abstr work

comments.md

@@ -1,33 +1,49 @@
+## Abstract
 * 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
+  - option 1: different
+  - option 2: similar
+  - option 3: same
+  - i.e. if there is/was a singular conclusive message of wide interest that could be pointed to, then perhaps that could point to an alternative title direction 
+  - but this title is still likely okay, especially with the results and figure data so far being the same as the biorxiv papers
 
-<!-- figure 2D: What is the value of the mean neural component curve when it levels off? Maybe around 1000 s or 16.6 min? -->
+Things to consider:
+  - domain maps. the non-random structure/overlap, area/lobe specific characteristics
+  - our work shows substantial overlap in the domain mosaic between recordings (at 1x mag with optimized information extraction) suggesting underlying, cortical organization-- structure. structured functional motifs in between the microscale (cellular) and macroscale (tissue level). What is left in between? Cell(s) with connections. Assemblies of cells. Cell groups. Functional units/motifs. Modules. Domains. Functions. Objects. Things. 
+  - domains or components per recording, spatial characteristics of the domains, diam
+  - jaccard overlap of the domain or region borders between sequential recordings or different animals
+
+motif (wn, noun)  
+: a design or figure that consists of recurring shapes or colors, as in architecture or decoration  
+
+* Q: Would the isofl data help with this manuscript? Or maybe the idea of integrating any new material is too much now 
+* Q: Should an information rate per recording be reported?
+  - the data amounts in and out of optimized vs non-optimized imaging sessions
+  - the avg signal power of data in and out of optimized recordings and pipeline
+* Q: figure 2D, What is the value of the mean neural component curve when it levels off? Maybe around 1000 s or 16.6 min? seventeen minutes.
 
 <!-- * 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? -->
+<!-- * 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? When we examined this once before, I think we figured out with the range of useful variance in our recordings for desired and undesired source signals (dim hemodynamics and tissue autofluorescence vs neural signals vs movement artifacts) that it is definitely more than 8bits but less than 14bits. -->
 
-* 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
+<!--
+three main points
+* the general solution to multichannel source signal separation, independent component analysis, can optimally recover neural signal content in recordings of neuronal cortical calcium dynamics  
+  - when captured at a magnification factor of one and at a sampling rate of 1.5×10⁶ pixels per one-hundred millisecond frame for seventeen minutes
+* a set of spatial and temporal metrics can be used to build a random forest classifier which separates neural activity and artifact components automatically at human performance
+* a functional segmentation of the mouse cerebral sheet, providing a map of 115 domains per {x} cm²-mouse-neocortical-hemisphere from which extracted time courses maximally represent the underlying signal in each recording 
+-->
 
----
-
-## Abstract
 
+### Considerations for orig abstr
 * [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
+* Q: Should statistics of our time series be commented on more?  
+  - i.e data density/sampling over relevant scales be emphasized?
     - Single plane sensor array pointed at a single living subject
     - widefield, pixelsize, reproduction ratio
+  - [ ] 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)
+  - discuss optimal information extraction
 * [ ] 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
@@ -35,8 +51,15 @@
 * [ ] 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
 
+
+### Considerations for orig intro
 * [ ] def 'primary sensory areas' 
 * [ ] def 'completely lack sub-regional divisions' 
 * [ ] def 'areas with high degree of interconnectedness, with overlaping functionality such as motor cortex'
@@ -61,8 +84,6 @@
     - 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.
@@ -71,6 +92,8 @@
 * [ ] 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
@@ -101,17 +124,18 @@
 
 ## 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
+* 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
 
 
+### Considerations for orig results
 * [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
@@ -129,18 +153,19 @@
 
 ## 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 
+* 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 
 
+### Considerations for orig methods
 * [ ] 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 
 

main.md

@@ -1,6 +1,7 @@
 # Data-driven segmentation of cortical calcium dynamics 
 
 <!--
+# Data-driven cortical domain maps
 # Data-driven domain maps of the cortical surface
 # Data-driven filtration and segmentation of cortical calcium dynamics 
 # Data-driven segmentation of cortical calcium dynamics maps a functional domain mosaic
@@ -24,7 +25,7 @@ Sydney C. Weiser¹•, Brian R. Mullen¹•, Desiderio Ascencio², & James B. Ac
 
 ## Abstract  
 
-Demixing neural signals from artifact signals in videos of neuronal calcium flux across the cerebral hemispheres could help map core functional features of cortical organization. Here we demonstrate that the general solution to multichannel source signal separation, independent component analysis, can optimally recover neural signal content in recordings of neuronal cortical calcium dynamics captured at a rate of 1.5×10⁶ pixels per one-hundred millisecond frame for seventeen minutes. We show that a set of spatial and temporal metrics can be used to build a random forest classifier which separates neural activity and artifact components automatically at human performance. We show how this data produces a functional segmentation of the neocortical sheet, providing a map of 230±14 domains from which extracted time courses maximally represent the underlying signal in each recording. This workflow of data-driven video decomposition and machine classification of signal sources will aid high quality mapping of complex cerebral dynamics.
+Demixing neural signals from artifact signals in videos of neuronal calcium flux across the cerebral hemispheres could help map core functional features of cortical organization. Here we demonstrate that the general solution to multichannel source signal separation, independent component analysis, can optimally recover neural signal content in recordings of neuronal cortical calcium dynamics captured at a magnification factor of one and at a sampling rate of 1.5×10⁶ pixels per one-hundred millisecond frame for seventeen minutes. We show that a set of spatial and temporal metrics can be used to build a random forest classifier which separates neural activity and artifact components automatically at human performance. Using this data, we establish a functional segmentation of the mouse cerebral sheet, providing a map of 115 domains per {x} cm²-mouse-neocortical-hemisphere from which extracted time courses maximally represent the underlying signal in each recording. This workflow of data-driven video decomposition and machine classification of signal sources will aid high quality mapping of complex cerebral dynamics.