arXiv subjects

Angelique C. Paulk

Publications and source records attributed to Angelique C. Paulk.

3 recordsLinked to original sources

arXiv2026

High-Density Multi-Depth Human Recordings Using 45 mm Long Neuropixels Probes

Neuropixels probes, initially developed for use in small animal models, have transformed basic neuroscience by enabling high-density, single-cell resolution recordings across multiple brain regions simultaneously. The recent development of Neuropixels 1.0 NHP Long, a longer probe designed for non-human primates, has expanded this capability, enabling unprecedented simultaneous access to multiple cortical layers and deep brain structures of large-brained animals. Here, we report the first use of these probes in humans, aiming to establish safe intraoperative use and assess feasibility for clinical and research applications. Nine patients undergoing neurosurgical procedures, including epilepsy or tumor resection and deep brain stimulation (DBS) implantation, were enrolled. Successful intraoperative recordings were obtained from surface and deep cortical structures without probe breakage or adverse events. Compared with conventional electrodes, the Neuropixels probe enabled dense sampling across multiple parenchymal depths with submillisecond temporal resolution. Recordings were obtained from deep targets including the hippocampus and cingulate cortex, as well as from regions that are challenging to access with single-unit precision, such as the superior frontal sulcus. Custom tools and refined workflows lowered technical barriers for operative use and improved recording stability. Neural activity was observed across all recordings. Neuropixels 1.0-NHP Long probes can be deployed in the human operating room, enabling simultaneous recordings from multiple brain structures at single-neuron resolution. These methods expand opportunities for studying human brain function and pathology in vivo, and may ultimately support the development of more precise neurosurgical interventions.

q-bio.NC↗

arXiv2023

Bayesian Time-Series Classifier for Decoding Simple Visual Stimuli from Intracranial Neural Activity

Understanding how external stimuli are encoded in distributed neural activity is of significant interest in clinical and basic neuroscience. To address this need, it is essential to develop analytical tools capable of handling limited data and the intrinsic stochasticity present in neural data. In this study, we propose a straightforward Bayesian time series classifier (BTsC) model that tackles these challenges whilst maintaining a high level of interpretability. We demonstrate the classification capabilities of this approach by utilizing neural data to decode colors in a visual task. The model exhibits consistent and reliable average performance of 75.55% on 4 patients' dataset, improving upon state-of-the-art machine learning techniques by about 3.0 percent. In addition to its high classification accuracy, the proposed BTsC model provides interpretable results, making the technique a valuable tool to study neural activity in various tasks and categories. The proposed solution can be applied to neural data recorded in various tasks, where there is a need for interpretable results and accurate classification accuracy.

cs.LG↗

arXiv2021

Human Brain Mapping with Multi-Thousand Channel PtNRGrids Resolves Novel Spatiotemporal Dynamics

Electrophysiological devices are critical for mapping eloquent and diseased brain regions and for therapeutic neuromodulation in clinical settings and are extensively utilized for research in brain-machine interfaces. However, the existing devices are often limited in either spatial resolution or cortical coverage, even including those with thousands of channels used in animal experiments. Here, we developed scalable manufacturing processes and dense connectorization to achieve reconfigurable thin-film, multi-thousand channel neurophysiological recording grids using platinum-nanorods (PtNRGrids). With PtNRGrids, we have achieved a multi-thousand channel array of small (30 μm) contacts with low impedance, providing unparalleled spatial and temporal resolution over a large cortical area. We demonstrate that PtNRGrids can resolve sub-millimeter functional organization of the barrel cortex in anesthetized rats that captured the histochemically-demonstrated structure. In the clinical setting, PtNRGrids resolved fine, complex temporal dynamics from the cortical surface in an awake human patient performing grasping tasks. Additionally, the PtNRGrids identified the spatial spread and dynamics of epileptic discharges in a patient undergoing epilepsy surgery at 1 mm spatial resolution, including activity induced by direct electrical stimulation. Collectively, these findings demonstrate the power of the PtNRGrids to transform clinical mapping and research with brain-machine interfaces and highlights a path toward novel therapeutics.

physics.app-ph↗