Quantifying a frequency modulation response biomarker in responsive neurostimulation.

Objective.Responsive neurostimulation (RNS) is an effective treatment for controlling seizures in patients with drug-resistant focal epilepsy who are not suitable candidates for resection surgery. A lack of tools for detecting and characterizing potential response biomarkers, however, contributes to a limited understanding of mechanisms by which RNS improves seizure control. We developed a method to quantify ictal frequency modulation, previously identified as a biomarker of clinical responsiveness to RNS.Approach.Frequency modulation is characterized by shifts in power across spectral bands during ictal events, over several months of neurostimulation. This effect was quantified by partitioning each seizure pattern into segments with distinct spectral content and measuring the extent of change from the baseline distribution of spectral content using the squared earth mover's distance.Main results.We analyzed intracranial electroencephalography data from 13 patients who received RNS therapy, six of whom exhibited frequency modulation on expert evaluation. Patients in the frequency modulation group had, on average, significantly larger and more sustained changes in their squared earth mover's distances (mean = 13.97 × 10-3± 1.197 × 10-3). In contrast, those patients without expert-identified frequency modulation exhibited statistically insignificant or negligible distances (mean = 4.994 × 10-3± 0.732 × 10-3).Significance.This method is the first step towards a quantitative, feedback-driven system for systematically optimizing RNS stimulation parameters, with an ultimate goal of truly personalized closed-loop therapy for epilepsy.

An Algorithm for Automated, Noninvasive Detection of Cortical Spreading Depolarizations Based on EEG Simulations.

OBJECTIVE: We present a novel signal processing algorithm for automated, noninvasive detection of cortical spreading depolarizations (CSDs) using electroencephalography (EEG) signals and validate the algorithm on simulated EEG signals. CSDs are waves of neurochemical changes that suppress the neuronal activity as they propagate across the brain's cortical surface. CSDs are believed to mediate secondary brain damage after brain trauma and cerebrovascular diseases like stroke. We address the following two key challenges in detecting CSDs from EEG signals: i) attenuation and loss of high spatial resolution information; and ii) cortical folds, which complicate tracking CSD waves. METHODS: Our algorithm detects and tracks "wavefronts" of a CSD wave, and stitch together data across space and time to make a detection. To test our algorithm, we provide different models of CSD waves, including different widths of CSD suppressions and different patterns, and use them to simulate scalp EEG signals using head models of four subjects. RESULTS AND CONCLUSION: Our results suggest that low-density EEG grids (40 electrodes) can detect CSD widths of 1.1 cm on average, while higher density EEG grids (340 electrodes) can detect CSD patterns as thin as 0.43 cm (less than minimum widths reported in prior works), among which single-gyrus CSDs are the hardest to detect because of their small suppression area. SIGNIFICANCE: The proposed algorithm is a first step toward noninvasive, automated detection of CSDs, which can help in reducing secondary brain damages.

Low-Cost Carbon Fiber-Based Conductive Silicone Sponge EEG Electrodes.

We propose a novel carbon fiber-based conductive silicone sponge for low electrode-skin impedance EEG recordings. When this sponge is used with water or saline solution, no gel is required, lowering the setup time drastically compared to classical wet electrodes. Moreover, the wet conductive carbon fiber silicone sponges achieve an electrode-skin impedance as low as $2.5\mathrm {k} \Omega $ at 1kHz when wet, making them better than state of the art gel electrodes. Additionally, even as the sponge dries out, it continues to remain conductive and performs as a reliable dry electrode. We demonstrate through experiments that these conductive carbon fiber silicone sponge electrodes, wet or dry, are able to measure alpha wave activity. Our carbon fiber conductive sponge electrodes are low-cost and are highly suitable for designs of portable high density EEG measurement systems.

Very high density EEG elucidates spatiotemporal aspects of early visual processing.

Standard human EEG systems based on spatial Nyquist estimates suggest that 20-30 mm electrode spacing suffices to capture neural signals on the scalp, but recent studies posit that increasing sensor density can provide higher resolution neural information. Here, we compared "super-Nyquist" density EEG ("SND") with Nyquist density ("ND") arrays for assessing the spatiotemporal aspects of early visual processing. EEG was measured from 128 electrodes arranged over occipitotemporal brain regions (14 mm spacing) while participants viewed flickering checkerboard stimuli. Analyses compared SND with ND-equivalent subsets of the same electrodes. Frequency-tagged stimuli were classified more accurately with SND than ND arrays in both the time and the frequency domains. Representational similarity analysis revealed that a computational model of V1 correlated more highly with the SND than the ND array. Overall, SND EEG captured more neural information from visual cortex, arguing for increased development of this approach in basic and translational neuroscience.