scholarly journals Experimental and computational study on motor control and recovery after stroke: towards a constructive loop between experimental and virtual embodied neuroscience

2020 ◽  
Author(s):  
Anna Letizia Allegra Mascaro ◽  
Egidio Falotico ◽  
Spase Petkoski ◽  
Maria Pasquini ◽  
Lorenzo Vannucci ◽  
...  
Keyword(s):  
Experimental Data ◽  
Motor Control ◽  
Computational Models ◽  
Computational Study ◽  
Motor Rehabilitation ◽  
Modeling Framework ◽  
First Results ◽  
Traditional Approaches ◽  
The Brain ◽  
Multiple Granularities

ABSTRACTBeing able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.

scholarly journals New Technologies of Motor Control and Rehabilitation

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Albertas Skurvydas
Keyword(s):  
Motor Control ◽  
New Technologies ◽  
Modern Science ◽  
Computational Approach ◽  
Neural Cells ◽  
Motor Rehabilitation ◽  
Principal Mechanism ◽  
Brain Stroke ◽  
Computational Paradigm ◽  
The Brain

Modern paradigms of motor control and rehabilitation are analyzed in the paper. Two main paradigms, i. e. computational approach and dynamical system approach are engaged in rivalry in motor control and learning research at present. From the standpoint of computational paradigm the principal mechanism of motor control and learning consists in the ability of the brain “to calculate” (acting as some kind of biological computer). According to the paradigm of dynamical systems the mechanism of motor control is time dependent. In other words, it can be different each time. The main principles of motor control and properties of movements are given considerable attention in the paper. Besides, modern methods of motor rehabilitation after stroke are emphasized in the paper. Fitting of neuroprosthesis and restoration of damaged neural cells are significant maiden steps in modern science. The scientists are engaged in search for: a) constraining such mechanism prosthesis that would submit to the efforts of human will and b) restoring neural cells damaged because of the brain stroke suffered.Keywords: motor control, rehabilitation, stroke.

Integrated Intrinsic and Dedicated Representations of Time: A Computational Study Involving Robotic Agents

2015 ◽  
Vol 3 (3-4) ◽  
pp. 246-268 ◽  
Author(s):  
Michail Maniadakis ◽  
Panos Trahanias
Keyword(s):  
Computational Models ◽  
Computational Study ◽  
A Priori ◽  
Design Procedure ◽  
Careful Examination ◽  
Time Representation ◽  
Simple Neural Network ◽  
Representation Scheme ◽  
Processing Mechanisms ◽  
The Brain

The computational modeling of cognitive processes provides a systematic means to study hidden and particularly complex aspects of brain functionality. Given our rather limited understanding of how the brain deals with the notion of time, the implementation of computational models addressing duration processing can be particularly informative for studying possible time representations in our brain. In the present work we adopt a connectionist modeling approach to study how time experiencing and time processing may be encoded in a simple neural network trained to accomplish time-based robotic tasks. A particularly interesting characteristic of the present study is the implementation of a single computational model to accomplish not only one but three different behavioral tasks that assume diverse manipulation of time intervals. This setup enables a multifaceted exploration of duration-processing mechanisms, revealing a rather plausible hypothesis of how our brain deals with time. The model is implemented through an evolutionary design procedure, making a very limited set of a priori assumptions regarding its internal structure and machinery. Artificial evolution facilitates the unconstrained self-organization of time representation and processing mechanisms in the brain of simulated robotic agents. Careful examination of the artificial brains has shown that the implemented mechanisms incorporate characteristics from both the ‘intrinsic’ time representation scheme and the ‘dedicated’ time representation scheme. Even though these two schemes are widely considered as contradictory, the present study shows that it is possible to effectively integrate them in the same cognitive system. This provides a new view on the possible representation of time in the brain, and paves the way for new and more comprehensive theories to address interval timing.

Brain Drug Delivery: Overcoming the Blood-brain Barrier to Treat Tauopathies

2020 ◽  
Vol 26 (13) ◽  
pp. 1448-1465 ◽  
Author(s):  
Jozef Hanes ◽  
Eva Dobakova ◽  
Petra Majerova
Keyword(s):  
Drug Delivery ◽  
Blood Brain Barrier ◽  
Neurodegenerative Disorders ◽  
Brain Barrier ◽  
Neuronal Function ◽  
Brain Drug Delivery ◽  
Naturally Occurring ◽  
Blood Brain ◽  
Traditional Approaches ◽  
The Brain

Tauopathies are neurodegenerative disorders characterized by the deposition of abnormal tau protein in the brain. The application of potentially effective therapeutics for their successful treatment is hampered by the presence of a naturally occurring brain protection layer called the blood-brain barrier (BBB). BBB represents one of the biggest challenges in the development of therapeutics for central nervous system (CNS) disorders, where sufficient BBB penetration is inevitable. BBB is a heavily restricting barrier regulating the movement of molecules, ions, and cells between the blood and the CNS to secure proper neuronal function and protect the CNS from dangerous substances and processes. Yet, these natural functions possessed by BBB represent a great hurdle for brain drug delivery. This review is concentrated on summarizing the available methods and approaches for effective therapeutics’ delivery through the BBB to treat neurodegenerative disorders with a focus on tauopathies. It describes the traditional approaches but also new nanotechnology strategies emerging with advanced medical techniques. Their limitations and benefits are discussed.

A combined experimental and computational study of mechanical properties after balloon kyphoplasty

2021 ◽  
pp. 095441192110139
Author(s):  
Philip Purcell ◽  
Fiona McEvoy ◽  
Stephen Tiernan ◽  
Derek Sweeney ◽  
Seamus Morris
Keyword(s):  
Computational Models ◽  
Computational Study ◽  
Case Reports ◽  
Experimental Tests ◽  
Balloon Kyphoplasty ◽  
Fatigue Tests ◽  
Vertebral Compression Fractures ◽  
Baseline Case ◽  
Strength And Stiffness ◽  
Stiffness Loss

Vertebral compression fractures rank among the most frequent injuries to the musculoskeletal system, with more than 1 million fractures per annum worldwide. The past decade has seen a considerable increase in the utilisation of surgical procedures such as balloon kyphoplasty to treat these injuries. While many kyphoplasty studies have examined the risk of damage to adjacent vertebra after treatment, recent case reports have also emerged to indicate the potential for the treated vertebra itself to re-collapse after surgery. The following study presents a combined experimental and computational study of balloon kyphoplasty which aims to establish a methodology capable of evaluating these cases of vertebral re-collapse. Results from both the experimental tests and computational models showed significant increases in strength and stiffness after treatment, by factors ranging from 1.44 to 1.93, respectively. Fatigue tests on treated specimens showed a 37% drop in the rate of stiffness loss compared to the untreated baseline case. Further analysis of the computational models concluded that inhibited PMMA interdigitation at the interface during kyphoplasty could reverse improvements in strength and stiffness that could otherwise be gained by the treatment.

Comparison of soot models for reacting sprays in diesel engine-like conditions

2021 ◽  
pp. 095440702110154
Author(s):  
Pavan Prakash Duvvuri ◽  
Rajesh Kumar Shrivastava ◽  
Sheshadri Sreedhara
Keyword(s):  
Experimental Data ◽  
Volume Fraction ◽  
Soot Volume Fraction ◽  
Modeling Framework ◽  
Aerosol Dynamics ◽  
Significant Difference ◽  
Polycyclic Aromatic ◽  
Order Of Magnitude ◽  
Detailed Kinetics ◽  
Soot Model

Stringent emission legislations and growing health concerns have contributed to the evolution of soot modeling in diesel engines from simple empirical relations to methods involving detailed kinetics and complex aerosol dynamics. In this paper, four different soot models have been evaluated for the high temperature, high pressure combusting dodecane spray cases of engine combustion network (ECN) spray A which mimics engine-relevant conditions. The soot models considered include an empirical, a multistep, a method of moments based, and a discrete sectional method soot model. Two experimental cases with ambient oxygen volume of 21% and 15% have been modeled. A good agreement between simulations and experiments for vapor penetration and heat release rate has been obtained. Quasi-steady soot volume fraction contours for the four soot models have been compared with experiments. Contours of the species and source terms involved in soot modeling have also been compared for a better understanding of soot processes. The empirical soot model results in higher magnitude and spread of soot due to a lack of modeling framework for oxidation through OH species. Among the four models studied, the multistep soot model has been observed to provide the most promising agreement with the experimental data in terms of distribution of soot and location of peak soot volume fraction. Due to a two-way coupling of soot models, the detailed models predict an upstream location for soot as compared to the multi-step soot model which is one way coupled. A significant difference (of an order of magnitude) in the concentration of PAH (polycyclic aromatic hydrocarbons) precursor between multistep and detailed soot models has been observed because of precursor consumption due to the coupling of detailed soot models with chemical kinetics. It is recommended that kinetic schemes, especially those concerning PAH, be validated with experimental data with a kinetics-coupled soot model.

scholarly journals Power Failure of Mitochondria and Oxidative Stress in Neurodegeneration and Its Computational Models

Antioxidants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 229
Author(s):  
JunHyuk Woo ◽  
Hyesun Cho ◽  
YunHee Seol ◽  
Soon Ho Kim ◽  
Chanhyeok Park ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Mitochondrial Dysfunction ◽  
Computational Models ◽  
Neuronal Damage ◽  
Living Organism ◽  
The Body ◽  
Mitochondrial Functions ◽  
A Cell ◽  
The Brain ◽  
And Oxidative Stress

The brain needs more energy than other organs in the body. Mitochondria are the generator of vital power in the living organism. Not only do mitochondria sense signals from the outside of a cell, but they also orchestrate the cascade of subcellular events by supplying adenosine-5′-triphosphate (ATP), the biochemical energy. It is known that impaired mitochondrial function and oxidative stress contribute or lead to neuronal damage and degeneration of the brain. This mini-review focuses on addressing how mitochondrial dysfunction and oxidative stress are associated with the pathogenesis of neurodegenerative disorders including Alzheimer’s disease, amyotrophic lateral sclerosis, Huntington’s disease, and Parkinson’s disease. In addition, we discuss state-of-the-art computational models of mitochondrial functions in relation to oxidative stress and neurodegeneration. Together, a better understanding of brain disease-specific mitochondrial dysfunction and oxidative stress can pave the way to developing antioxidant therapeutic strategies to ameliorate neuronal activity and prevent neurodegeneration.

Quantitative Models of Motor Responses Subject to Longitudinal, Lateral, and Preview Constraints

1978 ◽  
Vol 20 (1) ◽  
pp. 35-39 ◽  
Author(s):  
Tarald O. Kvålseth
Keyword(s):  
Experimental Data ◽  
Motor Control ◽  
Linear Models ◽  
Movement Time ◽  
Arm Movements ◽  
Second Order ◽  
Motor Responses ◽  
First Order ◽  
Different Types ◽  
Index Of Difficulty

First- and second-order linear models of mean movement time for serial arm movements aimed at a target and subject to preview constraints and lateral constraints were formulated as extensions of the so-called Fitts's law of motor control. These models were validated on the basis of experimental data from five subjects and found to explain from 80% to 85% of the variation in movement time in the case of the first-order models and from 93% to 95% of such variation for the second-order models. Fitts's index of difficulty (ID) was generally found to contribute more to the movement time than did either the preview ID or the lateral ID defined. Of the different types of errors, target overshoots occurred far more frequently than undershoots.

scholarly journals Inferring brain-computational mechanisms with models of activity measurements

2016 ◽  
Vol 371 (1705) ◽  
pp. 20160278 ◽  
Author(s):  
Nikolaus Kriegeskorte ◽  
Jörn Diedrichsen
Keyword(s):  
Computational Models ◽  
Brain Activity ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Summary Statistics ◽  
Activity Data ◽  
Individual Measurement ◽  
Activity Measurements ◽  
The Brain ◽  
The Way

High-resolution functional imaging is providing increasingly rich measurements of brain activity in animals and humans. A major challenge is to leverage such data to gain insight into the brain's computational mechanisms. The first step is to define candidate brain-computational models (BCMs) that can perform the behavioural task in question. We would then like to infer which of the candidate BCMs best accounts for measured brain-activity data. Here we describe a method that complements each BCM by a measurement model (MM), which simulates the way the brain-activity measurements reflect neuronal activity (e.g. local averaging in functional magnetic resonance imaging (fMRI) voxels or sparse sampling in array recordings). The resulting generative model (BCM-MM) produces simulated measurements. To avoid having to fit the MM to predict each individual measurement channel of the brain-activity data, we compare the measured and predicted data at the level of summary statistics. We describe a novel particular implementation of this approach, called probabilistic representational similarity analysis (pRSA) with MMs, which uses representational dissimilarity matrices (RDMs) as the summary statistics. We validate this method by simulations of fMRI measurements (locally averaging voxels) based on a deep convolutional neural network for visual object recognition. Results indicate that the way the measurements sample the activity patterns strongly affects the apparent representational dissimilarities. However, modelling of the measurement process can account for these effects, and different BCMs remain distinguishable even under substantial noise. The pRSA method enables us to perform Bayesian inference on the set of BCMs and to recognize the data-generating model in each case. This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.

scholarly journals The emulation theory of representation: Motor control, imagery, and perception

2004 ◽  
Vol 27 (3) ◽  
pp. 377-396 ◽  
Author(s):  
Rick Grush
Keyword(s):  
Motor Control ◽  
Sensory Feedback ◽  
Sensory Input ◽  
Neural Circuits ◽  
Sensory Information ◽  
The Body ◽  
Feedback Delay ◽  
Run Off ◽  
Effects Of Feedback ◽  
The Brain

The emulation theory of representation is developed and explored as a framework that can revealingly synthesize a wide variety of representational functions of the brain. The framework is based on constructs from control theory (forward models) and signal processing (Kalman filters). The idea is that in addition to simply engaging with the body and environment, the brain constructs neural circuits that act as models of the body and environment. During overt sensorimotor engagement, these models are driven by efference copies in parallel with the body and environment, in order to provide expectations of the sensory feedback, and to enhance and process sensory information. These models can also be run off-line in order to produce imagery, estimate outcomes of different actions, and evaluate and develop motor plans. The framework is initially developed within the context of motor control, where it has been shown that inner models running in parallel with the body can reduce the effects of feedback delay problems. The same mechanisms can account for motor imagery as the off-line driving of the emulator via efference copies. The framework is extended to account for visual imagery as the off-line driving of an emulator of the motor-visual loop. I also show how such systems can provide for amodal spatial imagery. Perception, including visual perception, results from such models being used to form expectations of, and to interpret, sensory input. I close by briefly outlining other cognitive functions that might also be synthesized within this framework, including reasoning, theory of mind phenomena, and language.

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