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A rapid and cost-effective noninvasive tool to detect and characterize neural silences can be of important benefit in diagnosing and treating many disorders. We propose an algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural silences, using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric cortical resection patients, using fewer than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53 mm), as well for 100 different simulated regions of silence based on a real human head model (12 ± 0.7 mm vs. 54 ± 2.2 mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function. Chamanzar et al. demonstrate that an algorithm, 'SilenceMap', results in rapid detection and localization of neural silences using just a few minutes of noninvasive scalp electroencephalography (EEG) recording. SilenceMap vastly outperformed existing algorithms in accuracy of localizing silences in human participants with circumscribed cortical resections, indicating its potential use for the monitoring of cortical function and health. [ABSTRACT FROM AUTHOR]

Objective: Individuals with migraine exhibit heightened sensitivity to visual input that continues beyond their migraine episodes. However, the contribution of color to visual sensitivity, and how it relates to neural activity, has largely been unexplored in these individuals. Background: Previously, it has been shown that, in non‐migraine individuals, patterns with greater chromaticity separation evoked greater cortical activity, regardless of hue, even when colors were isoluminant. Therefore, to investigate whether individuals with migraine experienced increased visual sensitivity, we compared the behavioral and neural responses to chromatic patterns of increasing separation in migraine and non‐migraine individuals. Methods: Seventeen individuals with migraine (12 with aura) and 18 headache‐free controls viewed pairs of colored horizontal grating patterns that varied in chromaticity separation. Color pairs were either blue‐green, red‐green, or red‐blue. Participants rated the discomfort of the gratings and electroencephalogram was recorded simultaneously. Results: Both groups showed increased discomfort ratings and larger N1/N2 event‐related potentials (ERPs) with greater chromaticity separation, which is consistent with increased cortical excitability. However, individuals with migraine rated gratings as being disproportionately uncomfortable and exhibited greater effects of chromaticity separation in ERP amplitude across occipital and parietal electrodes. Ratings of discomfort and ERPs were smaller in response to the blue‐green color pairs than the red‐green and red‐blue gratings, but this was to an equivalent degree across the 2 groups. Conclusions: Together, these findings indicate that greater chromaticity separation increases neural excitation, and that this effect is heightened in migraine, consistent with the theory that hyper‐excitability of the visual system is a key signature of migraine. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]