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@@ -158,11 +158,18 @@ An additional benefit is the highly compressed data format. The original or vide
Figures
<figure><img src="figs/figure1.png" width="500px"><figcaption>
Figure 1: Transcranial calcium imaging video data is separated into its underlying signal and artifact components, and can be rebuilt from only signal components for artifact filtration. A) Recording schematic and fluorescence image of transcranial calcium imaging preparation, cropped to cortical regions of interest. B) Sample video montage of raw video frames after dF/F calculation. C) ICA video decomposition workflow. A demeaned dF/F movie is decomposed into a series of statistically independent components that are either neural, artifact, or noise associated (not displayed). Each component has an associated time course from the ICA mixing matrix. Neural components can be rebuilt into a filtered movie (rICA). Alternatively, artifact components can be rebuilt into an artifact movie. Circular panels show higher resolution spatial structure in example in the rightmost components.
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<figure><img src="figs/figure2.png" width="500px"><figcaption>
Figure 2: ICA decomposition quality is sensitive to recording duration, spatial and temporal resolution. A) Distributions for lag-1 autocorrelation (black) and temporal variance (purple) are displayed for components 1-1200. A dotted line representing the cutoff determined from the distribution in the right panel. In the right panel, a horizontal histogram on the lag-1 autocorrelation with a two-peaked kernel density estimator (KDE) fit reveals a two peaked-histogram, summarized by a barbell line. Group data for each peak, as well as the central cutoff value is summarized by the boxplots on the right (n=16 videos; from 8 different animals). B) 2-peaked KDE fits of horizontal histogram distributions under various spatial downsampling conditions, with barbell summary lines on the right. After spatial resolution decreases beyond 41 µm pixel width (px), this two peak structure collapses, and an x denotes the primary histogram peak. C) 2-peaked KDE fits of horizontal histogram distributions under various temporal downsampling conditions, with barbell summary lines on the right. D) Component stabilization for different length video subsets of six 20- minute video samples. (n=6 videos from 3 different animals) Individual thin lines show polynomial fit to signal or artifact components under each time condition. Thick lines denote the curve fit of the mean number of components in each category across these six experiments. The group distribution of components at 20 minutes is summarized by the boxplot on the right (n=16 videos; from 8 different animals).
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Figure 3: Class identity cannot be established by any individual extracted feature. A) Examples of independent components of neural (n) signal, vascular (v) artifacts, and other (o) artifacts. Components are defined by both the spatial representation (eigenvector) and its temporal fluctuations. Circular windows magnify key portions of the eigenvector. Eigenvector values represented by colormap from blue to red. Temporal representation is in relative intensity (black time course under the eigenvector), only 1 minute of the full 20 minutes are shown. B) A comparison of the number of neural signal (GCaMP: dark blue; controls: light blue) and the artifact components (vascular: red; other : orange) with each animal shown (GCaMP components: N=12 animals, n=3851; mGFP components: N=3, n = 484; aGFP components: N=3, n = 442; WT components: N=3, n=229). C) Examples of binarization of the eigenvector. Histogram shows the full distribution of eigenvector values. The dynamic threshold method to generate binarized masks was used to identify the high eigenvector signal pixels (yellow) against the gaussian background (blue). Windowed spatial representation shows binarization on the key portions of the eigenvector. D) Examples of neural and artifact wavelet analysis shown in the to signal power-noise ratio (PNR) plots. 95% red-noise cutoff was used to create signal to noise ratio (black dashed lines). E) Histograms of example spatial metrics derived from GCaMP eigenvector values, F) morphometrics from the shape of the binarized primary region, G) temporal metrics derived from relative temporal intensities, H) frequency metrics derived from the PNR.
@@ -180,7 +187,12 @@ Figure 6: Time series extracted from domain maps outperform time series generate
Figure 7: Domain maps are created from ICA components and are unique to each recording, but highly similar among individual animals. Using data-guided methods to assign domains to cortical regions. A) Hierarchical clustering based on Pearson’s correlation produces a set of ∼ 13 regions across the cortical surface. B) Domains colored by various calculated spatial and temporal metrics to aid region assignment. Region area is calculated as a percent of the total cortical surface. Region extend ranges from 0 to 1 and calculates the relative area of a domain to its bounding box. Temporal standard deviation is calculated from the extracted time series, and frequency range size is calculated from wavelet significance. C) The Allen Brain atlas map31 is additionally used for anatomical reference. D) The final manually assigned region, with associated labels. E) Domain area and eccentricity by region. Population analysis of distribution of spatial characteristics in individual domains within defined regions across multiple recordings (n=16 videos; from 8 different animals). F) Example overlay of one domain map on another from the same animal. Individual domain or region overlap is calculated using the Jaccard index (intersect / union). Population analysis of the Jaccard index for domain (G) and region (H) overlap comparisons. Maps are generated from a different recording on the same animal, a littermate, a non-littermate, or a randomly generated voronoi map. Significance is calculated using a two-way ANOVA, followed by post-hoc t-test analysis with Holm-Sidak correction. Retrosplenial: R; Higher order visual: V+; Auditory: A; Somataosensory Seondary: Ss; Somataosensory Core: Sc; Somataosensory Barrel: Sb; Somatosensory other: S; Motor medial: Mm; Motor lateral: Ml; Olfactory: O
<figure><img src="figs/gui.png" width="500px"><figcaption>
Figure S1: A Tkinter-based graphical user interface (GUI) for browsing independent component analysis results. A) 15 independent components, order 60-74 by variance. Components displayed in grey are selected as artifact either manually or using a machine learning classifier. A click on the display for any given component manually toggles its classification as either signal or artifact associated. Components colored in the cool/warm colormap are signal associated. Components colored in the black/white colormap are artifact associated. Buttons on the bottom panel control GUI movement through the dataset. The text panel at the bottom displays where the index for the signal/noise cutoff. B) The component viewer displays additional temporal metrics about any given component. The top controls allow movement through the dataset by manual scrolling with (+/-) buttons, up/down keys, or through typing a desired component in the text box. PC timecourse displays the mixing matrix timecourse extracted by ICA for the given components. The Wavelet power spectrum is displayed in the bottom right, and an integrated wavelet or fourier representation is available on the bottom left. 0.95 significance as estimated by the AR(1) red-noise null hypothesis is displayed as a dot-dash line.C) The domain map correlation page shows the pearson cross correlation value between a selected seed domain and every other domain detected on the cortical surface. The seed domain can be changed through the arrow keys, the (+/-) buttons, or by clicking on a different domain on the displayed domain map. D) The Component region assignment page allows manual region assignment for each domain. After the region is selected from the menu on the right, each domain clicked on the domain map is assigned to that region.
</figcaption></figure>
Figure S2 (Video): ICA filtration removes artifacts for superior neural signal unmixing. Original video (left) is decomposed into artifact components and neural signal. The filtered artifact movie (center) can be rebuilt to visualize artifacts that were isolated and removed during the filtration process. The rebuilt neural signal (right) depicts just the filtered neural signal. 0.5Hz filtered mean is re-added to both filtered artifacts and neural signal (27). Video is a real-time 1-minute excerpt. Values displayed are in dF/F.