Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity

A Two-Gene Signature, SKI and SLAMF1, Predicts Time-to-Treatment in Previously Untreated Patients with Chronic Lymphocytic Leukemia

We developed and validated a two-gene signature that predicts prognosis in previously-untreated chronic lymphocytic leukemia (CLL) patients. Using a 65 sample training set, from a cohort of 131 patients, we identified the best clinical models to predict time-to-treatment (TTT) and overall survival (OS). To identify individual genes or combinations in the training set with expression related to prognosis, we cross-validated univariate and multivariate models to predict TTT. We identified four gene sets (5, 6, 12, or 13 genes) to construct multivariate prognostic models. By optimizing each gene set on the training set, we constructed 11 models to predict the time from diagnosis to treatment. Each model also predicted OS and added value to the best clinical models. To determine which contributed the most value when added to clinical variables, we applied the Akaike Information Criterion. Two genes were consistently retained in the models with clinical variables: SKI (v-SKI avian sarcoma viral oncogene homolog) and SLAMF1 (signaling lymphocytic activation molecule family member 1; CD150). We optimized a two-gene model and validated it on an independent test set of 66 samples. This two-gene model predicted prognosis better on the test set than any of the known predictors, including ZAP70 and serum β2-microglobulin

Antagonistic Regulation of PIN Phosphorylation by PP2A and PINOID Directs Auxin Flux

In plants, cell polarity and tissue patterning are connected by intercellular flow of the phytohormone auxin, whose directional signaling depends on polar subcellular localization of PIN auxin transport proteins. The mechanism of polar targeting of PINs or other cargos in plants is largely unidentified, with the PINOID kinase being the only known molecular component. Here, we identify PP2A phosphatase as an important regulator of PIN apical-basal targeting and auxin distribution. Genetic analysis, localization, and phosphorylation studies demonstrate that PP2A and PINOID both partially colocalize with PINs and act antagonistically on the phosphorylation state of their central hydrophilic loop, hence mediating PIN apical-basal polar targeting. Thus, in plants, polar sorting by the reversible phosphorylation of cargos allows for their conditional delivery to specific intracellular destinations. In the case of PIN proteins, this mechanism enables switches in the direction of intercellular auxin fluxes, which mediate differential growth, tissue patterning, and organogenesis

Robust and accurate online pose estimation algorithm via efficient three‐dimensional collinearity model

In this study, the authors propose a robust and high accurate pose estimation algorithm to solve the perspective‐N‐point problem in real time. This algorithm does away with the distinction between coplanar and non‐coplanar point configurations, and provides a unified formulation for the configurations. Based on the inverse projection ray, an efficient collinearity model in object–space is proposed as the cost function. The principle depth and the relative depth of reference points are introduced to remove the residual error of the cost function and to improve the robustness and the accuracy of the authors pose estimation method. The authors solve the pose information and the depth of the points iteratively by minimising the cost function, and then reconstruct their coordinates in camera coordinate system. In the following, the optimal absolute orientation solution gives the relative pose information between the estimated three‐dimensional (3D) point set and the 3D mode point set. This procedure with the above two steps is repeated until the result converges. The experimental results on simulated and real data show that the superior performance of the proposed algorithm: its accuracy is higher than the state‐of‐the‐art algorithms, and has best anti‐noise property and least deviation by the influence of outlier among the tested algorithms

Climbing Fiber Burst Size and Olivary Sub-threshold Oscillations in a Network Setting

The inferior olivary nucleus provides one of the two main inputs to the cerebellum: the so-called climbing fibers. Activation of climbing fibers is generally believed to be related to timing of motor commands and/or motor learning. Climbing fiber spikes lead to large all-or-none action potentials in cerebellar Purkinje cells, overriding any other ongoing activity and silencing these cells for a brief period of time afterwards. Empirical evidence shows that the climbing fiber can transmit a short burst of spikes as a result of an olivary cell somatic spike, potentially increasing the information being transferred to the cerebellum per climbing fiber activation. Previously reported results from in vitro studies suggested that the information encoded in the climbing fiber burst is related to the occurrence of the spike relative to the ongoing sub-threshold membrane potential oscillation of the olivary cell, i.e. that the phase of the oscillation is reflected in the size of the climbing fiber burst. We used a detailed three-compartmental model of an inferior olivary cell to further investigate the possible factors determining the size of the climbing fiber burst. Our findings suggest that the phase-dependency of the burst size is present but limited and that charge flow between soma and dendrite is a major determinant of the climbing fiber burst. From our findings it follows that phenomena such as cell ensemble synchrony can have a big effect on the climbing fiber burst size through dendrodendritic gap-junctional coupling between olivary cells

An exact duality theory for semidefinite programming based on sums of squares

Farkas' lemma is a fundamental result from linear programming providing linear certificates for infeasibility of systems of linear inequalities. In semidefinite programming, such linear certificates only exist for strongly infeasible linear matrix inequalities. We provide nonlinear algebraic certificates for all infeasible linear matrix inequalities in the spirit of real algebraic geometry: A linear matrix inequality is infeasible if and only if -1 lies in the quadratic module associated to it. We also present a new exact duality theory for semidefinite programming, motivated by the real radical and sums of squares certificates from real algebraic geometry.Comment: arXiv admin note: substantial text overlap with arXiv:1108.593

Using Linguistic Information and Machine Learning Techniques to Identify Entities from Juridical Documents

Information extraction from legal documents is an important and open problem. A mixed approach, using linguistic information and machine learning techniques, is described in this paper. In this approach, top-level legal concepts are identified and used for document classifica- tion using Support Vector Machines. Named entities, such as, locations, organizations, dates, and document references, are identified using se- mantic information from the output of a natural language parser. This information, legal concepts and named entities, may be used to popu- late a simple ontology, allowing the enrichment of documents and the creation of high-level legal information retrieval systems. The proposed methodology was applied to a corpus of legal documents - from the EUR-Lex site – and it was evaluated. The obtained results were quite good and indicate this may be a promising approach to the legal information extraction problem

On the Generation of Positivstellensatz Witnesses in Degenerate Cases

One can reduce the problem of proving that a polynomial is nonnegative, or more generally of proving that a system of polynomial inequalities has no solutions, to finding polynomials that are sums of squares of polynomials and satisfy some linear equality (Positivstellensatz). This produces a witness for the desired property, from which it is reasonably easy to obtain a formal proof of the property suitable for a proof assistant such as Coq. The problem of finding a witness reduces to a feasibility problem in semidefinite programming, for which there exist numerical solvers. Unfortunately, this problem is in general not strictly feasible, meaning the solution can be a convex set with empty interior, in which case the numerical optimization method fails. Previously published methods thus assumed strict feasibility; we propose a workaround for this difficulty. We implemented our method and illustrate its use with examples, including extractions of proofs to Coq.Comment: To appear in ITP 201

Balancing the playing field: collaborative gaming for physical training.

BACKGROUND: Multiplayer video games promoting exercise-based rehabilitation may facilitate motor learning, by increasing motivation through social interaction. However, a major design challenge is to enable meaningful inter-subject interaction, whilst allowing for significant skill differences between players. We present a novel motor-training paradigm that allows real-time collaboration and performance enhancement, across a wide range of inter-subject skill mismatches, including disabled vs. able-bodied partnerships. METHODS: A virtual task consisting of a dynamic ball on a beam, is controlled at each end using independent digital force-sensing handgrips. Interaction is mediated through simulated physical coupling and locally-redundant control. Game performance was measured in 16 healthy-healthy and 16 patient-expert dyads, where patients were hemiparetic stroke survivors using their impaired arm. Dual-player was compared to single-player performance, in terms of score, target tracking, stability, effort and smoothness; and questionnaires probing user-experience and engagement. RESULTS: Performance of less-able subjects (as ranked from single-player ability) was enhanced by dual-player mode, by an amount proportionate to the partnership's mismatch. The more abled partners' performances decreased by a similar amount. Such zero-sum interactions were observed for both healthy-healthy and patient-expert interactions. Dual-player was preferred by the majority of players independent of baseline ability and subject group; healthy subjects also felt more challenged, and patients more skilled. CONCLUSION: This is the first demonstration of implicit skill balancing in a truly collaborative virtual training task leading to heightened engagement, across both healthy subjects and stroke patients

Serotonin Differentially Regulates Short- and Long-Term Prediction of Rewards in the Ventral and Dorsal Striatum

BACKGROUND: The ability to select an action by considering both delays and amount of reward outcome is critical for maximizing long-term benefits. Although previous animal experiments on impulsivity have suggested a role of serotonin in behaviors requiring prediction of delayed rewards, the underlying neural mechanism is unclear. METHODOLOGY/PRINCIPAL FINDINGS: To elucidate the role of serotonin in the evaluation of delayed rewards, we performed a functional brain imaging experiment in which subjects chose small-immediate or large-delayed liquid rewards under dietary regulation of tryptophan, a precursor of serotonin. A model-based analysis revealed that the activity of the ventral part of the striatum was correlated with reward prediction at shorter time scales, and this correlated activity was stronger at low serotonin levels. By contrast, the activity of the dorsal part of the striatum was correlated with reward prediction at longer time scales, and this correlated activity was stronger at high serotonin levels. CONCLUSIONS/SIGNIFICANCE: Our results suggest that serotonin controls the time scale of reward prediction by differentially regulating activities within the striatum