Biology subjects

Sellers, K. K.

Publications and source records attributed to Sellers, K. K..

4 recordsLinked to original sources

bioRxiv2025

Towards individualized deep brain stimulation: A stereoencephalography-based workflow for unbiased neurostimulation target identification

ObjectivesDeep brain stimulation (DBS) is increasingly being used to treat a variety of neuropsychiatric conditions, many of which exhibit idiosyncratic symptom presentations and neural correlates across individuals. Thus, we have utilized inpatient stereoelectroencephalography (sEEG) to identify personalized therapeutic stimulation sites for chronic implantation of DBS. Informed by our experience, we have developed a statistics-driven framework for stimulation testing to identify therapeutic targets. Materials and MethodsFourteen participants (major depressive disorder = 6, chronic pain = 6, obsessive-compulsive disorder = 2) underwent inpatient testing using sEEG and symptom monitoring to identify personalized stimulation targets for subsequent DBS implantation. We present a structured approach to this sEEG testing, integrating a Stimulation Testing Decision Tree with power analysis and effect size considerations to inform adequately powered results to detect therapeutic stimulation sites with statistical rigor. ResultsEffect sizes (Hedges g) of stimulation-induced symptom score changes ranged from -1.5 to +2.39. The standard deviation of sham trial responses was a strong predictor of stimulation response variability, as confirmed by a leave-one-out cross-validated linear regression (R2 = 0.67, permutation p<0.001). Thus, early sham trial data could be used to estimate the variability of stimulation responses for power analysis calculations. We show that approximately 10 sham trials were needed to robustly estimate sham variability. Power analysis (using a paired-t test) showed that for effect sizes [≥] 1.1, roughly 10 trials should be used per stimulation site for sufficiently powered results. ConclusionsThe presented workflow is adaptable to multiple indications and is specifically designed to overcome key challenges experienced during stimulation site testing. Through incorporating sham trials, effect size calculations, and tolerability testing, the described approach can be used to identify personalized and clinically efficacious stimulation sites.

neuroscience↗

bioRxiv2023

DREDge: robust motion correction for high-density extracellular recordings across species

High-density microelectrode arrays (MEAs) have opened new possibilities for systems neuroscience in human and non-human animals, but brain tissue motion relative to the array poses a challenge for downstream analyses, particularly in human recordings. We introduce DREDge (Decentralized Registration of Electrophysiology Data), a robust algorithm which is well suited for the registration of noisy, nonstationary extracellular electrophysiology recordings. In addition to estimating motion from spikes in the action potential (AP) frequency band, DREDge enables automated tracking of motion at high temporal resolution in the local field potential (LFP) frequency band. In human intraoperative recordings, which often feature fast (period <1s) motion, DREDge correction in the LFP band enabled reliable recovery of evoked potentials, and significantly reduced single-unit spike shape variability and spike sorting error. Applying DREDge to recordings made during deep probe insertions in nonhuman primates demonstrated the possibility of tracking probe motion of centimeters across several brain regions while simultaneously mapping single unit electrophysiological features. DREDge reliably delivered improved motion correction in acute mouse recordings, especially in those made with an recent ultra-high density probe. We also implemented a procedure for applying DREDge to recordings made across tens of days in chronic implantations in mice, reliably yielding stable motion tracking despite changes in neural activity across experimental sessions. Together, these advances enable automated, scalable registration of electrophysiological data across multiple species, probe types, and drift cases, providing a stable foundation for downstream scientific analyses of these rich datasets.

neuroscience↗

bioRxiv2021

High density single-unit human cortical recordings using the Neuropixels probe

A fundamental unit of neural computation is the action potential. While significant advances have been made in the ability to sample action potentials of large numbers of individual neurons in animal models, translation of these methodologies to humans has been lacking due to clinical time constraints, electrical noise in the operating room, and reliability of the methodology. Here we present a reliable method for intraoperative recording of dozens of neurons in humans using the Neuropixels probe, yielding up to [~]100 simultaneously-recorded single-units (n=596 across 11 recordings in 8 participants). Most single-units were active within 1 minute of reaching target depth, compatible with clinical time constraints. Cell pairs active close in time were spatially closer in most recordings, demonstrating the power to resolve complex cortical dynamics. Altogether, this approach provides access to population single-unit activity across the depth of human neocortex at scales previously only accessible in animal models. HighlightsO_LISingle units in 8 patients, yielding 596 putative single units across 11 recordings C_LIO_LIThe majority of putative neurons fire at least 1 spike within one minute after reaching target depth C_LIO_LIPutative neurons take longer to fire at least one spike in anesthetized compared to awake participants C_LIO_LICell-pairs that fire action potentials close in time are spatially closer together than those fire further apart in time C_LI

neuroscience↗

bioRxiv2021

Analysis-rcs-data: Open-source toolbox for the ingestion, time-alignment, and visualization of sense and stimulation data from the Medtronic Summit RC+S system

Closed-loop neurostimulation is a promising therapy being tested and clinically implemented in a growing number of neurological and psychiatric indications. This therapy is enabled by chronically implanted, bidirectional devices including the Medtronic Summit RC+S system. In order to successfully optimize therapy for patients implanted with these devices, analyses must be conducted offline on the recorded neural data, in order to inform optimal sense and stimulation parameters. The file format, volume, and complexity of raw data from these device necessitate conversion, parsing, and time reconstruction ahead of time-frequency analyses and modeling common to standard neuroscientific analyses. Here, we provide an open-source toolbox written in Matlab which takes raw files from the Summit RC+S and transforms these data into a standardized format amenable to conventional analyses. Furthermore, we provide a plotting tool which can aid in the visualization of multiple data streams and sense, stimulation, and therapy settings. Finally, we describe an analysis module which replicates RC+S on-board power computations, functionality which can accelerate biomarker discovery. This toolbox aims to accelerate the research and clinical advances made possible by longitudinal neural recordings and adaptive neurostimulation in people with neurological and psychiatric illnesses.

neuroscience↗