Chamarthi, S. | bioRxiv Science

bioRxiv2026

Electrophysiological features of signals recorded from white matter

Intracranial neurophysiology studies have typically ignored signals from electrodes located in white matter (WM), assuming that their information content is artifactual or related to nearby gray matter (GM). Here, we tested the electrophysiological and functional features of signals recorded from different WM locations. Signals were recorded from 19 patients undergoing intracranial monitoring for drug-resistant epilepsy by means of stereo-electroencephalography (sEEG). Each sEEG electrode was classified into WM or GM based on the surrounding tissue. We obtained recordings from a total of 1,717 sEEG electrode contacts, 36% in WM, while the patients were in awake resting state (5 minutes). For each sEEG electrode, we employed a model-based spectral decomposition to separate periodic and aperiodic components, and we computed signal complexity metrics. For a subset of participants, we computed WM structural information from diffusion-weighted magnetic resonance imaging and we evaluated functional signals during a cognitive control task. Our results show that signals recorded from WM have different spectral features and higher complexity than GM. Complexity correlates positively with fractional anisotropy, and modulations related to behavior during the task were detected in WM. Overall, this indicates that WM signals carry information that may reflect signal propagation across WM fiber tracts.

neuroscience↗

bioRxiv2026

Estimation of neuronal tuning for word meaning from passively recorded naturalistic speech

The ability to derive neural-level language coding models holds great scientific and clinical potential. Current approaches are limited by the scale and ethological validity of input data; applications requiring large, rare, or naturalistic samples in particular would benefit from the ability to infer neural coding from incidental everyday speech. Here we present a novel pipeline designed to leverage spontaneous and incidental naturalistic speech. This pipeline performs transcription, segmentation, and video-assisted diarization, as well as alignment and spike detection of neural data. We apply this pipeline to a dataset derived from 21 patients (6+ days each, over 800 hours and 5 million words total). We benchmark both encoding and decoding models against extensive and rare ground-truth control datasets consisting of human-curated word-level temporal alignment and manually sorted spikes. We further validate our approach by quantifying representational drift, effect of dataset size, and differences between six brain areas. Together, these findings demonstrate that incidental natural speech is sufficiently processed in the brain to enable the estimation neural-level embeddings.

neuroscience↗