An automatic spike sorting algorithm based on adaptive spike detection and a mixture of skew-t distributions

An insect-based bioelectronic sensing system combining flexible dual-sided microelectrode array and insect olfactory circuitry for human lung cancer detection

Early detection of lung cancer significantly enhances treatment outcomes, yet current screening methods are limited by accessibility, sensitivity, and cost. This study introduces a bioelectronic sensing platform that integrates the highly sensitive locust olfactory system with a flexible dual-sided microelectrode array (MEA), for robust, noninvasive, and label-free detection of volatile lung cancer biomarkers. Using an innovative folding-annealing fabrication technique and PEDOT:PSS surface functionalization, we developed flexible, dual-sided MEAs with high electrode densities of 463, 687, and 766 channels/mm2 across prototypes, maintaining low impedance (within 4 × 104 Ω). These MEAs demonstrated mechanical flexibility and stability, enabling direct insertion into locust brain tissue without mechanical reinforcement and facilitating precise recording of neural activity in the antennal lobe triggered by lung cancer-related volatile organic compounds (VOCs) from low concentration (1 ppm). Advanced dimensionality reduction techniques applied to the electrophysiological recordings identified distinct neural response patterns to each VOC biomarker and the complex "scent" emitted from various cell lines. Using high-dimensional population neuronal response analysis with a leave-one-trial-out approach, the platform achieved a 100 % classification success rate for unknown VOCs. Additionally, varying concentrations (ppm-ppb) of individual VOC biomarkers were detected and classified with an accuracy of 86 %. The system was further tested for its ability to detect and classify human lung cancer cell lines based on the unique "scent" of cultured cells, including two non-small cell lung cancer (NSCLC) and two small cell lung cancer (SCLC) types. Quantitative assessments demonstrated that the platform achieved a classification accuracy of 85 % across these cell lines. These results substantiate the platform's potential for enhancing clinical diagnostics through the accurate identification of lung cancer stages and cell types. By integrating biological sensory systems with advanced bioelectronics, this study introduces a novel and efficient approach to lung cancer biomarker detection. It provides a non-invasive, brain-based cancer screening method, offering an accessible and innovative solution for early lung cancer diagnosis.

The impact of spatial frequency on hierarchical category representation in macaque temporal cortex

Objects are recognized in three hierarchical levels: superordinate, mid-level, and subordinate. Psychophysics shows that mid-level categories and low spatial frequency (LSF) information are rapidly recognized. However, the interaction between spatial frequency (SF) and abstraction is not well understood. To address this, we examine neural responses in the inferior temporal cortex and superior temporal sulcus of two male macaque monkeys. Our findings reveal that mid-level categories are well represented at both LSF and high SF (HSF), suggesting robust mid-level boundary maps in these areas, unaffected by SF changes. Conversely, superordinate category representation depends on HSF, indicating its crucial role in encoding global category information. The absence of subordinate representation in both LSF and HSF compared to intact stimuli further implies that full SF content is essential for fine-category processing. A supporting human psychophysics task confirms that superordinate categorization relies on HSF, while subordinate object recognition requires both LSF and HSF.

Foldable 3D opto-electro array for optogenetic neuromodulation and physiology recording

This paper presents a thin-film, three-dimensional (3D) opto-electro array featuring four addressable microscale light-emitting diodes (LEDs) for surface cortex illumination and nine penetrating electrodes for simultaneous recording of light-evoked neural activities. Inspired by the origami concept, we have developed a meticulously designed "bridge+trench" structure that facilitates the transformation of the array from 2D to 3D while preventing damage to the thin film metal. Prior to device transformation, the shape and dimensions of the 2D array can be customized, enhancing its versatility for various applications. In addition, the arched base offers strong mechanical support to facilitate the direct insertion of the probe into tissue without any mechanical reinforcement. The array was encapsulated using polyimide and epoxy to ensure mechanical flexibility and biocompatibility of the device. The efficacy of the device was evaluated through comprehensive in vitro and in vivo characterization.

Neural responses to light stimulation in the octopus arm

Octopuses are known to be visual animals. Beyond functions of the eyes, recent investigations have documented the importance of extraocular photoreception in behavior. Octopus arms have been shown to respond behaviorally to local light exposure with negative phototaxis. Moreover, light-activated chromatophore expansion (LACE) in octopus arms indicates that skin-based photoreception may contribute to light detection. In this study, we used electrophysiological recordings to investigate the neural activity of the arm's axial nerve cord in response to light on the arm. We tested the hypothesis that light stimulates the activity of neurons in the arm's axial nerve cord. We also aimed to determine sensitivity to different wavelengths of light. The results showed that the axial nerve cord is strongly responsive to light stimulation of the arm and that the response travels along the length of the axial nerve cord. Blue light generated the strongest neural activity while red and green light also induced responses. Light-induced neural activity was mediated through the aboral arm skin and by the oral-side skin and suckers. These findings reveal the role of the skin in the sensory abilities of octopuses and provide insights into the neural mechanisms underlying their response to light. Our study underscores the importance of extraocular photoreception in future investigations of cephalopod sensory and behavioral biology.

PseudoSorter: A self-supervised spike sorting approach applied to reveal Tau-induced reductions in neuronal activity

Microelectrode arrays (MEAs) permit recordings with high electrode counts, thus generating complex datasets that would benefit from precise neuronal spike sorting for meaningful data extraction. Nevertheless, conventional spike sorting methods face limitations in recognizing diverse spike shapes. Here, we introduce PseudoSorter, which uses self-supervised learning techniques, a density-based pseudolabeling strategy, and an iterative fine-tuning process to enhance spike sorting accuracy. Through benchmarking, we demonstrate the superior performance of PseudoSorter compared to other spike sorting algorithms before applying PseudoSorter on MEA recordings from hippocampal neurons exposed to subneuronal concentrations of monomeric Tau as a model for Alzheimer's disease. Our results unveil that Tau diminishes the firing rate of a subset of neurons, which complement our findings observed using conventional electrophysiology analysis, and demonstrate that PseudoSorter's high accuracy and throughput make it a valuable tool for studying neurodegenerative diseases, enhancing our understanding of their underlying mechanisms, as well as for therapeutic drug screening.

Comparison metrics and power trade-offs for BCI motor decoding circuit design

Brain signal decoders are increasingly being used in early clinical trials for rehabilitation and assistive applications such as motor control and speech decoding. As many Brain-Computer Interfaces (BCIs) need to be deployed in battery-powered or implantable devices, signal decoding must be performed using low-power circuits. This paper reviews existing hardware systems for BCIs, with a focus on motor decoding, to better understand the factors influencing the power and algorithmic performance of such systems. We propose metrics to compare the energy efficiency of a broad range of on-chip decoding systems covering Electroencephalography (EEG), Electrocorticography (ECoG), and Microelectrode Array (MEA) signals. Our analysis shows that achieving a given classification rate requires an Input Data Rate (IDR) that can be empirically estimated, a finding that is helpful for sizing new BCI systems. Counter-intuitively, our findings show a negative correlation between the power consumption per channel (PpC) and the Information Transfer Rate (ITR). This suggests that increasing the number of channels can simultaneously reduce the PpC through hardware sharing and increase the ITR by providing new input data. In fact, for EEG and ECoG decoding circuits, the power consumption is dominated by the complexity of signal processing. To better understand how to minimize this power consumption, we review the optimizations used in state-of-the-art decoding circuits.

Unveiling the content of frontal feedback in challenging object recognition

Object recognition under challenging real-world conditions, including partial occlusion, remains an enduring focus of investigation in cognitive visual neuroscience. This study addresses the insufficiently elucidated neural mechanisms and temporal dynamics involved in this complex process, concentrating on the persistent challenge of recognizing objects obscured by occlusion. Through the analysis of human EEG data, we decode feedback characteristics within frontotemporal networks, uncovering intricate neural mechanisms during occlusion coding, with a specific emphasis on processing complex stimuli such as occluded faces. Our findings elucidate the critical role of frontal feedback in the late processing stage of occluded face recognition, contributing to enhanced accuracy in identification. Temporal dynamics reveal distinct characteristics in both early and late processing stages, allowing the discernment of two unique types of occlusion processing that go beyond visual features, incorporating higher-order associations. The increased synchronized activity between frontal and temporal areas during the processing of occluded stimuli underscores the importance of frontotemporal coordination in challenging real-world conditions. A comparative analysis with macaque IT cortex recordings validates the contribution of the frontal cortex in the late stage of occluded face processing. Notably, the observed disparity between human EEG and two deep computational models, both with and without the consideration of feedback connection, emphasize the necessity for expanding models to accurately simulate frontal feedback.

Amylopectin-based Hydrogel Probes for Brain-machine Interfaces

Implantable neural probes hold promise for acquiring brain data, modulating neural circuits, and treating various brain disorders. However, traditional implantable probes face significant challenges in practical applications, such as balancing sensitivity with biocompatibility and the difficulties of in situ neural information monitoring and neuromodulation. To address these challenges, this study developed an implantable hydrogel probe capable of recording neural signals, modulating neural circuits, and treating stroke. Amylopectin is integrated into the hydrogels, which can induce reorientation of the poly(3,4-ethylenedioxythiophene) (PEDOT) chain and create compliant interfaces with brain tissues, enhancing both sensitivity and biocompatibility. The hydrogel probe shows the capability of continuously recording deep brain signals for 8 weeks. The hydrogel probe is effectively utilized to study deep brain signals associated with various physiological activities. Neuromodulation and neural signal monitoring are performed directly in the primary motor cortex of rats, enabling control over their limb behaviors through evoked signals. When applied to the primary motor cortex of stroke-affected rats, neuromodulation significantly reduced the brain infarct area, promoted synaptic reorganization, and restored motor functions and balance. This research represents a significant scientific breakthrough in the design of neural probes for brain monitoring, neural circuit modulation, and the development of brain disease therapies.

Microsaccade selectivity as discriminative feature for object decoding

Microsaccades, a form of fixational eye movements, help maintain visual stability during stationary observations. This study examines the modulation of microsaccadic rates by various stimulus categories in monkeys and humans during a passive viewing task. Stimulus sets were grouped into four primary categories: human, animal, natural, and man-made. Distinct post-stimulus microsaccade patterns were identified across these categories, enabling successful decoding of the stimulus category with accuracy and recall of up to 85%. We observed that microsaccade rates are independent of pupil size changes. Neural data showed that category classification in the inferior temporal (IT) cortex peaks earlier than changes in microsaccade rates, suggesting feedback from the IT cortex influences eye movements after stimulus discrimination. These results contribute to neurobiological models, enhance human-machine interfaces, optimize experimental visual stimuli, and deepen understanding of microsaccades' role in object decoding.

Graphene Microelectrode Arrays, 4D Structured Illumination Microscopy, and a Machine Learning Spike Sorting Algorithm Permit the Analysis of Ultrastructural Neuronal Changes During Neuronal Signaling in a Model of Niemann-Pick Disease Type C

Simultaneously recording network activity and ultrastructural changes of the synapse is essential for advancing understanding of the basis of neuronal functions. However, the rapid millisecond-scale fluctuations in neuronal activity and the subtle sub-diffraction resolution changes of synaptic morphology pose significant challenges to this endeavor. Here, specially designed graphene microelectrode arrays (G-MEAs) are used, which are compatible with high spatial resolution imaging across various scales as well as permit high temporal resolution electrophysiological recordings to address these challenges. Furthermore, alongside G-MEAs, an easy-to-implement machine learning algorithm is developed to efficiently process the large datasets collected from MEA recordings. It is demonstrated that the combined use of G-MEAs, machine learning (ML) spike analysis, and 4D structured illumination microscopy (SIM) enables monitoring the impact of disease progression on hippocampal neurons which are treated with an intracellular cholesterol transport inhibitor mimicking Niemann-Pick disease type C (NPC), and show that synaptic boutons, compared to untreated controls, significantly increase in size, leading to a loss in neuronal signaling capacity.

Assessing Cross-Contamination in Spike-Sorted Electrophysiology Data

Recent advances in extracellular electrophysiology now facilitate the recording of spikes from hundreds or thousands of neurons simultaneously. This has necessitated both the development of new computational methods for spike sorting and better methods to determine spike-sorting accuracy. One long-standing method of assessing the false discovery rate (FDR) of spike sorting-the rate at which spikes are assigned to the wrong cluster-has been the rate of interspike interval (ISI) violations. Despite their near ubiquitous usage in spike sorting, our understanding of how exactly ISI violations relate to FDR, as well as best practices for using ISI violations as a quality metric, remains limited. Here, we describe an analytical solution that can be used to predict FDR from the ISI violation rate (ISIv). We test this model in silico through Monte Carlo simulation and apply it to publicly available spike-sorted electrophysiology datasets. We find that the relationship between ISIv and FDR is highly nonlinear, with additional dependencies on firing frequency, the correlation in activity between neurons, and contaminant neuron count. Predicted median FDRs in public datasets recorded in mice were found to range from 3.1 to 50.0%. We found that stochasticity in the occurrence of ISI violations as well as uncertainty in cluster-specific parameters make it difficult to predict FDR for single clusters with high confidence but that FDR can be estimated accurately across a population of clusters. Our findings will help the growing community of researchers using extracellular electrophysiology assess spike-sorting accuracy in a principled manner.

Recapitulating the electrophysiological features of in vivo biological networks by using a real-time hardware Spiking Neural Network

Electroceutical methodologies utilized for treating neurological disorders, including stroke, can leverage neuromorphic engineering principles to design devices capable of seamlessly interfacing with the neural system. This paper introduces a bank of configurations for a real-time hardware Spiking Neural Network (SNN) based on the Hodgkin-Huxley formalism to mimic the electrophysiological behavior of an in vivo biological neural network (BNN). The neuronal activity in the rostral forelimb area of six anesthetized healthy rats was analyzed to extract peculiar electrophysiological features, such as the firing rate and the Inter-Spike-Interval, required for the customization of the SNN parameters. A set of different SNNs was built and comparative analyses between the electrical patterns generated by SNNs and the neural activity recorded from the BNNs were performed. The results indicate that it is possible to fine tune the SNN to achieve an electrophysiological behavior closely resembling that of a biological system.

BiœmuS: A new tool for neurological disorders studies through real-time emulation and hybridization using biomimetic Spiking Neural Network

Characterization and modeling of biological neural networks has emerged as a field driving significant advancements in our understanding of brain function and related pathologies. As of today, pharmacological treatments for neurological disorders remain limited, pushing the exploration of promising alternative approaches such as electroceutics. Recent research in bioelectronics and neuromorphic engineering have fostered the development of the new generation of neuroprostheses for brain repair. However, achieving their full potential necessitates a deeper understanding of biohybrid interaction. In this study, we present a novel real-time, biomimetic, cost-effective and user-friendly neural network capable of real-time emulation for biohybrid experiments. Our system facilitates the investigation and replication of biophysically detailed neural network dynamics while prioritizing cost-efficiency, flexibility and ease of use. We showcase the feasibility of conducting biohybrid experiments using standard biophysical interfaces and a variety of biological cells as well as real-time emulation of diverse network configurations. We envision our system as a crucial step towards the development of neuromorphic-based neuroprostheses for bioelectrical therapeutics, enabling seamless communication with biological networks on a comparable timescale. Its embedded real-time functionality enhances practicality and accessibility, amplifying its potential for real-world applications in biohybrid experiments.

A Cell-Type Selective Stimulation and Recording System for Retinal Ganglion Cells

Future retinal implants will require a stimulation selectivity between different sub-types of Retinal Ganglion Cells (RGCs) to evoke natural perceptions rather than phosphenes in patients. To achieve this, a cell-type specific stimulation pipeline is required that identifies target RGC sub-types from recorded input images and extracts the specific stimulation parameters to activate this cell-type selectively. Promising biological experiments showed that ON-/OFF- sustained/transient RGCs could be selectively activated by modulating repetition rate and amplitude of an electrical stimulation current in the kilohertz range. This research presents a 42 channel current controlled stimulation and recording system on chip (SoC) with parameter input from a real time target RGC selection algorithm. The SoC is able to stimulate retinal tissue with sinusoidal frequencies higher than 1 kHz at amplitudes of up to 200 μA at a supply voltage of 1.8 V. It also includes tunable recording units with an integrated action potential detection pipeline that are able to amplify signals between 1 Hz and 50 kHz. The required area of one stimulator is 0.0071 mm2, while one recording unit consumes an area of 0.0092 mm2. The application of sinusoidal stimulation currents in the kilohertz range towards retinal tissue leads to a suppressive response of only certain RGC sub-types that has not been oberved before, using electrical stimulation. Because this response is very similar to the natural light response of RGCs, this stimulation approach can lead to a more genuine visual perception for patients using retinal implants.

Control of polymers' amorphous-crystalline transition enables miniaturization and multifunctional integration for hydrogel bioelectronics

Soft bioelectronic devices exhibit motion-adaptive properties for neural interfaces to investigate complex neural circuits. Here, we develop a fabrication approach through the control of metamorphic polymers' amorphous-crystalline transition to miniaturize and integrate multiple components into hydrogel bioelectronics. We attain an about 80% diameter reduction in chemically cross-linked polyvinyl alcohol hydrogel fibers in a fully hydrated state. This strategy allows regulation of hydrogel properties, including refractive index (1.37-1.40 at 480 nm), light transmission (>96%), stretchability (139-169%), bending stiffness (4.6 ± 1.4 N/m), and elastic modulus (2.8-9.3 MPa). To exploit the applications, we apply step-index hydrogel optical probes in the mouse ventral tegmental area, coupled with fiber photometry recordings and social behavioral assays. Additionally, we fabricate carbon nanotubes-PVA hydrogel microelectrodes by incorporating conductive nanomaterials in hydrogel for spontaneous neural activities recording. We enable simultaneous optogenetic stimulation and electrophysiological recordings of light-triggered neural activities in Channelrhodopsin-2 transgenic mice.

Assessing cross-contamination in spike-sorted electrophysiology data

Recent advances in extracellular electrophysiology now facilitate the recording of spikes from hundreds or thousands of neurons simultaneously. This has necessitated both the development of new computational methods for spike sorting and better methods to determine spike sorting accuracy. One longstanding method of assessing the false discovery rate (FDR) of spike sorting - the rate at which spikes are misassigned to the wrong cluster - has been the rate of inter-spike-interval (ISI) violations. Despite their near ubiquitous usage in spike sorting, our understanding of how exactly ISI violations relate to FDR, as well as best practices for using ISI violations as a quality metric, remain limited. Here, we describe an analytical solution that can be used to predict FDR from ISI violation rate. We test this model in silico through Monte Carlo simulation, and apply it to publicly available spike-sorted electrophysiology datasets. We find that the relationship between ISI violation rate and FDR is highly nonlinear, with additional dependencies on firing rate, the correlation in activity between neurons, and contaminant neuron count. Predicted median FDRs in public datasets were found to range from 3.1% to 50.0%. We find that stochasticity in the occurrence of ISI violations as well as uncertainty in cluster-specific parameters make it difficult to predict FDR for single clusters with high confidence, but that FDR can be estimated accurately across a population of clusters. Our findings will help the growing community of researchers using extracellular electrophysiology assess spike sorting accuracy in a principled manner.

Mechanosensory signal transmission in the arms and the nerve ring, an interarm connective, of Octopus bimaculoides

Octopuses coordinate their arms in a range of complex behaviors. In addition to brain-based sensorimotor integration and control, interarm coordination also occurs through a nerve ring at the arms' base. Here, we examine responses to mechanosensory stimulation of the arms by recording neural activity in the stimulated arm, the nerve ring, and other arms in a preparation of only the ring and arms. Arm axial nerve cords show graded responses to mechanosensory input and activity is transmitted proximally and distally in the arm. Mechanostimulation of one arm generates spiking in the nerve ring and in other arms. Activity in the nerve ring decreases with distance from the stimulated arm. Spontaneous activity with a range of spiking patterns occurs in the axial nerve cords and the nerve ring. These data show rich interarm signaling that supports arm control and coordination occurring outside of the brain.

Electron Conductive and Transparent Hydrogels for Recording Brain Neural Signals and Neuromodulation

Recording brain neural signals and optogenetic neuromodulations open frontiers in decoding brain neural information and neurodegenerative disease therapeutics. Conventional implantable probes suffer from modulus mismatch with biological tissues and an irreconcilable tradeoff between transparency and electron conductivity. Herein, a strategy is proposed to address these tradeoffs, which generates conductive and transparent hydrogels with polypyrrole-decorated microgels as cross-linkers. The optical transparency of the electrodes can be attributed to the special structures that allow light waves to bypass the microgel particles and minimize their interaction. Demonstrated by probing the hippocampus of rat brains, the biomimetic electrode shows a prolonged capacity for simultaneous optogenetic neuromodulation and recording of brain neural signals. More importantly, an intriguing brain-machine interaction is realized, which involves signal input to the brain, brain neural signal generation, and controlling limb behaviors. This breakthrough work represents a significant scientific advancement toward decoding brain neural information and developing neurodegenerative disease therapies.

A study of autoencoders as a feature extraction technique for spike sorting

Spike sorting is the process of grouping spikes of distinct neurons into their respective clusters. Most frequently, this grouping is performed by relying on the similarity of features extracted from spike shapes. In spite of recent developments, current methods have yet to achieve satisfactory performance and many investigators favour sorting manually, even though it is an intensive undertaking that requires prolonged allotments of time. To automate the process, a diverse array of machine learning techniques has been applied. The performance of these techniques depends however critically on the feature extraction step. Here, we propose deep learning using autoencoders as a feature extraction method and evaluate extensively the performance of multiple designs. The models presented are evaluated on publicly available synthetic and real "in vivo" datasets, with various numbers of clusters. The proposed methods indicate a higher performance for the process of spike sorting when compared to other state-of-the-art techniques.

Different coding characteristics between flight and freezing in dorsal periaqueductal gray of mice during exposure to innate threats

BACKGROUND

Flight and freezing are two vital defensive behaviors that mice display to avoid natural enemies. When they are exposed to innate threats, visual cues are processed and transmitted by the visual system into the emotional nuclei and finally transmitted to the periaqueductal gray (PAG) to induce defensive behaviors. However, how the dorsal PAG (dPAG) encodes the two defensive behaviors is unclear.

METHODS

Multi-array electrodes were implanted in the dPAG nuclei of C57BL/6 mice. Two kinds of visual stimuli (looming and sweeping) were used to induce defensive behaviors in mice. Neural signals under different defense behaviors were recorded, and the encoding characteristics of the two behaviors were extracted and analyzed from spike firing and frequency oscillations. Finally, synchronization of neural activity during the defense process was analyzed.

RESULTS

The neural activity between flight and freezing behaviors showed different firing patterns, and the differences in the inter-spike interval distribution were mainly reflected in the 2-10 ms period. The frequency band activities under both defensive behaviors were concentrated in the theta band; the active frequency of flight was ~8 to 10 Hz, whereas that of freezing behavior was ~6 to 8 Hz. The network connection density under both defense behaviors was significantly higher than the period before and after defensive behavior occurred, indicating that there was a high synchronization of neural activity during the defense process.

CONCLUSIONS

The dPAG nuclei of mice have different coding features between flight and freezing behaviors; during strong looming stimulation, fast neuro-instinctive decision making is required while encountering weak sweeping stimulation, and computable planning late behavior is predicted in the early stage. The frequency band activities under both defensive behaviors were concentrated in the theta band. There was a high synchronization of neural activity during the defense process, which may be a key factor triggering different defensive behaviors.

Simultaneous monitoring of mouse grip strength, force profile, and cumulative force profile distinguishes muscle physiology following surgical, pharmacologic and diet interventions

Grip strength is a valuable preclinical assay to study muscle physiology in disease and aging by directly determining changes in muscle force generation in active laboratory mice. Existing methods to statistically evaluate grip strength, however, have limitations in the power and scope of the physiological features that are assessed. We therefore designed a microcontroller whose serial measure of resistance-based force enables the simultaneous readout of (1) peak grip strength, (2) force profile (the non-linear progress of force exerted throughout a standard grip strength trial), and (3) cumulative force profile (the integral of force with respect to time of a single grip strength trial). We hypothesized that muscle pathologies of different etiologies have distinct effects on these parameters. To test this, we used our apparatus to assess the three muscle parameters in mice with impaired muscle function resulting from surgically induced peripheral pain, genetic peripheral neuropathy, adverse muscle effects induced by statin drug, and metabolic alterations induced by a high-fat diet. Both surgically induced peripheral nerve injury and statin-associated muscle damage diminished grip strength and force profile, without affecting cumulative force profile. Conversely, genetic peripheral neuropathy resulting from lipin 1 deficiency led to a marked reduction to all three parameters. A chronic high-fat diet led to reduced grip strength and force profile when normalized to body weight. In high-fat fed mice that were exerted aerobically and allowed to recover for 30 min, male mice exhibited impaired force profile parameters, which female mice were more resilient. Thus, simultaneous analysis of peak grip strength, force profile and cumulative force profile distinguishes the muscle impairments that result from distinct perturbations and may reflect distinct motor unit recruitment strategies.

Highly Stretchable Hydrogels as Wearable and Implantable Sensors for Recording Physiological and Brain Neural Signals

Recording electrophysiological information such as brain neural signals is of great importance in health monitoring and disease diagnosis. However, foreign body response and performance loss over time are major challenges stemming from the chemomechanical mismatch between sensors and tissues. Herein, microgels are utilized as large crosslinking centers in hydrogel networks to modulate the tradeoff between modulus and fatigue resistance/stretchability for producing hydrogels that closely match chemomechanical properties of neural tissues. The hydrogels exhibit notably different characteristics compared to nanoparticles reinforced hydrogels. The hydrogels exhibit relatively low modulus, good stretchability, and outstanding fatigue resistance. It is demonstrated that the hydrogels are well suited for fashioning into wearable and implantable sensors that can obtain physiological pressure signals, record the local field potentials in rat brains, and transmit signals through the injured peripheral nerves of rats. The hydrogels exhibit good chemomechanical match to tissues, negligible foreign body response, and minimal signal attenuation over an extended time, and as such is successfully demonstrated for use as long-term implantable sensory devices. This work facilitates a deeper understanding of biohybrid interfaces, while also advancing the technical design concepts for implantable neural probes that efficiently obtain physiological information.