Cell-specific models of hiPSC-CMs developed by the gradient-based parameter optimization method fitting two different action potential waveforms.

Parameter optimization (PO) methods to determine the ionic current composition of experimental cardiac action potential (AP) waveform have been developed using a computer model of cardiac membrane excitation. However, it was suggested that fitting a single AP record in the PO method was not always successful in providing a unique answer because of a shortage of information. We found that the PO method worked perfectly if the PO method was applied to a pair of a control AP and a model output AP in which a single ionic current out of six current species, such as IKr, ICaL, INa, IKs, IKur or IbNSC was partially blocked in silico. When the target was replaced by a pair of experimental control and IKr-blocked records of APs generated spontaneously in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), the simultaneous fitting of the two waveforms by the PO method was hampered to some extent by the irregular slow fluctuations in the Vm recording and/or sporadic alteration in AP configurations in the hiPSC-CMs. This technical problem was largely removed by selecting stable segments of the records for the PO method. Moreover, the PO method was made fail-proof by running iteratively in identifying the optimized parameter set to reconstruct both the control and the IKr-blocked AP waveforms. In the lead potential analysis, the quantitative ionic mechanisms deduced from the optimized parameter set were totally consistent with the qualitative view of ionic mechanisms of AP so far described in physiological literature.

[To determine the value of iMAX, a new electrodiagnostic method, and its application in the evaluation of peripheral motor nerve excitability].

Objective: To test the new method of iMAX (the minimum stimulus current that elicits the maximum compound muscle action potential amplitude) electrodiagnosis, verify the feasibility of this method in evaluating the excitability of peripheral motor axons, and preliminarily explore the clinical application value. Methods: This study was a cross-sectional study. A total of 50 healthy subjects were recruited from the outpatient department of Peking University Third Hospital from June 2022 to March 2023, including 25 males and 25 females, aged 25-68 (48±8) years. Eleven patients with Charcot-Marie-Pain-1A (CMT1A), 7 males and 4 females, aged 19-55 (41±13) years and 21 patients with diabetic peripheral neuropathy (DPN), 10 males and 11 females, aged 28-79 (53±16) years were enrolled in this study. iMAX of bilateral median nerves, ulnar nerves and peroneal nerves were detected in all patients. Repeatable motor responses with minimum motor threshold and amplitude of at least 0.1 mV and the minimum stimulus current intensity, at which the maximum compound muscle action potential amplitude is elicited, were measured respectively [1 mA increment is called (iUP) and, 0.1 mA adjustment is called (iMAX)].Comparison of the parameters: the parameters of threshold, iUP and iMAX were compared among different age groups, genders and sides, body mass index(BMI) values and detection time , as well as between CMT1A patients, DPN patients and healthy subjects. Results: In healthy subjects, the threshold, iUP value and iMAX value were (1.8±0.7) mA, (4.4±1.2) mA, and (4.2±1.3) mA respectively; ulnar nerve (3.1±1.6) mA, (6.8±3.2) mA, (6.4±3.2) mA; peroneal nerve (3.7±2.0) mA, (7.8±2.8) mA, (7.4±2.9) mA. There were statistically significant differences in threshold, iUP value and iMAX value among different age groups (all P<0.001).With the increase of age, there was a trend of increasing threshold, iUP, and iMAX values in different nerves, and the differences are statistically significant (all P<0.001). There were no significant differences in gender, side and detection time threshold, iUP value and iMAX value (all P>0.05). The parameters of healthy subjects with high BMI value were higher than those of healthy subjects with low BMI value(all P<0.05). Compared with the healthy subjects, the parameters of 11 CMT1A patients were significantly increased (all P<0.05), and the parameters of 21 DPN patients were slightly increased (P<0.05). Conclusion: The new iMAX method reflects the excitability of motor axons and early axonal dysfunction, which is an important supplement to the traditional nerve conduction, and can be used to monitor motor axon excitability disorders.

Neural Drive and Motor Unit Characteristics of the Serratus Anterior in Individuals With Scapular Dyskinesis.

OBJECTIVE: Scapular dyskinesis is one of the causes of shoulder disorders and involves muscle weakness in the serratus anterior. This study investigated whether motor unit (MU) recruitment and firing property, which are important for muscle exertion, have altered in serratus anterior of the individuals with scapular dyskinesis. METHODS: Asymptomatic adults with (SD) and without (control) scapular dyskinesis were analyzed. Surface electromyography (sEMG) waveforms were collected at submaximal voluntary contraction of the serratus anterior. The sEMG waveform was decomposed into MU action potential amplitude (MUAPAMP), mean firing rate (MFR), and recruitment threshold. MUs were divided into low, moderate, and high thresholds, and MU recruitment and firing properties of the groups were compared. RESULTS: High-threshold MUAPAMP was significantly smaller in the SD group than in the control group. The control group also exhibited recruitment properties that reflected the size principle, however, the SD group did not. Furthermore, the SD group had a lower MFR than the control group. CONCLUSIONS: Individuals with scapular dyskinesis exhibit altered MU recruitment properties and lower firing rates of the serratus anterior; this may be detrimental to muscle performance. Thus, it may be necessary to improve the neural drive of the serratus anterior when correcting scapular dyskinesis.

Two Distinct Neuronal Populations in the Rat Parafascicular Nucleus Oppositely Encode the Engagement in Stimulus-driven Reward-seeking.

BACKGROUND: The thalamus is a phylogenetically well-preserved structure. Known to densely contact cortical regions, its role in the transmission of sensory information to the striatal complex has been widely reconsidered in recent years. METHODS: The parafascicular nucleus of the thalamus (Pf) has been implicated in the orientation of attention toward salient sensory stimuli. In a stimulus-driven reward-seeking task, we sought to characterize the electrophysiological activity of Pf neurons in rats. RESULTS: We observed a predominance of excitatory over inhibitory responses for all events in the task. Neurons responded more strongly to the stimulus compared to lever-pressing and reward collecting, confirming the strong involvement of the Pf in sensory information processing. The use of long sessions allowed us to compare neuronal responses to stimuli between trials when animals were engaged in action and those when they were not. We distinguished two populations of neurons with opposite responses: MOTIV+ neurons responded more intensely to stimuli followed by a behavioral response than those that were not. Conversely, MOTIV- neurons responded more strongly when the animal did not respond to the stimulus. In addition, the latency of excitation of MOTIV- neurons was shorter than that of MOTIV+ neurons. CONCLUSION: Through this encoding, the Pf could perform an early selection of environmental stimuli transmitted to the striatum according to motivational level.

GluN2A- and GluN2B-containing pre-synaptic N-methyl-d-aspartate receptors differentially regulate action potential-evoked Ca2+ influx via modulation of SK channels.

N-methyl-d-aspartate receptors (NMDARs) play a pivotal role in synaptic plasticity. While the functional role of post-synaptic NMDARs is well established, pre-synaptic NMDAR (pre-NMDAR) function is largely unexplored. Different pre-NMDAR subunit populations are documented at synapses, suggesting that subunit composition influences neuronal transmission. Here, we used electrophysiological recordings at Schaffer collateral-CA1 synapses partnered with Ca2+ imaging and glutamate uncaging at boutons of CA3 pyramidal neurones to reveal two populations of pre-NMDARs that contain either the GluN2A or GluN2B subunit. Activation of the GluN2B population decreases action potential-evoked Ca2+ influx via modulation of small-conductance Ca2+-activated K+ channels, while activation of the GluN2A population does the opposite. Critically, the level of functional expression of the subunits is subject to homeostatic regulation, bidirectionally affecting short-term facilitation, thus providing a capacity for a fine adjustment of information transfer. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.

Equal levels of pre- and postsynaptic potentiation produce unequal outcomes.

Which proportion of the long-term potentiation (LTP) expressed in the bulk of excitatory synapses is postsynaptic and which presynaptic remains debatable. To understand better the possible impact of either LTP form, we explored a realistic model of a CA1 pyramidal cell equipped with known membrane mechanisms and multiple, stochastic excitatory axo-spinous synapses. Our simulations were designed to establish an input-output transfer function, the dependence between the frequency of presynaptic action potentials triggering probabilistic synaptic discharges and the average frequency of postsynaptic spiking. We found that, within the typical physiological range, potentiation of the postsynaptic current results in a greater overall output than an equivalent increase in presynaptic release probability. This difference grows stronger at lower input frequencies and lower release probabilities. Simulations with a non-hierarchical circular network of principal neurons indicated that equal increases in either synaptic fidelity or synaptic strength of individual connections also produce distinct changes in network activity, although the network phenomenology is likely to be complex. These observations should help to interpret the machinery of LTP phenomena documented in situ. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.

Months-long tracking of neuronal ensembles spanning multiple brain areas with Ultra-Flexible Tentacle Electrodes.

We introduce Ultra-Flexible Tentacle Electrodes (UFTEs), packing many independent fibers with the smallest possible footprint without limitation in recording depth using a combination of mechanical and chemical tethering for insertion. We demonstrate a scheme to implant UFTEs simultaneously into many brain areas at arbitrary locations without angle-of-insertion limitations, and a 512-channel wireless logger. Immunostaining reveals no detectable chronic tissue damage even after several months. Mean spike signal-to-noise ratios are 1.5-3x compared to the state-of-the-art, while the highest signal-to-noise ratios reach 89, and average cortical unit yields are ~1.75/channel. UFTEs can track the same neurons across sessions for at least 10 months (longest duration tested). We tracked inter- and intra-areal neuronal ensembles (neurons repeatedly co-activated within 25 ms) simultaneously from hippocampus, retrosplenial cortex, and medial prefrontal cortex in freely moving rodents. Average ensemble lifetimes were shorter than the durations over which we can track individual neurons. We identify two distinct classes of ensembles. Those tuned to sharp-wave ripples display the shortest lifetimes, and the ensemble members are mostly hippocampal. Yet, inter-areal ensembles with members from both hippocampus and cortex have weak tuning to sharp wave ripples, and some have unusual months-long lifetimes. Such inter-areal ensembles occasionally remain inactive for weeks before re-emerging.

Anti-Hebbian plasticity drives sequence learning in striatum.

Spatio-temporal activity patterns have been observed in a variety of brain areas in spontaneous activity, prior to or during action, or in response to stimuli. Biological mechanisms endowing neurons with the ability to distinguish between different sequences remain largely unknown. Learning sequences of spikes raises multiple challenges, such as maintaining in memory spike history and discriminating partially overlapping sequences. Here, we show that anti-Hebbian spike-timing dependent plasticity (STDP), as observed at cortico-striatal synapses, can naturally lead to learning spike sequences. We design a spiking model of the striatal output neuron receiving spike patterns defined as sequential input from a fixed set of cortical neurons. We use a simple synaptic plasticity rule that combines anti-Hebbian STDP and non-associative potentiation for a subset of the presented patterns called rewarded patterns. We study the ability of striatal output neurons to discriminate rewarded from non-rewarded patterns by firing only after the presentation of a rewarded pattern. In particular, we show that two biological properties of striatal networks, spiking latency and collateral inhibition, contribute to an increase in accuracy, by allowing a better discrimination of partially overlapping sequences. These results suggest that anti-Hebbian STDP may serve as a biological substrate for learning sequences of spikes.

Electroacupuncture at different intensities inhibits nociceptive discharges of wide dynamic range neurons in spinal dorsal horn of rats. / ä¸åŒå¼ºåº¦ç”µé’ˆå¯¹å¤§é¼ è„Šé«“èƒŒè§’å¹¿åŠ¨åŠ›ç¥žç»å ƒä¼¤å®³æ€§æ”¾ç”µçš„æŠ‘åˆ¶ä½œç”¨.

OBJECTIVES: To observe the effect of electroacupuncture (EA) at different intensities on nociceptive discharges of wide dynamic range (WDR) neurons in the spinal dorsal horns (DHs) of rats, so as to explore its regulatory characteristics on nociceptive signals at the spinal level. METHODS: A total of 25 male SD rats were used in the present study. A microelectrode array was used to record the discharge activity of WDR neurons in the lumbar spinal DHs of normal rats. After finding the WDR neuron, electrical stimulation (pulse width of 2 ms) was administered to the plantar receptive field (RF) for determining its response component of discharges according to the latency of action potential generation (Aß ï¼»0 to 20 msï¼½, Aδ ï¼»20 to 90 msï¼½, C ï¼»90 to 500 msï¼½ and post-discharge ï¼»500 to 800 msï¼½). High-intensity electrical stimulation was continuously applied to the RF at the paw's plantar surface to induce DHs neuronal windup response. Subsequently, EA stimulation at different intensities (1 mA and 2 mA) was applied to the left "Zusanli"(ST36) at a frequency of 2 Hz/15 Hz for 10 min. The induction of WDR neuronal windup was then repeated under the same conditions. The quantity of nociceptive discharge components and the windup response of WDR neurons before and after EA stimulations at different intensities were compared. RESULTS: Compared to pre-EA, both EA1 mA and EA2 mA significantly reduced the number of Aδ and C component discharges of WDR neurons during stimulation, as well as post-discharge (P<0.01, P<0.001). The inhibitory rate of C component by EA2 mA was significantly higher than that by EA1 mA (P<0.05). Meanwhile, both EA1 mA and EA2 mA attenuated the windup response of WDR neurons (P<0.05, P<0.01), and the effect of EA2 mA was stronger than that of EA1 mA (P<0.05). Further analysis showed that when EA1 mA and EA2 mA respectively applied to both non-receptive field (non-RF) and RF, a significant reduction in the number of Aδ component, C component and post-discharge was observed (P<0.05, P<0.01). EA2 mA at the non-RF and RF demonstrated a significant inhibitory effect on the windup response of WDR neurons (P<0.01, P<0.05), but EA1 mA only at the non-RF showed a significant inhibitory effect on the windup response (P<0.01). CONCLUSIONS: EA can suppress nociceptive discharges of spinal DHs WDR neurons in rats. The inhibitory impact of EA is strongly correlated with the location and intensity of EA stimulation, and EA2 mA has a stronger inhibitory effect than EA1 mA.

The quality and complexity of pairwise maximum entropy models for large cortical populations.

We investigate the ability of the pairwise maximum entropy (PME) model to describe the spiking activity of large populations of neurons recorded from the visual, auditory, motor, and somatosensory cortices. To quantify this performance, we use (1) Kullback-Leibler (KL) divergences, (2) the extent to which the pairwise model predicts third-order correlations, and (3) its ability to predict the probability that multiple neurons are simultaneously active. We compare these with the performance of a model with independent neurons and study the relationship between the different performance measures, while varying the population size, mean firing rate of the chosen population, and the bin size used for binarizing the data. We confirm the previously reported excellent performance of the PME model for small population sizes N < 20. But we also find that larger mean firing rates and bin sizes generally decreases performance. The performance for larger populations were generally not as good. For large populations, pairwise models may be good in terms of predicting third-order correlations and the probability of multiple neurons being active, but still significantly worse than small populations in terms of their improvement over the independent model in KL-divergence. We show that these results are independent of the cortical area and of whether approximate methods or Boltzmann learning are used for inferring the pairwise couplings. We compared the scaling of the inferred couplings with N and find it to be well explained by the Sherrington-Kirkpatrick (SK) model, whose strong coupling regime shows a complex phase with many metastable states. We find that, up to the maximum population size studied here, the fitted PME model remains outside its complex phase. However, the standard deviation of the couplings compared to their mean increases, and the model gets closer to the boundary of the complex phase as the population size grows.

Uncertainty quantification and sensitivity analysis of neuron models with ion concentration dynamics.

This paper provides a comprehensive and computationally efficient case study for uncertainty quantification (UQ) and global sensitivity analysis (GSA) in a neuron model incorporating ion concentration dynamics. We address how challenges with UQ and GSA in this context can be approached and solved, including challenges related to computational cost, parameters affecting the system's resting state, and the presence of both fast and slow dynamics. Specifically, we analyze the electrodiffusive neuron-extracellular-glia (edNEG) model, which captures electrical potentials, ion concentrations (Na+, K+, Ca2+, and Cl-), and volume changes across six compartments. Our methodology includes a UQ procedure assessing the model's reliability and susceptibility to input uncertainty and a variance-based GSA identifying the most influential input parameters. To mitigate computational costs, we employ surrogate modeling techniques, optimized using efficient numerical integration methods. We propose a strategy for isolating parameters affecting the resting state and analyze the edNEG model dynamics under both physiological and pathological conditions. The influence of uncertain parameters on model outputs, particularly during spiking dynamics, is systematically explored. Rapid dynamics of membrane potentials necessitate a focus on informative spiking features, while slower variations in ion concentrations allow a meaningful study at each time point. Our study offers valuable guidelines for future UQ and GSA investigations on neuron models with ion concentration dynamics, contributing to the broader application of such models in computational neuroscience.

Characteristics of Right-Sided Accessory Pathways Associated With Right Cardiac Veins.

BACKGROUND: The anatomy of myocardial fibers around the right cardiac veins (RCVs) and their roles in accessory pathways (APs) are rarely reported. METHODS: Six RCV-APs were identified from 566 patients with right-sided APs. Mapping of retrograde atrial activation was performed using CARTO 3 system under orthodromic tachycardia or right ventricular pacing. Venography of RCVs was acquired at the earliest retrograde atrial activation. RESULTS: Patients enrolled had a median age of 30 (11-51) years, 5 of them were male. Venography of RCVs could be classified into 3 distinct patterns based on the identified ventricular branches, right marginal vein only (type I; n=3), both right marginal vein and anterior cardiac veins (type II; n=2), and anterior cardiac vein only (type III; n=1). Patients with type I venography had rS QRS pattern in lead V1, negative delta wave in lead III and negative or isoelectric delta wave in lead aVF. However, patients with type II and III venography had QS QRS patterns in lead V1 and variable patterns of delta wave in inferior leads. Earliest retrograde atrial activation was found at a median of 16.75 (14.60-20.00) mm away from the tricuspid annulus, all with A larger than V. At the earliest retrograde atrial activation, far-field ventricular electrogram was found 30 ms later than QRS onset in 1 patient under sinus rhythm. AP conduction was eliminated by mechanical pressure in 2 and by radiofrequency ablation in 4 at the ostium of the veins colocalizing with the earliest retrograde activation of the right atrium. No recurrence was observed during 36 (10-60) months follow-up. CONCLUSIONS: The RCV-AP is a rare form of right-sided APs characterized by atrial insertions distant from the annulus. ECG-speculated ventricular insertion sites conformed to the location of identified RCVs.

Drift of neural ensembles driven by slow fluctuations of intrinsic excitability.

Representational drift refers to the dynamic nature of neural representations in the brain despite the behavior being seemingly stable. Although drift has been observed in many different brain regions, the mechanisms underlying it are not known. Since intrinsic neural excitability is suggested to play a key role in regulating memory allocation, fluctuations of excitability could bias the reactivation of previously stored memory ensembles and therefore act as a motor for drift. Here, we propose a rate-based plastic recurrent neural network with slow fluctuations of intrinsic excitability. We first show that subsequent reactivations of a neural ensemble can lead to drift of this ensemble. The model predicts that drift is induced by co-activation of previously active neurons along with neurons with high excitability which leads to remodeling of the recurrent weights. Consistent with previous experimental works, the drifting ensemble is informative about its temporal history. Crucially, we show that the gradual nature of the drift is necessary for decoding temporal information from the activity of the ensemble. Finally, we show that the memory is preserved and can be decoded by an output neuron having plastic synapses with the main region.

Modeling the effect of magnetoelectric nanoparticles on neuronal electrical activity: An analog circuit approach.

This paper introduces a physical neuron model that incorporates magnetoelectric nanoparticles (MENPs) as an essential electrical circuit component to wirelessly control local neural activity. Availability of such a model is important as MENPs, due to their magnetoelectric effect, can wirelessly and noninvasively modulate neural activity, which, in turn, has implications for both finding cures for neurological diseases and creating a wireless noninvasive high-resolution brain-machine interface. When placed on a neuronal membrane, MENPs act as magnetic-field-controlled finite-size electric dipoles that generate local electric fields across the membrane in response to magnetic fields, thus allowing to controllably activate local ion channels and locally initiate an action potential. Herein, the neuronal electrical characteristic description is based on ion channel activation and inhibition mechanisms. A MENP-based memristive Hodgkin-Huxley circuit model is extracted by combining the Hodgkin-Huxley model and an equivalent circuit model for a single MENP. In this model, each MENP becomes an integral part of the neuron, thus enabling wireless local control of the neuron's electric circuit itself. Furthermore, the model is expanded to include multiple MENPs to describe collective effects in neural systems.

Role of tactile stimulation during the preweaning period on the development of the peripheral sensory sural (SU) nerve in adult artificially reared female rat.

Maternal deprivation, as a result of the artificial rearing (AR) paradigm, disturbs electrophysiological and histological characteristics of the peripheral sensory sural (SU) nerve of infant and adult male rats. Such changes are prevented by providing tactile or social stimulation during isolation. AR also affects the female rat's brain and behavior; however, it is unknown whether this early adverse experience also alters their SU nerve development or if tactile stimulation might prevent these possible developmental effects. To assess these possibilities, the electrophysiological and histological characteristics of the SU nerve from adult diestrus AR female rats that: (i) received no tactile stimulation (AR group), (ii) received tactile stimulation in the anogenital and body area (AR-Tactile group), or (iii) were mother reared (MR group) were determined. We found that the amplitude, but not the area, of the evoked compound action potential response in SU nerves of AR rats was lower than those of SU nerves of MR female rats. Tactile stimulation prevented these effects. Additionally, we found a reduction in the outer diameter and myelin thickness of axons, as well as a large proportion of axons with low myelin thickness in nerves of AR rats compared to the nerves of the MR and AR-Tactile groups of rats; however, tactile stimulation only partially prevented these effects. Our data indicate that maternal deprivation disturbs the development of sensory SU nerves in female rats, whereas tactile stimulation partially prevents the changes generated by AR. Considering that our previous studies have shown more severe effects of AR on male SU nerve development, we suggest that sex-associated factors may be involved in these processes.

An artificial visual neuron with multiplexed rate and time-to-first-spike coding.

Human visual neurons rely on event-driven, energy-efficient spikes for communication, while silicon image sensors do not. The energy-budget mismatch between biological systems and machine vision technology has inspired the development of artificial visual neurons for use in spiking neural network (SNN). However, the lack of multiplexed data coding schemes reduces the ability of artificial visual neurons in SNN to emulate the visual perception ability of biological systems. Here, we present an artificial visual spiking neuron that enables rate and temporal fusion (RTF) coding of external visual information. The artificial neuron can code visual information at different spiking frequencies (rate coding) and enables precise and energy-efficient time-to-first-spike (TTFS) coding. This multiplexed sensory coding scheme could improve the computing capability and efficacy of artificial visual neurons. A hardware-based SNN with the RTF coding scheme exhibits good consistency with real-world ground truth data and achieves highly accurate steering and speed predictions for self-driving vehicles in complex conditions. The multiplexed RTF coding scheme demonstrates the feasibility of developing highly efficient spike-based neuromorphic hardware.

Spiking Neural Membrane Systems with Adaptive Synaptic Time Delay.

Spiking neural membrane systems (or spiking neural P systems, SNP systems) are a new type of computation model which have attracted the attention of plentiful scholars for parallelism, time encoding, interpretability and extensibility. The original SNP systems only consider the time delay caused by the execution of rules within neurons, but not caused by the transmission of spikes via synapses between neurons and its adaptive adjustment. In view of the importance of time delay for SNP systems, which are a time encoding computation model, this study proposes SNP systems with adaptive synaptic time delay (ADSNP systems) based on the dynamic regulation mechanism of synaptic transmission delay in neural systems. In ADSNP systems, besides neurons, astrocytes that can generate adenosine triphosphate (ATP) are introduced. After receiving spikes, astrocytes convert spikes into ATP and send ATP to the synapses controlled by them to change the synaptic time delays. The Turing universality of ADSNP systems in number generating and accepting modes is proved. In addition, a small universal ADSNP system using 93 neurons and astrocytes is given. The superiority of the ADSNP system is demonstrated by comparison with the six variants. Finally, an ADSNP system is constructed for credit card fraud detection, which verifies the feasibility of the ADSNP system for solving real-world problems. By considering the adaptive synaptic delay, ADSNP systems better restore the process of information transmission in biological neural networks, and enhance the adaptability of SNP systems, making the control of time more accurate.

Assessing Rat Diaphragm Motor Unit Connectivity Outcome Measures as Quantitative Biomarkers of Phrenic Motor Neuron Degeneration and Compensation.

Loss of ventilatory muscle function is a consequence of motor neuron injury and neurodegeneration (e.g., cervical spinal cord injury and amyotrophic lateral sclerosis, respectively). Phrenic motor neurons are the final link between the central nervous system and muscle, and their respective motor units (groups of muscle fibers innervated by a single motor neuron) represent the smallest functional unit of the neuromuscular ventilatory system. Compound muscle action potential (CMAP), single motor unit potential (SMUP), and motor unit number estimation (MUNE) are established electrophysiological approaches that enable the longitudinal assessment of motor unit integrity in animal models over time but have mostly been applied to limb muscles. Therefore, the objectives of this study are to describe an approach in preclinical rodent studies that can be used longitudinally to quantify the phrenic MUNE, motor unit size (represented as SMUP), and CMAP, and then to demonstrate the utility of these approaches in a motor neuron loss model. Sensitive, objective, and translationally relevant biomarkers for neuronal injury, degeneration, and regeneration in motor neuron injury and diseases can significantly aid and accelerate experimental research discoveries to clinical testing.

Subthalamic nucleus synchronization between beta band local field potential and single-unit activity in Parkinson's disease.

Local field potential (LFP) oscillations in the beta band (13-30 Hz) in the subthalamic nucleus (STN) of Parkinson's disease patients have been implicated in disease severity and treatment response. The relationship between single-neuron activity in the STN and regional beta power changes remains unclear. We used spike-triggered average (STA) to assess beta synchronization in STN. Beta power and STA magnitude at the beta frequency range were compared in three conditions: STN versus other subcortical structures, dorsal versus ventral STN, and high versus low beta power STN recordings. Magnitude of STA-LFP was greater within the STN compared to extra-STN structures along the trajectory path, despite no difference in percentage of the total power. Within the STN, there was a higher percent beta power in dorsal compared to ventral STN but no difference in STA-LFP magnitude. Further refining the comparison to high versus low beta peak power recordings inside the STN to evaluate if single-unit activity synchronized more strongly with beta band activity in areas of high beta power resulted in a significantly higher STA magnitude for areas of high beta power. Overall, these results suggest that STN single units strongly synchronize to beta activity, particularly units in areas of high beta power.