Nat Neurosci

It remains an open question how neural responses in motor cortex relate to movement. We explored the hypothesis that motor cortex reflects dynamics appropriate for generating temporally patterned outgoing commands. To formalize this hypothesis, we trained recurrent neural networks to reproduce the muscle activity of reaching monkeys. Models had to infer dynamics that could transform simple inputs into temporally and spatially complex patterns of muscle activity. Analysis of trained models revealed that the natural dynamical solution was a low-dimensional oscillator that generated the necessary multiphasic commands. This solution closely resembled, at both the single-neuron and population levels, what was observed in neural recordings from the same monkeys. Notably, data and simulations agreed only when models were optimized to find simple solutions. An appealing interpretation is that the empirically observed dynamics of motor cortex may reflect a simple solution to the problem of generating temporally patterned descending commands.R01 MH093338/MH/NIMH NIH HHS/United StatesR01NS076460/NS/NINDS NIH HHS/United StatesR01 MH93338-02/MH/NIMH NIH HHS/United StatesR01 NS076460/NS/NINDS NIH HHS/United StatesDP2 NS083037/NS/NINDS NIH HHS/United StatesDP1 HD075623/HD/NICHD NIH HHS/United States8DP1HD075623/DP/NCCDPHP CDC HHS/United States2016-11-17T00:00:00Z26075643PMC511329

Spinal Control of Locomotion: Individual Neurons, Their Circuits and Functions

Systematic research on the physiological and anatomical characteristics of spinal cord interneurons along with their functional output has evolved for more than one century. Despite significant progress in our understanding of these networks and their role in generating and modulating movement, it has remained a challenge to elucidate the properties of the locomotor rhythm across species. Neurophysiological experimental evidence indicates similarities in the function of interneurons mediating afferent information regarding muscle stretch and loading, being affected by motor axon collaterals and those mediating presynaptic inhibition in animals and humans when their function is assessed at rest. However, significantly different muscle activation profiles are observed during locomotion across species. This difference may potentially be driven by a modified distribution of muscle afferents at multiple segmental levels in humans, resulting in an altered interaction between different classes of spinal interneurons. Further, different classes of spinal interneurons are likely activated or silent to some extent simultaneously in all species. Regardless of these limitations, continuous efforts on the function of spinal interneuronal circuits during mammalian locomotion will assist in delineating the neural mechanisms underlying locomotor control, and help develop novel targeted rehabilitation strategies in cases of impaired bipedal gait in humans. These rehabilitation strategies will include activity-based therapies and targeted neuromodulation of spinal interneuronal circuits via repetitive stimulation delivered to the brain and/or spinal cord

Spinal Control of Locomotion: Individual Neurons, Their Circuits and Functions

Systematic research on the physiological and anatomical characteristics of spinal cord interneurons along with their functional output has evolved for more than one century. Despite significant progress in our understanding of these networks and their role in generating and modulating movement, it has remained a challenge to elucidate the properties of the locomotor rhythm across species. Neurophysiological experimental evidence indicates similarities in the function of interneurons mediating afferent information regarding muscle stretch and loading, being affected by motor axon collaterals and those mediating presynaptic inhibition in animals and humans when their function is assessed at rest. However, significantly different muscle activation profiles are observed during locomotion across species. This difference may potentially be driven by a modified distribution of muscle afferents at multiple segmental levels in humans, resulting in an altered interaction between different classes of spinal interneurons. Further, different classes of spinal interneurons are likely activated or silent to some extent simultaneously in all species. Regardless of these limitations, continuous efforts on the function of spinal interneuronal circuits during mammalian locomotion will assist in delineating the neural mechanisms underlying locomotor control, and help develop novel targeted rehabilitation strategies in cases of impaired bipedal gait in humans. These rehabilitation strategies will include activity-based therapies and targeted neuromodulation of spinal interneuronal circuits via repetitive stimulation delivered to the brain and/or spinal cord

Neural Dynamics and the Geometry of Population Activity

A growing body of research indicates that much of the brain’s computation is invisible from the activity of individual neurons, but instead instantiated via population-level dynamics. According to this ‘dynamical systems hypothesis’, population-level neural activity evolves according to underlying dynamics that are shaped by network connectivity. While these dynamics are not directly observable in empirical data, they can be inferred by studying the structure of population trajectories. Quantification of this structure, the ‘trajectory geometry’, can then guide thinking on the underlying computation. Alternatively, modeling neural populations as dynamical systems can predict trajectory geometries appropriate for particular tasks. This approach of characterizing and interpreting trajectory geometry is providing new insights in many cortical areas, including regions involved in motor control and areas that mediate cognitive processes such as decision-making. In this thesis, I advance the characterization of population structure by introducing hypothesis-guided metrics for the quantification of trajectory geometry. These metrics, trajectory tangling in primary motor cortex and trajectory divergence in the Supplementary Motor Area, abstract away from task-specific solutions and toward underlying computations and network constraints that drive trajectory geometry. Primate motor cortex (M1) projects to spinal interneurons and motoneurons, suggesting that motor cortex activity may be dominated by muscle-like commands. Observations during reaching lend support to this view, but evidence remains ambiguous and much debated. To provide a different perspective, we employed a novel behavioral paradigm that facilitates comparison between time-evolving neural and muscle activity. We found that single motor cortex neurons displayed many muscle-like properties, but the structure of population activity was not muscle-like. Unlike muscle activity, neural activity was structured to avoid ‘trajectory tangling’: moments where similar activity patterns led to dissimilar future patterns. Avoidance of trajectory tangling was present across tasks and species. Network models revealed a potential reason for this consistent feature: low trajectory tangling confers noise robustness. We were able to predict motor cortex activity from muscle activity by leveraging the hypothesis that muscle-like commands are embedded in additional structure that yields low trajectory tangling. The Supplementary Motor Area (SMA) has been implicated in many higher-order aspects of motor control. Previous studies have demonstrated that SMA might track motor context. We propose that this computation necessitates that neural activity avoids ‘trajectory divergence’: moments where two similar neural states become dissimilar in the future. Indeed, we found that population activity in SMA, but not in M1, reliably avoided trajectory divergence, resulting in fundamentally different geometries: cyclical in M1 and helix-like in SMA. Analogous structure emerged in artificial networks trained without versus with context-related inputs. These findings reveal that the geometries of population activity in SMA and M1 are fundamentally different, with direct implications regarding what computations can be performed by each area. The characterization and statistical analysis of trajectory geometry promises to advance our understanding of neural network function by providing interpretable, cohesive explanations for observed population structure. Commonality between individuals and networks can be uncovered and more generic, task-invariant, fundamental aspects of neural response can be explored

Space-by-Time Modular Decomposition Effectively Describes Whole-Body Muscle Activity During Upright Reaching in Various Directions

The modular control hypothesis suggests that motor commands are built from precoded modules whose specific combined recruitment can allow the performance of virtually any motor task. Despite considerable experimental support, this hypothesis remains tentative as classical findings of reduced dimensionality in muscle activity may also result from other constraints (biomechanical couplings, data averaging or low dimensionality of motor tasks). Here we assessed the effectiveness of modularity in describing muscle activity in a comprehensive experiment comprising 72 distinct point-to-point whole-body movements during which the activity of 30 muscles was recorded. To identify invariant modules of a temporal and spatial nature, we used a space-by-time decomposition of muscle activity that has been shown to encompass classical modularity models. To examine the decompositions, we focused not only on the amount of variance they explained but also on whether the task performed on each trial could be decoded from the single-trial activations of modules. For the sake of comparison, we confronted these scores to the scores obtained from alternative non-modular descriptions of the muscle data. We found that the space-by-time decomposition was effective in terms of data approximation and task discrimination at comparable reduction of dimensionality. These findings show that few spatial and temporal modules give a compact yet approximate representation of muscle patterns carrying nearly all task-relevant information for a variety of whole-body reaching movements

Tensor Analysis and the Dynamics of Motor Cortex

Neural data often span multiple indices, such as neuron, experimental condition, trial, and time, resulting in a tensor or multidimensional array. Standard approaches to neural data analysis often rely on matrix factorization techniques, such as principal component analysis or nonnegative matrix factorization. Any inherent tensor structure in the data is lost when flattened into a matrix. Here, we analyze datasets from primary motor cortex from the perspective of tensor analysis, and develop a theory for how tensor structure relates to certain computational properties of the underlying system. Applied to the motor cortex datasets, we reveal that neural activity is best described by condition-independent dynamics as opposed to condition-dependent relations to external movement variables. Motivated by this result, we pursue one further tensor-related analysis, and two further dynamical systems-related analyses. First, we show how tensor decompositions can be used to denoise neural signals. Second, we apply system identification to the cortex- to-muscle transformation to reveal the intermediate spinal dynamics. Third, we fit recurrent neural networks to muscle activations and show that the geometric properties observed in motor cortex are naturally recapitulated in the network model. Taken together, these results emphasize (on the data analysis side) the role of tensor structure in data and (on the theoretical side) the role of motor cortex as a dynamical system

New approaches to the study of neurorehabilitation protocols in dogs and cats with acute or chronic spinal cord injury with or without deep pain sensation and possible spinal shock signs

Tese de Doutoramento em Ciências Veterinárias na especialidade Clínica, área científica de ClínicaABSTRACT - Intensive neurorehabilitation protocols (INRP) with rehabilitation modalities and weight supported treadmill training (BWSTT), are suggested as treatment to obtain ambulation in dogs and cats with complete (DPP-), discomplete and incomplete (DPP+) compressive or non-compressive spinal cord injury (SCI), similarly to what is performed in human medicine.The first study is a cohort, prospective, controlled and blinded study that was performed in 22 dogs with T11-L3 Hansen type I, revealing ambulation in 100% of the BWSTT group, within a mean of 4.6 weeks. One other study, a retrospective controlled clinical study, was developed in 367 acute post-surgical dogs, with T10-L3 Hansen type I. A new functional neurorehabilitation scale (FNRS-DPP-) was performed to evaluate the DPP- or discomplete dogs, that were able to achieve spinal reflex locomotion (SRL). A strong significance between groups was verified in the DPP+ (p<0.001), with 99.4% of ambulation. The same difference was seen in the DPP- (p=0.007) with 58,5% of ambulation and a tendency (p=0.058) was observed in regard to DPP recovery, with 37.2% achieving SRL, within a maximum of 3 months. INRP was demonstrated to be safe and ambulation recovery achieved earlier. The same population was included in another study, on 16 dogs with incomplete recovery 3 months after surgery. DPP- were under INRP associated with 4-aminopyridine administration, achieving 78% of SRL at day 45 and automatic micturition within a mean of 62 days. Also, 100% of ambulation in the DPP+ within a mean of 47 days and positive follow-up evolution. Ambulatory status was achieved in 88%, establishing this INRP as a therapeutic option to reduce euthanasia. Non-compressive myelopathies with contusive patterns were also referred in a prospective study of 9 cats that revealed 56% (n=5) of ambulation and 44% (n=4) of SRL, showing that INRP should be considered to improve quality of life and the well-being of our patients. Some dogs may develop spinal shock following SCI, including in acute noncompressive nucleus pulposus extrusion. Thus, a cohort prospective study applied a spinal shock scale as a monitoring tool, suggesting spinal shock as a negative factor for a quick recovery. INRP was shown to be safe, tolerable and feasible, allowing 32% of ambulation within 7 days and 94% within 60 days. Follow-ups until 4 years revealed a positive evolution. These studies should be continued, considering each limitationRESUMO - Nova abordagem aos protocolos de neuroreabilitação em cães e gatos com lesão medular aguda ou crónica, com/sem sensibilidade à dor profunda e choque espinhal. - Os protocolos de neuroreabilitação intensiva (INRP), com as modalidades de reabilitação e o treino locomotor em tapete rolante com suporte de peso (BWSTT), são sugeridos como terapêutica para obter a ambulação em cães e gatos de lesão medular compressiva / não compressiva, completa (DPP-), “discompleta” e incompleta (DPP+), tal como na medicina humana. Assim, apresenta-se o primeiro artigo, estudo de coorte, prospetivo, controlado e cego, em 22 cães com lesão T11-L3 Hansen tipo I, que demonstrou 100% de ambulação no grupo BWSTT, em média de 4.6 semanas. O segundo artigo refere-se ao estudo controlado e retrospetivo de 367 cães pós-cirúrgicos com lesão aguda T10-L3 de Hansen tipo I. A escala de neuroreabilitação funcional (FNRS-DPP-) foi elaborada e aplicada nestes cães, DPP- ou incompletos, capazes de atingir a locomoção espinhal por reflexos (SRL). Verificaram-se diferenças significativas entre grupos, nos DPP+ (p<0,001) com 99,4% de ambulação, e nos DPP- (p=0,007) com 58,5%. Em relação à recuperação da sensibilidade profunda (p=0,058), ocorreu 37.3% de SRL, no máximo em 3 meses. O INRP demonstrou-se seguro e a recuperação foi atingida de forma mais precoce. O mesmo foi estudado em 16 cães com recuperação incompleta 3 meses após cirurgia, sendo associada a administração de 4- aminopiridina nos DPP- com 78% de SRL até 45 dias e micção automática em ~62 dias. Obteve-se 100% de ambulação nos cães DPP+, em ~47 dias, com evolução positiva nas consultas de seguimento. A ambulação total foi de 88%, estabelecendo este INRP como opção terapêutica, reduzindo o número de eutanásias em âmbito clínico. As mielopatias não compressivas foram, também, estudadas. Assim sendo, estudo propectivo em 9 gatos revelou 56% de ambulação e 44% de SRL, demostrando que o INRP poderá ser considerado, no sentido de melhorar a qualidade de vida e bem-estar destes doentes. Após a lesão medular, alguns cães podem desenvolver o choque espinhal, principalmente na extrusão aguda não compressiva do núcleo pulposo. Assim, foi desenvolvido estudo propectivo coorte que elaborou e aplicou escala de choque espinhal para monitorização, sugerindo o choque espinhal como fator negativo para a rápida recuperação. Este INRP revelou-se seguro, tolerável e viável, com 32% de ambulação em 7 dias e 94% em 60 dias. Consultas de seguimento até 4 anos revelaram evolução positiva. Estes estudos devem ser continuados considerando as suas limitações.N/

Stochastic modeling and control of neural and small length scale dynamical systems

Recent advancements in experimental and computational techniques have created tremendous opportunities in the study of fundamental questions of science and engineering by taking the approach of stochastic modeling and control of dynamical systems. Examples include but are not limited to neural coding and emergence of behaviors in biological networks. Integrating optimal control strategies with stochastic dynamical models has ignited the development of new technologies in many emerging applications. In this direction, particular examples are brain-machine interfaces (BMIs), and systems to manipulate submicroscopic objects. The focus of this dissertation is to advance these technologies by developing optimal control strategies under various feedback scenarios and system uncertainties. Brain-machine interfaces (BMIs) establish direct communications between living brain tissue and external devices such as an artificial arm. By sensing and interpreting neuronal activity to actuate an external device, BMI-based neuroprostheses hold great promise in rehabilitating motor disabled subjects such as amputees. However, lack of the incorporation of sensory feedback, such as proprioception and tactile information, from the artificial arm back to the brain has greatly limited the widespread clinical deployment of these neuroprosthetic systems in rehabilitation. In the first part of the dissertation, we develop a systematic control-theoretic approach for a system-level rigorous analysis of BMIs under various feedback scenarios. The approach involves quantitative and qualitative analysis of single neuron and network models to the design of missing sensory feedback pathways in BMIs using optimal feedback control theory. As a part of our results, we show that the recovery of the natural performance of motor tasks in BMIs can be achieved by designing artificial sensory feedbacks in the proposed optimal control framework. The second part of the dissertation deals with developing stochastic optimal control strategies using limited feedback information for applications in neural and small length scale dynamical systems. The stochastic nature of these systems coupled with the limited feedback information has greatly restricted the direct applicability of existing control strategies in stabilizing these systems. Moreover, it has recently been recognized that the development of advanced control algorithms is essential to facilitate applications in these systems. We propose a novel broadcast stochastic optimal control strategy in a receding horizon framework to overcome existing limitations of traditional control designs. We apply this strategy to stabilize multi-agent systems and Brownian ensembles. As a part of our results, we show the optimal trapping of an ensemble of particles driven by Brownian motion in a minimum trapping region using the proposed framework