methodsPaper/comments.md

* 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.
  - our work shows non-random overlap in the mesoscale domain mosaic between recordings, suggesting cortical organization itinerant, persistent functional units/motifs 
* Q: Would the isofl data help with this manuscript? Or maybe the idea of integrating any new material is 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

<!-- figure 2D: What is the value of the mean neural component curve when it levels off? Maybe around 1000 s or 16.6 min? -->

<!-- * Q: What is the dynamic range of our cortical calcium signal before and after filtration? (e.g. raw data vs rICA data) -->
<!-- * Q: How many bits of the cameras ADC are needed to encode our calcium signal variance? What about for calcium signal variance+hemodynamic variance+vascular artifact variance all together on the same monochromatic channel? -->

* possible things to highlight: 
    - domains or components per recording, spatial characteristics of the domains, diam
    * jaccard overlap of the regions or domains between sequential recordings or different animals

---

## Abstract

* [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

* [ ] 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.
* [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

* [ ] 2. because of lack of spatial density sampling
    - def and expand this more
    - can add same is true for many or most other applications of ICA or other eigendecomp routines in neuro- (and proabably most fields?)
    - e.g. [^Mukamel:2009] ICA used with 2P laser scanning calcium imaging time series at microscale (cellular) level. Much lower data ingests 

* [ ] 3. IC model requires Gaussian baseline and independent message source 
    - must have one gaussian component
    - many other investigations do inter-subject grouping 
    - message source independence

* [ ] 6. def What is baseline
    - the controls are non/less-time variant tissue fluoresence vs high dynamic range neuronal calcium signals 
    - expl, cite resting state activity, fmri study timeframes? (which also do not usually have a quantified baseline timeframe)

* [ ] 7. local vs global signals
    - n components, size, px counts, common modes

* [ ] 8. Q: What is the median *significant* pixel count for a vascular artifact components vs a neural components?

* [ ] 9. Distributions of component shape, vacular vs hyperellipsoid neural blobs, small domains, possible types of cellular localization of calcium src

* [ ] 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

* [ ] highlight the age more? This work is on a cohort of of mice having a postnatal age of 21 days. 


## Results

### ICA separates signal sources from high resolution data 
### High resolution spontaneous activity improves noise separation and increasing data length results in a stable number of signal components
### Spatiotemporal metrics can be derived from each component to assess the classification of each signal source
### GCaMP mice have strong distinct globular domains that cover the entire cortical surface
### Spatial metrics best separate neural components from artifacts 
### Machine learning performs as well as human classification 
### Global mean needs a high-pass filter to account for removed artifacts before re-addition
### Domain maps optimize time course extraction from underlying data
### Animal specific domain maps can be regionalized based on reference maps and domain features


* [x] Check when Fig. S6 is referred to in text
* [ ] Check the um/px value for the lateral spatial resolution
* [ ] Check colormap dots overlay in figureS7
* [ ] Check figure7 labels
* [ ] Check writing of figure7 legend
* [ ] Check Allen Brain atlas map31 in figure7 legend
* [ ] rw "to test the meaning of these maps" 
* [ ] Highlight the substantial Jaccard index for region overlap (and domain overlap) between recordings and  

* [ ] Check phrase 'detected regions' as in 'We additionally quantified whether detected regions...'

* [ ] Fix Figure S6 "Nueral"
* [ ] rw "considered collecting spatial samples higher than our current resolution"


## Methods

### Mice 
### Surgical procedure 
### Recording calcium dynamics 
### ICA decomposition and saving 
### Dynamic Thresholding 
### ICA and Data processing 
### Metric generation and classification of Neural Independent Components 
### Map creation and comparisons 
### Compression and filtering residuals 
### Domain residuals and domain signal analyses 
### Statistical significance 

* [ ] rw 'components that are unsorted and often flipped'
* [ ] rw 'mean time series is pre-subtracted from the array before SVD'SVD used before defined in next paragraph. The mean signal effect is removed with the pre-whitening/sphereing step of SVD 

* [ ] rw, clarify Map creation domain size as in: 'This map was then further processed to get rid of domains smaller than 1/10th the mean domain. Any domain smaller than this size'
* [ ] rw, clarify 'Olfactory bulbs were included in map generation, but domains were highly variable, and were excluded'
* [ ] check sentence 'same number of domains or regions as the original map, n, and shares the same cortical mask as the original map. To create this map, n points were distributed randomly across the cortical mask. To turn these points into regions, the voronoi diagram was created using the scipy spatial package35'
* [ ] check sentence 'When comparing maps generated from different animals, the optimal alignment was calculated by shifting the second map up to 100 pixels in any direction. The optimal direction was determined by maximizing the Jaccard overlap.'