Movement is governed by rotational neural dynamics in spinal motor networks.

Although the generation of movements is a fundamental function of the nervous system, the underlying neural principles remain unclear. As flexor and extensor muscle activities alternate during rhythmic movements such as walking, it is often assumed that the responsible neural circuitry is similarly exhibiting alternating activity1. Here we present ensemble recordings of neurons in the lumbar spinal cord that indicate that, rather than alternating, the population is performing a low-dimensional 'rotation' in neural space, in which the neural activity is cycling through all phases continuously during the rhythmic behaviour. The radius of rotation correlates with the intended muscle force, and a perturbation of the low-dimensional trajectory can modify the motor behaviour. As existing models of spinal motor control do not offer an adequate explanation of rotation1,2, we propose a theory of neural generation of movements from which this and other unresolved issues, such as speed regulation, force control and multifunctionalism, are readily explained.

Sparse Firing in a Hybrid Central Pattern Generator for Spinal Motor Circuits.

Central pattern generators are circuits generating rhythmic movements, such as walking. The majority of existing computational models of these circuits produce antagonistic output where all neurons within a population spike with a broad burst at about the same neuronal phase with respect to network output. However, experimental recordings reveal that many neurons within these circuits fire sparsely, sometimes as rarely as once within a cycle. Here we address the sparse neuronal firing and develop a model to replicate the behavior of individual neurons within rhythm-generating populations to increase biological plausibility and facilitate new insights into the underlying mechanisms of rhythm generation. The developed network architecture is able to produce sparse firing of individual neurons, creating a novel implementation for exploring the contribution of network architecture on rhythmic output. Furthermore, the introduction of sparse firing of individual neurons within the rhythm-generating circuits is one of the factors that allows for a broad neuronal phase representation of firing at the population level. This moves the model toward recent experimental findings of evenly distributed neuronal firing across phases among individual spinal neurons. The network is tested by methodically iterating select parameters to gain an understanding of how connectivity and the interplay of excitation and inhibition influence the output. This knowledge can be applied in future studies to implement a biologically plausible rhythm-generating circuit for testing biological hypotheses.

Optoelectronic and mechanical properties of microstructured polymer optical fiber neural probes.

Multifunctional optical fiber-based neural interfaces have attracted significant attention for neural stimulation, recording, and photopharmacology towards understanding the central nervous system. In this work, we demonstrate the fabrication, optoelectrical characterization, and mechanical analysis of four types of microstructured polymer optical fiber neural probes using different soft thermoplastic polymers. The developed devices have integrated metallic elements for electrophysiology and microfluidic channels for localized drug delivery, and can be used for optogenetics in the visible spectrum at wavelengths spanning from 450 nm up to 800 nm. Their impedance, measured by electrochemical impedance spectroscopy, was found to be as low as 21 kΩ and 4.7 kΩ at 1kHz when indium and tungsten wires are used as the integrated electrodes, respectively. Uniform on-demand drug delivery can be achieved by the microfluidic channels with a measured delivery rate from 10 up to 1000 nL/min. In addition, we identified the buckling failure threshold (defined as the conditions for successful implantation) as well as the bending stiffness of the fabricated fibers. Using finite element analysis, we calculated the main critical mechanical properties of the developed probes to avoid buckling during implantation and maintain high flexibility of the probe within the tissue. Our results aim to demonstrate the impact of design, fabrication, and characteristics of the materials on the development of polymer fibers as next-generation implants and neural interfaces.

In vivo brain temperature mapping using polymer optical fiber Bragg grating sensors.

Variation of the brain temperature is strongly affected by blood flow, oxygen supply, and neural cell metabolism. Localized monitoring of the brain temperature is one of the most effective ways to correlate brain functions and diseases such as stroke, epilepsy, and mood disorders. While polymer optical fibers (POFs) are considered ideal candidates for temperature sensing in the brain, they have never been used so far in vivo. Here, we developed for the first, to the best of our knowledge, time an implantable probe based on a microstructured polymer optical fiber Bragg grating (FBG) sensor for intracranial brain temperature mapping. The temperature at different depths of the brain (starting from the cerebral cortex) and the correlation between the brain and body core temperature of a rat were recorded with a sensitivity of 33 pm/°C and accuracy <0.2°C. Our in vivo experimental results suggest that the proposed device can achieve real-time and high-resolution local temperature measurement in the brain, as well as being integrated with existing neural interfaces.

What are the gray and white matter volumes of the human spinal cord?

The gray matter of the spinal cord is the seat of somata of various types of neurons devoted to the sensory and motor activities of the limbs and trunk as well as a part of the autonomic nervous system. The volume of the spinal gray matter is an indicator of the local neuronal processing, and this can decrease due to atrophy associated with degenerative diseases and injury. Nevertheless, the absolute volume of the human spinal cord has rarely been reported, if ever. Here, we use high-resolution magnetic resonance imaging, with a cross-sectional resolution of 50 × 50 µm and a voxel size of 0.0005 mm3 to estimate the total gray and white matter volume of a post mortem human female spinal cord. Segregation of gray and white matter was accomplished using deep learning image segmentation. Furthermore, we include data from a male spinal cord of a previously published study. The gray and white matter volumes were found to be 2.87 and 11.33 mL, respectively, for the female and 3.55 and 19.33 mL, respectively, for the male. The gray and white matter profiles along the vertebral axis were found to be strikingly similar, and the volumes of the cervical, thoracic, and lumbosacral sections were almost equal.NEW & NOTEWORTHY Here, we combine high-field MRI (9.4 T) and deep learning for a post mortem reconstruction of the gray and white matter in human spinal cords. We report a minuscule total gray matter volume of 2.87 mL for a female and 3.55 mL for a male. For comparison, these volumes correspond approximately to the distal digit of the little finger.

Divisive gain modulation of motoneurons by inhibition optimizes muscular control.

When using muscles, the precision with which force is delivered is as important as the delivery of force itself. Force is regulated by both the number of recruited motoneurons and their spike frequency. While it is known that the recruitment is ordered to reduce variability in force, it remains unclear whether the motoneuron gain, i.e., the slope of the transformation between synaptic input and spiking output, is also modulated to reduce variability in force. To address this issue, we use turtle hindlimb scratching as a model for fine motor control, since this behavior involves precise limb movement to rub the location of somatic nuisance touch. We recorded intracellularly from motoneurons in a reduced preparation where the limbs were removed to increase mechanical stability and the motor nerve activity served as a surrogate for muscle force. We found that not only is the gain of motoneurons regulated on a subsecond timescale, it is also adjusted to minimize variability. The modulation is likely achieved via an expansive nonlinearity between spike rate and membrane potential with inhibition having a divisive influence. These findings reveal a versatile mechanism of modulating neuronal sensitivity and suggest that such modulation is fundamentally linked to optimization.

Premotor spinal network with balanced excitation and inhibition during motor patterns has high resilience to structural division.

Direct measurements of synaptic inhibition (I) and excitation (E) to spinal motoneurons can provide an important insight into the organization of premotor networks. Such measurements of flexor motoneurons participating in motor patterns in turtles have recently demonstrated strong concurrent E and I as well as stochastic membrane potentials and irregular spiking in the adult turtle spinal cord. These findings represent a departure from the widespread acceptance of feedforward reciprocal rate models for spinal motor function. The apparent discrepancy has been reviewed as an experimental artifact caused by the distortion of local networks in the transected turtle spinal cord. We tested this assumption in the current study by performing experiments to assess the integrity of motor functions in the intact spinal cord and the cord transected at segments D9/D10. Excitatory and inhibitory synaptic inputs to motoneurons were estimated during rhythmic motor activity and demonstrated primarily intense inputs that consisted of qualitatively similar mixed E/I before and after the transection. To understand this high functional resilience, we used mathematical modeling of networks with recurrent connectivity that could potentially explain the balanced E/I. Both experimental and modeling data support the concept of a locally balanced premotor network consisting of recurrent E/I connectivity, in addition to the well known reciprocal network activity. The multifaceted synaptic connections provide spinal networks with a remarkable ability to remain functional after structural divisions.

Synaptic inhibition and excitation estimated via the time constant of membrane potential fluctuations.

When recording the membrane potential, V, of a neuron it is desirable to be able to extract the synaptic input. Critically, the synaptic input is stochastic and nonreproducible so one is therefore often restricted to single-trial data. Here, we introduce means of estimating the inhibition and excitation and their confidence limits from single sweep trials. The estimates are based on the mean membrane potential, V, and the membrane time constant, τ. The time constant provides the total conductance (G = capacitance/τ) and is extracted from the autocorrelation of V. The synaptic conductances can then be inferred from V when approximating the neuron as a single compartment. We further employ a stochastic model to establish limits of confidence. The method is verified on models and experimental data, where the synaptic input is manipulated pharmacologically or estimated by an alternative method. The method gives best results if the synaptic input is large compared with other conductances, the intrinsic conductances have little or no time dependence or are comparably small, the ligand-gated kinetics is faster than the membrane time constant, and the majority of synaptic contacts are electrotonically close to soma (recording site). Although our data are in current clamp, the method also works in V-clamp recordings, with some minor adaptations. All custom made procedures are provided in Matlab.

Neural Decoding for Intracortical Brain-Computer Interfaces.

Brain-computer interfaces have revolutionized the field of neuroscience by providing a solution for paralyzed patients to control external devices and improve the quality of daily life. To accurately and stably control effectors, it is important for decoders to recognize an individual's motor intention from neural activity either by noninvasive or intracortical neural recording. Intracortical recording is an invasive way of measuring neural electrical activity with high temporal and spatial resolution. Herein, we review recent developments in neural signal decoding methods for intracortical brain-computer interfaces. These methods have achieved good performance in analyzing neural activity and controlling robots and prostheses in nonhuman primates and humans. For more complex paradigms in motor rehabilitation or other clinical applications, there remains more space for further improvements of decoders.

Soft monolithic infrared neural interface for simultaneous neurostimulation and electrophysiology.

Controlling neuronal activity using implantable neural interfaces constitutes an important tool to understand and develop novel strategies against brain diseases. Infrared neurostimulation is a promising alternative to optogenetics for controlling the neuronal circuitry with high spatial resolution. However, bi-directional interfaces capable of simultaneously delivering infrared light and recording electrical signals from the brain with minimal inflammation have not yet been reported. Here, we have developed a soft fibre-based device using high-performance polymers which are >100-fold softer than conventional silica glass used in standard optical fibres. The developed implant is capable of stimulating the brain activity in localized cortical domains by delivering laser pulses in the 2 µm spectral region while recording electrophysiological signals. Action and local field potentials were recorded in vivo from the motor cortex and hippocampus in acute and chronic settings, respectively. Immunohistochemical analysis of the brain tissue indicated insignificant inflammatory response to the infrared pulses while the signal-to-noise ratio of recordings still remained high. Our neural interface constitutes a step forward in expanding infrared neurostimulation as a versatile approach for fundamental research and clinically translatable therapies.

Stereological estimate of the total number of neurons in spinal segment D9 of the red-eared turtle.

The red-eared turtle is an important animal model for investigating the neural activity in the spinal circuit that generates motor behavior. However, basic anatomical features, including the number of neurons in the spinal segments involved, are unknown. In the present study, we estimate the total number of neurons in segment D9 of the spinal cord in the red-eared turtle (Trachemys scripta elegans) using stereological cell counting methods. In transverse spinal cord sections stained with modified Giemsa, motoneurons (MNs), interneurons (INs), and non-neuronal cells were distinguished according to location and morphology. Each cell type was then counted separately using an optical disector with the cell nucleus as counting item. The number of cells in segment D9 was as follows (mean ± SE): MNs, 2049 ± 74; INs, 16,135 ± 316; non-neuronal cells, 47,504 ± 478 (n = 6). These results provide the first estimate of the total number of neurons in a spinal segment in a terrestrial vertebrate based on unbiased stereological methods and an upper bound on the number of neurons involved in segmental sensorimotor activity. These findings also form a crucial quantitative foundation for integrating electrophysiological data into mathematical circuit models.

Viral strategies for targeting spinal neuronal subtypes in adult wild-type rodents.

Targeting specific subtypes of interneurons in the spinal cord is primarily restricted to a small group of genetic model animals. Since the development of new transgenic model animals can be expensive and labor intensive, it is often difficult to generalize these findings and verify them in other model organisms, such as the rat, ferret or monkey, that may be more beneficial in certain experimental investigations. Nevertheless, endogenous enhancers and promoters delivered using an adeno-associated virus (AAV) have been successful in providing expression in specific subtypes of neurons in the forebrain of wildtype animals, and therefore may introduce a shortcut. GABAergic interneurons, for instance, have successfully been targeted using the mDlx promoter, which has recently been developed and is now widely used in wild type animals. Here, we test the specificity and efficiency of the mDlx enhancer for robust targeting of inhibitory interneurons in the lumbar spinal cord of wild-type rats using AAV serotype 2 (AAV2). Since this has rarely been done in the spinal cord, we also test the expression and specificity of the CamKIIa and hSynapsin promoters using serotype 9. We found that AAV2-mDlx does in fact target many neurons that contain an enzyme for catalyzing GABA, the GAD-65, with high specificity and a small fraction of neurons containing an isoform, GAD-67. Expression was also seen in some motor neurons although with low correlation. Viral injections using the CamKIIa enhancer via AAV9 infected in some glutamatergic neurons, but also GABAergic neurons, whereas hSynapsin via AAV9 targets almost all the neurons in the lumbar spinal cord.

Bundle geodesic convolutional neural network for diffusion-weighted imaging segmentation.

Purpose: Applying machine learning techniques to magnetic resonance diffusion-weighted imaging (DWI) data is challenging due to the size of individual data samples and the lack of labeled data. It is possible, though, to learn general patterns from a very limited amount of training data if we take advantage of the geometry of the DWI data. Therefore, we present a tissue classifier based on a Riemannian deep learning framework for single-shell DWI data. Approach: The framework consists of three layers: a lifting layer that locally represents and convolves data on tangent spaces to produce a family of functions defined on the rotation groups of the tangent spaces, i.e., a (not necessarily continuous) function on a bundle of rotational functions on the manifold; a group convolution layer that convolves this function with rotation kernels to produce a family of local functions over each of the rotation groups; a projection layer using maximization to collapse this local data to form manifold based functions. Results: Experiments show that our method achieves the performance of the same level as state-of-the-art while using way fewer parameters in the model ( < 10 % ). Meanwhile, we conducted a model sensitivity analysis for our method. We ran experiments using a proportion (69.2%, 53.3%, and 29.4%) of the original training set and analyzed how much data the model needs for the task. Results show that this does reduce the overall classification accuracy mildly, but it also boosts the accuracy for minority classes. Conclusions: This work extended convolutional neural networks to Riemannian manifolds, and it shows the potential in understanding structural patterns in the brain, as well as in aiding manual data annotation.

Adaptive polymer fiber neural device for drug delivery and enlarged illumination angle for neuromodulation.

Objective. Optical fiber devices constitute significant tools for the modulation and interrogation of neuronal circuitry in the mid and deep brain regions. The illuminated brain area during neuromodulation has a direct impact on the spatio-temporal properties of the brain activity and depends solely on the material and geometrical characteristics of the optical fibers. In the present work, we developed two different flexible polymer optical fibers (POFs) with integrated microfluidic channels (MFCs) and an ultra-high numerical aperture (UHNA) for enlarging the illumination angle to achieve efficient neuromodulation.Approach. Three distinct thermoplastic polymers: polysulfone, polycarbonate, and fluorinated ethylene propylene were used to fabricate two step-index UHNA POF neural devices using a scalable thermal drawing process. The POFs were characterized in terms of their illumination map as well as their fluid delivery capability in phantom and adult rat brain slices. Main results.A 100-fold reduced bending stiffness of the proposed fiber devices compared to their commercially available counterparts has been found. The integrated MFCs can controllably deliver dye (trypan blue) on-demand over a wide range of injection rates spanning from 10 nl min-1to 1000 nl min-1. Compared with commercial silica fibers, the proposed UHNA POFs exhibited an increased illumination area by 17% and 21% under 470 and 650 nm wavelength, respectively. In addition, a fluorescent light recording experiment has been conducted to demonstrate the ability of our UHNA POFs to be used as optical waveguides in fiber photometry.Significance. Our results overcome the current technological limitations of fiber implants that have limited illumination area and we suggest that soft neural fiber devices can be developed using different custom designs for illumination, collection, and photometry applications. We anticipate our work to pave the way towards the development of next-generation functional optical fibers for neuroscience.

Influence of phasic and tonic dopamine release on receptor activation.

Tonic and phasic dopamine release is implicated in learning, motivation, and motor functions. However, the relationship between spike patterns in dopaminergic neurons, the extracellular concentration of dopamine, and activation of dopamine receptors remains unresolved. In the present study, we develop a computational model of dopamine signaling that give insight into the relationship between the dynamics of release and occupancy of D(1) and D(2) receptors. The model is derived from first principles using experimental data. It has no free parameters and offers unbiased estimation of the boundaries of dopaminergic volume transmission. Bursts primarily increase occupancy of D(1) receptors, whereas pauses translate into low occupancy of D(1) and D(2) receptors. Phasic firing patterns, composed of bursts and pauses, reduce the average D(2) receptor occupancy and increase average D(1) receptor occupancy compared with equivalent tonic firing. Receptor occupancy is crucially dependent on synchrony and the balance between tonic and phasic firing modes. Our results provide quantitative insight in the dynamics of volume transmission and complement experimental data obtained with electrophysiology, positron emission tomography, microdialysis, amperometry, and voltammetry.

Motoneuron membrane potentials follow a time inhomogeneous jump diffusion process.

Stochastic leaky integrate-and-fire models are popular due to their simplicity and statistical tractability. They have been widely applied to gain understanding of the underlying mechanisms for spike timing in neurons, and have served as building blocks for more elaborate models. Especially the Ornstein-Uhlenbeck process is popular to describe the stochastic fluctuations in the membrane potential of a neuron, but also other models like the square-root model or models with a non-linear drift are sometimes applied. Data that can be described by such models have to be stationary and thus, the simple models can only be applied over short time windows. However, experimental data show varying time constants, state dependent noise, a graded firing threshold and time-inhomogeneous input. In the present study we build a jump diffusion model that incorporates these features, and introduce a firing mechanism with a state dependent intensity. In addition, we suggest statistical methods to estimate all unknown quantities and apply these to analyze turtle motoneuron membrane potentials. Finally, simulated and real data are compared and discussed. We find that a square-root diffusion describes the data much better than an Ornstein-Uhlenbeck process with constant diffusion coefficient. Further, the membrane time constant decreases with increasing depolarization, as expected from the increase in synaptic conductance. The network activity, which the neuron is exposed to, can be reasonably estimated to be a threshold version of the nerve output from the network. Moreover, the spiking characteristics are well described by a Poisson spike train with an intensity depending exponentially on the membrane potential.

Why Firing Rate Distributions Are Important for Understanding Spinal Central Pattern Generators.

Networks in the spinal cord, which are responsible for the generation of rhythmic movements, commonly known as central pattern generators (CPGs), have remained elusive for decades. Although it is well-known that many spinal neurons are rhythmically active, little attention has been given to the distribution of firing rates across the population. Here, we argue that firing rate distributions can provide an important clue to the organization of the CPGs. The data that can be gleaned from the sparse literature indicate a firing rate distribution, which is skewed toward zero with a long tail, akin to a normal distribution on a log-scale, i.e., a "log-normal" distribution. Importantly, such a shape is difficult to unite with the widespread assumption of modules composed of recurrently connected excitatory neurons. Spinal modules with recurrent excitation has the propensity to quickly escalate their firing rate and reach the maximum, hence equalizing the spiking activity across the population. The population distribution of firing rates hence would consist of a narrow peak near the maximum. This is incompatible with experiments, that show wide distributions and a peak close to zero. A way to resolve this puzzle is to include recurrent inhibition internally in each CPG modules. Hence, we investigate the impact of recurrent inhibition in a model and find that the firing rate distributions are closer to the experimentally observed. We therefore propose that recurrent inhibition is a crucial element in motor circuits, and suggest that future models of motor circuits should include recurrent inhibition as a mandatory element.

Signaling in large-scale neural networks.

We examine the recent finding that neurons in spinal motor circuits enter a high conductance state during functional network activity. The underlying concomitant increase in random inhibitory and excitatory synaptic activity leads to stochastic signal processing. The possible advantages of this metabolically costly organization are analyzed by comparing with synaptically less intense networks driven by the intrinsic response properties of the network neurons.

Decoupling of timescales reveals sparse convergent CPG network in the adult spinal cord.

During the generation of rhythmic movements, most spinal neurons receive an oscillatory synaptic drive. The neuronal architecture underlying this drive is unknown, and the corresponding network size and sparseness have not yet been addressed. If the input originates from a small central pattern generator (CPG) with dense divergent connectivity, it will induce correlated input to all receiving neurons, while sparse convergent wiring will induce a weak correlation, if any. Here, we use pairwise recordings of spinal neurons to measure synaptic correlations and thus infer the wiring architecture qualitatively. A strong correlation on a slow timescale implies functional relatedness and a common source, which will also cause correlation on fast timescale due to shared synaptic connections. However, we consistently find marginal coupling between slow and fast correlations regardless of neuronal identity. This suggests either sparse convergent connectivity or a CPG network with recurrent inhibition that actively decorrelates common input.

Exploratory whisking by rat is not phase locked to the hippocampal theta rhythm.

The rat has a strong 6-9 Hz rhythm of electrical activity in the hippocampus, known as the theta rhythm. Exploratory whisking, i.e., the rhythmic movement of the rat's vibrissas to acquire tactile information, occurs within the same frequency range as the theta rhythm and provides a model system to examine the relationship between theta rhythm and active sensory movements. In particular, it has been postulated that these two rhythms are phase locked as a means to synchronize sensory and hippocampal processing. We tested this hypothesis in rats trained to whisk in air. Theta activity was measured via field electrodes in the hippocampus, and whisking was measured via the mystacial electromyogram. We calculated the spectral coherence between these two signals as a means to quantify the extent of phase locking. First, we found that the fraction of epochs with high coherence is not significantly greater than that expected by chance (seven of eight animals and as a population average). Second, we found that the trial-averaged coherence is low (coherence, < 0.1) and, as an average across all animals, statistically insignificant. We further asked whether the strength of the theta rhythm correlated with that of whisking, independent of the lack of cycle-by-cycle coherence. We observe that the correlation is weak and insignificant (six of eight animals and as a population average). We conclude that there is no relationship between the whisking and theta rhythms, at least when animals whisk in air.