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1

Neuromodulatory control of long-term synaptic plasticity in CA1 pyramidal neurons

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Jedrzejewska-Szmek J., Slawinski Z., Wojcik D. K.
Acta Neurobiologiae Experimentalis
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2019
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tom 79
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nr Suppl.1
INTRODUCTION: Long-term synaptic plasticity (LTSP) is a complex phenomenon. Experiments utilizing different LTSP-inducing paradigms have demonstrated activation of multiple signaling pathways and uncovered differences in neuromodulatory dependence. For example, dopaminergic (D1R) activation can retroactively convert spike-timing dependent depression to potentiation. On the other hand, inhibition of beta‑adrenergic (βAR) and not D1R activation can block induction of late, protein-synthesis dependent phase of LTSP (L‑LTSP) evoked by rate‑dependent paradigms (RTP). This is confusing because activation of both D1R and βAR increase cAMP activity and activate its targets. AIM(S): Understand the impact of differences in temporal patterns of synaptic and neuronal activity on activation of signaling pathways and signal transduction. METHOD(S): We used two detailed, multi-compartmental, morphologically realistic models of the CA1 neuron: 1) a conductance‑based neuron model, and 2) a stochastic reaction‑diffusion model of calcium‑, βAR‑, and D1R‑activated signaling pathways underlying LTSP. The latter model allows for simulating, monitoring, and controlling molecular concentrations in a dendritic spine and a dendritic segment. RESULTS: Modulation of dendritic potassium ion channels (e.g., SK, Kv1.1) by protein kinase A (PKA) may explain the observed differences in neuromodulatory requirements of STDP and RTP. To predict whether paradigms eliciting spike-timing dependent plasticity will induce L-LTSP, we studied the activity of key molecules implicated in plasticity, such as calcium calmodulin-dependent protein kinase II (CaMKII) and PKA. In the spine, we studied molecular species that are involved in actin cytoskeleton remodelling, and in the dendrite – particularly those that play a role in protein synthesis. CONCLUSIONS: These preliminary results suggest that molecular activity micro-spatial scales can predict the induction of L‑LTSP.
2

Kernel current source density revisited

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Chintaluri C., Kowalska M., Czerwinski M. B., Sredniawa W., Jedrzejewska-Szmek J., Dzik J. M., Wojcik D. K.
Acta Neurobiologiae Experimentalis
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2019
|
tom 79
|
nr Suppl.1
INTRODUCTION: Extracellular recordings reflect transmembrane currents of neural and glial cells and thus have long been the foundation of measurements of neural activity. Recorded potential reflects activity of the underlying neural network and is directly related to the distribution of current sources along the active cells (current source density, CSD). The long‑range of the electric field leads to significant correlations between recordings at distant sites, which complicates the analysis. However, data interpretation can be facilitated by reconstruction of current sources. AIM(S): Facilitate reconstruction of sources of brain activity with open software. METHOD(S): The Kernel Current Source Density method (KCSD) is a general non-parametric framework for CSD estimation based on kernel techniques, which are widely used in machine‑learning. KCSD allows for current source estimation from potentials recorded by arbitrarily distributed electrodes. Overfitting is prevented by constraining complexity of the inferred CSD model. RESULTS: Here, we revisit KCSD to present a new, open-source implementation in the form of a package, which includes new functionality and several additional tools for kCSD analysis and for validation of the results of analysis accompanied by extensive tutorials implemented in Jupyter notebook. Specifically, we have added 1) analysis of spectral properties of the method; 2) error map generation for assessment of reconstruction accuracy; and 3) L‑curve, a method for estimation of optimum reconstruction parameters. The new implementation allows for CSD reconstruction from potentials measured by 1D, 2D, and 3D experimental setups for a) sources distributed in the entire tissue, b) in a slice, or c) in a single cell with known morphology, provided that the potential is generated by that cell. CONCLUSIONS: New Python implementation of kCSD facilitates CSD analysis and allows for estimation of errors. The toolbox and tutorials are available at https:// github.com/Neuroinflab/kCSD‑python.
3

PyEcoHAB: a Python library for analysis of rodent behavioral data recorded with Eco-HAB

76%
Jedrzejewska-Szmek J., Maka J., Leski S., Winiarski M., Wojcik D. K., Knapska E.
Acta Neurobiologiae Experimentalis
|
2019
|
tom 79
|
nr Suppl.1
INTRODUCTION: How can we make mouse studies more reproducible? The obvious answer is the standardization of experimental conditions, minimization of human interference, automation of behavioral tests and data analysis, and introduction of data analysis pipelines to automate the process. Eco‑HAB, a system for automated measurement of social preference and in-cohort sociability in mice, provides a solution for the first two issues. Eco‑HAB closely follows murine ethology, providing a 4‑compartment apparatus with narrow tunnels, and minimizes contact between the experimenter and tested animals. Introduction of pyEcoHAB, a Python library for analysis of EcoHAB murine behavioral data. AIM(S): pyEcoHAB, a Python package, has been developed to automate and facilitate data analysis. METHOD(S): Combining data access and initial interpretation, pyEcoHAB removes the need to do this manually, and allows the researcher to build data analysis pipelines and automation of behavioral tests facilitating data interpretation. pyEcoHAB provides an object‑oriented application programming interface (API) and a data abstraction layer. Auxiliary utilities supporting development of analysis workflows are integrated with pyEcoHAB, including data validation and workflow configuration tools. Moreover, pyEcoHAB provides methods for assessment of mice social behavior, such as approach to social odor, total time spent by each pair of mice together in each compartment (in-cohort sociability), number of times each mouse follows other mice in narrow tunnels (following), and also, the number of times each mouse pushes other mice out of a narrow tunnel. The latter behaviorissimilarto tube dominance tests and is an example of how traditional behavioral tests can be automated. CONCLUSIONS: pyEcoHAB is a computational framework facilitating automatic analysis of behavioral data from EcoHAB system. FINANCIAL SUPPORT: This work was supported by the Polish National Science Centre grant 2017/27/B/ NZ4/02025.
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