bioRxiv Science⌕ Search

bioRxiv · 10.1101/2024.10.06.616879

A Method for Electrical Stimulus Artifact Removal Exploiting Neural Refractoriness: Validation by Contrasting Cathodic and Anodic Stimulation

Abstract

ObjectiveTo present a novel method for removing stimulus transient that exploits the absolute refractory period of electrically excitable neural tissues. BackgroundElectrical stimulation often generates significant signal artifacts that can obscure important physiological signals. Removal of the artifact and understanding latent information from these signals could provide objective measures of circuit engagement, potentially driving advancements in neuromodulation research and therapies. MethodsWe conducted intracranial physiology studies on five consecutive patients with Parkinsons disease who underwent deep brain stimulation (DBS) surgery as part of their routine care. Monopolar stimuli (either cathodic or anodic) were delivered in pairs through the DBS electrode across a range of inter-stimulus intervals. Recordings from adjacent unused electrode contacts used broadband sampling and precise synchronization to generate a robust template for the stimulus transient during the absolute refractory period. These templates of stimulus transient were then subtracted from recordings at different intervals to extract and analyze the residual neural potentials. ResultsAfter artifact removal, the residual signals exhibited absolute and relative refractory periods with timing indicative of neural activity. Cathodic and anodic DBS pulses generated distinct patterns of local tissue activation, showing phase independence from the prior stimulus. The earliest detectable neural responses occurred at short peak latencies (ranging from 0.19 to 0.38 ms post-stimulus) and were completely or partially obscured by the stimulus artifact prior to removal. Cathodic stimuli produced stronger local tissue responses than anodic stimuli, aligning with clinical observations of lower activation thresholds for cathodic stimulation. However, cathodic and anodic pulses induced artifact patterns that were equivalent but opposite. InterpretationThe proposed artifact removal technique enhances prior approaches by allowing direct measurement of local tissue responses without requirements for stimulus polarity reversal, template scaling, or specialized filters. This approach could be integrated into future neuromodulation systems to visualize stimulus-evoked neural potentials that would otherwise be obscured by stimulus artifacts.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Nakhmani, A., Block, J., Awad, M., Olson, J., Smith, R. J., Bentley, N. J., Holland, M. T., Brinkerhoff, S. A., Gonzalez, C. L., Moffitt, M., Walker, H. C.. 2024-10-07. A Method for Electrical Stimulus Artifact Removal Exploiting Neural Refractoriness: Validation by Contrasting Cathodic and Anodic Stimulation. https://doi.org/10.1101/2024.10.06.616879

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Dynamic Compression Platform for Live Imaging of Scaffold-Transmitted Cellular Mechanoresponses

Mechanical characterization of biomaterial scaffolds is essential to evaluate their capacity to meet the functional demands of target tissues in tissue engineering and regenerative medicine applications. Scaffolds designed to interface with living tissues must support the transmission of mechanical cues to resident cells and stimulate mechanosignaling pathways that are essential to their function. In joints, bone and cartilage cells act as primary mechanosensors, converting mechanical stimuli into biochemical signals that regulate tissue homeostasis and remodelling. Therefore, evaluating cellular mechanoresponses to scaffold-transmitted compression in vitro can inform the development of functional tissue-engineered constructs. For example, poly({epsilon}-caprolactone) (PCL) scaffolds are highly relevant for bone and cartilage tissue engineering due to their biocompatibility, stable mechanical properties and slow degradation. Here, we applied a custom-built device to study compression-induced mechanosignaling in MC3T3-E1 pre-osteoblast cells. The device is composed of a polydimethylsiloxane (PDMS) pillar, a force-sensing load cell, and a piezoelectric linear track. A protocol is described in which MC3T3-E1 cells are repeatedly compressed, while in parallel live tracking of force measurements and live imaging of intracellular calcium dynamics in MC3T3-E1 cells are recorded. PCL scaffolds fabricated by melt electrowriting (MEW) were subsequently integrated into the platform. Scaffold-transmitted compression triggered dynamic increases in cytosolic calcium; in MC3T3-E1 cells located directly under the PCL microfibers, but also in cells located in the interfiber spaces. This device and workflow facilitate in vitro investigations of real-time cellular mechanoresponses to dynamic compression applied with biomaterial scaffolds, and provides a testing platform for evaluating the mechanotransductive properties of scaffolds intended for tissue engineering applications.

bioengineering↗

Ultrasound Tracking Reveals Progressive Regional Strain Differences in Human Achilles Tendons During Fatigue Loading

Ultrasound is commonly used to assess structural changes in symptomatic Achilles tendons, but quantitative biomechanical metrics for progressive tendon deterioration remain limited. The goal of this study was to develop and validate an automated ultrasound tracking algorithm for regional tendon deformation and evaluate strain progression in survived and ruptured tendons during fatigue loading. We hypothesized that maximum strain, average strain, and strain heterogeneity would exhibit different trajectories between groups. Ten cadaveric Achilles tendons underwent cyclic loading with stress tests every 500 cycles until rupture or 150,000 cycles. Ultrasound images acquired during stress tests were analyzed using an automated tracking algorithm to generate spatially resolved regional strain fields. Ultrasound-derived bulk strain was highly correlated with actuator-derived strain in survived (R^2 = 0.968 +/- 0.017) and ruptured tendons (R^2 = 0.972 +/- 0.014). Maximum and average longitudinal strains progressively diverged between groups across fatigue life (Group x FatigueLife: p = 0.003 and p < 0.0001, respectively). During the first 10,000 cycles, average strain decreased in survived tendons ({beta} = -0.0268%, p = 0.0215) but not ruptured tendons ({beta} = 0.0147%, p = 0.1197), with a significant Group x Cycle interaction (p = 0.0061). This study demonstrates that the algorithm quantified Achilles tendon deformation with high fidelity and enabled spatially resolved strain assessment throughout fatigue loading. Maximum and average strain followed different trajectories between groups, whereas strain heterogeneity did not. Early differences in tendon biomechanics suggest that regional strain behavior may change before pronounced differences in absolute magnitude develop.

bioengineering↗

Brain organoid computing for robotic decision-making

Biomimicry has inspired the evolution of robotics toward greater autonomy, adaptability, and symbiosis with humans and dynamic environments. However, current robotic systems still face major challenges in recapitulating the high-efficiency decision-making capabilities of the human brain under complex and dynamic conditions. Here, we present Brainobot, a biohybrid robotic system that establishes a brain organoid controller as a high-level robotic decision-making layer for closed-loop embodiment. By leveraging brain organoid reservoir computing, Brainobot interacts with dynamic environments by receiving and processing sensory inputs and generating motor actions. As a proof-of-concept demonstration, Brainobot is implemented in a humanoid robotic system to perform real-world tasks, including object grasping and laser chasing. Interestingly, Brainobot exhibits unique features, including cross-task adaptivity, high computing efficiency, and low energy consumption. Thus, our approach may provide insights for advancing robotic embodiment and understanding biological decision-making.

bioengineering↗