Electrically Assisted Movement Therapy in Chronic Stroke Patients With Severe Upper Limb Paresis: A Pilot, Single-Blind, Randomized Crossover Study.

OBJECTIVE: To evaluate the effects of electrically assisted movement therapy (EAMT) in which patients use functional electrical stimulation, modulated by a custom device controlled through the patient's unaffected hand, to produce or assist task-specific upper limb movements, which enables them to engage in intensive goal-oriented training. DESIGN: Randomized, crossover, assessor-blinded, 5-week trial with follow-up at 18 weeks. SETTING: Rehabilitation university hospital. PARTICIPANTS: Patients with chronic, severe stroke (N=11; mean age, 47.9y) more than 6 months poststroke (mean time since event, 46.3mo). INTERVENTIONS: Both EAMT and the control intervention (dose-matched, goal-oriented standard care) consisted of 10 sessions of 90 minutes per day, 5 sessions per week, for 2 weeks. After the first 10 sessions, group allocation was crossed over, and patients received a 1-week therapy break before receiving the new treatment. MAIN OUTCOME MEASURES: Fugl-Meyer Motor Assessment for the Upper Extremity, Wolf Motor Function Test, spasticity, and 28-item Motor Activity Log. RESULTS: Forty-four individuals were recruited, of whom 11 were eligible and participated. Five patients received the experimental treatment before standard care, and 6 received standard care before the experimental treatment. EAMT produced higher improvements in the Fugl-Meyer scale than standard care (P<.05). Median improvements were 6.5 Fugl-Meyer points and 1 Fugl-Meyer point after the experimental treatment and standard care, respectively. The improvement was also significant in subjective reports of quality of movement and amount of use of the affected limb during activities of daily living (P<.05). CONCLUSIONS: EAMT produces a clinically important impairment reduction in stroke patients with chronic, severe upper limb paresis.

Disentangling constraints using viability evolution principles in integrative modeling of macromolecular assemblies.

Predicting the structure of large molecular assemblies remains a challenging task in structural biology when using integrative modeling approaches. One of the main issues stems from the treatment of heterogeneous experimental data used to predict the architecture of native complexes. We propose a new method, applied here for the first time to a set of symmetrical complexes, based on evolutionary computation that treats every available experimental input independently, bypassing the need to balance weight components assigned to aggregated fitness functions during optimization.

Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns.

The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs-locomotor bouts-matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior.

Artificial evolution by viability rather than competition.

Evolutionary algorithms are widespread heuristic methods inspired by natural evolution to solve difficult problems for which analytical approaches are not suitable. In many domains experimenters are not only interested in discovering optimal solutions, but also in finding the largest number of different solutions satisfying minimal requirements. However, the formulation of an effective performance measure describing these requirements, also known as fitness function, represents a major challenge. The difficulty of combining and weighting multiple problem objectives and constraints of possibly varying nature and scale into a single fitness function often leads to unsatisfactory solutions. Furthermore, selective reproduction of the fittest solutions, which is inspired by competition-based selection in nature, leads to loss of diversity within the evolving population and premature convergence of the algorithm, hindering the discovery of many different solutions. Here we present an alternative abstraction of artificial evolution, which does not require the formulation of a composite fitness function. Inspired from viability theory in dynamical systems, natural evolution and ethology, the proposed method puts emphasis on the elimination of individuals that do not meet a set of changing criteria, which are defined on the problem objectives and constraints. Experimental results show that the proposed method maintains higher diversity in the evolving population and generates more unique solutions when compared to classical competition-based evolutionary algorithms. Our findings suggest that incorporating viability principles into evolutionary algorithms can significantly improve the applicability and effectiveness of evolutionary methods to numerous complex problems of science and engineering, ranging from protein structure prediction to aircraft wing design.