Biology subjects

Karas, P. J.

Publications and source records attributed to Karas, P. J..

4 recordsLinked to original sources

bioRxiv2025

Building an Ecosystem of Seizure Localization Methods: Neural Fragility as the First Step

The current treatment for drug-resistant epilepsy (DRE) is surgical intervention, which relies on accurate identification of the seizure onset zone (SOZ) using intracranial EEG (iEEG) data. iEEG analysis with computational epileptogenic zone identification algorithms (CEZIAs) is a promising step towards better SOZ localization and surgical outcomes. A key step in validation and adoption of CEZIAs is to allow for widespread shared development and validation of code and data. We describe a set of three R packages to achieve this goal. Our ecosystem of seizure localization methods involves a straightforward analysis pipeline, standardized data formatting and storage, and completely documented and open-source code. The TableContainer package allows for easy storage and manipulation of table data, serving as groundwork for the Epoch package, which is specifically geared towards iEEG data. The Epoch package allows for cropping, resampling, and visualization of iEEG data and provides publicly downloadable iEEG data for reproducibility. Finally, the EZFragility package uses these two foundational packages to analyze iEEGs for SOZ localization using the Neural Fragility method described by Li et al. EZFragility was built using the same core principles as the original method but included several enhancements in computational efficiency and user experience. It accurately reproduces neural fragility results for both sample patients used in the original paper. This project serves as the first step towards building an open-source, reproducible ecosystem of seizure localization methods in R. Future steps include the addition of other CEZIAs using the framework and sample data already made available by these packages. Significance StatementLocalization of the seizure onset zone (SOZ) is a critical step in surgical treatment of drug-resistant epilepsy. Computational epileptogenic zone identification algorithms (CEZIAs) are promising potential tools to aid in clinical decision-making. However, shared development and verification of CEZIAs is difficult due to obscured source code, variable data structures, and limited data availability. The EZFragility software package1 is the first step in building a collaborative ecosystem of CEZIAs that can be downloaded, tested, and used without these roadblocks. EZFragility1 and its dependent packages TableContainer2 and Epoch3 are freely available on the Comprehensive R Archive Network (CRAN), with source code viewable on GitHub. They provide an open-source framework for CEZIA code, data formatting, and data access, all with extensive documentation.

neuroscience↗

bioRxiv2024

Integrating Data Across Oscillatory Power Bands Predicts the Epileptogenic Zone: the Frequency Range Explorer Epileptogenic Zone (FREEZ) Identification Algorithm

Accurate identification of the seizure onset zone (SOZ) using intracranial electroencephalography (iEEG) remains challenging. Although diverse methods have leveraged spectral features to classify patient outcomes, few approaches focus on identifying individual electrodes within the SOZ or integrate a broad spectrum of frequency ranges within a single model. We developed an interpretable machine learning model that integrates power across delta, theta, alpha, beta, gamma, and high-gamma frequencies over time to identify the SOZ. For 1,511 electrodes implanted across 21 patients, we computed the mean spectral power in each frequency band for the first 20 seconds after seizure onset and analyzed the differences in power between SOZ and non-SOZ electrodes. In patients who were seizure-free after surgery (n = 14), electrodes within the SOZ showed significantly higher area under the curve (AUC) for mean power over time in the first 20 seconds after seizure onset compared to electrodes outside the SOZ in the alpha (p = 0.0272), beta (p = 0.0263), gamma (p = 0.0013), and high gamma (p = 0.0086) ranges. Additionally, electrodes within the SOZ in patients that became seizure-free after surgery had significantly higher AUC compared to equivalent electrodes in patients who did not become seizure-free after surgery (n = 7) in the gamma (p = 0.0145) and high gamma (p = 0.0024) power ranges. We trained a stacked random forest ensemble model using these features over time to label electrodes within the SOZ. Leave-one-out patient cross validation of the machine learning model yielded a 96.6% positive predictive value and 99.9% specificity for identifying electrodes within the SOZ. Our dataset included a diverse array of seizure onset patterns, which were all classified accurately by the model. A second model was trained to predict post-operative seizure freedom, yielding 95.2% accuracy for predicting seizure outcome based on a planned resection. This two-model design mirrors clinical workflow, first localizing SOZ electrodes to support surgical planning, then predicting outcome based on a surgical plan. An advantage of our interpretable machine learning approach is the ability to interrogate our models to understand how predictions are made. For electrode classification, the model weighed beta (0.66 {+/-} 0.07), high gamma (0.54 {+/-} 0.06), and delta (0.51 {+/-} 0.06) power bands most heavily. Viewing the models frequency band weights over time reveals that the model identified a pattern resembling the "fingerprint of the epileptogenic zone", reinforcing the importance of this dominant fundamental neurophysiologic signature of seizure onset.

neuroscience↗

bioRxiv2020

Responses to Visual Speech in Human Posterior Superior Temporal Gyrus Examined with iEEG Deconvolution

Experimentalists studying multisensory integration compare neural responses to multisensory stimuli with responses to the component modalities presented in isolation. This procedure is problematic for multisensory speech perception since audiovisual speech and auditory-only speech are easily intelligible but visual-only speech is not. To overcome this confound, we developed intracranial encephalography (iEEG) deconvolution. Individual stimuli always contained both auditory and visual speech but jittering the onset asynchrony between modalities allowed for the time course of the unisensory responses and the interaction between them to be independently estimated. We applied this procedure to electrodes implanted in human epilepsy patients (both male and female) over the posterior superior temporal gyrus (pSTG), a brain area known to be important for speech perception. iEEG deconvolution revealed sustained, positive responses to visual-only speech and larger, phasic responses to auditory-only speech. Confirming results from scalp EEG, responses to audiovisual speech were weaker than responses to auditory- only speech, demonstrating a subadditive multisensory neural computation. Leveraging the spatial resolution of iEEG, we extended these results to show that subadditivity is most pronounced in more posterior aspects of the pSTG. Across electrodes, subadditivity correlated with visual responsiveness, supporting a model in visual speech enhances the efficiency of auditory speech processing in pSTG. The ability to separate neural processes may make iEEG deconvolution useful for studying a variety of complex cognitive and perceptual tasks. Significance statementUnderstanding speech is one of the most important human abilities. Speech perception uses information from both the auditory and visual modalities. It has been difficult to study neural responses to visual speech because visual-only speech is difficult or impossible to comprehend, unlike auditory-only and audiovisual speech. We used intracranial encephalography (iEEG) deconvolution to overcome this obstacle. We found that visual speech evokes a positive response in the human posterior superior temporal gyrus, enhancing the efficiency of auditory speech processing.

neuroscience↗

bioRxiv2019

Cross-modal suppression of auditory association cortex by visual speech as a mechanism for audiovisual speech perception

Vision provides a perceptual head start for speech perception because most speech is \"mouth-leading\": visual information from the talkers mouth is available before auditory information from the voice. However, some speech is \"voice-leading\" (auditory before visual). Consistent with a model in which vision modulates subsequent auditory processing, there was a larger perceptual benefit of visual speech for mouth-leading vs. voice-leading words (28% vs. 4%). The neural substrates of this difference were examined by recording broadband high-frequency activity from electrodes implanted over auditory association cortex in the posterior superior temporal gyrus (pSTG) of epileptic patients. Responses were smaller for audiovisual vs. auditory-only mouth-leading words (34% difference) while there was little difference (5%) for voice-leading words. Evidence for cross-modal suppression of auditory cortex complements our previous work showing enhancement of visual cortex (Ozker et al., 2018b) and confirms that multisensory interactions are a powerful modulator of activity throughout the speech perception network.\n\nImpact StatementHuman perception and brain responses differ between words in which mouth movements are visible before the voice is heard and words for which the reverse is true.

physiology↗