Пгу. мос. ру личный кабинет
На сайте pgu. mos. ru вы получите доступ к многочисленным информационным ресурсам города Москвы. После регистрации у Вас будет доступ к таким услугам как:
- Регистрация брака
- Запись в детский сад
- Оплата штрафов ПДД
- Подача показаний счетчиков воды и электроэнергии
- Запись на прием к врачу
- Электронный дневник школьника
И многим другим.
Портал предоставляет доступ к 285 услугам в системе, также доступ к порталу городских услуг,
порталу Наш город, к порталам Московский паркинг и Автокод.
Регистрация в Пгу. мос
Номер указанного Вами телефоно необходимо будет подтвердить. Для этого вам будет отправлено СМС с кодом поддверждения. Этот номер телефона будет использоваться для связи с Вами при возникновении проблем с доступом на портал pgu. mos. Например для восстановления пароля.
Что делать если СМС с кодом не пришла.
В случае, если код подтверждения с сайта пгу. мос. ру не приходит по указанному номеру,
возможно у вас запрещен прием СМС с коротких номеров. Попробуйте воспользоваться следующим способом:
При возникновении каких-то проблем со входом или регистрацией, Вы можете обратиться на горячую линию +7(495)539-55-55
или задать вопрос в форме обратной связи тут.
Личный кабинет пгу. мос. ру — популярные услуги
- Подать показания счетчика воды
- Подать показания счетчика электроэнергии
- Записать ребенка в детский сад
- Оплата штрафов за нарушение ПДД
- Получить Единый Платежный Документ (ЕПД)
- Оплатить ЕПД(Коммунальные платежи)
- Получить справку о составе семьи
- Электронный дневник школьника МРКО
Телефон горячей линии : +7 (495) 539-55-55 пн-вс, с 8:00 до 21:00
The technology of neuronal data acquisition using high density multi-electrode arrays (HD-MEAs) in tissue and cell cultures has grown dramatically over the past decade (Maccione et al. , 2013, 2014, 2015; Ingebrandt, 2015; Müller et al. , 2015; Dragas et al. , 2017; Viswam et al. , 2017; Steinmetz et al. , 2019; Paulk et al. , 2022). A brief history of MEA technology and advancement of these devices is discussed extensively by Didier et al. (2020). These ever-growing, state-of-the-art electrophysiology techniques (Stevenson and Kording, 2011; Lopez et al. , 2018; Miccoli et al. , 2019) now include HD-MEA devices capable of recording extracellular neuronal signals from cell cultures or brain slices from thousands of electrodes (Ronchi et al. , 2019). Pharmaceutical techniques have also expanded to include the high density multi-electrode arrays in new assay development (Kraushaar and Guenther, 2019). Several pharmaceutical applications and drug-testing protocols require long-duration recordings from 45 to 90 min (Codadu et al. , 2019a), which can result in large data files of 350 to 500 GB.
Researchers also require novel ways to track LFP activity over space and time, as calcium imaging is limited by slow kinetics (Tang et al. , 2015; Helassa et al. , 2016; Vanwalleghem et al. , 2020; Wei et al. , 2020) and current voltage-imaging techniques have several weaknesses, such as high-bleaching properties (Kulkarni and Miller, 2017; Xiao et al. , 2021). Recordings using high-resolution MEA systems offer a new way to explore network communication with a high degree of time and spatial resolution but require tools to tap into their full potential. Our new data pipeline offers an efficient and easy tool to analyze the spatial and time resolution offered by these MEA systems. We demonstrate the utility of this data pipeline with induction of seizure-like activity and generating example LFP raster plots over time and space, along with example traces from subregions of the brain. This bird’s-eye view of LFP activity within our GUI creates a new tool for investigation into novel insights into network dynamics, such as how the neocortex and hippocampus interact with each other. Furthermore, we demonstrate a novel seizure-tracking approach using the high density of electrophysiological channels with potential to be superior to large-scale calcium imaging to track seizure dynamics. We present data using this analysis tool that shows brain slices from Scn1aHet mice with a deficit in sodium channel NaV1. 1, an important channel for interneuron excitability, have more seizure-like events (SLE) than wild-type (WT) littermates in a low Mg2+ model. Furthermore, we show novel data that demonstrate an increased seizure-propagation rate in the Scn1aHet mice, likely due to the well-documented decreased firing rates of parvalbumin-positive interneurons in these mouse models (Martin et al. , 2010; Tai et al. , 2014; Favero et al. , 2018). We provide this new python-based software tool as an open-source, customizable solution for analysis and tracking of LFP activity using the 3Brain MEA recording system, but it can easily be adapted to any MEA recording platform. This GUI will also likely be suitable for analysis of large-scale EEG recordings and provide a useful mapping tool for in vivo LFP activity. Our current GUI has a particular utility for analysis of seizure-like activity but can be used for analysis of many other network LFP signals.
Materials and Methods
Statistics were done in GraphPad Prism 9. 1 (San Diego, CA, United States). Data was first checked for normal distribution using a Shapiro–Wilk normality test. Nonparametric data was analyzed with a Mann–Whitney test, and the parametric data was analyzed with an unpaired Student’s t-test. GraphPad Prism was used to graph scatter-point data. Significance was set at P ≤ 0. 05 for all analyses.
Data Analysis and Figures
The analysis platform and algorithms used were custom written in Python, including NumPy, pandas, SciPy, and visualizations using Plotly’s Dash libraries. The code and sample data files are provided through a GitHub repository1. Figures for the manuscript were created using diagrams, Inkscape 1.
Table 1. Benchmarking numbers for Xenon LFP analysis platform.
Channel Group Function
To generate data on subsets of channels within the GUI, groups of channels can be selected within the GUI for analysis. The channel group functions are useful for comparing two or three different regions of the brain slice and for comparing LFP activity summary measures from select brain regions of interest.
Local Field Potential Measures in Channel Groups
Maximum distance of spread of SLE: The Euclidean distance from the electrode at which the initiation of SLE is observed in the brain slice to the furthest point from the initiation point. The row and column number are used as the x and y coordinates, respectively. The Euclidean distance between the x, y co-ordinates have no unit. It is multiplied by the electrode spacing in micrometers to determine the distance of spread of seizure-like activity in the brain slice.
Duration of SLE: This is calculated for each channel in a selected group. The difference between the end time and the start time of the seizure-like event in the selected time window of the “Channel Raster (Groups)” gives the seizure duration for that channel. The mean and maximum duration are calculated for each group from the duration of seizure-like activity of all channels in that group.
Seizure propagation speed: For the selected time interval in the “Channel Raster (Groups),” the start time and end time of SLE are calculated for all channels in the group. The maximum distance of spread of the SLE is also calculated for that group. The seizure rate is the maximum distance of spread of the SLE divided by the mean difference in the start times of the seizure for each individual seizure.
Figure 2. Snapshot of the analysis GUI features. A view of the analysis GUI which is rendered in an html browser built in Python using Plotly’s Dash. The GUI has several interactive features from individual and group channel selection, low-pass, high-pass, and band-pass filtering, viewing entire trace or a small section of the trace, Fast Fourier Transformation (FFT) of sections from selected traces, customized raster plots, small groups of channels, and generation of group summary measures.
MEA Viewer Functions
Channel Group Functions
Figure 4. Channel groups and raster plot can be generated to visualize LFP activity in different regions of the brain slice. (A) Sensor locations corresponding to three different regions selected for analysis and the region-specific raster plots. Group 1 being the hippocampus, while groups 2 and 3 each being one half of the Neocortex. (B) Summary plots and measures that can be generated within the analysis platform.
Seizure Detection and Analysis Functions
The channel group raster is required to perform the seizure detection and analysis. Each group has a separate tab (Supplementary Video 3) under which individual channels can be selected to view seizure-like activity highlighted by the envelop (Figure 6A). Figure 6B demonstrates the raster plot for three different groups. Using the raster, a region can be selected with a potential SLE, as shown in Figure 6B (non-gray section), to generate summary measures and a visual of the channels that have an SLE within the selected section (Figure 6C). The channel dots highlighted in red are channels in the respective group that have an SLE, the blue dots are channels that did not participate in the SLE, while the gray dots have not been selected. The time interval shown in the summary table in Figure 6C is the selected time interval in the raster plot (Figure 6B non-gray section). The distance, duration, and seizure rate are calculated from the start and end times of seizure envelop in each of the channels in the group for the selected (zoomed in) seizure.
Figure 6. Seizure activity tracking over space and time. (A) Seizures in individual channels in a group are automatically detected. Their respective start and end times can be tracked across channels in that group. (B) Regions of the raster between time intervals can be selected as demonstrated to generate seizure maps of selected brain regions within the interval. (C) Seizure map for the time interval selected and channels in the group, including initiation site of the seizure, maximum distance the seizure spread from the initiation point, duration of the seizure, and the rate of seizure spread across the tissue.
Three metrics are calculated from the seizure envelop for all channels in the group: distance of spread, duration, and seizure propagation speed. The spatiotemporal origin of the seizure within a group is identified as the channel that first had spectral activity above the set threshold. This timestamp and the location of the channel is used to further calculate the distance and rate of the seizure spread. For example, in Figure 6C, it is the maximum distance from the green dot to the furthest red dots. If more than one channel is highlighted green, then they have similar start times, and the maximum distance from each point is calculated to find the overall maximum distance. The blue dots do not have a seizure-like event and are not included in the calculation. The x, y position on a 64X64 grid places the channels at 1 unit dimension from each other. The array spacing in micrometer is multiplied by the distance and seizure rate to get the final measure in micrometer and micrometer/second, respectively.
We next used this seizure tracking function to examine if neocortical seizure-like events from brain slices from Scn1aHet mice, which are heterozygous for NaV1. 1, have an altered phenotype in the low Mg2+ model of acute ictogenesis. The example raster plots demonstrate a likely difference in number of seizure-like events between WT littermates and the Scn1aHet animals (Figure 7A). Further analysis revealed that the Scn1aHet mice do have significantly more seizures than the WT littermates over the course of the 50-min recording (Figure 7B). Furthermore, we found that the start time to the first seizure-like event was significantly sooner in the Scn1aHet animals compared to controls; further demonstrating an increased seizure phenotype in animals with a deficit in NaV1. 1 expression (Figure 7C). Using our novel tracking algorithm for seizures within our GUI, we compared the speed of seizure propagation in brain slices from control mice versus the Scn1aHet mice. Interestingly, this analysis demonstrated a significantly faster rate of seizure propagation in brain slices from the Scn1aHet mice compared to control (Figure 7D). There was no significant difference found in the duration of the seizures between the control and Scn1aHet mice (Figure 7E). This data demonstrates novel phenotypic features of the Scn1aHet mice; a decreased time to the appearance of the first seizure-like event and an increased rate of seizure spread through the tissue, likely due to deficits in feed-forward inhibition provided by the somatostatin and parvalbumin interneurons (Trevelyan et al. , 2007; Cammarota et al. , 2013; Parrish et al. , 2019). These new analysis features provided by the Xenon LFP Analysis Platform provide new and exciting ways to understand phenotypic differences in transgenic animals, understand how pharmacology impacts neuronal network activity over space and time, and is customizable to fit any researcher’s needs.
Figure 7. Scn1aHet mice have an altered seizure pattern in the low Mg2+ model. (A) Example raster plots from a control brain slice and a brain slice from Scn1aHet mice. (B) Scn1aHet mice have significantly more SLE than littermate controls (Mann–Whitney test, p = 0. 04, n = 7–9 slices). (C) From the brain slices that displayed SLE, Scn1aHet demonstrated a significant increase in time to first seizure compared to littermate controls (unpaired t-test, p = 0. 006, n = 5–7 slices). (D) SLE from the Scn1aHet propagate significantly faster than seizures in the littermate controls (Mann–Whitney test, p = 0. 0059, n = 10–14 seizures from 5 to 7 slices). The first two seizure from the slices that had SLE were used in this analysis. (E) Seizure duration was not different between Scn1aHet and littermate controls (Mann–Whitney test, p = 0. 66, n = 10–14 seizures from 5 to 7 slices). The first two seizures from the slices that had SLE were used in this analysis. *P < 0. 05, **P < 0.
With the advent of larger recording systems, allowing for up to six brain slices and over 1,000 channels per slice during a single recording session, tools like this GUI are timely. These new systems will allow for immense screening of transgenic animals to elucidate aberrant network behavior (Mackenzie-Gray Scott et al. , 2022) and large-scale drug screening of biological tissue. Furthermore, with epilepsy and other disorders, there is a need to understand how different brain regions interact with each other when challenged in media that induces increased network activity or when stimulated electrically or optogenetically (Rafiq et al. , 2003; Codadu et al. , 2019b; Cela and Sjostrom, 2020). While we now have the recording platforms to facilitate these research needs, we are still limited by analysis tools. Here we directly address some of these needs in our GUI and set important groundwork for further developments within this platform. We also perceive that this GUI will be useful in other large-scale electrophysiological recording systems where the researcher wants to understand interactions between LFP activity at different recording sites over space and time. For example, it would be particularly interesting to visualize multichannel human EEG recordings within the framework of this GUI, which could provide easy and efficient visualization of channel recruitment during various behavioral states with the current built-in features and custom additions.
RP conceived the work. AM, NC, and RP designed the computational methods and edited and approved the final draft. AM wrote the code and designed the visualizations. RP collected the data. AM and RP analyzed the data and wrote the manuscript. All authors contributed to the article and approved the submitted version.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.