Cortical Responses to Input From Distant Areas are Modulated by Local Spontaneous Alpha/Beta Oscillations

The Neurostimulationist will see you now: prescribing direct electrical stimulation therapies for the human brain in epilepsy and beyond

As the pace of research in implantable neurotechnology increases, it is important to take a step back and see if the promise lives up to our intentions. While direct electrical stimulation applied intracranially has been used for the treatment of various neurological disorders, such as Parkinson's, epilepsy, clinical depression, and Obsessive-compulsive disorder, the effectiveness can be highly variable. One perspective is that the inability to consistently treat these neurological disorders in a standardized way is due to multiple, interlaced factors, including stimulation parameters, location, and differences in underlying network connectivity, leading to a trial-and-error stimulation approach in the clinic. An alternate view, based on a growing knowledge from neural data, is that variability in this input (stimulation) and output (brain response) relationship may be more predictable and amenable to standardization, personalization, and, ultimately, therapeutic implementation. In this review, we assert that the future of human brain neurostimulation, via direct electrical stimulation, rests on deploying standardized, constrained models for easier clinical implementation and informed by intracranial data sets, such that diverse, individualized therapeutic parameters can efficiently produce similar, robust, positive outcomes for many patients closer to a prescriptive model. We address the pathway needed to arrive at this future by addressing three questions, namely: (1) why aren't we already at this prescriptive future?; (2) how do we get there?; (3) how far are we from this Neurostimulationist prescriptive future? We first posit that there are limited and predictable ways, constrained by underlying networks, for direct electrical stimulation to induce changes in the brain based on past literature. We then address how identifying underlying individual structural and functional brain connectivity which shape these standard responses enable targeted and personalized neuromodulation, bolstered through large-scale efforts, including machine learning techniques, to map and reverse engineer these input-output relationships to produce a good outcome and better identify underlying mechanisms. This understanding will not only be a major advance in enabling intelligent and informed design of neuromodulatory therapeutic tools for a wide variety of neurological diseases, but a shift in how we can predictably, and therapeutically, prescribe stimulation treatments the human brain.

Single-pulse electrical stimulation artifact removal using the novel matching pursuit-based artifact reconstruction and removal method (MPARRM)

Objective.Single-pulse electrical stimulation (SPES) has been widely used to probe effective connectivity. However, analysis of the neural response is often confounded by stimulation artifacts. We developed a novel matching pursuit-based artifact reconstruction and removal method (MPARRM) capable of removing artifacts from stimulation-artifact-affected electrophysiological signals.Approach.To validate MPARRM across a wide range of potential stimulation artifact types, we performed a bench-top experiment in which we suspended electrodes in a saline solution to generate 110 types of real-world stimulation artifacts. We then added the generated stimulation artifacts to ground truth signals (stereoelectroencephalography signals from nine human subjects recorded during a receptive speech task), applied MPARRM to the combined signal, and compared the resultant denoised signal with the ground truth signal. We further applied MPARRM to artifact-affected neural signals recorded from the hippocampus while performing SPES on the ipsilateral basolateral amygdala in nine human subjects.Main results.MPARRM could remove stimulation artifacts without introducing spectral leakage or temporal spread. It accommodated variable stimulation parameters and recovered the early response to SPES within a wide range of frequency bands. Specifically, in the early response period (5-10 ms following stimulation onset), we found that the broadband gamma power (70-170 Hz) of the denoised signal was highly correlated with the ground truth signal (R=0.98±0.02, Pearson), and the broadband gamma activity of the denoised signal faithfully revealed the responses to the auditory stimuli within the ground truth signal with94%±1.47%sensitivity and99%±1.01%specificity. We further found that MPARRM could reveal the expected temporal progression of broadband gamma activity along the anterior-posterior axis of the hippocampus in response to the ipsilateral amygdala stimulation.Significance.MPARRM could faithfully remove SPES artifacts without confounding the electrophysiological signal components, especially during the early-response period. This method can facilitate the understanding of the neural response mechanisms of SPES.

An objective model for diagnosing comorbid cognitive impairment in patients with epilepsy based on the clinical-EEG functional connectivity features

Objective

Cognitive impairment (CI) is a common disorder in patients with epilepsy (PWEs). Objective assessment method for diagnosing CI in PWEs would be beneficial in reality. This study proposed to construct a diagnostic model for CI in PWEs using the clinical and the phase locking value (PLV) functional connectivity features of the electroencephalogram (EEG).

Methods

PWEs who met the inclusion and exclusion criteria were divided into a cognitively normal (CON) group (n = 55) and a CI group (n = 76). The 23 clinical features and 684 PLV _EEG features at the time of patient visit were screened and ranked using the Fisher score. Adaptive Boosting (AdaBoost) and Gradient Boosting Decision Tree (GBDT) were used as algorithms to construct diagnostic models of CI in PWEs either with pure clinical features, pure PLV _EEG features, or combined clinical and PLV _EEG features. The performance of these models was assessed using a five-fold cross-validation method.

Results

GBDT-built model with combined clinical and PLV _EEG features performed the best with accuracy, precision, recall, F1-score, and an area under the curve (AUC) of 90.11, 93.40, 89.50, 91.39, and 0.95%. The top 5 features found to influence the model performance based on the Fisher scores were the magnetic resonance imaging (MRI) findings of the head for abnormalities, educational attainment, PLV _EEG in the beta (β)-band C3-F4, seizure frequency, and PLV _EEG in theta (θ)-band Fp1-Fz. A total of 12 of the top 5% of features exhibited statistically different PLV _EEG features, while eight of which were PLV _EEG features in the θ band.

Conclusion

The model constructed from the combined clinical and PLV _EEG features could effectively identify CI in PWEs and possess the potential as a useful objective evaluation method. The PLV _EEG in the θ band could be a potential biomarker for the complementary diagnosis of CI comorbid with epilepsy.

A prediction model integrating synchronization biomarkers and clinical features to identify responders to vagus nerve stimulation among pediatric patients with drug-resistant epilepsy

Aims

Vagus nerve stimulation (VNS) is a neuromodulation therapy for children with drug‐resistant epilepsy (DRE). The efficacy of VNS is heterogeneous. A prediction model is needed to predict the efficacy before implantation.

Methods

We collected data from children with DRE who underwent VNS implantation and received regular programming for at least 1 year. Preoperative clinical information and scalp video electroencephalography (EEG) were available in 88 children. Synchronization features, including phase lag index (PLI), weighted phase lag index (wPLI), and phase‐locking value (PLV), were compared between responders and non‐responders. We further adapted a support vector machine (SVM) classifier selected from 25 clinical and 18 synchronization features to build a prediction model for efficacy in a discovery cohort (n = 70) and was tested in an independent validation cohort (n = 18).

Results

In the discovery cohort, the average interictal awake PLI in the high beta band was significantly higher in responders than non‐responders (p < 0.05). The SVM classifier generated from integrating both clinical and synchronization features had the best prediction efficacy, demonstrating an accuracy of 75.7%, precision of 80.8% and area under the receiver operating characteristic (AUC) of 0.766 on 10‐fold cross‐validation. In the validation cohort, the prediction model demonstrated an accuracy of 61.1%.

Conclusion

This study established the first prediction model integrating clinical and baseline synchronization features for preoperative VNS responder screening among children with DRE. With further optimization of the model, we hope to provide an effective and convenient method for identifying responders before VNS implantation.

MRIES: A Matlab Toolbox for Mapping the Responses to Intracranial Electrical Stimulation

Propose

Directed cortical responses to intracranial electrical stimulation are a good standard for mapping inter-regional direct connectivity. Cortico-cortical evoked potential (CCEP), elicited by single pulse electrical stimulation (SPES), has been widely used to map the normal and abnormal brain effective network. However, automated processing of CCEP datasets and visualization of connectivity results remain challenging for researchers and clinicians. In this study, we develop a Matlab toolbox named MRIES (Mapping the Responses to Intracranial Electrical Stimulation) to automatically process CCEP data and visualize the connectivity results.

Method

The MRIES integrates the processing pipeline of the CCEP datasets and various methods for connectivity calculation based on low- and high-frequency signals with stimulation artifacts removed. The connectivity matrices are saved in different folders for visualization. Different visualization patterns (connectivity matrix, circle map, surface map, and volume map) are also integrated to the graphical user interface (GUI), which makes it easy to intuitively display and compare different connectivity measurements. Furthermore, one sample CCEP data set collected from eight epilepsy patients is used to validate the MRIES toolbox.

Result

We show the GUI and visualization functions of MRIES using one example CCEP data that has been described in a complete tutorial. We applied this toolbox to the sample CCEP data set to investigate the direct connectivity between the medial temporal lobe and the insular cortex. We find bidirectional connectivity between MTL and insular that are consistent with the findings of previous studies.

Conclusion

MRIES has a friendly GUI and integrates the full processing pipeline of CCEP data and various visualization methods. The MRIES toolbox, tutorial, and example data can be freely downloaded. As an open-source package, MRIES is expected to improve the reproducibility of CCEP findings and facilitate clinical translation.

How Alpha Rhythm Spatiotemporally Acts Upon the Thalamus-Default Mode Circuit in Idiopathic Generalized Epilepsy

GOAL

Idiopathic generalized epilepsy (IGE) represents generalized spike-wave discharges (GSWD) and distributed changes in thalamocortical circuit. The purpose of this study is to investigate how the ongoing alpha oscillation acts upon the local temporal dynamics and spatial hyperconnectivity in epilepsy.

METHODS

We evaluated the spatiotemporal regulation of alpha oscillations in epileptic state based on simultaneous EEG-fMRI recordings in 45 IGE patients. The alpha-BOLD temporal consistency, as well as the effect of alpha power windows on dynamic functional connectivity strength (dFCS) was analyzed. Then, stable synchronization networks during GSWD were constructed, and the spatial covariation with alpha-based network integration was investigated.

RESULTS

Increased temporal covariation was demonstrated between alpha power and BOLD fluctuations in thalamus and distributed cortical regions in IGE. High alpha power had inhibition effect on dFCS in healthy controls, while in epilepsy, high alpha windows arose along with the enhancement of dFCS in thalamus, caudate and some default mode network (DMN) regions. Moreover, synchronization networks in GSWD-before, GSWD-onset and GSWD-after stages were constructed, and the connectivity strength in prominent hub nodes (precuneus, thalamus) was associated with the spatially disturbed alpha-based network integration.

CONCLUSION

The results indicated spatiotemporal regulation of alpha in epilepsy by means of the increased power and decreased coherence communication. It provided links between alpha rhythm and the altered temporal dynamics, as well as the hyperconnectivity in thalamus-default mode circuit.

SIGNIFICANCE

The combination between neural oscillations and epileptic representations may be of clinical importance in terms of seizure prediction and non-invasive interventions.

Connectivity Gradient in the Human Left Inferior Frontal Gyrus: Intraoperative Cortico-Cortical Evoked Potential Study

In the dual-stream model of language processing, the exact connectivity of the ventral stream to the anterior temporal lobe remains elusive. To investigate the connectivity between the inferior frontal gyrus (IFG) and the lateral part of the temporal and parietal lobes, we integrated spatiotemporal profiles of cortico-cortical evoked potentials (CCEPs) recorded intraoperatively in 14 patients who had undergone surgical resection for a brain tumor or epileptic focus. Four-dimensional visualization of the combined CCEP data showed that the pars opercularis (Broca’s area) is connected to the posterior temporal cortices and the supramarginal gyrus, whereas the pars orbitalis is connected to the anterior lateral temporal cortices and angular gyrus. Quantitative topographical analysis of CCEP connectivity confirmed an anterior–posterior gradient of connectivity from IFG stimulus sites to the temporal response sites. Reciprocality analysis indicated that the anterior part of the IFG is bidirectionally connected to the temporal or parietal area. This study shows that each IFG subdivision has different connectivity to the temporal lobe with an anterior–posterior gradient and supports the classical connectivity concept of Dejerine; that is, the frontal lobe is connected to the temporal lobe through the arcuate fasciculus and also a double fan-shaped structure anchored at the limen insulae.

Stimulus probability affects the visual N700 component of the event-related potential

OBJECTIVE

To examine whether the occipito-temporal visual N700 component of the event-related potential is sensitive to stimulus probabilities.

METHODS

P1, N1, P3, and, in particular, the occipito-temporal N700 component of the event-related potential were analysed in response to frequent and rare non-target letters of a continuous performance task in 200 healthy adolescents. Additionally, amplitude habituation with time was examined for the occipito-temporal N700 and N1 components.

RESULTS

The visual P1, N1, and occipito-temporal N700 components were significantly larger in response to rare letters than to frequent letters, whereas the P3 component demonstrated no amplitude difference. Over time, the occipito-temporal N700 amplitude decreased in response to the rare letters, while the N1 amplitude increased, to both, frequent and rare letters.

CONCLUSIONS

This study provides first evidence that the visual occipito-temporal N700 is sensitive to stimulus probabilities, suggesting an enhanced post-processing of rare stimuli in secondary visual areas. The distinct habituation patterns of occipito-temporal N700 and N1 amplitudes distinguish repetition effects on stimulus post-processing (N700) from those on perception (N1).

SIGNIFICANCE

The enhanced N700 component to rare stimuli might reflect an orienting response and underlying attentional processes. The N700 sensitivity to stimulus probabilities should be examined in patient groups with attentional deficits.

The neural tides of sleep and consciousness revealed by single-pulse electrical brain stimulation

Wakefulness and sleep arise from global changes in brain physiology that may also govern the flow of neural activity between cortical regions responsible for perceptual processing versus planning and action. To test whether and how the sleep/wake cycle affects the overall propagation of neural activity in large-scale brain networks, we applied single-pulse electrical stimulation (SPES) in patients implanted with intracranial EEG electrodes for epilepsy surgery. SPES elicited cortico-cortical spectral responses at high-gamma frequencies (CCSRHG, 80-150 Hz), which indexes changes in neuronal population firing rates. Using event-related causality (ERC) analysis, we found that the overall patterns of neural propagation among sites with CCSRHG were different during wakefulness and different sleep stages. For example, stimulation of frontal lobe elicited greater propagation toward parietal lobe during slow-wave sleep than during wakefulness. During REM sleep, we observed a decrease in propagation within frontal lobe, and an increase in propagation within parietal lobe, elicited by frontal and parietal stimulation, respectively. These biases in the directionality of large-scale cortical network dynamics during REM sleep could potentially account for some of the unique experiential aspects of this sleep stage. Together these findings suggest that the regulation of conscious awareness and sleep is associated with differences in the balance of neural propagation across large-scale frontal-parietal networks.

Phase-Locked Stimulation during Cortical Beta Oscillations Produces Bidirectional Synaptic Plasticity in Awake Monkeys

The functional role of cortical beta oscillations, if any, remains unresolved. During oscillations, the periodic fluctuation in excitability of entrained cells modulates transmission of neural impulses and periodically enhances synaptic interactions. The extent to which oscillatory episodes affect activity-dependent synaptic plasticity remains to be determined. In nonhuman primates, we delivered single-pulse electrical cortical stimulation to a "stimulated" site in sensorimotor cortex triggered on a specific phase of ongoing beta (12-25 Hz) field potential oscillations recorded at a separate "triggering" site. Corticocortical connectivity from the stimulated to the triggering site as well as to other (non-triggering) sites was assessed by cortically evoked potentials elicited by test stimuli to the stimulated site, delivered outside of oscillatory episodes. In separate experiments, connectivity was assessed by intracellular recordings of evoked excitatory postsynaptic potentials. The conditioning paradigm produced transient (1-2 s long) changes in connectivity between the stimulated and the triggering site that outlasted the duration of the oscillatory episodes. The direction of the plasticity effect depended on the phase from which stimulation was triggered: potentiation in depolarizing phases, depression in hyperpolarizing phases. Plasticity effects were also seen at non-triggering sites that exhibited oscillations synchronized with those at the triggering site. These findings indicate that cortical beta oscillations provide a spatial and temporal substrate for short-term, activity-dependent synaptic plasticity in primate neocortex and may help explain the role of oscillations in attention, learning, and cortical reorganization.