates of individual finger movements can be revealed using multivoxel pattern analysis (MVPA) of fMRI data. Neurofeedback of MVPA, known as decoded neurofeedback, has manipulated behaviors such as visual perception and confidence judgements. However, this technique has yet to be applied to sensorimotor behaviors associated with individual fingers. Here we investigated how best to decode patterns of neural activity from sensorimotor cortex in real-time. To set key parameters for the experiment, we used offline simulations of decoded neurofeedback using previously recorded fMRI data to predict neurofeedback performance. We show that these predictions align with real neurofeedback performance at the group level and can also explain individual differences in neurofeedback success. Finally, we investigated if people could learn to shift the neural patterns related to their own finger movements, and how this might affect fine motor skills. Deficits in individual finger movements after stroke are associated with weakened, overlapping neural activity patterns. Here we investigated whether neural activity patterns of fingers in sensorimotor cortex could be shifted using decoded neurofeedback in healthy individuals. This is meant to provide the groundwork to using neurofeedback on stroke patients, except in the opposite direction, by decreasing confusion between finger pairs instead of increasing confusion between them. We discovered that participants could learn to shift the pattern associated with their ring finger but not that of their middle finger. We also found that participants' finger movement behaviors changed in the ring and little fingers, but not in the index or middle fingers. Our results show that neural activity and behaviors associated with the ring finger are more readily modulated than those associated with the middle finger. These results have broader implications for rehabilitation of individual finger movements, which may be limited or enhanced by individual finger plasticity after neurological injury.Mechanical Engineerin

Basal ganglia-cortical connectivity underlies self-regulation of brain oscillations in humans

Brain-Computer Interface操作の得手不得手に関わる脳回路を発見 --操作を「考える」か「感じる」か、個人差に合わせた技術開発へ期待--. 京都大学プレスリリース. 2022-08-10.Brain-computer interfaces provide an artificial link by which the brain can directly interact with the environment. To achieve fine brain-computer interface control, participants must modulate the patterns of the cortical oscillations generated from the motor and somatosensory cortices. However, it remains unclear how humans regulate cortical oscillations, the controllability of which substantially varies across individuals. Here, we performed simultaneous electroencephalography (to assess brain-computer interface control) and functional magnetic resonance imaging (to measure brain activity) in healthy participants. Self-regulation of cortical oscillations induced activity in the basal ganglia-cortical network and the neurofeedback control network. Successful self-regulation correlated with striatal activity in the basal ganglia-cortical network, through which patterns of cortical oscillations were likely modulated. Moreover, basal ganglia-cortical network and neurofeedback control network connectivity correlated with strong and weak self-regulation, respectively. The findings indicate that the basal ganglia-cortical network is important for self-regulation, the understanding of which should help advance brain-computer interface technology

Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review

First published: 25 April 2020Neurofeedback training using real-time functional magnetic resonance imaging (rtfMRI-NF) allows subjects voluntary control of localised and distributed brain activity. It has sparked increased interest as a promising non-invasive treatment option in neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance are yet to be determined. In this work, we present the first extensive review of acquisition, processing and quality control methods available to improve the quality of the neurofeedback signal. Furthermore, we investigate the state of denoising and quality control practices in 128 recently published rtfMRI-NF studies. We found: (a) that less than a third of the studies reported implementing standard real-time fMRI denoising steps, (b) significant room for improvement with regards to methods reporting and (c) the need for methodological studies quantifying and comparing the contribution of denoising steps to the neurofeedback signal quality. Advances in rtfMRI-NF research depend on reproducibility of methods and results. Notably, a systematic effort is needed to build up evidence that disentangles the various mechanisms influencing neurofeedback effects. To this end, we recommend that future rtfMRI-NF studies: (a) report implementation of a set of standard real-time fMRI denoising steps according to a proposed COBIDAS-style checklist (https://osf.io/kjwhf/), (b) ensure the quality of the neurofeedback signal by calculating and reporting community-informed quality metrics and applying offline control checks and (c) strive to adopt transparent principles in the form of methods and data sharing and support of open-source rtfMRI-NF software. Code and data for reproducibility, as well as an interactive environment to explore the study data, can be accessed at https://github. com/jsheunis/quality-and-denoising-in-rtfmri-nf.LSH‐TKI, Grant/Award Number: LSHM16053‐SGF; Philips Researc

Control freaks: Towards optimal selection of control conditions for fMRI neurofeedback studies

fMRI Neurofeedback research employs many different control conditions. Currently, there is no consensus as to which control condition is best, and the answer depends on what aspects of the neurofeedback-training design one is trying to control for. These aspects can range from determining whether participants can learn to control brain activity via neurofeedback to determining whether there are clinically significant effects of the neurofeedback intervention. Lack of consensus over criteria for control conditions has hampered the design and interpretation of studies employing neurofeedback protocols. This paper presents an overview of the most commonly employed control conditions currently used in neurofeedback studies and discusses their advantages and disadvantages. Control conditions covered include no control, treatment-as-usual, bidirectional-regulation control, feedback of an alternative brain signal, sham feedback, and mental-rehearsal control. We conclude that the selection of the control condition(s) should be determined by the specific research goal of the study and best procedures that effectively control for relevant confounding factor

Modeling subject perception and behaviour during neurofeedback training

International audienceNeurofeedback training (NFT) describes a closed-loop paradigm in which a subject is provided with a real time evaluation of his/her brain activity. As a learning process, it is designed to help the subject learn to apprehend his/her own cognitive states and better modulate them through mental actions. Its use for therapeutic purposes has gained a lot of traction in the public sphere in the last decade, but conflicting evidence concerning its efficacy has led to a two-pronged effort from the scientific community. First, a call for experimental protocols and reports standardization [1], aiming to reduce the variability of the results and provide a reliable set of data to describe empirical findings. Second, an effort towards a formal description of the neurofeedback loop and the main hypotheses that guide the design of our experiments, in order to explain or even predict the effects of such training [2,3]

Operant conditioning of monosynaptic spinal reflexes and its computational modeling and simulation

Spasticity, or more specifically hyperreflexia, is a common impairment following neurological injury such as stroke. Current clinical interventions aimed at reducing rectus femoris (RF) hyperreflexia have shown modest effect but entails side effects and limited clinical evidence. My previous research has shown that RF hyperreflexia is associated with reduced knee flexion in people post-stroke with Stiff-Knee gait (SKG). I posit that reducing RF hyperreflexia should improve walking following SKG after stroke. I developed a non-pharmacological procedure using operant H-reflex conditioning of the RF, which allows the patient to self-modulate one’s spinal reflex activity, elicited via electrical stimulation on peripheral nerve. With current evidence that operant H-reflex conditioning enhances gait function in individuals with SCI, I conducted a proof-of-concept study to examine the feasibility of this procedure on the RF for healthy and post-stroke individuals. Operant conditioning of neural activation has a high incidence of non-responders, and delineating the explicit response to feedback can help determine why some individuals may not respond to neurofeedback training. I developed a simulated operant H-reflex conditioning neurofeedback environment that separated the ability to self-regulate the neurofeedback signal from its perception by using an explicit, unskilled visuomotor task. Main outcomes indicated that biological variability modulates performance and operant strategy depending on the feedback type. While previous results provided a holistic view of the effect of feedback parameters on overall performance and operant strategy, the next approach focused on determining whether such decisions could be predicted based on feedback on a trial-by-trial basis. I observed that the feedback sensitivity was modulated by biological variability and reward threshold. I used computational models to investigate the best estimate of learning resulting in feedback-weighted averages of previous decisions. This thesis introduces a novel simulated operant H-reflex conditioning environment that serves as a simple and robust model to quickly examine learning mechanisms, optimize learning, and potentially identify non-responders.Mechanical Engineerin

Real-time fMRI connectivity neurofeedback for modulation of the motor system

Advances in functional magnetic resonance imaging (fMRI) have enabled an understanding of the neural mechanisms underlying human brain functions such as motor functions. In recent decades fMRI, which is a non-invasive and highresolution technique, has been used to investigate the functions of the human brain using the blood oxygen level dependent (BOLD) response as an indirect measurement of brain neural activities. Real-time fMRI (rt-fMRI) has been used as neurofeedback to enable individuals to regulate their neural activity to achieve improvements in their health and performance, such as their motor performance. Neurofeedback can be defined as the measurement of the neural activity of a participant that is presented to them as visual or auditory signals that enable self-regulation of neural activity. Rt-fMRI has also been used to provide feedback about the connectivity between brain regions. Such connectivity neurofeedback can be a more effective feedback strategy than providing feedback from a single region. Recently, connectivity neurofeedback has been explored to examine how functional connectivity of cortical areas and subcortical areas of the brain can be modulated. Enhancing connectivity between cortical and subcortical regions holds promise for the improvement of performance, particularly motor function performance. The aim of this PhD research was to modulate connectivity neurofeedback by using real-time fMRI neurofeedback (rt-fMRI-NF) between brain regions and to investigate whether any possible enhancement in the activation due to a successful fMRI-NF will translate into changes in behavioural measures. The thesis research began with experimental work to establish the experimental paradigm. This included work, using fMRI, to develop and test localisers for different motor areas such as primary motor cortex (M1), supplementary motor cortex (SMA), the motor cerebellum and the motor thalamus. The results showed that the execution of actions, such as hand clenching, can be used to functionally activate many motor areas including M1, SMA and the cerebellum. The motor thalamus was localised using a motor thalamus mask that was created offline using the Talairach atlas. All localisers tested in this research were feasible and able to be used for applications such as rt-fMRI-NF research to define the regions of interest. The first rt-fMRI connectivity neurofeedback experimental study of this thesis was conducted to determine whether healthy participants can use neurofeedback to enhance the connectivity between M1 and the thalamus using rt-fMRI. It also aimed to investigate whether successful rt-fMRI-NF of M1- thalamus connectivity could translate into changes in behavioural measures. For this purpose, the behavioural tasks were conducted before and after each MRI session. Two behavioural tasks were used in this experiment: Go/No Go and switching tasks. The results of this experiment showed a significant increase in connectivity neurofeedback in the experimental group (M1-thalamus), hence, rt-fMRI-NF is a useful tool to modulate functional connectivity between M1 and the thalamus using motor imagery and it facilitates the learning by participants of new mental strategies to upregulate M1-thalamus connectivity. The behavioural tasks showed a significant reduction in the switching time in the experimental group while Go/No Go task did not show a significant reduction in the reaction time in the experimental group. The second rt-fMRI connectivity neurofeedback experimental study of this thesis was conducted to investigate the ability of neurofeedback to modulate M1-cerebellum connectivity using motor imagery based rt-fMRI-NF. The results of this research showed enhanced connectivity between M1 and the cerebellum in each participant. However, this enhancement was not statistically significant. In summary, this PhD thesis extends and validates the usefulness of connectivity neurofeedback using motor imagery based rt-fMRI to modulate the correlation between cortical and subcortical brain regions. Successful modulation using this technique has the potential to lead to an enhancement in motor functions. Thereby, the results of this PhD research may help to advance connectivity neurofeedback for use as a supplementary treatment for many brain disorders such as stroke recovery and Parkinson’s disease