Using high-frequency oscillations from brief intraoperative neural recordings to predict the seizure onset zone.

Background: While high-frequency oscillations (HFOs) and their stereotyped clusters (sHFOs) have emerged as potential neuro-biomarkers for the rapid localization of the seizure onset zone (SOZ) in epilepsy, their clinical application is hindered by the challenge of automated elimination of pseudo-HFOs originating from artifacts in heavily corrupted intraoperative neural recordings. This limitation has led to a reliance on semi-automated detectors, coupled with manual visual artifact rejection, impeding the translation of findings into clinical practice.

Methods: In response, we have developed a computational framework that integrates sparse signal processing and ensemble learning to automatically detect genuine HFOs of intracranial EEG data.

Assessing expert reliability in determining intracranial EEG channel quality and introducing the automated bad channel detection algorithm.

To evaluate the inter- and intra-rater reliability for the identification of bad channels among neurologists, EEG Technologists, and naïve research personnel, and to compare their performance with the automated bad channel detection (ABCD) algorithm for detecting bad channels.Six Neurologists, ten EEG Technologists, and six naïve research personnel (22 raters in total) were asked to rate 1440 real intracranial EEG channels as good or bad. Intra- and interrater kappa statistics were calculated for each group.

Distinct computational mechanisms of uncertainty processing explain opposing exploratory behaviors in anxiety and apathy.

Decision-making in uncertain environments often leads to varied outcomes. Understanding how individuals interpret the causes of unexpected feedback is crucial for adaptive behavior and mental well-being. Uncertainty can be broadly categorized into two components: volatility and stochasticity.

Diagnosis and Management of Riga-Fede Disease: A Case Series.

In this case series, we present four unique cases of Riga-Fede disease (RFD), a rare disorder characterized by mucosal trauma as a result of repetitive tongue protrusion against the incisors, leading to the development of a large oral mass/ulceration. Due to the rapid development and growth of these lesions mimicking malignancy, it is important for the general and pediatric otolaryngologist to correctly diagnose and treat this benign disorder. This series highlights the variable clinical presentations, along with comorbidities of RFD, as well as the importance of interdisciplinary care between the pediatric otolaryngologist and pediatric dentist in its management.

Flexible multi-step hypothesis testing of human ECoG data using cluster-based permutation tests with GLMEs.

Background: Time series analysis is critical for understanding brain signals and their relationship to behavior and cognition. Cluster-based permutation tests (CBPT) are commonly used to analyze a variety of electrophysiological signals including EEG, MEG, ECoG, and sEEG data without a priori assumptions about specific temporal effects. However, two major limitations of CBPT include the inability to directly analyze experiments with multiple fixed effects and the inability to account for random effects (e.