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Data processing software

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The Inscopix Data Processing Software is a comprehensive solution for the analysis and visualization of neural data collected using Inscopix's nVista and nVoke imaging systems. The software provides tools for preprocessing, segmentation, and tracking of neural signals, as well as the ability to perform various analytical and statistical operations on the data.

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Lab products found in correlation

Matlab, by MathWorks (4 mentions) Nvista, by Inscopix (2 mentions) Data acquisition software, by Inscopix (2 mentions) Grin lens, by Inscopix (2 mentions) Nvista 3, by Inscopix (1 mentions) Matlab 2018a, by MathWorks (1 mentions)

22 protocols using data processing software

1

Miniature Fluorescence Microscopy Protocol

Cited 3 times
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All miniature fluorescent microscope videos were recorded with a fluorescence power of 0.5–0.8 mW/mm2 at a frame rate of 20 Hz at 1280 × 800. Following acquisition, raw videos were spatially downsampled by a binning factor of 4 (16x spatial downsample) and temporally downsampled by a binning factor of 2 (down to 10 frames per second) using Inscopix Data Processing Software (Inscopix). Furthermore, the videos were motion-corrected with reference to a single reference frame to match the XY positions of each frame throughout the video using Inscopix Data Processing Software in order to correct for motion artifacts. The motion-corrected 10 Hz video of raw Ca2+ activity was then saved as a .TIFF and used for cell identification. Ca2+ signals were extracted using modified constrained non-negative matrix factorization scripts in MATLAB (Pnevmatikakis et al., 2016 (link); Zhou et al., 2018b (link)), which allows for denoising, deconvolving, and demixing of microendoscopic imaging data in order to estimate temporally constrained instances of calcium activity for each neuron. Cross-session neurons were matched using CellReg through comparison of contours and centroid locations (Sheintuch et al., 2017 (link)).
Li A., Liu Y., Zhang Q., Friesner I., Jee H.J., Chen Z.S, & Wang J. (2021). Disrupted population coding in the prefrontal cortex underlies pain aversion. Cell reports, 37(6), 109978.
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2

Calcium Imaging Data Processing Protocol

Cited 2 times
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After motion correction, each image frame was re-expressed in units of relative changes in fluorescence, ΔF(t)/F0 = (F(t) − F0)/F0, where F0 is the mean image obtained by averaging the entire video. Spatial filters corresponding to individual neurons were identified using Inscopix Data Processing Software (v1.0.0.2273, Inscopix, Palo Alto, CA) based on principal and independent component analyses 54 (link). Cells spatial filters were identified based on the calcium data acquired over the entire session. For each filter, all pixels were then zeroed with values <50% of that filter’s maximum intensity. To obtain time traces of calcium activity, each cell thresholded spatial filter was added to the ΔF(t)/F0 movie. As previously described 55 (link), the extracted spatial filters generally had sizes, morphologies and activity traces that were characteristic of individual neurons. Every cell included in the analyses was validated by visual inspection.
Cell registration across 10 recording sessions using Inscopix Data Processing Software (v1.0.0.2273, Inscopix, Palo Alto, CA). This corrected for potential slight translations, rotations, or focus-dependent magnification changes between sessions and yielded the location of each cell in the reference coordinate system.
Besnard A., Gao Y., Kim M.T., Twarkowski H., Reed A.K., Langberg T., Feng W., Xu X., Saur D., Zweifel L.S., Davison I, & Sahay A. (2019). Dorsolateral septum somatostatin interneurons gate mobility to calibrate context specific behavioral fear responses. Nature neuroscience, 22(3), 436-446.
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3

Miniature Fluorescent Microscopy for In Vivo Calcium Imaging

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All miniature fluorescent microscope videos were recorded at a frame rate of 20 Hz, with a laser power of 0.6–0.8mW/mm2. Using the Inscopix Data Processing Software (Inscopix), raw videos were downsampled spatially by a binning factor of 4 (16 × spatial downsample) and temporally by a binning factor of 2 (down to 10 frames per second). After downsampling, the videos were motion-corrected relative to a single reference frame to match the XY positions of each frame throughout the video using the Inscopix Data Processing Software. The motion-corrected 10 Hz video of raw Ca2+ activity was saved as a.TIFF file and was used to for cell identification. Using modified constrained non-negative matrix factorization scripts (CNMF_E) in MATLAB, Ca2+ signals were extracted to estimate temporally constrained instances of calcium activity for each neuronal region of interest (ROI).
Jee H.J., Zhu E., Sun M., Liu W., Zhang Q, & Wang J. (2023). Anterior cingulate cortex regulates pain catastrophizing-like behaviors in rats. Molecular Brain, 16, 71.
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4

Miniature Fluorescence Microscopy Protocol

Cited 3 times
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All miniature fluorescent microscope videos were recorded with a fluorescence power of 0.5–0.8 mW/mm2 at a frame rate of 20 Hz at 1280 × 800. Following acquisition, raw videos were spatially downsampled by a binning factor of 4 (16x spatial downsample) and temporally downsampled by a binning factor of 2 (down to 10 frames per second) using Inscopix Data Processing Software (Inscopix). Furthermore, the videos were motion-corrected with reference to a single reference frame to match the XY positions of each frame throughout the video using Inscopix Data Processing Software in order to correct for motion artifacts. The motion-corrected 10 Hz video of raw Ca2+ activity was then saved as a .TIFF and used for cell identification. Ca2+ signals were extracted using modified constrained non-negative matrix factorization scripts in MATLAB (Pnevmatikakis et al., 2016 (link); Zhou et al., 2018b (link)), which allows for denoising, deconvolving, and demixing of microendoscopic imaging data in order to estimate temporally constrained instances of calcium activity for each neuron. Cross-session neurons were matched using CellReg through comparison of contours and centroid locations (Sheintuch et al., 2017 (link)).
Li A., Liu Y., Zhang Q., Friesner I., Jee H.J., Chen Z.S, & Wang J. (2021). Disrupted population coding in the prefrontal cortex underlies pain aversion. Cell reports, 37(6), 109978.
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5

Calcium Imaging Data Processing Protocol

Cited 2 times
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After motion correction, each image frame was re-expressed in units of relative changes in fluorescence, ΔF(t)/F0 = (F(t) − F0)/F0, where F0 is the mean image obtained by averaging the entire video. Spatial filters corresponding to individual neurons were identified using Inscopix Data Processing Software (v1.0.0.2273, Inscopix, Palo Alto, CA) based on principal and independent component analyses 54 (link). Cells spatial filters were identified based on the calcium data acquired over the entire session. For each filter, all pixels were then zeroed with values <50% of that filter’s maximum intensity. To obtain time traces of calcium activity, each cell thresholded spatial filter was added to the ΔF(t)/F0 movie. As previously described 55 (link), the extracted spatial filters generally had sizes, morphologies and activity traces that were characteristic of individual neurons. Every cell included in the analyses was validated by visual inspection.
Cell registration across 10 recording sessions using Inscopix Data Processing Software (v1.0.0.2273, Inscopix, Palo Alto, CA). This corrected for potential slight translations, rotations, or focus-dependent magnification changes between sessions and yielded the location of each cell in the reference coordinate system.
Besnard A., Gao Y., Kim M.T., Twarkowski H., Reed A.K., Langberg T., Feng W., Xu X., Saur D., Zweifel L.S., Davison I, & Sahay A. (2019). Dorsolateral septum somatostatin interneurons gate mobility to calibrate context specific behavioral fear responses. Nature neuroscience, 22(3), 436-446.
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6

Calcium Imaging of Neuronal Activity

Cited 2 times
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Imaging frames were spatially downsampled by a factor of two in the
x and y dimensions. Frames collected
over the course of a single day (always including both female and male
intruder trials) were concatenated into a single stack and registered to
each other to correct for motion artefacts, using the Inscopix Data
Processing software. To extract single cells and their
Ca2+-activity traces from the fluorescent imaging frames, we used
the constrained non-negative matrix factorization for micro-endoscopic data
(CNMF-E)42 (link)algorithm. CNMF-E outputs for putative individual neurons were individually
inspected manually, and those that did not appear to correspond to single
neurons were discarded.
Traces were normalized to units of σ with
respect to the baseline fluorescence of the neuron before the first trial of
resident–intruder interactions on a given day of imaging, as
previously published6 (link). In
brief, for a given neuron with extracted calcium trace
F0(t), we computed the mean
(μ) and standard deviation
(σ) of
F0(t) over a
‘baseline’ period of 30 or more seconds during which the mouse
was in its home cage with no intruder present. Normalized calcium activity
was then computed as F(t) =
(F0(t) −
μ)/σ.
Karigo T., Kennedy A., Yang B., Liu M., Tai D., Wahle I.A, & Anderson D.J. (2020). Distinct hypothalamic control of same- and opposite-sex mounting behaviour in mice. Nature, 589(7841), 258-263.
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7

GRIN Lens Implantation for In Vivo Calcium Imaging

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A gradient refractive index (GRIN) lens with prism (diameter, 1 mm, length, 4.3 mm, 1 mm × 1 mm prism attached, 1050-004601, Inscopix, CA, USA) was implanted ~0.1 mm lateral to layer 5a of the prelimbic cortex (1.84 mm anterior and 0.5–0.55 mm lateral to bregma; 2 mm ventral to brain surface; targeting IT neurons located at 1.34–2.31 mm anterior to bregma and 1–2 mm ventral to brain surface). The baseplate for nVista 2.0 microscope (Inscopix, CA, USA) was attached to the skull 4–6 weeks since lens implantation. Calcium signals were acquired through the implanted GRIN lens and nVista microscope at 30 Hz frame rate using LED power of 0.42–0.54 mW/mm2. Spatial downsampling (× 1/4) and motion correction of calcium imaging data were performed using Inscopix Data Processing Software (version 1.3.1). The processed video was exported in TIFF format and analyzed with the CNMF-E algorithm70 to extract single unit signals. Details of lens implantation and image processing are described in a previous report71 (link).
Bae J.W., Jeong H., Yoon Y.J., Bae C.M., Lee H., Paik S.B, & Jung M.W. (2021). Parallel processing of working memory and temporal information by distinct types of cortical projection neurons. Nature Communications, 12, 4352.
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8

Miniaturized Microscope Imaging Protocol

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Images were acquired with a head-mounted miniaturized microscope (nVista, Inscopix) at 4 frames per second over 1 minute (240 ms exposure time, 10% to 20% LED illumination, 1.5 to 2.5x gain). The 1-minute movies were spatially filtered (high-pass filter, cutoff at 40 µm), corrected for motion artifacts (Inscopix Data Processing Software), and saved as stacks of frames in TIFF format. Regions of interest (ROI) were defined as nonoverlapping bright regions that displayed fast activity in any stack. They were applied to every stack (ImageJ), and numerical data were saved as text files for further processing in Matlab (MathWorks).
El Cheikh Hussein L., Fontanaud P., Mollard P, & Bonnefont X. (2022). Nested calcium dynamics support daily cell unity and diversity in the suprachiasmatic nuclei of free-behaving mice. PNAS Nexus, 1(3), pgac112.
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9

In vivo Calcium Imaging of Respiratory Neurons

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All mice were trained with dummy camera and habituated to plethysmography chamber at least twice before imaging. The miniature microscope with integrated 475 nm LED (Inscopix, Palo Alto, CA, USA) was secured to the baseplate. GCaMP6 fluorescence was visualised through the GRIN lens, using nVista 2 HD acquisition software (Inscopix, Palo Alto, CA, USA). Calcium fluorescence was optimised for each experiment so that the histogram range was ~150–600, with average recording parameters set at 10–20 frames/sec with the LED power set to 10–20 mW of light and a digital gain of 1.0–4.0. A TTL pulse was used to synchronize the calcium signalling to the plethysmography trace. All images were processed using Inscopix data processing software (Inscopix, Palo Alto, CA, USA). GCaMP6 movies were ran through: preprocessing algorithm (with temporal downsampling), crop, spatial filter algorithm (0.005–0.5 Hz), motion correction and cell identification through manual regions of interest (ROIs) operation to generate the identified cell sets. Cell sets were imported into Spike2 software for processing.
Bhandare A., van de Wiel J., Roberts R., Braren I., Huckstepp R, & Dale N. (2022). Analyzing the brainstem circuits for respiratory chemosensitivity in freely moving mice. eLife, 11, e70671.
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10

Miniscope Data Preprocessing and Filtering

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Miniscope data were acquired using the Inscopix Data Acquisition Software as 2x down-sampled .isxd files. Preprocessing and motion correction was performed using the Inscopix Data Processing Software. Briefly, raw imaging data was cropped, 2x down-sampled, median filtered and motion corrected. A spatial band-pass filter was then applied to remove out of focus background. The filtered imaging data was temporal down-sampled to 10Hz and exported as a .tiff image stack.
Yang B., Karigo T, & Anderson D.J. (2022). TRANSFORMATIONS OF NEURAL REPRESENTATIONS IN A SOCIAL BEHAVIOR NETWORK. Nature, 608(7924), 741-749.
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