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## Introduction
* [ ] Specify what is underdeveloped
* [x] Specify what is underdeveloped
* [ ] expand on what has been done, utility of work till now, setting upfor the caveats later
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## Results
* [ ] Check when Fig. S6 is referred to in text
* [x] Check when Fig. S6 is referred to in text

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## Introduction
Understanding cerebral dynamics at multiple scales is important for exploring how environmental and genetic influences give rise to altered neural connectivity patterns linked to behavioral phenotypes 8,9.
Optical techniques have long been used to monitor the functional dynamics in sets of neuronal elements ranging from isolated crustacean nerve fibers1 to entire regions of mammalian cerebral cortex 2,3. Imaging calcium flux with calcium sensors 4,5 allows neural activity monitoring across the entire neocortex with high enough spatiotemporal resolution to identify sub-areal networks of the neocortex 6,7. These techniques have the potential to map group function at unprecedented resolution and scale across the neocortical sheet in awake behaving mice; however identifying neural signals from calcium imaging sessions is challenging due to numerous confounding signal sources.
Optical techniques have long been used to monitor the functional dynamics in sets of neuronal elements ranging from isolated crustacean nerve fibers[^Cohen:1968] to entire regions of mammalian cerebral cortex[^Grinvald:1986][^Grinvald:2004]. Imaging calcium flux with calcium sensors[^Tsien:1989][^Chen:2013a] allows neural activity monitoring across the entire neocortex with high enough spatiotemporal resolution to identify sub-areal networks of the neocortex[^Ackman2014c][^Vanni2014]. These techniques have the potential to map group function at unprecedented resolution and scale across the neocortical sheet in awake behaving mice; however identifying neural signals from calcium imaging sessions is challenging due to numerous confounding signal sources.
Wide-field imaging of neuronal calcium flux offers mesoscale observation of cortical neural dynamics and allows for viewing the supracellular group organization between microscale (cell) and macroscale (tissue lobe) investigations; however, it is affected by issues common to optical imaging recordings. Body or facial movements can create large fluctuations in autofluorescence of the brain and blood vessels, which produce significant artifacts in the data. Vascular artifacts are commonly seen due to vasodynamics and the resulting changes in blood flow to meet the energy demands of surrounding tissue. Fluid exchange between vascular and neural tissue causes cortical hemodynamics, resulting in region specific changes of optical properties among cerebral lobes 8. Further, though the skull is fixed to a specific location during the experiment, slight brain movements occur within the cranium, thereby influencing the recordings. Any optical property differences that originate from the experimental preparation may be highlighted in the dataset as signal due to changes in tissue contrast.
<!-- Understanding cerebral dynamics at multiple scales is important for exploring how environmental and genetic influences give rise to altered neural connectivity patterns linked to behavioral phenotypes [^Ma:2016][^Kozberg:2016] -->
Eigendecompositions can be used to identify and filter components of signal,9–11 and present a flexible method of filtering that is not hardware dependent, and can be applied to any video dataset regardless of the recording hardware or parameters. Independent Component Analysis (ICA) 12 has been previously applied to fMRI and EEG data with varying success; for example, identifying both intrinsic connectivity networks rather than individual areas, and artifacts that represent large-scale effects rather than spatially localized effects 13–16. We hypothesize that this is due to the lower density of spatial sampling in fMRI and EEG data. Wide-field calcium imaging provides a unique combination of spatially and temporally resolved dynamics across the cortical surface, with scale ranging from complex activation patterns in high-order circuits, to discrete activations hundreds of micrometers in diameter, to whole cortical lobe activity patterns 6,7. Researchers have recorded wide field calcium dynamics at frame rates ranging from 5-100Hz 6,17,18. In addition, spatial resolution varies between different researchers’ setups, but is typically in the range of 256x256 to 512x512 pixels (0.06 to 0.2 megapixels) for the entire cortical surface, and is often further spatially reduced for processing 6,17,19. Selection of resolution is often dependent on the video observer’s perceived quality of the data or available computational resources, rather than a quantified comparison of signal content.
Wide-field imaging of neuronal calcium flux offers mesoscale observation of cortical neural dynamics and allows for viewing the supracellular group organization between microscale (cell) and macroscale (tissue lobe) investigations; however, it is affected by issues common to optical imaging recordings. Body or facial movements can create large fluctuations in autofluorescence of the brain and blood vessels, which produce significant artifacts in the data. Vascular artifacts are commonly seen due to vasodynamics and the resulting changes in blood flow to meet the energy demands of surrounding tissue. Fluid exchange between vascular and neural tissue causes cortical hemodynamics, resulting in region specific changes of optical properties among cerebral lobes[^Ma:2016]. Further, though the skull is fixed to a specific location during the experiment, slight brain movements occur within the cranium, thereby influencing the recordings. Any optical property differences that originate from the experimental preparation may be highlighted in the dataset as signal due to changes in tissue contrast.
Eigendecompositions can be used to identify and filter components of signal[^Kozberg:2016][^Patel2015][^Pnevmatikakis2016], and present a flexible method of filtering that is not hardware dependent, and can be applied to any video dataset regardless of the recording hardware or parameters. Independent Component Analysis (ICA)[^Hyvarinen:2000] has been previously applied to fMRI and EEG data with varying success; for example, identifying both intrinsic connectivity networks rather than individual areas, and artifacts that represent large-scale effects rather than spatially localized effects 13–16. We hypothesize that this is due to the lower density of spatial sampling in fMRI and EEG data. Wide-field calcium imaging provides a unique combination of spatially and temporally resolved dynamics across the cortical surface, with scale ranging from complex activation patterns in high-order circuits, to discrete activations hundreds of micrometers in diameter, to whole cortical lobe activity patterns[^Ackman2014c][^Vanni2014]. Researchers have recorded wide field calcium dynamics at frame rates ranging from 5-100Hz[^Ackman2014c],17,18. In addition, spatial resolution varies between different researchers’ setups, but is typically in the range of 256x256 to 512x512 pixels (0.06 to 0.2 megapixels) for the entire cortical surface, and is often further spatially reduced for processing[^Ackman2014c],17,19. Selection of resolution is often dependent on the video observer’s perceived quality of the data or available computational resources, rather than a quantified comparison of signal content.
It is common to use sensory stimulation to identify specific regions in the neocortex and align a reference map based on the location of these defined regions 19–21. Even if these maps are reliable for locating primary sensory areas, they often lack specificity for higher order areas, or even completely lack sub-regional divisions. This is especially true in areas with a high degree of interconnectedness and overlapping functionality, such as motor cortex 22. Moreover, there is evidence that the shape and location of higher order regions can vary from subject to subject 23,24. Improper map alignment or misinformed regional boundaries can lead to a loss in dynamic range between signals across a regional border. Thus, to extract the most information from a recorded dataset, the level of parcellation must reflect the quality and sources present within the data. Thus, a flexible data-driven method is necessary and must also respect functional boundaries of the cortex and be sensitive to age, genotype and individual variation.
@@ -65,7 +66,10 @@ Artifact components can take many forms, including those from blood vessels, mov
Video data can be reconstructed using any combination of these components. In particular, a filtered video can be constructed by excluding all artifact components. The artifact movie can also be reconstructed to verify that desired signal was not removed with the artifact filtration (Fig. S2 video).
<figure><img src="figs/methods-figureS2.png" width="400px"><figcaption>
<figure>
<!-- <img src="figs/methods-figureS2.png" width="400px"> -->
<video src="figs/methods-figureS2-1min_filtering_clip.mp4" width="400px" controls></video>
<figcaption>
**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.
</figcaption></figure>
@@ -209,7 +213,10 @@ Removal of artifact components will ensure that neural signals are the dominant
**Figure S8** Mean filtration to minimize global slow oscillations seen in GFP control data. A) 30sec examples of the global mean that was subtracted and stored at the initiation of the pipeline, before the decomposition into eigenvectors for GCaMP, mGFP, aGFP and Bl6. B) Global wavelet spectrum (top) and its corresponding power to noise ratio (PNR; bottom) of GCaMP (N=4), mGFP (N=3), aGFP (N=3), and Bl6 (N=3), red indicates the omitted frequencies from our applied high pass filter. C) High-pass filtration results of the same 30 sec in A.
</figcaption></figure>
<figure><img src="figs/methods-figureS9.png" width="400px"><figcaption>
<figure>
<!-- <img src="figs/methods-figureS9.png" width="400px"> -->
<video src="figs/methods-figureS9-mean_filter_clip.mp4" width="400px" controls></video>
<figcaption>
**Figure S9 (Video)** Readdition of the global mean with high-pass filter better represents neural signal. Rebuilt video from all components (left) or with only neural components (center, right) with mean re-addition from the original global mean (left, center) or the global mean with 0.5 Hz high pass filtration (right). All movies are on the same scale of change in fluorescence over mean fluorescence (rainbow colorbar).
</figcaption></figure>
@@ -243,7 +250,9 @@ The residuals between the mosaic movies and the filtered movies were compared to
Saving these extracted time courses and all associated metadata results in a file size of ∼ 100MB, representing an additional ∼ 60-fold compression compared to saving the full ICA compressed dataset. One potential benefit to accounting for the underlying regions of the brain while extracting time courses is reducing the amount of times that an extracted mean signal is diluted by signal from a neighboring region. Properly restricting time series extraction to statistically independent units should enhance the dynamic range between extracted time series.
<figure><img src="figs/methods-figureS11.png" width="400px"><figcaption>
<figure>
<!-- <img src="figs/methods-figureS11.png" width="400px"> -->
<video src="figs/methods-figureS11-mosaic_comparison_clip.mp4" width="400px" controls></video>
**Figure S11 (Video)** The mosaic movie represents the neural signal captured from time series extracted from domains across the cortical surface. Signal from the filtered video (left) is segmented by the data-driven domain map (left overlay). Average time series from these segmented domains can be visualized as a mosaic movie, where each domain is represented by its averaged time series. The filtered video contains 1.77 megapixels representing the cortical signal, while the mosaic movie contains only 300 unique time series to describe the same signal with a 5900x compression rate. Video is a real-time 1-minute excerpt. Values displayed are in dF/F.
</figcaption></figure>
@@ -274,7 +283,7 @@ We additionally quantified whether detected regions were similar across map comp
Wide-field calcium imaging has grown in popularity in the last decade due to advances in genetically encoded calcium indicators, however, the methods used to isolate neural signal sources are underdeveloped 29. Here we use an ICA-based algorithm that overcomes many of these limitationsEigendecomposition algorithms have been essential to understand signals across neuroscience. Another recent eigendecomposition pipeline has been developed to explore the functional activities across wide-field imaging of the cortex, but is limited by the use of a reference map and was not able to separate artifact signals from neural activations 29. The methods presented here are able to achieve similar expository results with artifact removal, allowing researchers to explore datasets of any age, treatment, genotype, or strain that would be impeded by the use of a reference map.
High resolution imaging of mesoscale cortical calcium dynamics combined with data-driven decomposition using ICA results in an optimized extraction of neural source signals. We have built de novo anour own Independent Component Analysis (ICA) based pipeline to that can not onlynot only achieve (isolate?, identify? sub-regional neural components, but also can to show utilization of components to quantify the impact on data quality based on recording parameters, to improve data quality through removal of artifacts, and to build domain maps based on the limitations of the fluorescent signal sources. We demonstrate that these methods provide precise isolation and filtration of video artifacts due to movement, optical deformations, or blood vessel dynamics while recovering cortical source signals with minimal alteration. Our lab and another have successfully implemented an ICA-based filtration to isolate the neural signal from artifacts 30,31. This approach can either be used alone, or in conjunction with techniques to correct calcium dynamics from tissue hemodynamics 8,18.
High resolution imaging of mesoscale cortical calcium dynamics combined with data-driven decomposition using ICA results in an optimized extraction of neural source signals. We have built de novo anour own Independent Component Analysis (ICA) based pipeline to that can not onlynot only achieve (isolate?, identify? sub-regional neural components, but also can to show utilization of components to quantify the impact on data quality based on recording parameters, to improve data quality through removal of artifacts, and to build domain maps based on the limitations of the fluorescent signal sources. We demonstrate that these methods provide precise isolation and filtration of video artifacts due to movement, optical deformations, or blood vessel dynamics while recovering cortical source signals with minimal alteration. Our lab and another have successfully implemented an ICA-based filtration to isolate the neural signal from artifacts 30,31. This approach can either be used alone, or in conjunction with techniques to correct calcium dynamics from tissue hemodynamics[^Ma:2016],18.
Signal separation from mesoscale calcium dynamics recorded across the cortical surface was the most complete at the highest spatial resolution tested (pixel size of 6.9 µm/px). Our recordings consisted of large sets of densely sampled image frames having at least 12 bits of dynamic range across pixel intensities. Temporal resolution had less of an effect on ICA signal separation; we found that a 10Hz sampling rate was sufficient for spatial segregation. These metrics for signal quality are automatically generated by our algorithm, and can be used to optimize signal collection on any given experimental setup. The number of components identified is highly stable after recording sufficient duration of dynamics, and provides a metric for spatial complexity of neural signals across the neocortex. Compared with the high density optical recordings we used here, other neurophysiological techniques remain limited in the number of available spatial samples; as such, the effect of signal recording resolution on ICA decomposition of neural signal sources had not previously been reported.
@@ -393,18 +402,35 @@ Statistical significance was calculated using OLS models from statsmodel.formula
## References
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2. Grinvald, A., Lieke, E., Frostig, R. D., Gilbert, C. D. & Wiesel, T. N. Functional architecture of cortex revealed by optical imaging of intrinsic signals. Nature 324, 361–4 (1986).
3. Grinvald, A. & Hildesheim, R. VSDI: a new era in functional imaging of cortical dynamics. Nat Rev Neurosci 5, 874–85 (2004).
4. Tsien, R. Y. Fluorescent probes of cell signaling. Annu Rev Neurosci 12, 227–53 (1989).
5. Chen, T.-W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).
6. Ackman, J. B., Zeng, H. & Crair, M. C. Structured dynamics of neural activity across developing neocortex. bioRxiv (2014) doi:10.1101/012237.
7. Vanni, M. P. & Murphy, T. H. Mesoscale Transcranial Spontaneous Activity Mapping in GCaMP3 Transgenic Mice Reveals Extensive Reciprocal Connections between Areas of Somatomotor Cortex. J. Neurosci. 34, 15931–15946 (2014).
8. Ma, Y. et al. Wide-field optical mapping of neural activity and brain haemodynamics: considerations and novel approaches. Philos. Trans. R. Soc. B 371, 20150360 (2016).
9. Kozberg, M. G., Ma, Y., Shaik, M. A., Kim, S. H. & Hillman, E. M. C. Rapid Postnatal Expansion of Neural Networks Occurs in an Environment of Altered Neurovascular and Neurometabolic Coupling. J. Neurosci. 36, 6704–6717 (2016).
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[^Cohen:1968]: Cohen LB, Keynes RD, Hille B. Light scattering and birefringence changes during nerve activity. Nature. (1968). 218:438–41. pmid:5649693
[^Grinvald:1986]: Grinvald A, Lieke E, Frostig RD, Gilbert CD, Wiesel TN. Functional architecture of cortex revealed by optical imaging of intrinsic signals. Nature. (1986). 324:361–4. pmid:3785405
[^Grinvald:2004]: Grinvald A, Hildesheim R. VSDI: A new era in functional imaging of cortical dynamics. Nat Rev Neurosci. (2004). 5:874–85. pmid:15496865
[^Tsien:1989]: Tsien RY. Fluorescent probes of cell signaling. Annu Rev Neurosci. (1989). 12:227–53. pmid:2648950
[^Chen:2013a]: Chen T-W, Wardill TJ, Sun Y, Pulver SR, Renninger SL, Baohan A, et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature. (2013). 499:295–300. doi:10.1038/nature12354 pmid:23868258
[^Ackman2014c]: Ackman JB, Zeng H, Crair MC. Structured dynamics of neural activity across developing neocortex. bioRxiv. (2014). doi:10.1101/012237
[^Vanni2014]: Vanni MP, Murphy TH. Mesoscale transcranial spontaneous activity mapping in GCaMP3 transgenic mice reveals extensive reciprocal connections between areas of somatomotor cortex. J Neurosci. (2014). 34:15931–46. doi:10.1523/JNEUROSCI.1818-14.2014 pmid:25429135
[^Ma:2016]: Ma Y, Shaik MA, Kim SH, Kozberg MG, Thibodeaux DN, Zhao HT, et al. Wide-field optical mapping of neural activity and brain haemodynamics: Considerations and novel approaches. Philos Trans R Soc Lond B Biol Sci. (2016). 371. doi:10.1098/rstb.2015.0360 pmid:27574312
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<!-- 9,10,11 -->
[^Kozberg:2016]: Kozberg MG, Ma Y, Shaik MA, Kim SH, Hillman EMC. Rapid postnatal expansion of neural networks occurs in an environment of altered neurovascular and neurometabolic coupling. J Neurosci. (2016). 36:6704–17. doi:10.1523/JNEUROSCI.2363-15.2016 pmid:27335402
[^Patel2015]: Patel TP, Man K, Firestein BL, Meaney DF. Automated quantification of neuronal networks and single-cell calcium dynamics using calcium imaging. J Neurosci Methods. (2015). 243:26–38. doi:10.1016/j.jneumeth.2015.01.020
[^Pnevmatikakis2016]: Pnevmatikakis EA, Soudry D, Gao Y, Machado TA, Merel J, Pfau D, et al. Simultaneous denoising, deconvolution, and demixing of calcium imaging data. Neuron. (2016). 89:285–99. doi:10.1016/j.neuron.2015.11.037
<!-- 12 -->
[^Hyvarinen:2000]: Hyvärinen A, Oja E. Independent component analysis: Algorithms and applications. Neural Netw. (2000). 13:411–30. pmid:10946390
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