Biology subjects

Bahador, N.

Publications and source records attributed to Bahador, N..

5 recordsLinked to original sources

bioRxiv2025

DIME: Data-driven Importance MEtric Guides Localization of the Seizure Onset Zone from Intracranial EEG Data

Epilepsy affects over 50 million individuals, many of whom require surgical treatment that is dependent on accurate localization of the seizure onset zone (SOZ). Conventional SOZ biomarkers are based on strictly defined intracranial electroen-cephalography (iEEG) phenomena and cannot benefit from increased datasets. The scarcity of SOZ-labeled iEEG data impedes biomarker development. We introduce the Data-driven Importance MEtric (DIME) to guide SOZ localization in an interpretable pipeline that improves with ictal-labeled iEEG data. We apply DIME to an open-source dataset (n=21; 13 successful; 8 failed) for SOZ localization and surgical outcome prediction. The highest DIME-ranked electrode belonged to the clinically annotated SOZ for 69.2% of patients with successful surgery (p < 0.001). DIME scores were significantly higher in SOZ electrodes than nonSOZ electrodes in both successful and failed surgeries (p < 0.001), though the DIME distribution for successful cases differed from failed cases (p = 0.002). DIME predicted surgical outcome with 92.3% recall and 66.7% accuracy.

neuroscience↗

bioRxiv2025

Semi-Automated Detection, Annotation, and Prognostic Assessment of Ictal Chirps in Intracranial EEG from Patients with Epilepsy

We analyzed the spectro-temporal characteristics of ictal "chirp" events in intracranial EEG (iEEG) recordings from 13 epilepsy patients, using a custom derivative dataset of 22,721 spectrograms that we generated from the Epilepsy-iEEG-Multicenter Dataset. Ictal chirps, transient frequency-modulated patterns, were semi-automatically annotated to assess their relationship with seizure onset zones (SOZs) and surgical outcomes. Preprocessing included notch filtering (60 Hz, 120 Hz) and bandpass filtering (1-60 Hz), followed by segmentation into 60-second windows. Spectrograms were generated via Short-Time Fourier Transform (STFT) with a Hann window (87.5% overlap) and converted to dB scale. Chirps were annotated by manually drawing bounding boxes, followed by automated ridge detection, model fitting, and feature extraction (start/end time-frequency, duration, direction, RMSE, R2). Spatial, Spectro-temporal, and clinical features were analyzed using heatmaps, hierarchical clustering, statistical tests (Mann-Whitney U), and outcome prediction models. Patient-channel mappings revealed clustering of chirp patterns among specific patient pairs, correlating with shared clinical profiles. Flow-based analysis demonstrated prognostic value: very high spectral durations in SOZ regions were associated with favorable surgical outcomes (80.43% success rate), whereas very high temporal durations in SOZ correlated with poorer outcomes (51.35% risk). Statistical comparisons showed significant differences between SOZ and non-SOZ chirps: SOZ chirps exhibited longer spectral durations (10.13 {+/-} 6.35 Hz vs. 8.51 {+/-} 5.66 Hz, *p* < 0.001), shorter temporal durations (6.76 {+/-} 5.83 s vs. 7.14 {+/-} 5.39 s, *p* = 0.006), and higher spectro-temporal ratios (2.66 vs. 1.92, *p* < 0.001). Distribution analyses further indicated that prolonged temporal chirps were more prevalent in non-SOZ regions.

neuroscience↗

bioRxiv2025

A Software for Identification and Characterization of Theta Rhythms in the Hippocampus

Characterizing theta rhythms in the hippocampus provides a window into understanding memory processing. An inquiry that arises when an animal sustains a pathological state is how theta rhythms are affected. In pathological states like epilepsy or Alzheimers, these rhythms change in specific ways. Statistically robust changes in these rhythms could serve as potential biomarkers, indicating the severity of the animals condition and the effectiveness of a drug. However, this understanding depends on how the data is analyzed. There are currently no standard criteria for recognizing theta dominance in experimental recordings. To address this, we have developed novel MATLAB-based software with an easy-to-use graphical user interface which enables identifying and analyzing theta rhythms in a standard way. We discuss the softwares functionality and its underlying algorithms. The algorithms were developed using previously acquired EEG/LFP data recorded from the hippocampus of a mouse kindling model of epilepsy. Two primary analyses were conducted to test the softwares functionality: first, comparing theta rhythms during the baseline period versus during spontaneous recurrent seizures; second, analyzing the timing of theta rhythms relative to the seizure event. Our illustrative results indicate that our developed software can robustly identify theta events with statistically significant feature differences. Further, the examination presented here with two mice shows that while theta events can occur just before seizures, it takes tens of minutes post-seizure before theta rhythms occur again. Our software thus provides the user with the ability to robustly identify and characterize theta rhythms and their feature changes. Significance StatementTheta rhythms in the hippocampus are fundamental for spatial navigation and memory formation. Their observed changes during several pathological states such as epilepsy and Alzheimers make them highly interesting to be able to serve as biomarkers. However, their variability (in terms of the time of occurrence, duration, and frequency range) makes them challenging to quantify. The absence of available tools for the automatic extraction of these rhythms from extended datasets significantly hampers processing efficiency and reduces accuracy and consistency. Clinicians and researchers often manually inspect their data to identify these rhythms, a process that is not only time-consuming but also inherently subjective. We have thus developed a MATLAB software for precise, automatic analysis of theta rhythms in EEG/LFP recordings.

neuroscience↗

bioRxiv2024

Ictal-Related Chirp as a Biomarker for Monitoring Seizure Progression

Despite being prevalent, the causes, mechanisms, and progression of epilepsy--a chronic neurological disorder with unprovoked seizures--are not well understood, complicating drug development for treatment. This study used a comprehensive mouse epilepsy kindling model dataset to investigate frequency modulation (chirp) as a potential indicator of distinct states of epilepsy (early evoked discharge, late evoked discharge, spontaneous recurrent seizure, and drug state). Employing time-frequency ridge extraction, chirp identification, and statistical testing, our analyses revealed that chirp patterns occur in the majority of ictal discharges (>81.6%), persisting across evoked and spontaneous seizures. While the focus was on hippocampal recordings, chirps were also detected in the piriform peripheral cortex. Significant frequency and duration changes in chirp patterns during the transition from early to late evoked ictal events suggest their potential as the screening tool for seizure progression. Additionally, detailed analyses illuminate the impact of Lorazepam, a GABAA enhancer, on chirp characteristics, providing insights into how increased inhibitory tone quantifiably influences excitatory-inhibitory balances during seizures.

neuroscience↗

bioRxiv2023

Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings using SVD-based Adaptive Filtering

Deep brain stimulation (DBS) is widely used as a treatment option for patients with movement disorders. In addition to its clinical impact, DBS has been utilized in the field of cognitive neuroscience wherein the answers to several fundamental questions underpinning the mechanisms of neuromodulation in decision making rely on how a burst of DBS pulses, usually delivered at clinical frequency, i.e., 130 Hz, perturb participants choices. It was observed that neural activities recorded during DBS were contaminated with stereotype large artifacts, which lasts for a few milliseconds, as well as a low-frequency (slow) signal ([~]1-2 Hz) that can persist for hundreds of milliseconds. While the focus of the most of methods for removing DBS artifact was on the former, the artifact removal of the slow signal has not been addressed. In this work, we propose a new method based on combining singular value decomposition (SVD) and normalized adaptive filtering to remove both large (fast) and slow artifacts in local field potentials recorded during a cognitive task in which bursts of DBS were utilized. Using synthetic data, we show that our proposed algorithm outperforms four commonly used techniques in the literature, namely, (1) Normalized least mean square adaptive filtering, (2) Optimal FIR Wiener filtering, (3) Gaussian model matching, and (4) Moving average. The algorithms capabilities are further demonstrated by its ability to effectively remove DBS artifacts in local field potentials recorded from the subthalamic nucleus during a verbal Stroop task, highlighting its utility in real-world applications.

neuroscience↗