Dynamic Information Flow Based on EEG and Diffusion MRI in Stroke: A Proof-of-Principle Study

Cortical Reorganization of Early Somatosensory Processing in Hemiparetic Stroke

The cortical motor system can be reorganized following a stroke, with increased recruitment of the contralesional hemisphere. However, it is unknown whether a similar hemispheric shift occurs in the somatosensory system to adapt to this motor change, and whether this is related to movement impairments. This proof-of-concept study assessed somatosensory evoked potentials (SEPs), P50 and N100, in hemiparetic stroke participants and age-matched controls using high-density electroencephalograph (EEG) recordings during tactile finger stimulation. The laterality index was calculated to determine the hemispheric dominance of the SEP and re-confirmed with source localization. The study found that latencies of P50 and N100 were significantly delayed in stroke brains when stimulating the paretic hand. The amplitude of P50 in the contralateral (to stimulated hand) hemisphere was negatively correlated with the Fügl-Meyer upper extremity motor score in stroke. Bilateral cortical responses were detected in stroke, while only contralateral cortical responses were shown in controls, resulting in a significant difference in the laterality index. These results suggested that somatosensory reorganization after stroke involves increased recruitment of ipsilateral cortical regions, especially for the N100 SEP component. This reorganization delays the latency of somatosensory processing after a stroke. This research provided new insights related to the somatosensory reorganization after stroke, which could enrich future hypothesis-driven therapeutic rehabilitation strategies from a sensory or sensory-motor perspective.

Structure supports function: Informing directed and dynamic functional connectivity with anatomical priors

The dynamic repertoire of functional brain networks is constrained by the underlying topology of structural connections. Despite this intrinsic relationship between structural connectivity (SC) and functional connectivity (FC), integrative and multimodal approaches to combine the two remain limited. Here, we propose a new adaptive filter for estimating dynamic and directed FC using structural connectivity information as priors. We tested the filter in rat epicranial recordings and human event-related EEG data, using SC priors from a meta-analysis of tracer studies and diffusion tensor imaging metrics, respectively. We show that, particularly under conditions of low signal-to-noise ratio, SC priors can help to refine estimates of directed FC, promoting sparse functional networks that combine information from structure and function. In addition, the proposed filter provides intrinsic protection against SC-related false negatives, as well as robustness against false positives, representing a valuable new tool for multimodal imaging in the context of dynamic and directed FC analysis.

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.

Are early measured resting-state EEG parameters predictive for upper limb motor impairment six months poststroke?

OBJECTIVES

Investigate whether resting-state EEG parameters recorded early poststroke can predict upper extremity motor impairment reflected by the Fugl-Meyer motor score (FM-UE) after six months, and whether they have prognostic value in addition to FM-UE at baseline.

METHODS

Quantitative EEG parameters delta/alpha ratio (DAR), brain symmetry index (BSI) and directional BSI (BSIdir) were derived from 62-channel resting-state EEG recordings in 39 adults within three weeks after a first-ever ischemic hemispheric stroke. FM-UE scores were acquired within three weeks (FM-UE_baseline) and at 26 weeks poststroke (FM-UE_w26). Linear regression analyses were performed using a forward selection procedure to predict FM-UE_w26.

RESULTS

BSI calculated over the theta band (BSI_theta) (β = -0.40; p = 0.013) was the strongest EEG-based predictor regarding FM-UE_w26. BSI_theta (β = -0.27; p = 0.006) remained a significant predictor when added to a regression model including FM-UE_baseline, increasing explained variance from 61.5% to 68.1%.

CONCLUSION

Higher BSI_theta values, reflecting more power asymmetry over the hemispheres, predict more upper limb motor impairment six months after stroke. Moreover, BSI_theta shows additive prognostic value regarding FM-UE_w26 next to FM-UE_baseline scores, and thereby contains unique information regarding upper extremity motor recovery.

SIGNIFICANCE

To our knowledge, we are the first to show that resting-state EEG parameters can serve as prognostic biomarkers of stroke recovery, in addition to FM-UE_baseline scores.

Limited capacity for ipsilateral secondary motor areas to support hand function post-stroke

KEY POINTS

Ipsilateral-projecting corticobulbar pathways, originating primarily from secondary motor areas, innervate the proximal and even distal portions, although they branch more extensively at the spinal cord. It is currently unclear to what extent these ipsilateral secondary motor areas and subsequent cortical projections may contribute to hand function following stroke-induced damage to one hemisphere. In the present study, we provide both structural and functional evidence indicating that individuals increasingly rely on ipsilateral secondary motor areas, although at the detriment of hand function. Increased activity in ipsilateral secondary motor areas was associated with increased involuntary coupling between shoulder abduction and finger flexion, most probably as a result of the low resolution of these pathways, making it increasingly difficult to open the hand. These findings suggest that, although ipsilateral secondary motor areas may support proximal movements, they do not have the capacity to support distal hand function, particularly for hand opening.

ABSTRACT

Recent findings have shown connections of ipsilateral cortico-reticulospinal tract (CRST), predominantly originating from secondary motor areas to not only proximal, but also distal muscles of the arm. Following a unilateral stroke, CRST from the ipsilateral side remains intact and thus has been proposed as a possible backup system for post-stroke rehabilitation even for the hand. We argue that, although CRST from ipsilateral secondary motor areas can provide control for proximal joints, it is insufficient to control either hand or coordinated shoulder and hand movements as a result of its extensive spinal branching compared to contralateral corticospinal tract. To address this issue, we combined magnetic resonance imaging, high-density EEG, and robotics in 17 individuals with severe chronic hemiparetic stroke and 12 age-matched controls. We tested for changes in structural morphometry of the sensorimotor cortex and found that individuals with stroke demonstrated higher grey matter density in secondary motor areas ipsilateral to the paretic arm compared to controls. We then measured cortical activity when participants were attempting to generate hand opening either supported on a table or when lifting against a shoulder abduction load. The addition of shoulder abduction during hand opening increased reliance on ipsilateral secondary motor areas in stroke, but not controls. Crucially, the increased use of ipsilateral secondary motor areas was associated with decreased hand opening ability when lifting the arm as a result of involuntary coupling between the shoulder and wrist/finger flexors. Taken together, this evidence implicates a compensatory role for ipsilateral (i.e. contralesional) secondary motor areas post-stroke, although with no apparent capacity to support hand function.

Determining the Online Measurable Input Variables in Human Joint Moment Intelligent Prediction Based on the Hill Muscle Model

Introduction: Human joint moment is a critical parameter to rehabilitation assessment and human-robot interaction, which can be predicted using an artificial neural network (ANN) model. However, challenge remains as lack of an effective approach to determining the input variables for the ANN model in joint moment prediction, which determines the number of input sensors and the complexity of prediction. Methods: To address this research gap, this study develops a mathematical model based on the Hill muscle model to determining the online input variables of the ANN for the prediction of joint moments. In this method, the muscle activation, muscle-tendon moment velocity and length in the Hill muscle model and muscle-tendon moment arm are translated to the online measurable variables, i.e. muscle electromyography (EMG), joint angles and angular velocities of the muscle span. To test the predictive ability of these input variables, an ANN model is designed and trained to predict joint moments. The ANN model with the online measurable input variables is tested on the experimental data collected from ten healthy subjects running with the speeds of 2, 3, 4 and 5 m/s on a treadmill. The variance accounted for (VAF) between the predicted and inverse dynamics moment is used to evaluate the prediction accuracy. Results: The results suggested that the method can predict joint moments with a higher accuracy (mean VAF = 89.67±5.56 %) than those obtained by using other joint angles and angular velocities as inputs (mean VAF = 86.27±6.6%) evaluated by jack-knife cross-validation. Conclusions: The proposed method provides us with a powerful tool to predict joint moment based on online measurable variables, which establishes the theoretical basis for optimizing the input sensors and detection complexity of the prediction system. It may facilitate the research on exoskeleton robot control and real-time gait analysis in motor rehabilitation.

MEG Source Imaging and Group Analysis Using VBMEG

Variational Bayesian Multimodal EncephaloGraphy (VBMEG) is a MATLAB toolbox that estimates distributed source currents from magnetoencephalography (MEG)/electroencephalography (EEG) data by integrating functional MRI (fMRI) (https://vbmeg.atr.jp/). VBMEG also estimates whole-brain connectome dynamics using anatomical connectivity derived from a diffusion MRI (dMRI). In this paper, we introduce the VBMEG toolbox and demonstrate its usefulness. By collaborating with VBMEG's tutorial page (https://vbmeg.atr.jp/docs/v2/static/vbmeg2_tutorial_neuromag.html), we show its full pipeline using an open dataset recorded by Wakeman and Henson (2015). We import the MEG data and preprocess them to estimate the source currents. From the estimated source currents, we perform a group analysis and examine the differences of current amplitudes between conditions by controlling the false discovery rate (FDR), which yields results consistent with previous studies. We highlight VBMEG's characteristics by comparing these results with those obtained by other source imaging methods: weighted minimum norm estimate (wMNE), dynamic statistical parametric mapping (dSPM), and linearly constrained minimum variance (LCMV) beamformer. We also estimate source currents from the EEG data and the whole-brain connectome dynamics from the MEG data and dMRI. The observed results indicate the reliability, characteristics, and usefulness of VBMEG.