An active electronic bidirectional interface for high resolution interrogation of the spinal cord.

Epidural electrical stimulation (EES) has shown promise as both a clinical therapeutic tool and research aid in the study of nervous system function. However, available clinical paddles are limited to using a small number of contacts due to the burden of wires necessary to connect each contact to the therapeutic device. Here, we introduce for the first time the integration of a hermetic active electronic multiplexer onto the electrode paddle array itself, removing this interconnect limitation. We evaluated the chronic implantation of an active electronic 60-contact paddle (the HD64) on the lumbosacral spinal cord of two sheep. The HD64 was implanted for 13 months and 15 months, with no device-related malfunctions or adverse events. We identified increased selectivity in EES-evoked motor responses using dense stimulating bipoles. Further, we found that dense recording bipoles decreased the spatial correlation between channels during recordings. Finally, spatial electrode encoding enabled a neural network to accurately perform EES parameter inference for unseen stimulation electrodes, reducing training data requirements. A high-density EES paddle, containing active electronics safely integrated into neural interfaces, opens new avenues for the study of nervous system function and new therapies to treat neural injury and dysfunction.

Fast inference of spinal neuromodulation for motor control using amortized neural networks.

Objective.Epidural electrical stimulation (EES) has emerged as an approach to restore motor function following spinal cord injury (SCI). However, identifying optimal EES parameters presents a significant challenge due to the complex and stochastic nature of muscle control and the combinatorial explosion of possible parameter configurations. Here, we describe a machine-learning approach that leverages modern deep neural networks to learn bidirectional mappings between the space of permissible EES parameters and target motor outputs.Approach.We collected data from four sheep implanted with two 24-contact EES electrode arrays on the lumbosacral spinal cord. Muscle activity was recorded from four bilateral hindlimb electromyography (EMG) sensors. We introduce a general learning framework to identify EES parameters capable of generating desired patterns of EMG activity. Specifically, we first amortize spinal sensorimotor computations in a forward neural network model that learns to predict motor outputs based on EES parameters. Then, we employ a second neural network as an inverse model, which reuses the amortized knowledge learned by the forward model to guide the selection of EES parameters.Main results.We found that neural networks can functionally approximate spinal sensorimotor computations by accurately predicting EMG outputs based on EES parameters. The generalization capability of the forward model critically benefited our inverse model. We successfully identified novel EES parameters, in under 20 min, capable of producing desired target EMG recruitment duringin vivotesting. Furthermore, we discovered potential functional redundancies within the spinal sensorimotor networks by identifying unique EES parameters that result in similar motor outcomes. Together, these results suggest that our framework is well-suited to probe spinal circuitry and control muscle recruitment in a completely data-driven manner.Significance.We successfully identify novel EES parameters within minutes, capable of producing desired EMG recruitment. Our approach is data-driven, subject-agnostic, automated, and orders of magnitude faster than manual approaches.

Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience.

In cognitive neuroscience, computational modeling can formally adjudicate between theories and affords quantitative fits to behavioral/brain data. Pragmatically, however, the space of plausible generative models considered is dramatically limited by the set of models with known likelihood functions. For many models, the lack of a closed-form likelihood typically impedes Bayesian inference methods. As a result, standard models are evaluated for convenience, even when other models might be superior. Likelihood-free methods exist but are limited by their computational cost or their restriction to particular inference scenarios. Here, we propose neural networks that learn approximate likelihoods for arbitrary generative models, allowing fast posterior sampling with only a one-off cost for model simulations that is amortized for future inference. We show that these methods can accurately recover posterior parameter distributions for a variety of neurocognitive process models. We provide code allowing users to deploy these methods for arbitrary hierarchical model instantiations without further training.

Cognitive neuroscience studies the links between the physical brain and cognition. Computational models that attempt to describe the relationships between the brain and specific behaviours quantitatively is becoming increasingly popular in this field. This approach may help determine the causes of certain behaviours and make predictions about what behaviours will be triggered by specific changes in the brain. Many of the computational models used in cognitive neuroscience are built based on experimental data. A good model can predict the results of new experiments given a specific set of conditions with few parameters. Candidate models are often called 'generative': models that can simulate data. However, typically, cognitive neuroscience studies require going the other way around: they need to infer models and their parameters from experimental data. Ideally, it should also be possible to properly assess the remaining uncertainty over the parameters after having access to the experimental data. To facilitate this, the Bayesian approach to statistical analysis has become popular in cognitive neuroscience. Common software tools for Bayesian estimation require a 'likelihood function', which measures how well a model fits experimental data for given values of the unknown parameters. A major obstacle is that for all but the most common models, obtaining precise likelihood functions is computationally costly. In practice, this requirement limits researchers to evaluating and comparing a small subset of neurocognitive models for which a likelihood function is known. As a result, it is convenience, rather than theoretical interest, that guides this process. In order to provide one solution for this problem, Fengler et al. developed a method that allows users to perform Bayesian inference on a larger number of models without high simulation costs. This method uses likelihood approximation networks (LANs), a computational tool that can estimate likelihood functions for a broad class of models of decision making, allowing researchers to estimate parameters and to measure how well models fit the data. Additionally, Fengler et al. provide both the code needed to build networks using their approach and a tutorial for users. The new method, along with the user-friendly tool, may help to discover more realistic brain dynamics underlying cognition and behaviour as well as alterations in brain function.

Discriminating between sleep and exercise-induced fatigue using computer vision and behavioral genetics.

Following prolonged swimming, Caenorhabditis elegans cycle between active swimming bouts and inactive quiescent bouts. Swimming is exercise for C. elegans and here we suggest that inactive bouts are a recovery state akin to fatigue. It is known that cGMP-dependent kinase (PKG) activity plays a conserved role in sleep, rest, and arousal. Using C. elegans EGL-4 PKG, we first validate a novel learning-based computer vision approach to automatically analyze C. elegans locomotory behavior and an edge detection program that is able to distinguish between activity and inactivity during swimming for long periods of time. We find that C. elegans EGL-4 PKG function impacts timing of exercise-induced quiescent (EIQ) bout onset, fractional quiescence, bout number, and bout duration, suggesting that previously described pathways are engaged during EIQ bouts. However, EIQ bouts are likely not sleep as animals are feeding during the majority of EIQ bouts. We find that genetic perturbation of neurons required for other C. elegans sleep states also does not alter EIQ dynamics. Additionally, we find that EIQ onset is sensitive to age and DAF-16 FOXO function. In summary, we have validated behavioral analysis software that enables a quantitative and detailed assessment of swimming behavior, including EIQ. We found novel EIQ defects in aged animals and animals with mutations in a gene involved in stress tolerance. We anticipate that further use of this software will facilitate the analysis of genes and pathways critical for fatigue and other C. elegans behaviors.

Fully automated leg tracking of Drosophila neurodegeneration models reveals distinct conserved movement signatures.

Some neurodegenerative diseases, like Parkinsons Disease (PD) and Spinocerebellar ataxia 3 (SCA3), are associated with distinct, altered gait and tremor movements that are reflective of the underlying disease etiology. Drosophila melanogaster models of neurodegeneration have illuminated our understanding of the molecular mechanisms of disease. However, it is unknown whether specific gait and tremor dysfunctions also occur in fly disease mutants. To answer this question, we developed a machine-learning image-analysis program, Feature Learning-based LImb segmentation and Tracking (FLLIT), that automatically tracks leg claw positions of freely moving flies recorded on high-speed video, producing a series of gait measurements. Notably, unlike other machine-learning methods, FLLIT generates its own training sets and does not require user-annotated images for learning. Using FLLIT, we carried out high-throughput and high-resolution analysis of gait and tremor features in Drosophila neurodegeneration mutants for the first time. We found that fly models of PD and SCA3 exhibited markedly different walking gait and tremor signatures, which recapitulated characteristics of the respective human diseases. Selective expression of mutant SCA3 in dopaminergic neurons led to a gait signature that more closely resembled those of PD flies. This suggests that the behavioral phenotype depends on the neurons affected rather than the specific nature of the mutation. Different mutations produced tremors in distinct leg pairs, indicating that different motor circuits were affected. Using this approach, fly models can be used to dissect the neurogenetic mechanisms that underlie movement disorders.

Emergent rules for codon choice elucidated by editing rare arginine codons in Escherichia coli.

The degeneracy of the genetic code allows nucleic acids to encode amino acid identity as well as noncoding information for gene regulation and genome maintenance. The rare arginine codons AGA and AGG (AGR) present a case study in codon choice, with AGRs encoding important transcriptional and translational properties distinct from the other synonymous alternatives (CGN). We created a strain of Escherichia coli with all 123 instances of AGR codons removed from all essential genes. We readily replaced 110 AGR codons with the synonymous CGU codons, but the remaining 13 "recalcitrant" AGRs required diversification to identify viable alternatives. Successful replacement codons tended to conserve local ribosomal binding site-like motifs and local mRNA secondary structure, sometimes at the expense of amino acid identity. Based on these observations, we empirically defined metrics for a multidimensional "safe replacement zone" (SRZ) within which alternative codons are more likely to be viable. To evaluate synonymous and nonsynonymous alternatives to essential AGRs further, we implemented a CRISPR/Cas9-based method to deplete a diversified population of a wild-type allele, allowing us to evaluate exhaustively the fitness impact of all 64 codon alternatives. Using this method, we confirmed the relevance of the SRZ by tracking codon fitness over time in 14 different genes, finding that codons that fall outside the SRZ are rapidly depleted from a growing population. Our unbiased and systematic strategy for identifying unpredicted design flaws in synthetic genomes and for elucidating rules governing codon choice will be crucial for designing genomes exhibiting radically altered genetic codes.