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* Q: Should the title be the same as the methods-paper version published on biorxiv?
- Q: Is there conclusive message of wide interest that could be pointed to?
* Refresh, simplify abstract
* Q: Should data density/sampling over relevant scales be emphasized?
* Q: Should the statistics of our time series be commented on?
* Q: Would the isofl data help?
* Q: Should the title be the same as the `methods-paper` version published on bioRxiv?
- i.e. If there is/was a singular conclusive message of wide interest that could be pointed to, then perhaps that could be the title, but this one is still likely good, especially with the Results and figure data being the same.
* Q: Would the isofl data help with this manuscript? Or maybe the idea of integrating any new material is a too much now
* Q: Should a measure of information rate per recording be reported?
- i.e. the data flow amounts in and out of optimized vs non-optimized imaging sessions
---
## Abstract
* [ ] contrast 'supervised' with 'data-driven' | unsupervised
* [ ] def 'limited references' better
- the statistical model baseline
- statistical power of multivariate *sufficiently dense* sampling within a space-time window (scale | frame of reference | local viewport | field of view)
* [ ] replicative (repl.) information about 'segments independent functional units' and 'produce segmentations of the cortical surface' possibly (unless surface segmentations are cortical areas containing the units?)
* [x] repl. sentences, information at end of abstr
* [ ] Expand, rewrite (rw) to focus 'unique to each individual's functional patterning' better
- perhaps from first sentence
- the dense sampling in space and time **from single individuals** is key. The gaussian baseline estimate from long enough recordings. Compared to the group averaged studies and less-than-optimal baseline assumptions that are typically utilized in most studies and applications using either unsupervised or supervised ML implementations
* [ ] 1. Expand on how this is optimal information extraction
- Single plane multivariate sensor
- widefield, pixelsize, reproduction ratio
* [x] repl. sentences, information at end of abstr
* [ ] contrast 'supervised' with ('data-driven' | unsupervised)
* Q: Should data density/sampling over relevant scales be emphasized?
* Q: Should the statistics of our time series be commented on more?
* [ ] def 'limited references' better
- the statistical model baseline
- statistical power of multivariate *sufficiently dense* sampling within a space-time window (scale | frame of reference | local viewport | field of view)
* [ ] Expand on how this is optimal information extraction
- Single plane sensor array pointed at a single living subject
- widefield, pixelsize, reproduction ratio
* [ ] Expand to focus 'unique to each individual's functional patterning'?
- perhaps from first sentence
- the dense sampling in space and time **from single individuals** is key. The gaussian baseline estimate from analyzing movies of sufficient duration. Compared to the group averaged studies and less-than-optimal baseline assumptions that are typically utilized in most studies and applications using either unsupervised or supervised ML implementations
* [ ] Clarify 'compare control data recorded in glial cell reporter and non-fluorescent mouse lines...'
* [ ] Possibly replicative information about 'segments independent functional units' and 'produce segmentations of the cortical surface'? (unless 'surface segmentations' are the cortical areas containing the unitsin which case we have two different uses of 'segmentation'?)
## Introduction
* [x] Specify what is underdeveloped
* [ ] expand on what has been done, utility of work till now, setting upfor the caveats later
* [ ] rm last line of first para.
* [ ] def 'primary sensory areas'
* [ ] def 'completely lack sub-regional divisions'
* [ ] def 'areas with high degree of interconnectedness, with overlaping functionality such as motor cortex'
* [ ] def 'lead to loss in dynamic range between signals...'
* [ ] def 'recorded dataset'
* [ ] def 'parcellation'
* [ ] def 'quality and source present within the data'
* [ ] def 'respects functional boundaries of the cortex'
* [ ] rw 'is sensitive to age, genotype...'
* [ ] def 'global mean timecourse'
* [ ] def 'functional regions of the cortex'
* [ ] def 'control wide-field imaging data corroborates'
* [ ] def 'The decomposition'
* [ ] def 'resolution-dependent effect'
* [ ] def 'find a quantified increase in ICA signal separation'
* [ ] def 'functional regions of the cortex'
* [ ] merge ICA parts of third and fifth para.
- parts of the fifth para are almost replicative with the third
* [ ] rw merge calcium imaging parts of third para. with that of first and second para.
* [ ] def mesoscale observation better?
- Should it be more rigorously defined?
- time-space scale; pixel, temporal sampling, supracellular etc
---
* [ ] rm last line of first para.
* [ ] rw start of second para.
* [ ] rw start of third para.
* [ ] rw start of fourth para.
* [ ] merge ICA parts of third and fifth para.
- parts of the fifth para are almost replicative with the third
* [ ] rw merge calcium imaging parts of third para. with that of first and second para.
* [x] Specify what is underdeveloped
* [ ] Add blurb about a combination of technologies protein reporter, imaging sensors, computational power? Maybe not.
* [ ] expand on what has been done, utility of work till now, setting up for the caveats later
* [ ] 1. First usage of term 'data-driven method' is not till near end of fourth paragraph, but should be clearly made associated with any introductions of ICA earlier or unsupervised ML learning techniques in general so that better contrast is made with the supervised ML classifier methods, as we should carefully do in the abstr as well rw
@@ -49,10 +73,6 @@
- many other investigations do inter-subject grouping
- message source independence
* [ ] 5. def mesoscale observation
- Can it be more rigorously defined? If not, should a rough def be tied to something on sensor parameters
- CMOS array, pixel sampling, size, supraneuronal
* [ ] 6. def What is baseline
- the controls are non/less-time variant tissue fluoresence vs high dynamic range neuronal calcium signals
@@ -65,6 +85,9 @@
* [ ] 10. Then leads to: Where the calcium flux comes from, multi spots, small comp in opp hemisphere from the larger singular src blobs, axon traj or max prob of tissue src origination
## Results
* [x] Check when Fig. S6 is referred to in text

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main.md
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@@ -53,7 +53,7 @@ Neural components represent a distinct area of cortical tissue, which we refer t
**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.
</figcaption></figure>
Artifact components can take many forms, including those from blood vessels, movement, and optical distortions on the imaging surface. The left two artifact examples (Fig 1C) likely represent hemodynamics from the superior sagittal sinus vein with the bottom artifact likely representing blood flow through the middle cerebral artery26. A very high resolution map of the vessel patterns can be rebuilt from these components, with branching structures as small as 12 µm in diameter (shown in Fig 1C). Noise components lack a spatial domain, and have little to no temporal structure. Signal and artifact components can be sorted manually in graphical user interface (Fig. S1) or with a machine learning classifier.
Artifact components can take many forms, including those from blood vessels, movement, and optical distortions on the imaging surface. The left two artifact examples (Fig 1C) likely represent hemodynamics from the superior sagittal sinus vein with the bottom artifact likely representing blood flow through the middle cerebral artery[^Xiong2017]. A very high resolution map of the vessel patterns can be rebuilt from these components, with branching structures as small as 12 µm in diameter (shown in Fig 1C). Noise components lack a spatial domain, and have little to no temporal structure. Signal and artifact components can be sorted manually in graphical user interface (Fig. S1) or with a machine learning classifier.
@@ -71,7 +71,7 @@ Video data can be reconstructed using any combination of these components. In pa
<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.
**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[^Wei2017]. Video is a real-time 1-minute excerpt. Values displayed are in dF/F.
</figcaption></figure>
@@ -150,7 +150,7 @@ To investigate how well these metrics captured features of each component, we ex
Thresholded GCaMP neural components have high densities in the olfactory bulbs and posterolateral portions of the cortex, including visual, auditory, and somatosensory systems. There is less dense localization of centroids along the anteromedial portions of the cortex, including motor and retrosplenial cortices. Further, in both the GCaMP and control mice, we see the majority of artifact components localize along anatomical brain vasculature. The major venous systems including the rostral rhinal vein - the superior sagittal sinus, and the transverse sinus - all show high densities of artifact centroid locations 26. The cerebral arteries are less consistent in localizing the primary domain of their respective components. We see that many of the other artifacts align with the sagittal and lambda cranial sutures 27 .
Thresholded GCaMP neural components have high densities in the olfactory bulbs and posterolateral portions of the cortex, including visual, auditory, and somatosensory systems. There is less dense localization of centroids along the anteromedial portions of the cortex, including motor and retrosplenial cortices. Further, in both the GCaMP and control mice, we see the majority of artifact components localize along anatomical brain vasculature. The major venous systems including the rostral rhinal vein - the superior sagittal sinus, and the transverse sinus - all show high densities of artifact centroid locations[^Xiong2017]. The cerebral arteries are less consistent in localizing the primary domain of their respective components. We see that many of the other artifacts align with the sagittal and lambda cranial sutures[^Wei2017].
<figure><img src="figs/methods-figure4.png" width="400px"><figcaption>
@@ -235,7 +235,7 @@ In addition to their applications for filtering, the components also are a rich
At full resolution, there are approximately 1.5 million pixels along the surface of the cortex– an impractical number of sources for most network analyses, which work best on 10-300 time series28. As such, data-driven domain maps are an optimal method for extracting time courses from the cortical surface. Time series were extracted by averaging the filtered movie under each domain. This results in a series of 230±14 time series per video recording, representing a ∼ 6,500-fold reduction in size (Fig. 6A; bottom).
At full resolution, there are approximately 1.5 million pixels along the surface of the cortex– an impractical number of sources for most network analyses, which work best on 10-300 time series[^Richiardi2013]. As such, data-driven domain maps are an optimal method for extracting time courses from the cortical surface. Time series were extracted by averaging the filtered movie under each domain. This results in a series of 230±14 time series per video recording, representing a ∼ 6,500-fold reduction in size (Fig. 6A; bottom).
To test how well the full filtered video was represented in these time series, we rebuilt ‘mosaic movies’, where each domain is represented by its mean extracted signal at any given time point (Fig. 6B, Fig. S11 video). By comparing the borders of the large higher order visual activation, one can see visually that the data appears more distorted in the voronoi and grid. To numerically compare whether this method of time course extraction was superior to alternate methods, we also compared mosaic movies rebuilt with either grid or voronoi maps.
@@ -281,15 +281,15 @@ We additionally quantified whether detected regions were similar across map comp
## Discussion
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.
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[^Saxena2020]. 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[^Saxena2020]. 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[^Ma:2016][^Valley2020].
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[^Lu2021][^West2021]. This approach can either be used alone, or in conjunction with techniques to correct calcium dynamics from tissue hemodynamics[^Ma:2016][^Valley2020].
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.
The data rebuild of identified neural components with mean filtration is a statistically valid process for isolating neural signals. We hypothesize that these sampling conditions coupled with a strong neuronal GCaMP signal-to-noise ratio optimizes ICA’s signal de-mixing ability to functionally isolate discrete patches of cerebral cortex from other physiological signals. In control recordings lacking a calcium sensor to report neuronal dynamics, high quality isolation of signal components was not attained given equivalent video sampling conditions. The dynamics of vascular and neural tissues are energetically and thus physiologically linked and the interplay between the hemodynamic responses and neural signals is known 32,33. Even so, we found that neural GCaMP components comprise discrete units across the cortex. In tissue expressing a contrast agent, such as GFP, the optical hemodynamics are enhanced and result in widespread regional effects among the cerebral lobes from control animals. Wavelet analysis on the global mean and individual neural components show the dominant signals extracted from GCaMP animals as being in a faster frequency range than cortical hemodynamics (>1 Hz). Our results indicate that neural GCaMP signals heavily outweigh the neocortical hemodynamic signals in decomposed independent components of densely sampled wide-field calcium imaging videos.
The data rebuild of identified neural components with mean filtration is a statistically valid process for isolating neural signals. We hypothesize that these sampling conditions coupled with a strong neuronal GCaMP signal-to-noise ratio optimizes ICA’s signal de-mixing ability to functionally isolate discrete patches of cerebral cortex from other physiological signals. In control recordings lacking a calcium sensor to report neuronal dynamics, high quality isolation of signal components was not attained given equivalent video sampling conditions. The dynamics of vascular and neural tissues are energetically and thus physiologically linked and the interplay between the hemodynamic responses and neural signals is known[^Ma:2016][^Esposito2005]. Even so, we found that neural GCaMP components comprise discrete units across the cortex. In tissue expressing a contrast agent, such as GFP, the optical hemodynamics are enhanced and result in widespread regional effects among the cerebral lobes from control animals. Wavelet analysis on the global mean and individual neural components show the dominant signals extracted from GCaMP animals as being in a faster frequency range than cortical hemodynamics (>1 Hz). Our results indicate that neural GCaMP signals heavily outweigh the neocortical hemodynamic signals in decomposed independent components of densely sampled wide-field calcium imaging videos.
Maximal segmentation of the cortex was achieved after 20 minutes of spontaneous recordings, resulting in specific domain maps generated from individual animals. We describe a method for using these ICA-based components to perform data-driven mapping of the captured cortical dynamics, resulting in a superior isolation of the various signal sources on the cortical surface. Sufficient numbers of activations resulted in a fully segmented cortex with higher density of domains in primary sensory regions. Detected units vary in shape and size across the cortical surface, and have features that resemble known cortical morphology. These maps help elucidate changes in functional structure across the cortical surface across different experimental groups known to change cortical functional or spatial structure 34. Interestingly, these maximal segmented maps may highlight the limitations of this imaging technique theoretically outlined in the field[^Waters2020].
Maximal segmentation of the cortex was achieved after 20 minutes of spontaneous recordings, resulting in specific domain maps generated from individual animals. We describe a method for using these ICA-based components to perform data-driven mapping of the captured cortical dynamics, resulting in a superior isolation of the various signal sources on the cortical surface. Sufficient numbers of activations resulted in a fully segmented cortex with higher density of domains in primary sensory regions. Detected units vary in shape and size across the cortical surface, and have features that resemble known cortical morphology. These maps help elucidate changes in functional structure across the cortical surface across different experimental groups known to change cortical functional or spatial structure[^Perna2021]. Interestingly, these maximal segmented maps may highlight the limitations of this imaging technique theoretically outlined in the field[^Waters2020].
Functional imaging in unanesthetized, behaving animals gives insight into the nature of physiological processes; however, nontrivial challenges arise during such sessions with intermixed sets of time varying signals. The methods presented here address the most common issues in analyzing large wide-field mesoscale datasets, including filtration of vessel artifacts, spatial mapping, and optimized time series analysis. This work demonstrates that signal components having maximal statistical independence captured in sufficiently sampled mono-chromatic calcium flux videos exhibit a combination of spatiotemporal features that allow machine classification of signal type. Implementation of automated machine classifiers for neural signals is practical given densely captured arrays of spatially and temporally variant data gathered from individual subjects .With these tools, neuroscientists can easily collect and analyze high quality neural dynamics across the cortical surface, allowing the investigation of complex networks at unprecedented scale.
@@ -315,7 +315,7 @@ An additional benefit is the highly compressed data format. The original or vide
All animal studies were conducted in accordance with the UCSC Office of Animal Research Oversight and Institutional Animal Care and Use Committee protocols. P21-22 Snap25 GCaMP6s transgenic mice (JAX: 025111), Cx3cr1 GFP (JAX: 005582), and Aldh1 GFP (MGI: 3843271) were maintained on a C57/Bl6 background in UCSCs mouse facilities. To identify Snap25 GCaMP expressing mice, a single common forward primer (5’-CCC AGT TGA GAT TGG AAA GTG-3’) was used in conjunction with either transgene specific reverse primer (5’-ACT TCG CAC AGG ATC CAA GA-3’; 230 band size) or control reverse primer (5’-CTG GTT TTG TTG GAA TCA GC-3’; 498 band size). The expression of this transgene resulted in pan-neuronal expression of GCaMP6s throughout the nervous system. To identify GFP expressing mice a forward (5’-CCT ACG GCG TGC AGT GCT TCA GC-3’) and reverse (5’-CGG CGA GCT GCA CGC TGC GTC CTC-3’; 400 band size) PCR amplification was used to identify which animals had the GFP transgene. At the end of each recording session, the animal was either euthanized or perfused and the brain dissected.
7 animals used in this study were to experiment and control mice to study perinatal penicillin exposure effects on cerebral networks34. These methods work independent of experimental conditions and the perinatal penicillin had little effect on domain parcellation.
7 animals used in this study were to experiment and control mice to study perinatal penicillin exposure effects on cerebral networks[^Perna2021]. These methods work independent of experimental conditions and the perinatal penicillin had little effect on domain parcellation.
### Surgical procedure
@@ -333,7 +333,7 @@ Spatial resolution analyses were performed on a single 10 minute recording at 10
### ICA decomposition and saving
ICA was performed using FastICA12, implemented through python’s sklearn decomposition36. The ICA decomposition was applied to the spatially flattened (xy,t) 2-D representation of the video data under the cortical ROI mask. The mean time series is pre-subtracted from the array before SVD decomposition or ICA decomposition, since ICA cannot separate sources with a mean signal effect. The filtered, unfiltered mean, ICA components, mixing matrix, and associated metadata are all saved. Data is stored and saved in this flattened format for storage optimization. Components are locally spatially reconstructed for visualization in the GUI.
ICA was performed using FastICA[^Hyvarinen:2000], implemented through python’s sklearn decomposition[^Pedregosa2011]. The ICA decomposition was applied to the spatially flattened (xy,t) 2-D representation of the video data under the cortical ROI mask. The mean time series is pre-subtracted from the array before SVD decomposition or ICA decomposition, since ICA cannot separate sources with a mean signal effect. The filtered, unfiltered mean, ICA components, mixing matrix, and associated metadata are all saved. Data is stored and saved in this flattened format for storage optimization. Components are locally spatially reconstructed for visualization in the GUI.
Requesting the full number of components resulted in extremely lengthy processing times. To reduce the processing time, the data was preprocessed through Singular Value Decomposition (SVD) whitening, and noise components were cropped. To ensure that no signal was lost, and there were ample dimensions left for ICA separation, the inflection point between SVD signal and noise floor was identified, and SVD components were reduced to include components equal to 5 times the SVD signal to noise cutoff value. This multiple cutoff can be adjusted while ICA projecting.
@@ -355,7 +355,7 @@ ICA decompositions of videos at full spatial resolution and duration (20 min) we
### Metric generation and classification of Neural Independent Components
An ensemble random forest classifier from the scikit-learn packages 37,38 was used to train and classify between human scored signal and artifact components39, based on features calculated from each component. Wavelet Mean filtration Wavelet decomposition on the time series signals were performed with a ω = 4 morlet wavelet family, code adapted from C. Torrence and G. Compo40 available at URL: http://paos.colorado.edu/research/wavelets/ Significance was determined using the 95th percentile of a red-noise model fit to the time series autocorrelation. Frequency distributions are all displayed as the ratio of the global wavelet spectrum, relative to the noise cutoff. For wavelet filtering, the original signal was rebuilt excluding all frequency signals in a certain range.
An ensemble random forest classifier from the scikit-learn packages[^Virtanen2020][^Abraham2014] was used to train and classify between human scored signal and artifact components[^Geron2017], based on features calculated from each component. Wavelet Mean filtration Wavelet decomposition on the time series signals were performed with a ω = 4 morlet wavelet family, code adapted from C. Torrence and G. Compo[^Torrence1998] available at <http://paos.colorado.edu/research/wavelets/>. Significance was determined using the 95th percentile of a red-noise model fit to the time series autocorrelation. Frequency distributions are all displayed as the ratio of the global wavelet spectrum, relative to the noise cutoff. For wavelet filtering, the original signal was rebuilt excluding all frequency signals in a certain range.
### Map creation and comparisons
@@ -392,9 +392,11 @@ To quantify the amount of signal present in the original movie that was not incl
Statistical significance was calculated using OLS models from statsmodel.formula.api with Holm-Sidak multiple testing correction (p ≤ 0.5: *; p ≤ 0.01: **; p ≤ 0.001: *** ). Model significance is determined by the F-statistic, and significance of two-group analyses (p>|t|) are calculated with t-tests.
**Acknowledgements** The authors acknowledge C. Santo Thomas for maintaining the lab mouse lines, and University of California Santa Cruz's Hummingbird Computational Cluster for support and node maintenance. This work was supported by Startup funds from University of California, Santa Cruz, Division of Physical and Biological Sciences, grants from the National Institutes of Health, USA (NIH T32 GM 133391) to S.C.W. and B.R.M, and by a Hellman Fellows Fund Award to J.B.A. Funding for D.A. was provided by the UCSC Maximizing Access to Research Careers (MARC) program (T32-GM007910) and the UCSC Initiative for Maximizing Student Development (IMSD) (R25-GM058903).
---
**Contributions** ICA filtering, exploratory GUI, map creation and time series extraction and analysis code, was written by S.C.W. All recordings, metric extractions, mean frequency analysis, feature extraction and analysis, machine learning pipeline were performed by B.R.M. Optimizing and determination of hyperparameters was done by D.A. J.B.A. oversaw the project and provided feedback to experimental design, results, and paper preparation. The manuscript was prepared by B.R.M. and S.C.W, with input from all authors.
**Acknowledgements** The authors acknowledge C. Santo Thomas for maintaining the lab mouse lines, and University of California Santa Cruz's Hummingbird Computational Cluster for support and node maintenance. This work was supported by Startup funds from University of California, Santa Cruz, Division of Physical and Biological Sciences, grants from the National Institutes of Health, USA (NIH T32 GM 133391) to S.C.W. and B.R.M, and by a Hellman Fellows Fund Award to J.B.A. Funding for D.A. was provided by the UCSC Maximizing Access to Research Careers (MARC) program (T32-GM007910) and the UCSC Initiative for Maximizing Student Development (IMSD) (R25-GM058903).
**Contributions** ICA filtering, exploratory GUI, map creation and time series extraction and analysis code, was written by S.C.W. All recordings, metric extractions, mean frequency analysis, feature extraction and analysis, machine learning pipeline were performed by B.R.M. Optimizing and determination of hyperparameters was done by D.A. J.B.A. oversaw the project and provided feedback to experimental design, results, and paper preparation. The manuscript was prepared by B.R.M. and S.C.W, with input from all authors.
**Competing Interests** The authors declare that they have no competing financial interests.
@@ -412,66 +414,74 @@ Statistical significance was calculated using OLS models from statsmodel.formula
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