Low impedance electrodes improve detection of high frequency oscillations in the intracranial EEG

OBJECTIVE Epileptic fast ripple oscillations (FR, 250-500 Hz) indicate epileptogenic tissue with high specificity. However, their low amplitude makes detection demanding against noise. Since thermal noise is reduced by low impedance electrodes (LoZ), we investigate here whether this noise reduction is relevant in the FR frequency range. METHODS We analyzed intracranial electrocorticography during neurosurgery of 10 patients where a low impedance electrode was compared to a standard electrode (HiZ) with equal surface area during stimulation of the somatosensory evoked potential, which evokes a robust response in the FR frequency range. To estimate the noise level, we computed the difference between sweep 2n and sweep 2n + 1 for all sweeps. RESULTS The power spectral density of the noise spectrum improved for the LoZ over all frequencies. In the FR range, the median noise level improved from HiZ (0.153 µV) to LoZ (0.089 µV). For evoked FR, the detection rate improved (91% for HiZ vs. 100% for LoZ). CONCLUSIONS Low impedance electrodes for intracranial EEG reduce noise in the FR frequency range and may thereby improve FR detection. SIGNIFICANCE Improving the measurement chain may enhance the diagnostic value of FR as biomarkers for epileptogenic tissue

Motor-evoked potentials (MEP) during brainstem surgery to preserve corticospinal function

Background: Brainstem surgery bears a risk of damage to the corticospinal tract (CST). Motor-evoked potentials (MEPs) are used intraoperatively to monitor CST function in order to detect CST damage at a reversible stage and thus impede permanent neurological deficits. While the method of MEP is generally accepted, warning criteria in the context of brainstem surgery still have to be agreed on. Method: We analyzed 104 consecutive patients who underwent microsurgical resection of lesions affecting the brainstem. Motor grade was documented prior to surgery, early postoperatively and at discharge. A baseline MEP stimulation intensity threshold was defined and intraoperative testing aimed to keep MEP response amplitude constant. MEPs were considered deteriorated and the surgical team was notified whenever the threshold was elevated by ≥20mA or MEP response fell under 50%. Findings: On the first postoperative day, 18 patients experienced new paresis that resolved by discharge in 11. MEPs deteriorated in 39 patients, and 16 of these showed new postoperative paresis, indicating a 41% risk of new paresis. In the remaining 2/18 patients, intraoperative MEPs were stable, although new paresis appeared postoperatively. In one of these patients, intraoperative hemorrhage caused postoperative swelling, and the new motor deficit persisted until discharge. Of all 104 patients, 7 deteriorated in motor grade at discharge, 92 remained unchanged, and 5 patients have improved. Conclusions: Adjustment of surgical strategy contributed to good motor outcome in 33/39 patients. MEP monitoring may help significantly to prevent motor deficits during demanding neurosurgical procedures on the brainste

An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG

In this work, we present a neuromorphic system that combines for the first time a neural recording headstage with a signal-to-spike conversion circuit and a multi-core spiking neural network (SNN) architecture on the same die for recording, processing, and detecting High Frequency Oscillations (HFO), which are biomarkers for the epileptogenic zone. The device was fabricated using a standard 0.18$\mu$m CMOS technology node and has a total area of 99mm$^{2}$. We demonstrate its application to HFO detection in the iEEG recorded from 9 patients with temporal lobe epilepsy who subsequently underwent epilepsy surgery. The total average power consumption of the chip during the detection task was 614.3$\mu$W. We show how the neuromorphic system can reliably detect HFOs: the system predicts postsurgical seizure outcome with state-of-the-art accuracy, specificity and sensitivity (78%, 100%, and 33% respectively). This is the first feasibility study towards identifying relevant features in intracranial human data in real-time, on-chip, using event-based processors and spiking neural networks. By providing "neuromorphic intelligence" to neural recording circuits the approach proposed will pave the way for the development of systems that can detect HFO areas directly in the operation room and improve the seizure outcome of epilepsy surgery.Comment: 16 pages. A short video describing the rationale underlying the study can be viewed on https://youtu.be/NuAA91fdma

Optimization of signal-to-noise ratio in short-duration SEP recordings by variation of stimulation rate

Objective: The intraoperative averaging of the somatosensory evoked potential (SEP) requires reliable recordings within the shortest possible duration. We here systematically optimized the repetition rate of stimulus presentation. Methods: We recorded medianus and tibial nerve SEP during 22 surgeries and varied the rate of stimulus presentation between 2.7 Hz and 28.7 Hz. We randomly sampled a number of sweeps corresponding to recording durations up to 20 s and calculated the signal-to-noise ratio (SNR). Results: For the medianus nerve at 5 s recording duration, SEP stimulation rate at 12.7 Hz obtained the highest median SNR = 22.9 for the N20, which was higher than for rate 4.7 Hz (p = 1.5e-4). When increasing the stimulation rate, latency increased and amplitude decayed for cortical but not for peripheral recording sites. For the tibial nerve, the rate 4.7 Hz achieved the highest SNR for all durations. Conclusions: We determined the time-dependence of SNR for N20 and elucidated the underlying physiology. For short recordings, rapid reduction of noise through averaging at high stimulation rate outweighs the disadvantage of smaller amplitude. Significance: For a short duration of medianus nerve SEP recording only, it may be advantageous to stimulate with a repetition rate of 12.7 Hz. Keywords: Erb’s point; High frequency oscillation; Intraoperative neuromonitoring; Neurosurgery; Peripheral nerve conduction; Stimulation frequency

Increased EEG power and slowed dominant frequency in patients with neurogenic pain

To study the mechanisms of chronic neurogenic pain, we compared the power spectra of the resting EEG of patients (n = 15, 38-75 years, median 64 years, 6 women) and healthy controls (n = 15, 41-71 years, median 60 years, 8 women). On an average, the patient group exhibited higher spectral power over the frequency range of 2-25 Hz, and the dominant peak was shifted towards lower frequencies. Maximal differences appeared in the 7-9 Hz band in all electrodes. Frontal electrodes contributed most to this difference in the 13-15 Hz band. Bicoherence analysis suggests an enhanced coupling between theta (4-9 Hz) and beta (12-25 Hz) frequencies in patients. The subgroup of six patients free from centrally acting medication showed higher spectral power in the 2-18 Hz frequency range. On an individual basis, the combination of peak height and peak frequency discriminated between patient and control groups: discriminant analysis classified 87% of all subjects correctly. After a therapeutic lesion in the thalamus (central lateral thalamotomy, CLT) we carried out follow-up for a subgroup of seven patients. Median pain relief was 70 and 95% after 3 and 12 months, respectively. The average EEG power of all seven patients gradually decreased in the theta band and approached normal values only after 12 months. The excess theta EEG power in patients and its decrease after thalamic surgery suggests that both EEG and neurogenic pain are determined by tightly coupled thalamocortical loops. The small therapeutic CLT lesion is thought to initiate a progressive normalization in the affected thalamocortical system, which is reflected in both decrease of EEG power and pain relie

Persistent neuronal firing in the medial temporal lobe supports performance and workload of visual working memory in humans

The involvement of the medial temporal lobe (MTL) in working memory is controversially discussed. Recent findings suggest that persistent neural firing in the hippocampus during maintenance in verbal working memory is associated with workload. Here, we recorded single neuron firing in 13 epilepsy patients (7 male) while they performed a visual working memory task. The number of coloured squares in the stimulus set determined the workload of the trial. Performance was almost perfect for low workload (1 and 2 squares) and dropped at high workload (4 and 6 squares), suggesting that high workload exceeded working memory capacity. We identified maintenance neurons in MTL neurons that showed persistent firing during the maintenance period. More maintenance neurons were found in the hippocampus for trials with correct compared to incorrect performance. Maintenance neurons increased and decreased firing in the hippocampus and increased firing in the entorhinal cortex for high compared to low workload. Population firing predicted workload particularly during the maintenance period. Prediction accuracy of workload based on single-trial activity during maintenance was strongest for neurons in the entorhinal cortex and hippocampus. The data suggest that persistent neural firing in the MTL reflects a domain-general process of maintenance supporting performance and workload of multiple items in working memory below and beyond working memory capacity. Persistent neural firing during maintenance in the entorhinal cortex may be associated with its preference to process visual-spatial arrays

A Spiking Neural Network (SNN) for detecting High Frequency Oscillations (HFOs) in the intraoperative ECoG

To achieve seizure freedom, epilepsy surgery requires the complete resection of the epileptogenic brain tissue. In intraoperative ECoG recordings, high frequency oscillations (HFOs) generated by epileptogenic tissue can be used to tailor the resection margin. However, automatic detection of HFOs in real-time remains an open challenge. Here we present a spiking neural network (SNN) for automatic HFO detection that is optimally suited for neuromorphic hardware implementation. We trained the SNN to detect HFO signals measured from intraoperative ECoG on-line, using an independently labeled dataset. We targeted the detection of HFOs in the fast ripple frequency range (250-500 Hz) and compared the network results with the labeled HFO data. We endowed the SNN with a novel artifact rejection mechanism to suppress sharp transients and demonstrate its effectiveness on the ECoG dataset. The HFO rates (median 6.6 HFO/min in pre-resection recordings) detected by this SNN are comparable to those published in the dataset (58 min, 16 recordings). The postsurgical seizure outcome was "predicted" with 100% accuracy for all 8 patients. These results provide a further step towards the construction of a real-time portable battery-operated HFO detection system that can be used during epilepsy surgery to guide the resection of the epileptogenic zone.Comment: 11 pages, 3 figures, 2 tables. The results of this publication were obtained by simulating our hardware platform, built for online processing of biological signals. This hardware combines neural recording headstages with a multi-core neuromorphic processor arxiv.org/abs/2009.1124

Information flows from hippocampus to auditory cortex during replay of verbal working memory items

The maintenance of items in working memory (WM) relies on a widespread network of cortical areas and hippocampus where synchronization between electrophysiological recordings reflects functional coupling. We investigated the direction of information flow between auditory cortex and hippocampus while participants heard and then mentally replayed strings of letters in WM by activating their phonological loop. We recorded local field potentials from the hippocampus, reconstructed beamforming sources of scalp EEG, and - additionally in four participants - recorded from subdural cortical electrodes. When analyzing Granger causality, the information flow was from auditory cortex to hippocampus with a peak in the [4 8] Hz range while participants heard the letters. This flow was subsequently reversed during maintenance while participants maintained the letters in memory. The functional interaction between hippocampus and the cortex and the reversal of information flow provide a physiological basis for the encoding of memory items and their active replay during maintenance

Correction: clinical utility and limitations of intraoperative monitoring of visual evoked potentials

This corrects the article DOI: 10.1371/journal.pone.0120525

Functional specialization and interaction in the amygdala-hippocampus circuit during working memory processing

Both the hippocampus and amygdala are involved in working memory (WM) processing. However, their specific role in WM is still an open question. Here, we simultaneously recorded intracranial EEG from the amygdala and hippocampus of epilepsy patients while performing a WM task, and compared their representation patterns during the encoding and maintenance periods. By combining multivariate representational analysis and connectivity analyses with machine learning methods, our results revealed a functional specialization of the amygdala-hippocampal circuit: The mnemonic representations in the amygdala were highly distinct and decreased from encoding to maintenance. The hippocampal representations, however, were more similar across different items but remained stable in the absence of the stimulus. WM encoding and maintenance were associated with bidirectional information flow between the amygdala and the hippocampus in low-frequency bands (1-40 Hz). Furthermore, the decoding accuracy on WM load was higher by using representational features in the amygdala during encoding and in the hippocampus during maintenance, and by using information flow from the amygdala during encoding and that from the hippocampus during maintenance, respectively. Taken together, our study reveals that WM processing is associated with functional specialization and interaction within the amygdala-hippocampus circuit