Biology subjects

Diaz-Montiel, A. A.

Publications and source records attributed to Diaz-Montiel, A. A..

3 recordsLinked to original sources

bioRxiv2026

Biophysical modeling reveals how recording geometry shapes representations of cortical activity

Extracellular electrophysiological recordings reflect both the organization of neural activity and the geometry through which that activity is sampled. Here, we used biophysical modeling and experimental recordings from the macaque auditory cortex to determine how cortical activity is transformed across neural sources, extracellular signals, and recording geometries. In a biophysically detailed thalamocortical model, four separately instantiated cortical-column conditions spanning graded thalamic recruitment exhibited a graded response hierarchy across local field potential, current-source density, and multiunit activity, despite signal-dependent differences in the contributions of cortical neuronal populations. We then arranged independently simulated cortical columns into controlled spatial configurations and sampled the same underlying sources using virtual linear laminar and Directional and Scalable (DiSc) electrode arrays. The linear geometry primarily represented variation along cortical depth, whereas DiSc additionally represented circumferential variation associated with lateral source organization, demonstrating that recording geometry determines which spatial dimensions of common underlying activity are represented in extracellular measurements. Experimental recordings obtained with the corresponding geometries were consistent with these model-derived predictions: broadband-noise responses exhibited spatial organization shared across tone-evoked responses in both multidimensional DiSc maps and laminar profiles, despite substantial variation in frequency-dependent response magnitude across penetrations. Together, these results show that extracellular measurements represent an interaction between biological source organization and recording geometry. Biophysical forward modeling therefore provides a framework for determining which features of underlying circuit organization remain identifiable after extracellular sampling and for relating neural-interface design to the dimensions of cortical activity accessible experimentally.

neuroscience↗

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↗

bioRxiv2023

Graph representations of iEEG data for seizure detection with graph neural networks

Epilepsy is a neurological disorder that affects over 50 million individuals worldwide. Today, the gold-standard treatment for those who are drug resistant, meaning that symptoms cannot be controlled with medication, is to surgically remove the seizure onset zone (SOZ), the area of the brain believed to cause seizures: the main symptom of epilepsy. Unfortunately, around 50% of drug resistant patients are not resective candidates, which can be attributed in part to poor SOZ localization. SOZ localization is a complex and lengthy procedure, requiring visual inspection and manual processing by human experts that first need to localize and isolate seizure events. The intracranial electroencephalography (iEEG) is a tool that records electrophysiological activity of the inner brain at different regions and depths, and provides critical information on the SOZ. However, iEEG data processing methodologies are not standardized, and practice and resources vary across hospitals and clinics. To assist human experts with systematic processing of iEEG data, we propose a data processing pipeline that generates graph representations of iEEG data. We evaluate 9 different graph representations of publicly available iEEG data from 25 patients with epilepsy with a graph neural network model trained to detect seizures. Our results suggest that graph representations of iEEG data that leverage electrode and functional connectivity features are powerful data structures to analyze and interpret iEEG data in the context of epilepsy. We anticipate that our data pipeline that provides a systematic processing of neural data with graphs can integrate other data modalities like neuroimaging data. Moreover, methods used in the data pipeline have potentials to apply to other neurological disorders such as Parkinsons disease or major depression disorder.

neuroscience↗