3.8 KiB
- 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?
Abstract
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contrast 'supervised' with 'data-driven' | unsupervised
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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)
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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?)
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repl. sentences, information at end of abstr
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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
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1. Expand on how this is optimal information extraction
- Single plane multivariate sensor
- widefield, pixelsize, reproduction ratio
Introduction
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Specify what is underdeveloped
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expand on what has been done, utility of work till now, setting upfor the caveats later
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rm last line of first para.
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rw start of second para.
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rw start of third para.
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rw start of fourth para.
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merge ICA parts of third and fifth para.
- parts of the fifth para are almost replicative with the third
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rw merge calcium imaging parts of third para. with that of first and second para.
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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
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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
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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
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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
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6. def What is baseline
- the controls are non/less-time variant tissue fluoresence vs high dynamic range neuronal calcium signals
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7. local vs global signals
- n components, size, px counts, common modes
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8. Q: What is the median significant pixel count for a vascular artifact components vs a neural components?
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9. Distributions of component shape, vacular vs hyperellipsoid neural blobs, small domains, possible types of cellular localization of calcium src
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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
- Check when Fig. S6 is referred to in text