Biology subjects

Zariffa, J.

Publications and source records attributed to Zariffa, J..

7 recordsLinked to original sources

bioRxiv2026

Integrating Electrical Components into a Printed Self-folding Cuff Electrode for Chronic Peripheral Nerve Interfaces

Neuroelectronic thin-film implants hold promise for advancing fundamental understanding of the peripheral nervous system and offer potential for targeted treatments using specific stimulation. However, challenges in establishing robust and durable connections to soft and flexible implants limit their widespread adoption and long-term utility. Here, we present a novel method for integrating rigid electrical components, such as a standard USB-C connector, directly into a printed stretchable self-folding cuff electrode for chronic peripheral nerve interfacing. Our multi-material printing approach provides a gradual stiffness transition, effectively mitigating common failure points associated with mechanical stress at soft-rigid boundary. We demonstrate the integration of a wireless stimulation circuit and a robust USB-C implantable port, offering a plug- and-play solution for stable chronic electrophysiology experiments. Chronic implant studies in free-running locusts with USB-C connectors show reliable nerve recordings, capturing behavioral differences. The concept is further validated as a transcutaneous implanted port in rats for vagus nerve recordings. This work addresses a critical bottleneck in neurotechnology by enabling robust connectivity for implanted devices, which is essential for advancing peripheral nervous electrophysiology experiments in freely moving small vertebrates and insects.

neuroscience↗

bioRxiv2024

Precise Timing Matters: Modulating Human Memory by Synchronizing Hippocampal Stimulation to Saccadic Event Related Potentials

Episodic memory, the ability to record and relive experiences, is intricately connected to visual exploration in most humans. This study explores the possibility that eye movements create physiological states relevant for memory, analogous to those associated with hippocampal theta. Previous work has demonstrated that saccadic eye movements, which occur roughly at theta frequency, elicit hippocampal event-related potentials (ERPs). Building on the Separate Phases of Encoding and Retrieval (SPEAR) model, we asked if the peaks and troughs of this saccadic ERP are differentially important for memory formation. Specifically, we applied saccade-contingent hippocampal electrical stimulation at estimated ERP peaks and troughs while individuals with epilepsy visually explored natural scenes across 59 sessions. We subsequently assessed their recognition memory for scenes and their recall of associated targets. Results indicate that memory is robust when stimulation precisely targets the peak or trough, contrasting with impairments observed with random stimulation. Moreover, memory impairment is prominent when stimulation is applied within 100 ms of saccade initiation, a time that reflects high medial temporal lobe inhibition. Our findings suggest that the hippocampus rapidly evolves through memory-relevant states following each eye movement, while also challenging the assumption that human saccadic ERP peaks and troughs mirror the encoding and retrieval phases of theta rhythms studied in rodents. The study sheds light on the dynamic interplay between eye movements, hippocampal activity, and memory formation, offering theoretical insights with potential applications for memory modulation in neurological disorders. Significance StatementWhy do eye-movements enhance memory formation? Here, we causally tested if eye-movements initiate short-lived states critical for memory formation within the hippocampus, a brain region known to support memory. We built a system that could precisely apply hippocampal electrical stimulation at key moments after eye-movements to test how the timing of this stimulation influenced peoples ability to form memories. We found that stimulation was particularly disruptive to memory formation when applied within 100 ms of initiating an eye movement. By contrast, memory was robust to stimulation precisely timed to the peak and trough of hippocampal eye-movement responses. We interpret this temporal evolution of memory-relevant states within a prominent model of how theta phases relate to rodent memory.

neuroscience↗

bioRxiv2023

Closed-loop control of functional electrical stimulation using a selectively recording and bidirectional nerve cuff interface

BackgroundDiscriminating recorded afferent neural information can provide sensory feedback for closed-loop control of functional electrical stimulation, which restores movement to paralyzed limbs. Previous work achieved state-of-the-art off-line classification of electrical activity in different neural pathways recorded by a multi-contact nerve cuff electrode, by applying deep learning to spatiotemporal neural patterns. ObjectiveTo incorporate this approach into closed-loop stimulation. MethodsAcute in vivo experiments were conducted on 11 Long Evans rats to demonstrate closed-loop stimulation. A 64-channel (8 x 8) nerve cuff electrode was implanted on each rats sciatic nerve for recording and stimulation. A convolutional neural network (CNN) was trained with spatiotemporal signal recordings associated with 3 different states of the hindpaw (dorsiflexion, plantarflexion, and pricking of the heel). After training, firing rates were reconstructed from the classifier outputs for each of the three target classes. A rule-based closed-loop controller was implemented to produce ankle movement trajectories using neural stimulation, based on the classified nerve recordings. Closed-loop stimulation was initiated by the detection of a heel prick, and induced dorsiflexion. The detection of dorsiflexion triggered stimulation to induce plantarflexion, and vice versa. A single trial began with a heel prick and ended when an incorrect state transition occurred or when a second heel prick was detected. ResultsClosed-loop stimulation was successfully demonstrated in 6 subjects. Number of successful trials per subject ranged from 1-17 and number of correct state transitions per trial ranged from 3-53. ConclusionThis work demonstrates that a CNN applied to multi-contact nerve cuff recordings can be used for closed-loop control of functional electrical stimulation.

neuroscience↗

bioRxiv2022

Resource-efficient Neural Network Architectures forClassifying Nerve Cuff Recordings on Implantable Devices

BackgroundClosed-loop control of functional electrical stimulation involves using recorded nerve signals to make decisions regarding nerve stimulation in real-time. Surgically implanted devices that can implement this strategy have significant potential to restore natural movement after paralysis. Previous work demonstrated the use of convolutional neural networks (CNNs) to discriminate between activity from different neural pathways recorded by a high-density multi-contact nerve cuff electrode. Despite state-of-the-art performance, that approach required too much data storage, power and computation time for a practical implementation on surgically implanted hardware. ObjectiveTo reduce resource utilization for an implantable implementation, with a minimal performance loss for CNNs that can discriminate between neural pathways in multi-contact nerve cuff electrode recordings. MethodsNeural network (NN) architectures were evaluated on a dataset of rat sciatic nerve recordings previously collected using 56-channel (7 x 8) spiral nerve cuff electrodes to capture spatiotemporal neural activity patterns. The NNs were trained to classify individual, natural compound action potentials (nCAPs) elicited by sensory stimuli. Three architecture types were explored: the previously reported ESCAPE-NET, a fully convolutional network, and a recurrent neural network. Variations of each architecture yielded NNs with a range in the number of weights and required floating-point operations (FLOPs). Each NN was evaluated based on F1-score and resource requirements. ResultsNNs were identified that, when compared to ESCAPE-NET, required 1,132-1,787x fewer weights, 389-995x less memory, and 6-11,073x fewer FLOPs, while maintaining macro F1-scores of 0.70-0.71 compared to a baseline of 0.75. Memory requirements range from 22.69 KB to 58.11 KB, falling within the range of on-chip memory sizes from several published deep learning accelerators fabricated in 65nm ASIC technology. ConclusionReduced versions of ESCAPE-NET require significantly fewer resources without significant accuracy loss, thus can be more easily incorporated into a surgically implantable device that performs closed-loop real-time responsive neural stimulation.

neuroscience↗

bioRxiv2020

Compensation Strategies for Bioelectric Signal Changes in Chronic Selective Nerve Cuff Recordings: A Simulation Study

Peripheral nerve interfaces (PNIs) allow us to extract motor, sensory and autonomic information from the nervous system and use it as control signals in neuroprosthetic and neuromodulation applications. Recent efforts have aimed to improve the recording selectivity of PNIs, including by using spatiotemporal patterns from multi-contact nerve cuff electrodes as input to a convolutional neural network (CNN). Before such a methodology can be translated to humans, its performance in chronic implantation scenarios must be evaluated. In this simulation study, approaches were evaluated for maintaining selective recording performance in the presence of two chronic implantation challenges: the growth of encapsulation tissue and rotation of the nerve cuff electrode. Performance over time was examined in three conditions: training the CNN at baseline only, supervised re-training with explicitly labeled data at periodic intervals, and a semi-supervised self-learning approach. This study demonstrated that a selective recording algorithm trained at baseline will likely fail over time due to changes in signal characteristics resulting from the chronic challenges. Results further showed that periodically recalibrating the selective recording algorithm can maintain its performance over time, and that a self-learning approach has the potential to reduce the frequency of recalibration.

bioengineering↗

bioRxiv2020

Automatic Three-Dimensional Reconstruction of Fascicles in Peripheral Nerves from Histological Images

Computational studies can be used to support the development of peripheral nerve interfaces, but currently use simplified models of nerve anatomy, which may impact the applicability of simulation results. To better quantify and model neural anatomy across the population, we have developed an algorithm to automatically reconstruct accurate peripheral nerve models from histological cross-sections. We acquired serial median nerve cross-sections from human cadaveric samples, staining one set with hematoxylin and eosin (H&E) and the other using immunohistochemistry (IHC) with anti-neurofilament antibody. We developed a four-step processing pipeline involving registration, fascicle detection, segmentation, and reconstruction. We compared the output of each step to manual ground truths, and additionally compared the final models to commonly used extrusions, via intersection-over-union (IOU). Fascicle detection and segmentation required the use of a neural network and active contours in H&E-stained images, but only simple image processing methods for IHC-stained images. Reconstruction achieved an IOU of 0.42{+/-}0.07 for H&E and 0.37{+/-}0.16 for IHC images, with errors partially attributable to global misalignment at the registration step, rather than poor reconstruction. This work provides a quantitative baseline for fully automatic construction of peripheral nerve models. Our models provided fascicular shape and branching information that would be lost via extrusion.

neuroscience↗

bioRxiv2018

Classification of naturally evoked compound action potentials in peripheral nerve spatiotemporal recordings

Peripheral neural signals have the potential to provide the necessary motor, sensory or autonomic information for robust control in many neuroprosthetic and neuromodulation applications. However, developing methods to recover information encoded in these signals is a significant challenge. We introduce the idea of using spatiotemporal signatures extracted from multi-contact nerve cuff electrode recordings to classify naturally evoked compound action potentials (CAP). 9 Long-Evan rats were implanted with a 56-channel nerve cuff on the sciatic nerve. Afferent activity was selectively evoked in the different fascicles of the sciatic nerve (tibial, peroneal, sural) using mechano-sensory stimuli. Spatiotemporal signatures of recorded CAPs were used to train three different classifiers. Performance was measured based on the classification accuracy, F1-score, and the ability to reconstruct original firing rates of neural pathways. The mean classification accuracies, for a 3-class problem, for the best performing classifier was 0.686 {+/-} 0.126 and corresponding mean F1-score was 0.605 {+/-} 0.212. The mean Pearson correlation coefficients between the original firing rates and estimated firing rates found for the best classifier was 0.728 {+/-} 0.276. The proposed method demonstrates the possibility of classifying individual naturally evoked CAPs in peripheral neural signals recorded from extraneural electrodes, allowing for more precise control signals in neuroprosthetic applications.

neuroscience↗