Mapping of the central sulcus using non-invasive ultra-high-density brain recordings.

Brain mapping is vital in understanding the brain's functional organization. Electroencephalography (EEG) is one of the most widely used brain mapping approaches, primarily because it is non-invasive, inexpensive, straightforward, and effective. Increasing the electrode density in EEG systems provides more neural information and can thereby enable more detailed and nuanced mapping procedures. Here, we show that the central sulcus can be clearly delineated using a novel ultra-high-density EEG system (uHD EEG) and somatosensory evoked potentials (SSEPs). This uHD EEG records from 256 channels with an inter-electrode distance of 8.6 mm and an electrode diameter of 5.9 mm. Reconstructed head models were generated from T1-weighted MRI scans, and electrode positions were co-registered to these models to create topographical plots of brain activity. EEG data were first analyzed with peak detection methods and then classified using unsupervised spectral clustering. Our topography plots of the spatial distribution from the SSEPs clearly delineate a division between channels above the somatosensory and motor cortex, thereby localizing the central sulcus. Individual EEG channels could be correctly classified as anterior or posterior to the central sulcus with 95.2% accuracy, which is comparable to accuracies from invasive intracranial recordings. Our findings demonstrate that uHD EEG can resolve the electrophysiological signatures of functional representation in the brain at a level previously only seen from surgically implanted electrodes. This novel approach could benefit numerous applications, including research, neurosurgical mapping, clinical monitoring, detection of conscious function, brain-computer interfacing (BCI), rehabilitation, and mental health.

Intraoperative language mapping guided by real-time visualization of gamma band modulation electrocorticograms: Case report and proof of concept.

Background: Electrocorticography (ECoG) language mapping is often performed extraoperatively, frequently involves offline processing, and relationships with direct cortical stimulation (DCS) remain variable. We sought to determine the feasibility and preliminary utility of an intraoperative language mapping approach guided by real-time visualization of electrocorticograms. Methods: A patient with astrocytoma underwent awake craniotomy with intraoperative language mapping, utilizing a dual iPad stimulus presentation system coupled to a real-time neural signal processing platform capable of both ECoG recording and delivery of DCS. Gamma band modulations in response to 4 language tasks at each electrode were visualized in real-time. Next, DCS was conducted for each neighboring electrode pair during language tasks. Results: All language tasks resulted in strongest heat map activation at an electrode pair in the anterior to mid superior temporal gyrus. Consistent speech arrest during DCS was observed for Object and Action naming tasks at these same electrodes, indicating good correspondence with ECoG heat map recordings. This region corresponded well with posterior language representation via preoperative functional MRI. Conclusions: Intraoperative real-time visualization of language task-based ECoG gamma band modulation is feasible and may help identify targets for DCS. If validated, this may improve the efficiency and accuracy of intraoperative language mapping.

Long-latency gamma modulation after median nerve stimulation delineates the central sulcus and contrasts the states of consciousness.

OBJECTIVE: To evaluate the functional use of sub-band modulations in somatosensory evoked potentials (SSEPs) to discriminate between the primary somatosensory (S1) and motor (M1) areas and contrast the states of consciousness. METHODS: During routine intraoperative cortical mapping, SSEPs were recorded with electrocorticography (ECoG) grids from the sensorimotor cortex of eight patients in the anesthetized and awake states. We conducted a time-frequency analysis on the SSEP trace to extract the spectral modulations in each state and visualize their spatial topography. RESULTS: We observed late gamma modulation (60-250 Hz) in all subjects approximately 50 ms after stimulation onset, extending up to 250 ms in each state. The late gamma activity enhancement was predominant in S1 in the awake state, where it discriminated S1 from M1 at a higher accuracy (92 %) than in the anesthetized state (accuracy = 70 %). CONCLUSIONS: These results showed that sensorimotor mapping does not need to rely on only SSEP phase reversal. The long latency gamma modulation can serve as a biomarker for primary sensorimotor localization and monitor the level of consciousness in neurosurgical practice. SIGNIFICANCE: While the intraoperative assessment of SSEP phase reversal with ECoG is widely employed to delineate the central sulcus, the median nerve stimulation-induced spatio-spectral patterns can distinctly localize it and distinguish between conscious states.

Real-Time Delineation of the Central Sulcus with the Spatial Profile of SSEPs Captured with High-Density Ecog Grid.

Cortical mapping is widely employed to define the sensorimotor area and delineate the central sulcus (CS) during awake craniotomies. The approach involves the gold standard somatosensory evoked potentials (SSEPs) recorded with electrocorticogram (ECoG) strip electrodes. However, the evoked response can be misconstrued from the manual peak interpretation due to the poor spatial resolution of the strip electrode or when the electrode does not precisely cover the desired cortical area. This can lead to unintentional damage to the eloquent cortex. We present a soft real-time computer based visualization system that uses recorded SSEPs with a subdural grid to aid in cortical mapping. The neural data during electrical stimulation of the median nerve at 0.6Hz are picked up with a bio-amplifier at 2.4kHz. The stimulation artifact recorded from the bipolar electromyogram (EMG) is used as the stimulation onset. The ECoG data are assessed online with MATLAB Simulink to process and visualize the SSEPs waveform. The visualization system is programmed to display the SSEPs peak activation as a heat map on a 2D grid and projected onto a screen, showcasing the nature of the cortical activities over the contact surface area. Since the grid occupies a large cortical surface, the heatmap is able to delineate the central sulcus. The map can be viewed at any time point along the SSEP trace without the need for peak interpretation. With the goal to provide additional information during cortical mapping and facilitate interpretation of ECoG grid data, we believe that this visualization system will aid in rapid definition of the sensorimotor area during surgical planning. Clinical Relevance- This real-time visualization system can be used to delineate the central sulcus in a short time during awake craniotomies.

Translational Organic Neural Interface Devices at Single Neuron Resolution.

Recording from the human brain at the spatiotemporal resolution of action potentials provides critical insight into mechanisms of higher cognitive functions and neuropsychiatric disease that is challenging to derive from animal models. Here, organic materials and conformable electronics are employed to create an integrated neural interface device compatible with minimally invasive neurosurgical procedures and geared toward chronic implantation on the surface of the human brain. Data generated with these devices enable identification and characterization of individual, spatially distribute human cortical neurons in the absence of any tissue penetration (n = 229 single units). Putative single-units are effectively clustered, and found to possess features characteristic of pyramidal cells and interneurons, as well as identifiable microcircuit interactions. Human neurons exhibit consistent phase modulation by oscillatory activity and a variety of population coupling responses. The parameters are furthermore established to optimize the yield and quality of single-unit activity from the cortical surface, enhancing the ability to investigate human neural network mechanisms without breaching the tissue interface and increasing the information that can be safely derived from neurophysiological monitoring.

Unsupervised machine learning can delineate central sulcus by using the spatiotemporal characteristic of somatosensory evoked potentials.

Objective.Somatosensory evoked potentials (SSEPs) recorded with electrocorticography (ECoG) for central sulcus (CS) identification is a widely accepted procedure in routine intraoperative neurophysiological monitoring. Clinical practices test the short-latency SSEPs for the phase reversal over strip electrodes. However, assessments based on waveform morphology are susceptible to variations in interpretations due to the hand area's localized nature and usually require multiple electrode placements or electrode relocation. We investigated the feasibility of unsupervised delineation of the CS by using the spatiotemporal patterns of the SSEP captured with the ECoG grid.Approach. Intraoperatively, SSEPs were recorded from eight patients using ECoG grids placed over the sensorimotor cortex. Neurosurgeons blinded to the electrophysiology identified the sensory and motor gyri using neuronavigation based on sulcal anatomy. We quantified the most discriminatory time points in SSEPs temporal profile between the primary motor (M1) and somatosensory (S1) cortex using the Fisher discrimination criterion. We visualized the amplitude gradient of the SSEP over a 2D heat map to provide visual feedback for the delineation of the CS based on electrophysiology. Subsequently, we employed spectral clustering using the entire the SSEP waveform without selecting any time points and grouped ECoG channels in an unsupervised fashion.Main results.Consistently in all patients, two different time points provided almost equal discrimination between anterior and posterior channels, which vividly outlined the CS when we viewed the SSEP amplitude distribution as a spatial 2D heat map. The first discriminative time point was in proximity to the conventionally favored ∼20 ms peak (N20), and the second time point was slightly later than the markedly high ∼30 ms peak (P30). Still, the location of these time points varied noticeably across subjects. Unsupervised clustering approach separated the anterior and posterior channels with an accuracy of 96.3% based on the time derivative of the SSEP trace without the need for a subject-specific time point selection. In contrast, the raw trace resulted in an accuracy of 88.0%.Significance. We show that the unsupervised clustering of the SSEP trace assessed with subdural electrode grids can delineate the CS automatically with high precision, and the constructed heat maps can localize the motor cortex. We anticipate that the spatiotemporal patterns of SSEP fused with machine learning can serve as a useful tool to assist in surgical planning.

Power Modulations of ECoG Alpha/Beta and Gamma Bands Correlate With Time-Derivative of Force During Hand Grasp.

It is well-known that motor cortical oscillatory components are modulated in their amplitude during voluntary and imagined movements. These patterns have been used to develop brain-machine interfaces (BMI) which focused mostly on movement kinematics. In contrast, there have been only a few studies on the relation between brain oscillatory activity and the control of force, in particular, grasping force, which is of primary importance for common daily activities. In this study, we recorded intraoperative high-density electrocorticography (ECoG) from the sensorimotor cortex of four patients while they executed a voluntary isometric hand grasp following verbal instruction. The grasp was held for 2 to 3 s before being instructed to relax. We studied the power modulations of neural oscillations during the whole time-course of the grasp (onset, hold, and offset phases). Phasic event-related desynchronization (ERD) in the low-frequency band (LFB) from 8 to 32 Hz and event-related synchronization (ERS) in the high-frequency band (HFB) from 60 to 200 Hz were observed at grasp onset and offset. However, during the grasp holding period, the magnitude of LFB-ERD and HFB-ERS decreased near or at the baseline level. Overall, LFB-ERD and HFB-ERS show phasic characteristics related to the changes of grasp force (onset/offset) in all four patients. More precisely, the fluctuations of HFB-ERS primarily, and of LFB-ERD to a lesser extent, correlated with the time-course of the first time-derivative of force (yank), rather than with force itself. To the best of our knowledge, this is the first study that establishes such a correlation. These results have fundamental implications for the decoding of grasp in brain oscillatory activity-based neuroprosthetics.

A portable platform to collect and review behavioral data simultaneously with neurophysiological signals.

This paper presents a portable platform to collect and review behavioral data simultaneously with neurophysiological signals. The whole system is comprised of four parts: a sensor data acquisition interface, a socket server for real-time data streaming, a Simulink system for real-time processing and an offline data review and analysis toolbox. A low-cost microcontroller is used to acquire data from external sensors such as accelerometer and hand dynamometer. The micro-controller transfers the data either directly through USB or wirelessly through a bluetooth module to a data server written in C++ for MS Windows OS. The data server also interfaces with the digital glove and captures HD video from webcam. The acquired sensor data are streamed under User Datagram Protocol (UDP) to other applications such as Simulink/Matlab for real-time analysis and recording. Neurophysiological signals such as electroencephalography (EEG), electrocorticography (ECoG) and local field potential (LFP) recordings can be collected simultaneously in Simulink and fused with behavioral data. In addition, we developed a customized Matlab Graphical User Interface (GUI) software to review, annotate and analyze the data offline. The software provides a fast, user-friendly data visualization environment with synchronized video playback feature. The software is also capable of reviewing long-term neural recordings. Other featured functions such as fast preprocessing with multithreaded filters, annotation, montage selection, power-spectral density (PSD) estimate, time-frequency map and spatial spectral map are also implemented.