Journal: Frontiers in Neuroinformatics

Article Title: NeuroMatic: An Integrated Open-Source Software Toolkit for Acquisition, Analysis and Simulation of Electrophysiological Data

doi: 10.3389/fninf.2018.00014

Figure Lengend Snippet: Workflow pattern within NeuroMatic enabling recursive analysis. Numbers denote workflow steps: (1) Work within NeuroMatic begins by either acquiring data via the Clamp tab, computing simulations via the Pulse or Model tab, or opening a pre-existing data file from a disk. The acquired/simulated/imported waves (i.e., time-series arrays) reside inside a NeuroMatic data folder (e.g., “nmFolder0”) and are named with the same wave prefix (e.g., “Record”), followed by a channel letter (e.g., A or B), followed by a sequence number (e.g., 0, 1, 2, etc.). (2) The user selects which data folder, waves, channels and sets to analyze (see Figure ). The user can also select temporary transforms to apply to the waves, e.g., filter or baseline (see Figure ). (3) The selected data is then analyzed using one or more of NeuroMatic's analysis tabs. (4) Outputs of the analysis often consist of waves that are displayed in graphs and tables. These output waves reside in the selected NeuroMatic data folder (green arrow) and can therefore be selected for further analysis via the wave-prefix select (2) (e.g., “Avg_”). Outputs also include notes printed to Igor's Command Window, including NeuroMatic function commands and analysis output, thereby providing documentation of the user's workflow and a means of creating user-defined functions. Notes are also attached to individual data waves if the waves are permanently transformed or created by an analysis function.

Article Snippet: NeuroMatic runs within Igor Pro (WaveMetrics, Portland, Oregon; https://www.wavemetrics.com ; RRID:SCR_000325 ), here referred to as Igor, a platform-independent software environment for scientists and engineers that has an extensive library of built-in functions, a command line for executing built-in and user-defined functions, a history window for reviewing the user's command workflow and the ability to generate publication-quality graphics.

Techniques: Sequencing, Transformation Assay

Journal: Frontiers in Neuroinformatics

Article Title: NeuroMatic: An Integrated Open-Source Software Toolkit for Acquisition, Analysis and Simulation of Electrophysiological Data

doi: 10.3389/fninf.2018.00014

Figure Lengend Snippet: NeuroMatic's GUI. After installation, NeuroMatic's GUI automatically appears once Igor starts. Red numbers (added to this screen capture) denote GUI controls of interest: (1) The folder select is used to specify which NeuroMatic data folder to analyze, or to execute a folder function such as Open, Save or New. NeuroMatic data folders contain acquired/simulated/imported waves, plus all variables, waves and subfolders necessary to run NeuroMatic. (2) The wave-prefix select is used to specify which waves in the selected folder are to be processed. For example, “Record” selects waves named “RecordA0,” “RecordA1,” etc. Selected waves are sorted alphanumerically via their channel letter (e.g., “A”) and sequence number (0, 1, 2, etc.). (3) The wave-number select controls (set-variable, slider, < > buttons) are used to specify the wave sequence number to display in NeuroMatic's channel graphs (Figure ). (4) Data waves can be categorized into sets (e.g., Set1 or Set2) via checkboxes or the sets Edit Panel. Total wave counts are displayed to the right of each set. Data waves can also be categorized into groups (e.g., Group0, Group1, etc.) via the groups (G) control. (5) The channel select is used to specify which data channel(s) to analyze (e.g., A, B, or All) for a multi-channel time series. (6) The wave/set select is used to specify which collection of waves to analyze (e.g., All or Set1 or Group0). The total wave count is displayed to the right. (7) NeuroMatic tabs can be switched in and out (+ tab) without the need to devote more screen space to new panels that clutter the desktop. Tabs perform analysis on the currently selected waves, or perform data acquisition (Clamp) or simulations (Pulse and Model), and are controlled by NeuroMatic's tab module. Here, the Stats tab is activated, showing results of a rise-time analysis of those waves in Set1. Users can also create their own tab using the Demo tab as a template (Supplementary Figure ). (8) NeuroMatic configurations can be changed and saved for future use. Clicking the Configs checkbox activates NeuroMatic's configuration listbox for the currently selected tab. General NeuroMatic configurations are listed on the “NM” tab. More NeuroMatic functionality can be found under Igor's main menu (Menu/NeuroMatic), including keyboard shortcuts for repetitive tasks like changing the wave-number select (e.g., Ctrl+0 for next wave) or a wave's set categorization (e.g., Ctrl+1 for Set1 toggle). To install NeuroMatic simply place its package folder inside Igor's Procedures folder before starting Igor.

Article Snippet: NeuroMatic runs within Igor Pro (WaveMetrics, Portland, Oregon; https://www.wavemetrics.com ; RRID:SCR_000325 ), here referred to as Igor, a platform-independent software environment for scientists and engineers that has an extensive library of built-in functions, a command line for executing built-in and user-defined functions, a history window for reviewing the user's command workflow and the ability to generate publication-quality graphics.

Techniques: Sequencing, Control

Journal: Frontiers in Neuroinformatics

Article Title: NeuroMatic: An Integrated Open-Source Software Toolkit for Acquisition, Analysis and Simulation of Electrophysiological Data

doi: 10.3389/fninf.2018.00014

Figure Lengend Snippet: NeuroMatic's channel graph. Screen capture of the channel A graph (Ch A) for the Stats-tab rise-time analysis displayed in Figure , showing a voltage-clamp recording (RecordA2, black line) acquired from the cell body of a granule cell (GC) in a cerebellar slice, where extracellular stimulation of a single mossy-fiber (MF) synaptic input at 50 ms (see stimulus artifact, which has been clipped for display purposes) evoked an EPSC (inward current). Red circle and horizontal line denote the minimum of the EPSC computed within the dashed vertical red lines ( xbgn, xend ). An average baseline value (horizontal green line) has been computed before the EPSC within the dashed vertical green lines. Blue dashes denote where the EPSC reaches 10 and 90% of the peak amplitude from baseline. The time between these points is the 10–90% rise time (0.20 ms). Data was filtered by a binomial smooth function (2 smoothing operations). Note, each channel graph has options to apply temporary transforms to the data (top) such as filter, baseline, normalize and differentiate. Various display options are listed within the top-left popup control, such as overlay, grid lines and axis scaling. More options are listed by clicking inside an Igor marquee (dashed square), created by clicking and dragging the mouse diagonally to frame a region of interest. Data was acquired via the Clamp tab from a cerebellar slice from a P24 mouse, where access resistance (R a ) = 14 MΩ, membrane capacitance (C mem ) = 2.5 pF and the holding potential (V hold ) = −76.3 mV.

Article Snippet: NeuroMatic runs within Igor Pro (WaveMetrics, Portland, Oregon; https://www.wavemetrics.com ; RRID:SCR_000325 ), here referred to as Igor, a platform-independent software environment for scientists and engineers that has an extensive library of built-in functions, a command line for executing built-in and user-defined functions, a history window for reviewing the user's command workflow and the ability to generate publication-quality graphics.

Techniques: Control, Membrane

Journal: Frontiers in Neuroinformatics

Article Title: NeuroMatic: An Integrated Open-Source Software Toolkit for Acquisition, Analysis and Simulation of Electrophysiological Data

doi: 10.3389/fninf.2018.00014

Figure Lengend Snippet: NeuroMatic's hierarchical folder structure. Screen capture of Igor's Data Browser (Menu/Data/Data Browser), a useful tool for navigating through NeuroMatic's data folder hierarchy. Here, all folders and variables were created by NeuroMatic inside Igor's local-memory root directory. The top two folders beginning with “nm” were created by the Clamp tab after executing a stimulus protocol called “Search.” Red arrow denotes the folder selected for processing (Figure , control 1) which was clicked open for browsing. Inside the folder are waves with prefix “Record” and channel letter “A,” and a string variable CurrentPrefix containing the value of the wave-prefix select (Figure , control 2) whose current value is “Record,” in which case the prefix subfolder “NMPrefix_Record” is activated. This prefix subfolder has been opened, showing string variables containing wave name lists for channel A, values for the channel and wave/set selects (Figure , controls 5 and 6), wave name lists for Set1, Set2 and SetX, and a subfolder (ChanA) containing channel graph information like axis scales and wave transformations. Other subfolders were created by the Clamp tab (Notes and Search), Stats tab (Stats_Record_Set1_A) and by entering “Avg_” for the wave-prefix select (NMPrefix_Avg_). Here, wave RecordA0 has been selected (blue), revealing its “Info” at the bottom, including Igor information and NeuroMatic acquisition and analysis notes; this information can be printed to the Command Window or an Igor notebook via the Main tab (Display/Print Notes; Supplementary Figure ). Note, some waves, string variables and wave notes have been removed for display purposes, and numeric variables are not shown. NeuroMatic's private data for nearly all its functionality is located in the root:Packages:NeuroMatic folder.

Article Snippet: NeuroMatic runs within Igor Pro (WaveMetrics, Portland, Oregon; https://www.wavemetrics.com ; RRID:SCR_000325 ), here referred to as Igor, a platform-independent software environment for scientists and engineers that has an extensive library of built-in functions, a command line for executing built-in and user-defined functions, a history window for reviewing the user's command workflow and the ability to generate publication-quality graphics.

Techniques: Control

Journal: Frontiers in Neuroinformatics

Article Title: NeuroMatic: An Integrated Open-Source Software Toolkit for Acquisition, Analysis and Simulation of Electrophysiological Data

doi: 10.3389/fninf.2018.00014

Figure Lengend Snippet: NeuroMatic commands and notes printed to Igor's Command Window. (A) Screen capture of Igor's Command Window (Menu/Windows/Command Window), a useful tool that creates a historical log of the researcher's workflow, from acquisition to analysis and simulation. Both Igor and NeuroMatic print function commands and analysis/transform notes to this window. Function commands are preceded by a small circular bullet. Analysis/transform notes sometimes appear after the function command. NeuroMatic commands can also be printed to an Igor notebook (GUI/Configs/NM/CmdHistory/Notebook). (B) Screen capture of an Igor procedure window (Menu/Windows/New/Procedure…) named “MyFunctions” containing a function called “MyNewFunction.” NeuroMatic function commands were copied from Igor's Command Window (A) and pasted into this function. A simple macro named “CallMyNewFunction” was created to call MyNewFunction. Macros are automatically listed in Igor's Macros menu where they can be readily selected and executed. In this way users can create functions of repetitive tasks that need to be performed on multiple data files. Creating such user-defined functions improves research efficiency, documentation and reproducibility.

Article Snippet: NeuroMatic runs within Igor Pro (WaveMetrics, Portland, Oregon; https://www.wavemetrics.com ; RRID:SCR_000325 ), here referred to as Igor, a platform-independent software environment for scientists and engineers that has an extensive library of built-in functions, a command line for executing built-in and user-defined functions, a history window for reviewing the user's command workflow and the ability to generate publication-quality graphics.

Techniques:

Journal: Frontiers in Neuroinformatics

Article Title: NeuroMatic: An Integrated Open-Source Software Toolkit for Acquisition, Analysis and Simulation of Electrophysiological Data

doi: 10.3389/fninf.2018.00014

Figure Lengend Snippet: NeuroMatic acquisition, detection and analysis of spontaneous EPSCs. Spontaneous EPSCs with isolated AMPA receptor (AMPAR) component were recorded from the cell body of a GC. The stimulus protocol called Minis was configured via the Clamp tab to perform continuous whole-cell voltage-clamp recordings with 50 kHz sampling. (A1) Screen capture of the channel A graph during negative event threshold detection via the Event tab with criteria shown in Figure . Blue line denotes the sliding baseline window (5 ms). Green line denotes the negative threshold search criteria (2.5 ms ahead of the center of the baseline window, 6 pA below the baseline average). Red dash denotes the threshold crossing point of the detected EPSC. Red square and circle denote results of the EPSC onset and peak detection, respectively. Data (black line) was low-pass filtered at 1 kHz during event detection. Inset , first 39 of 199 detected EPSCs (thresholds and peaks) of the 170-s recording; x- and y-scale bars, 5 s and 20 pA. (A2) Detected EPSCs ( n = 199) were copied to new waves (Event/Events 2 Waves) and selected for analysis via the wave-prefix select (“Event0_”). Black lines are the first 39 EPSCs overlaid (low-pass filtered at 5 kHz for display purposes). EPSCs were aligned to their onset ( t 0 ), as described in (B2) . Red line is the average of all 199 aligned unfiltered EPSCs. Event detection was repeated using this average for template matching (average normalized to have a baseline of 0 and peak of 1, truncated to 3 ms length with 0.2 ms baseline; detection criteria level = −3) resulting in 194 detected EPSCs whose average is plotted in blue. The difference between the two averages was near 0 (gray line). To run template matching, place NeuroMatic's MatchTemplate XOP (see http://www.neuromatic.thinkrandom.com/NMInstall.html ) inside Igor's Extensions folder, activate “template matching” on the Event tab, select the template wave from the current data folder and click the Event tab's Match button. Scale bars are for data here and (B2) . (B1) Screen capture of the channel A graph during the curve fit (red line) of the same EPSC in (A1) to the multi-exponential function NMSynExp3 (Equation 1; second decay component was set to zero) via the Fit tab. EPSCs were fit between 4 and 6 ms (dashed blue lines), a window that contained the EPSC onset, peak and initial decay. Data was unfiltered during the fits. (B2) The first 39 of all 199 fits to the detected EPSCs. Fits were selected for analysis (wave prefix “Fit_Event0_”) and aligned on their onset (GUI/Main/X-scale/Align/align by a wave of values/FT_Syn3_X0_EV0All_A0). Inset , histogram of the onset ( t 0 ; see Equation 1) of all 199 fits (0.01 ms bins). (C1) Screen capture of the channel A graph during Stats 10–90% rise-time analysis of the curve fit in (B1) (red line). Black circle denotes the minimum peak value and small black lines denote times when the fit reached 10 and 90% of the peak value. Dashed blue lines denote window for minimum peak detection. The EPSC in (B1) was added to the background for display purposes (gray line). (C2) Histogram of 10–90% rise times (top; 0.01 ms bins; 2 EPSCs with slow rise times 0.5–0.7 ms are not shown) and minimum peaks (bottom; 2 pA bins) for the 199 fits computed via the Stats tab (Stats2/Functions/Histogram). The rise-time histogram was fit to a Gaussian function (red line; peak center at 0.11 ms, STDV = 0.03 ms; Menu/Analysis/Curve Fitting…/gauss). GC experimental details: cerebellar slice from a P25 rat, R a = 15.5 MΩ, C mem = 2.6 pF, V hold = −80 mV. The external solution contained 10 mM MNI-glutamate. Data is from DiGregorio et al. .

Article Snippet: NeuroMatic runs within Igor Pro (WaveMetrics, Portland, Oregon; https://www.wavemetrics.com ; RRID:SCR_000325 ), here referred to as Igor, a platform-independent software environment for scientists and engineers that has an extensive library of built-in functions, a command line for executing built-in and user-defined functions, a history window for reviewing the user's command workflow and the ability to generate publication-quality graphics.

Techniques: Isolation, Sampling

Journal: eLife

Article Title: Identification of an inhibitory neuron subtype, the L-stellate cell of the cochlear nucleus

doi: 10.7554/eLife.54350

Figure Lengend Snippet:

Article Snippet: Electrophysiological data were analyzed using pClamp 10.4 software (Molecular Devices), Axograph, or IGOR Pro v6.3 or v8 (WaveMetrics) and NeuroMatic ( ).

Techniques: Software