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.
* 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

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## 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

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* [ ] 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 

* [ ] 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




## Results

* [x] Check when Fig. S6 is referred to in text