* 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?

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## Abstract
* [ ] reread old abstracts and compare lines
* [ ] 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?)
* [ ] replicative (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

## Introduction
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 

4. 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

* [ ] 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.

* [ ] rw start of second para.
* [ ] rw start of third para.
* [ ] rw start of fourth para.

* [ ] merge ICA parts of third and fifth para.
* [ ] rw merge calcium imaging parts of third para. with that of first and second para.