Dynamic Causal Modeling of the Cortical Responses to Wrist Perturbations

Low-Cost Dynamometer for Measuring and Regulating Wrist Extension and Flexion Motor Tasks in Electroencephalography Experiments

A brain-computer interface could control a bionic hand by interpreting electroencephalographic (EEG) signals associated with wrist extension (WE) and wrist flexion (WF) movements. Misinterpretations of the EEG may stem from variations in the force, speed and range of these movements. To address this, we designed, constructed and tested a novel dynamometer, the IsoReg, which regulates WE and WF movements during EEG recording experiments. The IsoReg restricts hand movements to isometric WE and WF, controlling their speed and range of motion. It measures movement force using a dual-load cell system that calculates the percentage of maximum voluntary contraction and displays it to help users control movement force. Linearity and measurement accuracy were tested, and the IsoReg's performance was evaluated under typical EEG experimental conditions with 14 participants. The IsoReg demonstrated consistent linearity between applied and measured forces across the required force range, with a mean accuracy of 97% across all participants. The visual force gauge provided normalised force measurements with a mean accuracy exceeding 98.66% across all participants. All participants successfully controlled the motor tasks at the correct relative forces (with a mean accuracy of 89.90%) using the IsoReg, eliminating the impact of inherent force differences between typical WE and WF movements on the EEG analysis. The IsoReg offers a low-cost method for measuring and regulating movements in future neuromuscular studies, potentially leading to improved neural signal interpretation.

Modeling the cortical response elicited by wrist manipulation via a nonlinear delay differential embedding

Regarding motor processes, modeling healthy people's brains is essential to understand the brain activity in people with motor impairments. However, little research has been undertaken when external forces disturb limbs, having limited information on physiological pathways. Therefore, in this paper, a nonlinear delay differential embedding model is used to estimate the brain response elicited by externally controlled wrist movement in healthy individuals. The aim is to improve the understanding of the relationship between a controlled wrist movement and the generated cortical activity of healthy people, helping to disclose the underlying mechanisms and physiological relationships involved in the motor event. To evaluate the model, a public database from the Delft University of Technology is used, which contains electroencephalographic recordings of ten healthy subjects while wrist movement was externally provoked by a robotic system. In this work, the cortical response related to movement is identified via Independent Component Analysis and estimated based on a nonlinear delay differential embedding model. After a cross-validation analysis, the model performance reaches 90.21% ± 4.46% Variance Accounted For, and Correlation 95.14% ± 2.31%. The proposed methodology allows to select the model degree, to estimate a general predominant operation mode of the cortical response elicited by wrist movement. The obtained results revealed two facts that had not previously been reported: the movement's acceleration affects the cortical response, and a common delayed activity is shared among subjects. Going forward, identifying biomarkers related to motor tasks could aid in the evaluation of rehabilitation treatments for patients with upper limbs motor impairments.

Dynamic Causal Modeling on the Identification of Interacting Networks in the Brain: A Systematic Review

Dynamic causal modeling (DCM) has long been used to characterize effective connectivity within networks of distributed neuronal responses. Previous reviews have highlighted the understanding of the conceptual basis behind DCM and its variants from different aspects. However, no detailed summary or classification research on the task-related effective connectivity of various brain regions has been made formally available so far, and there is also a lack of application analysis of DCM for hemodynamic and electrophysiological measurements. This review aims to analyze the effective connectivity of different brain regions using DCM for different measurement data. We found that, in general, most studies focused on the networks between different cortical regions, and the research on the networks between other deep subcortical nuclei or between them and the cerebral cortex are receiving increasing attention, but far from the same scale. Our analysis also reveals a clear bias towards some task types. Based on these results, we identify and discuss several promising research directions that may help the community to attain a clear understanding of the brain network interactions under different tasks.

Nonlinear Modeling of Cortical Responses to Mechanical Wrist Perturbations Using the NARMAX Method

OBJECTIVE

Nonlinear modeling of cortical responses (EEG) to wrist perturbations allows for the quantification of cortical sensorimotor function in healthy and neurologically impaired individuals. A common model structure reflecting key characteristics shared across healthy individuals may provide a reference for future clinical studies investigating abnormal cortical responses associated with sensorimotor impairments. Thus, the goal of our study is to identify this common model structure and therefore to build a nonlinear dynamic model of cortical responses, using nonlinear autoregressive-moving-average model with exogenous inputs (NARMAX).

METHODS

EEG was recorded from ten participants when receiving continuous wrist perturbations. A common model structure detection method was developed for identifying a common NARMAX model structure across all participants, with individualized parameter values. The results were compared to conventional subject-specific models.

RESULTS

The proposed method achieved 93.91% variance accounted for (VAF) when implementing a one-step-ahead prediction and around 50% VAF for a k-step ahead prediction (k = 3), without a substantial drop of VAF as compare to subject-specific models. The estimated common structure suggests that the measured cortical response is a mixed outcome of the nonlinear transformation of external inputs and local neuronal interactions or inherent neuronal dynamics at the cortex.

CONCLUSION

The proposed method well determined the common characteristics across subjects in the cortical responses to wrist perturbations.

SIGNIFICANCE

It provides new insights into the human sensorimotor nervous system in response to somatosensory inputs and paves the way for future translational studies on assessments of sensorimotor impairments using our modeling approach.

Assessing Neural Connectivity and Associated Time Delays of Muscle Responses to Continuous Position Perturbations

Both linear and nonlinear electromyographic (EMG) connectivity has been reported during the expression of stretch reflexes, though it is not clear whether they are generated by the same neural pathways. To answer this question, we aim to distinguish linear and nonlinear connectivity, as well as their delays in muscle responses, resulting from continuous elbow joint perturbations. We recorded EMG from Biceps Brachii muscle when eight able-bodied participants were performing a steady elbow flexion torque while simultaneously receiving a continuous position perturbation. Using a recently developed phase coupling metric, we estimated linear and nonlinear connectivity as well as their associated delays between Biceps EMG responses and perturbations. We found that the time delay for linear connectivity (24.5 ± 5.4 ms) is in the range of short-latency stretch reflex period (< 35 ms), while that for nonlinear connectivity (53.8 ± 3.2 ms) is in the range of long-latency stretch reflex period (40-70 ms). These results suggest that the estimated linear connectivity between EMG and perturbations is very likely generated by the mono-synaptic spinal stretch reflex loop, while the nonlinear connectivity may be associated with multi-synaptic supraspinal stretch reflex loops. As such, this study provides new evidence of the nature of neural connectivity related to the stretch reflex.

The influence of nociceptive and neuropathic pain states on the processing of acute electrical nociceptive stimulation: A dynamic causal modeling study

INTRODUCTION

Despite the worldwide increase in prevalence of chronic pain and the subsequent scientific interest, researchers studying the brain and brain mechanisms in pain patients have not yet clearly identified the exact underlying mechanisms. Quantifying the neuronal interactions in electrophysiological data could help us gain insight into the complexity of chronic pain. Therefore, the aim of this study is to examine how different underlying pain states affect the processing of nociceptive information.

METHODS

Twenty healthy participants, 20 patients with non-neuropathic low back-related leg pain and 20 patients with neuropathic failed back surgery syndrome received nociceptive electrical stimulation at the right sural nerve with simultaneous electroencephalographic recordings. Dynamic Causal Modeling (DCM) was used to infer hidden neuronal states within a Bayesian framework.

RESULTS

Pain intensity ratings and stimulus intensity of the nociceptive stimuli did not differ between groups. Compared to healthy participants, both patient groups had the same winning DCM model, with an additional forward and backward connection between the somatosensory cortex and right dorsolateral prefrontal cortex.

DISCUSSION

The additional neuronal connection with the prefrontal cortex as seen in both pain patient groups could be a reflection of the higher attention towards pain in pain patients and might be explained by the higher levels of pain catastrophizing in these patients.

CONCLUSION

In contrast to the similar pain intensity ratings of an acute nociceptive electrical stimulus between pain patients and healthy participants, the brain is processing these stimuli in a different way.

Quantifying Altered Neural Connectivity of the Stretch Reflex in Chronic Hemiparetic Stroke

Post-stroke flexion synergy limits arm/hand function and is also linked to hyperactive stretch reflexes or spasticity. It is implicated in the increased role of indirect motor pathways following damage to direct corticospinal projections. We hypothesized that this maladaptive neuroplasticity also affects stretch reflexes. Specifically, multi-synaptic interactions in indirect motor pathways may increase nonlinear neural connectivity and time lag between stretch and reflex muscle response. Continuous position perturbations were applied to the elbow joint when eleven participants with stroke generated two levels of shoulder abduction (SABD) torques with their paretic arm to induce synergy-related spasticity. Likewise, the perturbations were applied to eleven control subjects while performing SABD and elbow flexion levels matching the synergy torques in stroke. We quantified linear and non-linear connectivity and the corresponding time lags between perturbations and muscle activity. Enhanced nonlinear connectivity with a prolonged time lag was found in stroke as compared to controls. Non-linear connectivity and time lag also increased with the expression of the flexion synergy, as induced by greater SABD load levels, in stroke. This study provides new evidence of changes in neural connectivity and long-latency time lag in the stretch reflex response post-stroke. The results suggest the contribution of indirect motor pathways to synergy-related spasticity.

Shoulder reflexes integrate elbow information at "long-latency" delay throughout a corrective action

Previous studies have demonstrated a progression of function when healthy subjects counter a sudden mechanical load. Short-latency reflexes are linked to local stretch of the particular muscle and its antagonist. Long-latency reflexes integrate stretch information from both local sources and muscles crossing remote joints appropriate for a limb's mechanical interactions. Unresolved is how sensory information is processed throughout the corrective response, since capabilities at some time can be produced by circuits acting at that delay and at briefer delays. One possibility is that local abilities are always expressed at a short-latency delay and integrative abilities are always expressed at a long-latency delay. Alternatively, the neural circuits may be altered over time, leading to a temporal shift in expressing certain abilities; a refractory period could retard integrative responses to a second perturbation, whereas priming could enable integrative responses at short latency. We tested between these three hypotheses in a shoulder muscle by intermixing trials of step torque with either torque pulses ( experiment 1) or double steps of torque ( experiment 2). The second perturbation occurred at 35, 60, and 110 ms after the first perturbation to probe processing throughout the corrective action. The second perturbation reliably evoked short-latency responses in the shoulder muscle linked to only shoulder motion and long-latency responses linked to both shoulder and elbow motion. This pattern is best accounted by the continuous action of controllers with fixed functions. NEW & NOTEWORTHY Sudden displacement of the limb evokes a short-latency reflex, 20-50 ms, based on local muscle stretch and a long-latency reflex based on integrating muscle stretch at different joints. A novel double-perturbation paradigm tested if these abilities are temporally conserved throughout the corrective response or are shifted (retarded or delayed) due to functional changes in the responsible circuits. Multi-joint integration was reliably expressed at a long-latency delay consistent with the continuous operation of circuits with fixed abilities.