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Volga Neuroscience School 2016 Astroglial control of rhythm genesis in the brain Acknowledgements
This work was supported by the Russian Science Foundation (project 15-14-30000).
Identification of Novel Mutations Causing Malformations in Cortical Development by ENU Induced Mutagenesis in the Mouse
E.V. Borisova *, A.A. Babaev, M.V. Turovskaya, E.A. Turovsky, V.S. Tarabykin
Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia. * Presenting e-mail: [email protected]
Abstract. The cerebral cortex, is the seat of our higher cognitive capacity that distinguish humans from other species. The development of the cerebral cortex is a complex and highly orchestrated process whose disruption can result in a wide range of developmental disorders that are recognized as malformations of cortical development. Malformations of the cerebral cortex can frequently cause of epilepsy, developmental delay, neurological deficits, and mental retardation in humans. Intellectual disability and epilepsy caused by neurodevelopmental disorders has a high prevalence of about 2% in human population. Consequently identification of novel mouse mutants with malformations of cortical development and identification and characterization of the causal genes will improve our understanding of the genetic regulation of cortical development and pathogenesis of malfunctioning of the brain.
Aims
The main goal of this project is to identify novel genes that control proper establishment of the cortical structure and functions. For this study we generate mouse mutants by N-ethyl-N-nitrosurea (ENU) directed mutagenesis with disrupted structure and function of the cerebral cortex. We will identify mutants with epileptiform activity and then identify respective genes with positional cloning. Also we would like to characterize the behavior of these mutants and underlying molecular mechanisms.
Methods
To screen for recessive mutations we use back cross three generation scheme. Male C3H mice (12 weeks old) are treated with three intraperitoneal injections in a dose of 80, 90, 100, 120 and 150 mg/kg of ENU at weekly intervals. Following injections ENU induces a variable period (10-15 weeks) of sterility during which mutagenized spermatogonial stem cells repopulate the testis. The surviving males after that period were used for mating with the C3H females to produce G1 offspring.
On next step we will take males from G1 and mated them with C57BL6 females fron Satb2-LacZ line, their upperlayer neurons of brain cortex express LacZ as a reporter (Dobreva G. et al., 2006). This makes it easy to visualize changes cy-toarchitecture cortex and connections between cells. To detect the presence of LacZ transgene in line Satb2-LacZ mice and genotyping PCR protocol was developed.
Results
The optimal concentration of ENU was determined (100 mg/kg). Also we assume that a fertile period after injections of ENU longer than 12 weeks.
The protocol of genotyping Satb2-LacZ mice was developed. As a samples for PCR we use tail cuts of mice from this line, then lysing performed, we use the phenol-chloroform method for extraction of DNA and after that PCR. Besides brain drugs was optimized staining protocol mice Satb2-LacZ-reporters. Beta-galactosidase (p-gal), encoded by the gene LacZ, hydrolyses beta-galactosides, resulting in the appearance of a blue color. Females, genotyped and carrying the reporter Satb2-LacZ, were further crossed with G1 males.
Conclusions
Thus males after ENU injections were obtained and females with help of the developed genotyping protocol were selected for mating to produce G1 population.
Acknowledgements
Research carried out with the financial support of the grant of the Russian Scientific Foundation (project №15-14-10021)
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Volga Neuroscience School 2016 Astroglial control of rhythm genesis in the brain References
1. G. Dobreva, M Chahrour, M Dautzenberg L. ChiriveDa, B. Kanzler, I. Fariñas, G. Karsenty andR Grosschedl, Cell,2006, 125(5), 971-986.
Human-Machine Interface Built on sEMG Toolkit with Artificial Neural Network Feature Classifier
I.A. Kastalskiy1 *, V.A. Makarov1,2 and S.A. Lobov1
1 Lobachevsky State University of Nizhny Novgorod, Russia;
2 Universidad Complutense de Madrid, Spain. * Presenting e-mail: [email protected]
EMG (electromyographic) signal is a superposition of action potentials generated by motor units. Signal recording typically implement via the surface electrodes placed on the skin [1,2]. It is known that sEMG (surface EMG) toolkit is widely used to interpret bioelectric patterns for controlling a variety of devices [3-8]. The aim of this study was to investigate the possibility of using a wearable sEMG bracelet to control electronic devices, including interaction with the personal computer. In order to execute the set of commands needed to control the position of the cursor on the computer screen, we propose an artificial neural network (ANN) driven by the signals recorded on arm. The ANN passed through a supervised learning is able to measure the degree of arm muscles effort during movement and classify gestures. It has been shown that the human-computer interface allows controlling the cursor remotely by hand movements and simulating mouse clicks by clenched fist. The average classification accuracy of six gestures (right, left, up, down, left (single) click, right (double) click) varies around 97%.
We used the bracelet MYO (Thalmic Labs) with eight equispaced sEMG sensors acquiring raw myographic signals which were being sent through a bluetooth interface to a PC. The software allows for recognition of hand gestures and estimating muscle efforts that control the cursor on the screen in a way similar that one can achieve with ordinary computer mouse. We used root mean square (RMS) value calculating to evaluate the EMG signal obtained by each electrode. The RMS data, as a composite feature of the current hand gesture, are fed into an ANN. The network neurons apply weighted sum over inputs and use sigmoidal activation function to generate the output. The learning, i.e., adjustment of the neuron weights, is achieved by the back-propagation algorithm [9].
As a result, the cursor movement direction is defined by gestures, while its speed is controlled by the degree of muscle contraction. This significantly improved the user experience. Experimental data shows that all users were able to move the cursor and simulate left and right mouse clicks.
Acknowledgements
This work was supported by the Russian Science Foundation under project 15-12-10018. References
1. Bishop MD, Pathare N. Considerations for the use of surface electromyography. Phys Ther Korea. 2004;11(4):61-69.
2. Pullman SL, Goodin DS, Marquinez AI, Tabbal S, Rubin M. Clinical utility of surface EMG Report of the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology. Neurology. 2000;55(2):171-177.
3. Chowdhury A, Ramadas R, Karmakar S. Muscle Computer Interface: A Review. In: Chakrabarti A, Prakash RV, eds. ICoRD'13, Lecture Notes in Mechanical Engineering. Springer India 2013; 2013:411-421.
4. Hahne JM, Biessmann F, Jiang N, et al. Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control. IEEE Trans Neural Syst Rehabil Eng. 2014;22(2):269-279.
5. Lorrain T, Jiang N, Farina D. Influence of the training set on the accuracy of surface EMG classification in dynamic contractions for the control of multifunction prostheses. J Neuroeng Rehabil. 2011;8:25.
6. Mironov VI, Lobov SA, Kastalskiy IA, Kazantsev VB. Myoelectric Control System of Lower Limb Exoskeleton for Re-training Motion Deficiencies. Lect Notes Comput Sci. 2015;9492:428-435.
7. Naik GR, Kumar DK, Palaniswami M. Multi run ICA and surface EMG based signal processing system for recognizing hand gestures. In: 8th IEEE International Conference on Computer and Information Technology, 2008. IEEE; 2008:700-705.
8. Peerdeman B, Boere D, Witteveen HJB, et al. Myoelectric forearm prostheses: State of the art from a user-centered perspective. J Rehabil Res Dev. 2011;4s(6):719-738.
9. Rumelhart DE, Hinton GE, Williams RJ. Learning Internal Representations by Error Propagation. In: Parallel Distributed Processing. San Diego: La Jolla Institute for Cognitive Science, California University; 1985:318-362.
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