Early Findings on Functional Connectivity Correlates of Behavioral Outcomes of Brain-Computer Interface Stroke Rehabilitation Using Machine Learning

Predicting upper limb motor recovery in subacute stroke patients via fNIRS-measured cerebral functional responses induced by robotic training

BACKGROUND

Neural activation induced by upper extremity robot-assisted training (UE-RAT) helps characterize adaptive changes in the brains of poststroke patients, revealing differences in recovery potential among patients. However, it remains unclear whether these task-related neural activities can effectively predict rehabilitation outcomes. In this study, we utilized functional near-infrared spectroscopy (fNIRS) to measure participants' neural activity profiles during resting and UE-RAT tasks and developed models via machine learning to verify whether task-related functional brain responses can predict the recovery of upper limb motor function.

METHODS

Cortical activation and brain network functional connectivity (FC) in brain regions such as the superior frontal cortex, premotor cortex, and primary motor cortex were measured using fNIRS in 82 subacute stroke patients in the resting state and during UE-RAT. The Fugl-Meyer Upper Extremity Assessment Scale (FMA-UE) was chosen as the index for assessing upper extremity motor function, and clinical information such as demographic and neurophysiological data was also collected. Robust features were screened in 100 randomly divided training sets using the least absolute shrinkage and selection operator (LASSO) method. Based on the selected robust features, machine learning algorithms were used to develop clinical models, fNIRS models, and combined models that integrated both clinical and fNIRS features. Finally, Shapley Additive Explanations (SHAP) was applied to interpret the prediction process and analyze key predictive factors.

RESULTS

Compared to the resting state, task-related FC is a more robust feature for modeling, with screening frequencies above 90%. The combined models built using artificial neural networks (ANNs) and support vector machines (SVMs) significantly outperformed the other algorithms, with an average AUC of 0.861 (± 0.087) for the ANN and an average correlation coefficient (r) of 0.860 (± 0.069) for the SVM. Furthermore, predictive factor analysis of the models revealed that FC measured during tasks is the most important factor for predicting upper limb motor function.

CONCLUSION

This study confirmed that UE-RAT-induced FC can serve as an important predictor of rehabilitation, especially when combined with clinical information, further enhancing the accuracy of model predictions. These findings provide new insights for the early prediction of patients' recovery potential, which may contribute to personalized rehabilitation decisions.

Carry-Over Effect of Deep Cerebellar Stimulation-Mediated Motor Recovery in a Rodent Model of Traumatic Brain Injury

BACKGROUND

We previously demonstrated that deep brain stimulation (DBS) of lateral cerebellar nucleus (LCN) can enhance motor recovery and functional reorganization of perilesional cortex in rodent models of stroke or TBI.

OBJECTIVE

Considering the treatment-related neuroplasticity observed at the perilesional cortex, we hypothesize that chronic LCN DBS-enhanced motor recovery observed will carry-over even after DBS has been deactivated.

METHODS

Here, we directly tested the enduring effects of LCN DBS in male Long Evans rats that underwent controlled cortical impact (CCI) injury targeting sensorimotor cortex opposite their dominant forepaw followed by unilateral implantation of a macroelectrode into the LCN opposite the lesion. Animals were randomized to DBS or sham treatment for 4 weeks during which the motor performance were characterize by behavioral metrics. After 4 weeks, stimulation was turned off, with assessments continuing for an additional 2 weeks. Afterward, all animals were euthanized, and tissue was harvested for further analyses.

RESULTS

Treated animals showed significantly greater motor improvement across all behavioral metrics relative to untreated animals during the 4-week treatment, with functional gains persisting across 2-week post-treatment. This motor recovery was associated with the increase in CaMKIIα and BDNF positive cell density across perilesional cortex in treated animals.

CONCLUSIONS

LCN DBS enhanced post-TBI motor recovery, the effect of which was persisted up to 2 weeks beyond stimulation offset. Such evidence should be considered in relation to future translational efforts as, unlike typical DBS applications, treatment may only need to be provided until such time as a new function plateau is achieved.

Investigating functional connectivity related to stroke recovery: A systematic review

INTRODUCTION

Stroke recovery is a complex process influenced by various factors, including specific neural reorganization. The objective of this systematic review was to identify important functional connectivity (FC) changes in resting-state fMRI data that were often correlated with motor, emotional, and cognitive outcome improvement.

METHOD

A systematic search using PubMed and SCOPUS databases was conducted to identify relevant studies published between 2010 and 2023.

RESULTS

A total of 766 studies were identified, of which 20 studies (602 S individuals) met the inclusion criteria. Fourteen studies focussed on motor recovery while six on cognitive recovery. All studies reported interhemispheric FC to be strongly associated with motor and cognitive recovery. The preservation and changes of M1-M1 (eight incidences) and M1-SMA (nine incidences) FC were found to be strongly correlated with motor function improvement. For cognitive recovery, restoration and preservation of FC with and between default mode network (DMN)-related regions were important for the process.

CONCLUSIONS

This review identified specific patterns of FC that were consistently reported with recovery of motor and cognitive function. The findings may serve in refining future management strategies to enhance patient outcomes.

BCI-FES With Multimodal Feedback for Motor Recovery Poststroke

An increasing number of research teams are investigating the efficacy of brain-computer interface (BCI)-mediated interventions for promoting motor recovery following stroke. A growing body of evidence suggests that of the various BCI designs, most effective are those that deliver functional electrical stimulation (FES) of upper extremity (UE) muscles contingent on movement intent. More specifically, BCI-FES interventions utilize algorithms that isolate motor signals-user-generated intent-to-move neural activity recorded from cerebral cortical motor areas-to drive electrical stimulation of individual muscles or muscle synergies. BCI-FES interventions aim to recover sensorimotor function of an impaired extremity by facilitating and/or inducing long-term motor learning-related neuroplastic changes in appropriate control circuitry. We developed a non-invasive, electroencephalogram (EEG)-based BCI-FES system that delivers closed-loop neural activity-triggered electrical stimulation of targeted distal muscles while providing the user with multimodal sensory feedback. This BCI-FES system consists of three components: (1) EEG acquisition and signal processing to extract real-time volitional and task-dependent neural command signals from cerebral cortical motor areas, (2) FES of muscles of the impaired hand contingent on the motor cortical neural command signals, and (3) multimodal sensory feedback associated with performance of the behavioral task, including visual information, linked activation of somatosensory afferents through intact sensorimotor circuits, and electro-tactile stimulation of the tongue. In this report, we describe device parameters and intervention protocols of our BCI-FES system which, combined with standard physical rehabilitation approaches, has proven efficacious in treating UE motor impairment in stroke survivors, regardless of level of impairment and chronicity.

Neural correlates of user learning during long-term BCI training for the Cybathlon competition

Background

Brain-computer interfaces (BCIs) are systems capable of translating human brain patterns, measured through electroencephalography (EEG), into commands for an external device. Despite the great advances in machine learning solutions to enhance the performance of BCI decoders, the translational impact of this technology remains elusive. The reliability of BCIs is often unsatisfactory for end-users, limiting their application outside a laboratory environment.

Methods

We present the analysis on the data acquired from an end-user during the preparation for two Cybathlon competitions, where our pilot won the gold medal twice in a row. These data are of particular interest given the mutual learning approach adopted during the longitudinal training phase (8 months), the long training break in between the two events (1 year) and the demanding evaluation scenario. A multifaceted perspective on long-term user learning is proposed: we enriched the information gathered through conventional metrics (e.g., accuracy, application performances) by investigating novel neural correlates of learning in different neural domains.

Results

First, we showed that by focusing the training on user learning, the pilot was capable of significantly improving his performance over time even with infrequent decoder re-calibrations. Second, we revealed that the analysis of the within-class modifications of the pilot’s neural patterns in the Riemannian domain is more effective in tracking the acquisition and the stabilization of BCI skills, especially after the 1-year break. These results further confirmed the key role of mutual learning in the acquisition of BCI skills, and particularly highlighted the importance of user learning as a key to enhance BCI reliability.

Conclusion

We firmly believe that our work may open new perspectives and fuel discussions in the BCI field to shift the focus of future research: not only to the machine learning of the decoder, but also in investigating novel training procedures to boost the user learning and the stability of the BCI skills in the long-term. To this end, the analyses and the metrics proposed could be used to monitor the user learning during training and provide a marker guiding the decoder re-calibration to maximize the mutual adaptation of the user to the BCI system.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12984-022-01047-x.

Functional connectivity drives stroke recovery: shifting the paradigm from correlation to causation

Stroke is a leading cause of disability, with deficits encompassing multiple functional domains. The heterogeneity underlying stroke poses significant challenges in the prediction of post-stroke recovery, prompting the development of neuroimaging-based biomarkers. Structural neuroimaging measurements, particularly those reflecting corticospinal tract injury, are well-documented in the literature as potential biomarker candidates of post-stroke motor recovery. Consistent with the view of stroke as a 'circuitopathy', functional neuroimaging measures probing functional connectivity may also prove informative in post-stroke recovery. An important step in the development of biomarkers based on functional neural network connectivity is the establishment of causality between connectivity and post-stroke recovery. Current evidence predominantly involves statistical correlations between connectivity measures and post-stroke behavioural status, either cross-sectionally or serially over time. However, the advancement of functional connectivity application in stroke depends on devising experiments that infer causality. In 1965, Sir Austin Bradford Hill introduced nine viewpoints to consider when determining the causality of an association: (i) strength; (ii) consistency; (iii) specificity; (iv) temporality; (v) biological gradient; (vi) plausibility; (vii) coherence; (viii) experiment; and (ix) analogy. Collectively referred to as the Bradford Hill Criteria, these points have been widely adopted in epidemiology. In this review, we assert the value of implementing Bradford Hill's framework to stroke rehabilitation and neuroimaging. We focus on the role of neural network connectivity measurements acquired from task-oriented and resting-state functional MRI, EEG, magnetoencephalography and functional near-infrared spectroscopy in describing and predicting post-stroke behavioural status and recovery. We also identify research opportunities within each Bradford Hill tenet to shift the experimental paradigm from correlation to causation.

Ipsilesional Mu Rhythm Desynchronization Correlates With Improvements in Affected Hand Grip Strength and Functional Connectivity in Sensorimotor Cortices Following BCI-FES Intervention for Upper Extremity in Stroke Survivors

Stroke is a leading cause of acquired long-term upper extremity motor disability. Current standard of care trajectories fail to deliver sufficient motor rehabilitation to stroke survivors. Recent research suggests that use of brain-computer interface (BCI) devices improves motor function in stroke survivors, regardless of stroke severity and chronicity, and may induce and/or facilitate neuroplastic changes associated with motor rehabilitation. The present sub analyses of ongoing crossover-controlled trial NCT02098265 examine first whether, during movements of the affected hand compared to rest, ipsilesional Mu rhythm desynchronization of cerebral cortical sensorimotor areas [Brodmann’s areas (BA) 1-7] is localized and tracks with changes in grip force strength. Secondly, we test the hypothesis that BCI intervention results in changes in frequency-specific directional flow of information transmission (direct path functional connectivity) in BA 1-7 by measuring changes in isolated effective coherence (iCoh) between cerebral cortical sensorimotor areas thought to relate to electrophysiological signatures of motor actions and motor learning. A sample of 16 stroke survivors with right hemisphere lesions (left hand motor impairment), received a maximum of 18–30 h of BCI intervention. Electroencephalograms were recorded during intervention sessions while outcome measures of motor function and capacity were assessed at baseline and completion of intervention. Greater desynchronization of Mu rhythm, during movements of the impaired hand compared to rest, were primarily localized to ipsilesional sensorimotor cortices (BA 1-7). In addition, increased Mu desynchronization in the ipsilesional primary motor cortex, Post vs. Pre BCI intervention, correlated significantly with improvements in hand function as assessed by grip force measurements. Moreover, the results show a significant change in the direction of causal information flow, as measured by iCoh, toward the ipsilesional motor (BA 4) and ipsilesional premotor cortices (BA 6) during BCI intervention. Significant iCoh increases from ipsilesional BA 4 to ipsilesional BA 6 were observed in both Mu [8–12 Hz] and Beta [18–26 Hz] frequency ranges. In summary, the present results are indicative of improvements in motor capacity and behavior, and they are consistent with the view that BCI-FES intervention improves functional motor capacity of the ipsilesional hemisphere and the impaired hand.

Weighted neurofeedback facilitates greater self-regulation of functional connectivity between the primary motor area and cerebellum

Objective.Brain-computer interface (BCI) is a tool that can be used to train brain self-regulation and influence specific activity patterns, including functional connectivity, through neurofeedback. The functional connectivity of the primary motor area (M1) and cerebellum play a critical role in motor recovery after a brain injury, such as stroke. The objective of this study was to determine the feasibility of achieving control of the functional connectivity between M1 and the cerebellum in healthy subjects. Additionally, we aimed to compare the brain self-regulation of two different feedback modalities and their effects on motor performance.Approach.Nine subjects were trained with a real-time functional magnetic resonance imaging BCI system. Two groups were conformed: equal feedback group (EFG), which received neurofeedback that weighted the contribution of both regions of interest (ROIs) equally, and weighted feedback group (WFG) that weighted each ROI differentially (30% cerebellum; 70% M1). The magnitude of the brain activity induced by self-regulation was evaluated with the blood-oxygen-level-dependent (BOLD) percent change (BPC). Functional connectivity was assessed using temporal correlations between the BOLD signal of both ROIs. A finger-tapping task was included to evaluate the effect of brain self-regulation on motor performance.Main results.A comparison between the feedback modalities showed that WFG achieved significantly higher BPC in M1 than EFG. The functional connectivity between ROIs during up-regulation in WFG was significantly higher than EFG. In general, both groups showed better tapping speed in the third session compared to the first. For WFG, there were significant correlations between functional connectivity and tapping speed.Significance.The results show that it is possible to train healthy individuals to control M1-cerebellum functional connectivity with rtfMRI-BCI. Besides, it is also possible to use a weighted feedback approach to facilitate a higher activity of one region over another.

Classification of EEG Data Based on the Spatio-Temporo-Rhythmic Characteristics of the Task-Discriminating Functional Sub-networks

We propose a new approach that utilizes the dynamic state of cortical functional connectivity for the classification of task-based electroencephalographic (EEG) data. We introduce a novel feature extraction framework that locates functional networks in the cortex as they convene at different time intervals across different frequency bands. The framework starts by applying the wavelet transform to isolate, then augment, EEG frequency bands. Next, the time intervals of stationary functional states, within the augmented data, are identified using the source-informed segmentation algorithm. Functional networks are localized in the brain, during each segment, using a singular value decomposition-based approach. For feature selection, we propose a discriminative-associative algorithm, and use it to find the sub-networks showing the highest recurrence rate differences across the target tasks. The sequences of augmented functional networks are projected onto the identified sub-networks, for the final sequences of features. A dynamic recurrent neural network classifier is then used for classification. The proposed approach is applied to experimental EEG data to classify motor execution and motor imagery tasks. Our results show that an accuracy of 90% can be achieved within the first 500 msec of the cued task-planning phase.

Impact of industry 4.0 to create advancements in orthopaedics

Scientists and health professional are focusing on improving the medical sciences for the betterment of patients. The fourth industrial revolution, which is commonly known as Industry 4.0, is a significant advancement in the field of engineering. Industry 4.0 is opening a new opportunity for digital manufacturing with greater flexibility and operational performance. This development is also going to have a positive impact in the field of orthopaedics. The purpose of this paper is to present various advancements in orthopaedics by the implementation of Industry 4.0. To undertake this study, we have studied the available literature extensively on Industry 4.0, technologies of Industry 4.0 and their role in orthopaedics. Paper briefly explains about Industry 4.0, identifies and discusses the major technologies of Industry 4.0, which will support development in orthopaedics. Finally, from the available literature, the paper identifies twelve significant advancements of Industry 4.0 in orthopaedics. Industry 4.0 uses various types of digital manufacturing and information technologies to create orthopaedics implants, patient-specific tools, devices and innovative way of treatment. This revolution is to be useful to perform better spinal surgery, knee and hip replacement, and invasive surgeries.

Self-Regulation of SMR Power Led to an Enhancement of Functional Connectivity of Somatomotor Cortices in Fibromyalgia Patients

Neuroimaging studies have demonstrated that altered activity in somatosensory and motor cortices play a key role in pain chronification. Neurofeedback training of sensorimotor rhythm (SMR) is a tool which allow individuals to self-modulate their brain activity and to produce significant changes over somatomotor brain areas. Several studies have further shown that neurofeedback training may reduce pain and other pain-related symptoms in chronic pain patients. The goal of the present study was to analyze changes in SMR power and brain functional connectivity of the somatosensory and motor cortices elicited by neurofeedback task designed to both synchronize and desynchronize the SMR power over motor and somatosensory areas in fibromyalgia patients. Seventeen patients were randomly assigned to the SMR training (n = 9) or to a sham protocol (n = 8). All participants were trained during 6 sessions, and fMRI and EEG power elicited by synchronization and desynchronization trials were analyzed. In the SMR training group, four patients achieved the objective of SMR modulation in more than 70% of the trials from the second training session (good responders), while five patients performed the task at the chance level (bad responders). Good responders to the neurofeedback training significantly reduced pain and increased both SMR power modulation and functional connectivity of motor and somatosensory related areas during the last neurofeedback training session, whereas no changes in brain activity or pain were observed in bad responders or participants in the sham group. In addition, we observed that good responders were characterized by reduced impact of fibromyalgia and pain symptoms, as well as by increased levels of health-related quality of life during the pre-training sessions. In summary, the present study revealed that neurofeedback training of SMR elicited significant brain changes in somatomotor areas leading to a significant reduction of pain in fibromyalgia patients. In this sense, our research provide evidence that neurofeedback training is a promising tool for a better understanding of brain mechanisms involved in pain chronification.

Brain Functional Networks Study of Subacute Stroke Patients With Upper Limb Dysfunction After Comprehensive Rehabilitation Including BCI Training

Brain computer interface (BCI)-based training is promising for the treatment of stroke patients with upper limb (UL) paralysis. However, most stroke patients receive comprehensive treatment that not only includes BCI, but also routine training. The purpose of this study was to investigate the topological alterations in brain functional networks following comprehensive treatment, including BCI training, in the subacute stage of stroke. Twenty-five hospitalized subacute stroke patients with moderate to severe UL paralysis were assigned to one of two groups: 4-week comprehensive treatment, including routine and BCI training (BCI group, BG, n = 14) and 4-week routine training without BCI support (control group, CG, n = 11). Functional UL assessments were performed before and after training, including, Fugl-Meyer Assessment-UL (FMA-UL), Action Research Arm Test (ARAT), and Wolf Motor Function Test (WMFT). Neuroimaging assessment of functional connectivity (FC) in the BG was performed by resting state functional magnetic resonance imaging. After training, as compared with baseline, all clinical assessments (FMA-UL, ARAT, and WMFT) improved significantly (p < 0.05) in both groups. Meanwhile, better functional improvements were observed in FMA-UL (p < 0.05), ARAT (p < 0.05), and WMFT (p < 0.05) in the BG. Meanwhile, FC of the BG increased across the whole brain, including the temporal, parietal, and occipital lobes and subcortical regions. More importantly, increased inter-hemispheric FC between the somatosensory association cortex and putamen was strongly positively associated with UL motor function after training. Our findings demonstrate that comprehensive rehabilitation, including BCI training, can enhance UL motor function better than routine training for subacute stroke patients. The reorganization of brain functional networks topology in subacute stroke patients allows for increased coordination between the multi-sensory and motor-related cortex and the extrapyramidal system. Future long-term, longitudinal, controlled neuroimaging studies are needed to assess the effectiveness of BCI training as an approach to promote brain plasticity during the subacute stage of stroke.