Disentangling Somatosensory Evoked Potentials of the Fingers: Limitations and Clinical Potential

Cortical activations induced by electrical versus vibrotactile finger stimulation using EEG

Somatosensory evoked potentials (SEPs) recorded with electroencephalography offer insights into cortical responses to tactile stimulation, typically elicited through temporally precise electrical stimulation. Although vibrotactile stimulation is more ecologically valid but less common, studies directly comparing EEG responses to both electrical and vibrotactile finger stimulation are limited. This study examines and compares (a) cortical responses, (b) connectivity patterns, and (c) somatotopic accuracy of these stimulation types on the fingers. In two experiments, SEPs were recorded from healthy participants' right-hand finger stimulation, using either electrical (experiment 1, n = 22) or vibrotactile (experiment 2, n = 22) stimulation. Vibrotactile stimuli were delivered at 10, 50, and 250 Hz, targeting different ranges of tactile mechanoreceptors activations. Electrical stimulation reliability was assessed across two days, showing consistent SEP amplitudes and latencies. Both stimulation types generated three early components (P1, N1, P2), but all vibrotactile components were increasingly delayed compared to electrical stimulation. Vibrotactile stimulation exhibited stronger localized connectivity in early components (P1, N1) in the left hemisphere, while electrical stimulation showed broader connectivity at P2. Electrical stimulation provided a clearer somatotopic organization in the postcentral gyrus than vibrotactile stimulation. These findings suggest distinct processing for electrical versus vibrotactile finger stimulation. These two stimulation methods are not interchangeable in somatosensory studies: the temporal shift in vibrotactile responses reflects selective activation of Pacinian corpuscles, whereas electrical stimulation yields stronger generalized cortical processing. Electrical stimulation may engage a serial processing pathway starting in the primary somatosensory cortex, while vibrotactile stimulation could involve parallel processing in both primary and secondary somatosensory cortices.

Investigation of sensory attenuation in the somatosensory domain using EEG in a novel virtual reality paradigm

We are not only passively immersed in a sensorial world, but we are active agents that directly produce stimulations. Understanding what is unique about sensory consequences can give valuable insight into the action-perception-cycle. Sensory attenuation is the phenomenon that self-produced stimulations are perceived as less intense compared to externally-generated ones. Studying this phenomenon, however, requires considering a plethora of factors that could otherwise interfere with its interpretation, such as differences in stimulus properties, attentional resources, or temporal predictability. We therefore developed a novel Virtual Reality (VR) setup which allows control over several of these confounding factors. Furthermore, we modulated the expectation of receiving a somatosensory stimulation across self-production and passive perception through a simple probabilistic learning task, allowing us to test to what extent the electrophysiological correlates of sensory attenuation are impacted by stimulus expectation. Therefore, the aim of the present study was twofold: first we aimed validating a novel VR paradigm during electroencephalography (EEG) recoding to investigate sensory attenuation in a highly controlled setup; second, we tested whether electrophysiological differences between self- and externally-generated sensations could be better explained by stimulus predictability factors, corroborating the validity of sensory attenuation. Results of 26 participants indicate that early (P100), mid-latency (P200) and later negative contralateral potentials were significantly attenuated by self-generated sensations, independent of the stimulus expectation. Moreover, a component around 200 ms post-stimulus at frontal sites was found to be enhanced for self-produced stimuli. The P300 was influenced by stimulus expectation, regardless of whether the stimulation was actively produced or passively attended. Together, our results demonstrate that VR opens up new possibilities to study sensory attenuation in more ecological valid yet well-controlled paradigms, and that sensory attenuation is not significantly modulated by stimulus predictability, suggesting that sensory attenuation relies on motor-specific predictions about their sensory outcomes. This not only supports the phenomenon of sensory attenuation, but is also consistent with previous research and the concept that action actually plays a crucial role in perception.

How the Somatosensory System Adapts to the Motor Change in Stroke: A Hemispheric Shift?

Previous studies found that post-stroke motor impairments are associated with damage to the lesioned corticospinal tract and a maladaptive increase in indirect contralesional motor pathways. How the somatosensory system adapts to the change in the use of motor pathways and the role of adaptive sensory feedback to the abnormal movement control of the paretic arm remains largely unknown. We hypothesize that following a unilateral stroke, there is an adaptive hemispheric shift of somatosensory processing toward the contralesional sensorimotor areas to provide sensory feedback support to the contralesional indirect motor pathways. This research could provide new insights related to somatosensory reorganization after stroke, which could enrich future hypothesis-driven therapeutic rehabilitation strategies from a sensory or sensory-motor perspective. Understanding how somatosensory information shifts may provide a target for a novel method to therapeutically prevent and mitigate the emergence and expression of upper limb motor impairments, following a stroke.

Cortical activations associated with spatial remapping of finger touch using EEG

The spatial coding of tactile information is functionally essential for touch-based shape perception and motor control. However, the spatiotemporal dynamics of how tactile information is remapped from the somatotopic reference frame in the primary somatosensory cortex to the spatiotopic reference frame remains unclear. This study investigated how hand position in space or posture influences cortical somatosensory processing. Twenty-two healthy subjects received electrical stimulation to the right thumb (D1) or little finger (D5) in three position conditions: palm down on right side of the body (baseline), hand crossing the body midline (effect of position), and palm up (effect of posture). Somatosensory-evoked potentials (SEPs) were recorded using electroencephalography. One early-, two mid-, and two late-latency neurophysiological components were identified for both fingers: P50, P1, N125, P200, and N250. D1 and D5 showed different cortical activation patterns: compared with baseline, the crossing condition showed significant clustering at P1 for D1, and at P50 and N125 for D5; the change in posture showed a significant cluster at N125 for D5. Clusters predominated at centro-parietal electrodes. These results suggest that tactile remapping of fingers after electrical stimulation occurs around 100-125 ms in the parietal cortex.

Afferent volley from the digital nerve induces short-latency facilitation of perceptual sensitivity and primary sensory cortex excitability

The present study examined whether the perceptual sensitivity and excitability of the primary sensory cortex are modulated by the afferent volley from the digital nerve of a conditioned finger within a short period of time. The perceptual threshold of an electrical stimulus to the index finger (test stimulus) was decreased by a conditioning stimulus to the index finger 4 or 6 ms before the test stimulus, or by a stimulus to the middle or ring finger 2 ms before that. This is explained by the view that the afferent volleys from the digital nerves of the fingers converge in the somatosensory areas, causing spatial summation of the afferent inputs through a small number of synaptic relays, leading to the facilitation of perceptual sensitivity. The N20 component of the somatosensory-evoked potential was facilitated by a conditioning stimulus to the middle finger 4 ms before a test stimulus or to the thumb 2 ms before the test stimulus. This is explained by the view that the afferent volley from the digital nerve of the finger adjacent to the tested finger induces lateral facilitation of the representation of the tested finger in the primary sensory cortex through a small number of synaptic relays.

Deep neural networks constrained by neural mass models improve electrophysiological source imaging of spatiotemporal brain dynamics

Many efforts have been made to image the spatiotemporal electrical activity of the brain with the purpose of mapping its function and dysfunction as well as aiding the management of brain disorders. Here, we propose a non-conventional deep learning-based source imaging framework (DeepSIF) that provides robust and precise spatiotemporal estimates of underlying brain dynamics from noninvasive high-density electroencephalography (EEG) recordings. DeepSIF employs synthetic training data generated by biophysical models capable of modeling mesoscale brain dynamics. The rich characteristics of underlying brain sources are embedded in the realistic training data and implicitly learned by DeepSIF networks, avoiding complications associated with explicitly formulating and tuning priors in an optimization problem, as often is the case in conventional source imaging approaches. The performance of DeepSIF is evaluated by 1) a series of numerical experiments, 2) imaging sensory and cognitive brain responses in a total of 20 healthy subjects from three public datasets, and 3) rigorously validating DeepSIF's capability in identifying epileptogenic regions in a cohort of 20 drug-resistant epilepsy patients by comparing DeepSIF results with invasive measurements and surgical resection outcomes. DeepSIF demonstrates robust and excellent performance, producing results that are concordant with common neuroscience knowledge about sensory and cognitive information processing as well as clinical findings about the location and extent of the epileptogenic tissue and outperforming conventional source imaging methods. The DeepSIF method, as a data-driven imaging framework, enables efficient and effective high-resolution functional imaging of spatiotemporal brain dynamics, suggesting its wide applicability and value to neuroscience research and clinical applications.

The Cortical Response Evoked by Robotic Wrist Perturbations Reflects Level of Proprioceptive Impairment After Stroke

Background: Proprioception is important for regaining motor function in the paretic upper extremity after stroke. However, clinical assessments of proprioception are subjective and require verbal responses from the patient to applied proprioceptive stimuli. Cortical responses evoked by robotic wrist perturbations and measured by electroencephalography (EEG) may be an objective method to support current clinical assessments of proprioception.

Objective: To establish whether evoked cortical responses reflect proprioceptive deficits as assessed by clinical scales and whether they predict upper extremity motor function at 26 weeks after stroke.

Methods: Thirty-one patients with stroke were included. In week 1, 3, 5, 12, and 26 after stroke, the upper extremity sections of the Erasmus modified Nottingham Sensory Assessment (EmNSA-UE) and the Fugl-Meyer Motor Assessment (FM-UE) and the EEG responses (64 channels) to robotic wrist perturbations were measured. The extent to which proprioceptive input was conveyed to the affected hemisphere was estimated by the signal-to-noise ratio (SNR) of the evoked response. The relationships between SNR and EmNSA-UE as well as SNR and time after stroke were investigated using linear regression. Receiver-operating-characteristic curves were used to compare the predictive values of SNR and EmNSA-UE for predicting whether patients regained some selective motor control (FM-UE > 22) or whether they could only move their paretic upper extremity within basic limb synergies (FM-UE ≤ 22) at 26 weeks after stroke.

Results: Patients (N = 7) with impaired proprioception (EmNSA-UE proprioception score < 8) had significantly smaller SNR than patients with unimpaired proprioception (N = 24) [EmNSA-UE proprioception score = 8, t(29) = 2.36, p = 0.03]. No significant effect of time after stroke on SNR was observed. Furthermore, there was no significant difference in the predictive value between EmNSA-UE and SNR for predicting motor function at 26 weeks after stroke.

Conclusion: The SNR of the evoked cortical response does not significantly change as a function of time after stroke and differs between patients with clinically assessed impaired and unimpaired proprioception, suggesting that SNR reflects persistent damage to proprioceptive pathways. A similar predictive value with respect to EmNSA-UE suggests that SNR may be used as an objective predictor next to clinical sensory assessments for predicting motor function at 26 weeks after stroke.

Feasibility and reproducibility of electroencephalography-based corticokinematic coherence

The most important message in this report is that the corticokinematic coherence (CKC) method is a feasible and reproducible tool to quantify, map, and follow cortical proprioceptive (“the movement sense”) processing using EEG that is more widely available for CKC recordings than previously used magnetoencephalographic designs, in basic research, but especially in clinical environments. We provide useful recommendations for optimal EEG derivations for cost-effective experimental designs, allowing large sample size studies.

Position-Cortical Coherence as a Marker of Afferent Pathway Integrity Early Poststroke: A Prospective Cohort Study

Background. Addressing the role of somatosensory impairment, that is, afferent pathway integrity, in poststroke motor recovery may require neurophysiological assessment. Objective. We investigated the longitudinal construct validity of position-cortical coherence (PCC), that is, the agreement between mechanically evoked wrist perturbations and electroencephalography (EEG), as a measure of afferent pathway integrity. Methods. PCC was measured serially in 48 patients after a first-ever ischemic stroke in addition to Fugl-Meyer motor assessment of the upper extremity (FM-UE) and Nottingham Sensory Assessment hand-finger subscores (EmNSA-HF, within 3 and at 5, 12, and 26 weeks poststroke. Changes in PCC over time, represented by percentage presence of PCC (%PCC), mean amplitude of PCC over the affected (Amp-A) and nonaffected hemisphere (Amp-N) and a lateralization index (L-index), were analyzed, as well as their association with FM-UE and EmNSA-HF. Patients were retrospectively categorized based on FM-UE score at baseline and 26 weeks poststroke into high- and low-baseline recoverers and non-recoverers. Results. %PCC increased from baseline to 12 weeks poststroke (β = 1.6%, CI = 0.32% to 2.86%, P = .01), which was no longer significant after adjusting for EmNSA-HF and FM-UE. A significant positive association was found between %PCC, Amp-A, and EmNSA-HF. Low-baseline recoverers (n = 8) showed longitudinally significantly higher %PCC than high-baseline recoverers (n = 23). Conclusions. We demonstrated the longitudinal construct validity of %PCC and Amp-A as a measure of afferent pathway integrity. A high %PCC in low-baseline recoverers suggests that this measure also contains information on cortical excitability. Use of PCC as an EEG-based measure to address the role of somatosensory integrity to motor recovery poststroke requires further attention.

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

In hemiparetic stroke, functional recovery of paretic limb may occur with the reorganization of neural networks in the brain. Neuroimaging techniques, such as magnetic resonance imaging (MRI), have a high spatial resolution which can be used to reveal anatomical changes in the brain following a stroke. However, low temporal resolution of MRI provides less insight of dynamic changes of brain activity. In contrast, electro-neurophysiological techniques, such as electroencephalography (EEG), have an excellent temporal resolution to measure such transient events, however are hindered by its low spatial resolution. This proof-of-principle study assessed a novel multimodal brain imaging technique namely Variational Bayesian Multimodal Encephalography (VBMEG), which aims to improve the spatial resolution of EEG for tracking the information flow inside the brain and its changes following a stroke. The limitations of EEG are complemented by constraints derived from anatomical MRI and diffusion weighted imaging (DWI). EEG data were acquired from individuals suffering from a stroke as well as able-bodied participants while electrical stimuli were delivered sequentially at their index finger in the left and right hand, respectively. The locations of active sources related to this stimulus were precisely identified, resulting in high Variance Accounted For (VAF above 80%). An accurate estimation of dynamic information flow between sources was achieved in this study, showing a high VAF (above 90%) in the cross-validation test. The estimated dynamic information flow was compared between chronic hemiparetic stroke and able-bodied individuals. The results demonstrate the feasibility of VBMEG method in revealing the changes of information flow in the brain after stroke. This study verified the VBMEG method as an advanced computational approach to track the dynamic information flow in the brain following a stroke. This may lead to the development of a quantitative tool for monitoring functional changes of the cortical neural networks after a unilateral brain injury and therefore facilitate the research into, and the practice of stroke rehabilitation.