diff --git a/comments.md b/comments.md
index 1634a94..7e316cc 100644
--- a/comments.md
+++ b/comments.md
@@ -1,9 +1,17 @@
 * 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 
+  - 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
 
+<!-- * 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
@@ -75,6 +83,7 @@
 
 * [ ] 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
@@ -85,9 +94,31 @@
 
 * [ ] 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
 
 * [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 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"
+
+
+## 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 
+
+* [ ] 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.'
+
diff --git a/main.md b/main.md
index afac3c7..28e7ef3 100644
--- a/main.md
+++ b/main.md
@@ -1,4 +1,16 @@
-# Data-driven filtration and segmentation of mesoscale neural dynamics 
+# Data-driven segmentation of cortical calcium dynamics 
+
+<!--
+# 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
+
+# Data-driven segmentation of cortical calcium dynamics reveals a mesocale functional mosaic
+
+# Seventeeen minute maps of cortical calcium domains in mouse at postnatal day 21
+# Mapping neuronal calcium domains in mouse neocortex at postnatal day 21
+-->
+
 
 **Authors and Affiliated Institutions:**  
 Sydney C. Weiser¹•, Brian R. Mullen¹•, Desiderio Ascencio², & James B. Ackman¹  
@@ -10,12 +22,63 @@ Sydney C. Weiser¹•, Brian R. Mullen¹•, Desiderio Ascencio², & James B. Ac
 
 ## Abstract  
 
-In order to understand how information flows across cerebral networks we need to be able to record neuronal activity across the cortical hemispheres from awake behaving animals. One method recently developed in mice uses calcium imaging data to access information flow. However, analyzing video recordings of calcium imaging comes with challenges, including optical and movement-associated blood vessel artifacts and limited references for time series extraction. Here we present a data-driven workflow that isolates artifacts from calcium activity patterns, and segments independent functional units across the cortical surface using Independent Component Analysis (ICA). ICA utilizes the statistical interdependence of pixel activation to completely unmix signals from background noise, given sufficient spatial and temporal samples. We characterize each component using a set of extracted spatial and temporal metrics and develop the tools for building a supervised learning classifier that automatically separates neural activity and artifact components. We demonstrate that the performance of the machine classifier matches human identification of signal components in novel data sets. We also analyze and compare control data recorded in a glial cell reporter and non-fluorescent mouse lines that validates human and machine identification of functional component class. We additionally utilize isolated signal components to produce segmentations of the cortical surface, unique to each individual’s functional patterning. Time series extraction from these maps maximally represent the underlying signal in a highly compressed format. This workflow of data-driven video decomposition and machine classification of signal sources will aid robust and scalable mapping of complex cerebral dynamics. These improved techniques for data pre-processing, spatial segmentation, and time series extraction result in optimal signals for further analysis and model development. 
+Demixing neural signals from artifact signals in videos of neuronal calcium flux across the cerebral hemispheres could reveal key functional features of cortical organization. Here we demonstrate that the multichannel blind source deconvolution algorithm, independent component analysis, can optimally recover neural signal content in CMOS sensor imaging of pan-neuronal cortical calcium dynamics at a sampling of 1.5M cortical pixels per 0.1 s for 17 min. We characterize each component using a set of spatial and temporal metrics and build a random forest classifier that separates neural activity and artifact components automatically with human performance. We show how this data produces a functional tesselation 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.
+
+
+
+<!-- figure 2D: What is the value of the mean neural component curve when the derivative approaches zero? Around 900 s or 15 min? 
+-->
+
+<!--
+We additionally utilize isolated signal components to produce segmentations of the cortical surface, unique to each individual’s functional patterning. 
+
+Time series extraction from these maps maximally represent the underlying signal in a highly compressed format. 
+This workflow of data-driven video decomposition and machine classification of signal sources will aid the mapping of complex cerebral dynamics.
+
+Methods for analyzing cortical calcium imaging videos are underdeveloped and progress has been hindered by the challenges of live recordings, such as intermixing of artifact sources with neural signals. Here, we build a calcium flux video processing pipeline that uncovers the set of signals which optimally represent the underlying structure in observed neocortical imaging data. 
+
+We demonstrate the critical importance of choosing sufficient spatial and temporal sampling parameters for optimizing extraction of source signals and gaining the maximum information from a given recording, capturing and processing x samples 
+
+However, there are challenges with analyzing cortical calcium imaging videos, including optical artifacts and litte report on the samplif lack of u
+
+(how to deal with undesired signal sources | optical artifacts) and (little work on optimizing the baseline sampling parameters that give best extraction of neuronal calcium source signals) | limited references) for time series extraction. 
+
+Here we present a data-driven workflow that isolates artifacts from calcium activity patterns, and segments independent functional units across the cortical surface using Independent Component Analysis (ICA). 
+
+ICA utilizes the statistical interdependence of pixel activation to completely unmix signals from background noise, given sufficient spatial and temporal samples. 
+
+We characterize each component using a set of extracted spatial and temporal metrics and develop the tools for building a supervised learning classifier that automatically separates neural activity and artifact components. 
+
+We demonstrate that the performance of the machine classifier matches human identification of signal components in novel data sets. 
+
+We also analyze and compare control data recorded in a glial cell reporter and non-fluorescent mouse lines that validates human and machine identification of functional component class. 
+-->
+
+
+
+<!-- These improved techniques for data pre-processing, spatial segmentation, and time series extraction result in optimal signals for further analysis and model development.  -->
+
+<!--
+In order to understand how information flows across cerebral networks we need to be able to record neuronal activity across the cortical hemispheres from awake behaving animals. 
+One method recently developed in mice uses calcium imaging data to access information flow. 
+
+Functional imaging of neuronal activity is important for understanding cerebral cortical dynamics. 
+
+Imaging neuronal calcium dynamics across the hemispheres is an important method for investigating the functional organization of cortical networks. 
+
+functional organization
+investigate
+important method
+calcium dynamics
+cortical networks
+-->
 
 
 ## Introduction
 
-Optical techniques have long been used to monitor the functional dynamics in sets of neuronal elements ranging from isolated crustacean nerve fibers[^Cohen:1968] to entire regions of mammalian cerebral cortex[^Grinvald:1986][^Grinvald:2004]. Imaging calcium flux with calcium sensors[^Tsien:1989][^Chen:2013a] allows neural activity monitoring across the entire neocortex with high enough spatiotemporal resolution to identify sub-areal networks of the neocortex[^Ackman2014c][^Vanni2014]. These techniques have the potential to map group function at unprecedented resolution and scale across the neocortical sheet in awake behaving mice; however identifying neural signals from calcium imaging sessions is challenging due to numerous confounding signal sources. 
+<!-- Capturing maps of functional tissue organization is a key task -->
+
+Optical techniques have long been used to monitor the functional dynamics in sets of neuronal elements ranging from isolated invertebrate nerve fibers[^Cohen:1968][^Salzberg:1977] to entire regions of mammalian visual cortex in vivo[^Grinvald:1986][^Grinvald:2004][^Ackman:2012]. Imaging calcium flux with calcium sensors[^Tsien:1989][^Chen:2013a] allows neural activity monitoring across the entire neocortex with high enough spatiotemporal resolution to identify sub-areal networks of the neocortex[^Ackman2014c][^Vanni2014]. These techniques have the potential to map supracellular group function at unprecedented resolution and scale across the neocortical sheet in awake behaving mice; however identifying neural signals from calcium imaging sessions is challenging due to numerous confounding signal sources. 
 
 <!-- Understanding cerebral dynamics at multiple scales is important for exploring how environmental and genetic influences give rise to altered neural connectivity patterns linked to behavioral phenotypes [^Ma:2016][^Kozberg:2016] -->
 
@@ -269,6 +332,7 @@ To test the meaning of these maps, a series of comparisons were performed. Pairs
 <figure><img src="figs/methods-figure7.png" width="400px"><figcaption>
 
 **Figure 7** Domain maps are created from ICA components and are unique to each recording, but highly similar among individual animals. Using data-guided methods to assign domains to cortical regions. A) Hierarchical clustering based on Pearson’s correlation produces a set of ∼ 13 regions across the cortical surface. B) Domains colored by various calculated spatial and temporal metrics to aid region assignment. Region area is calculated as a percent of the total cortical surface. Region extend ranges from 0 to 1 and calculates the relative area of a domain to its bounding box. Temporal standard deviation is calculated from the extracted time series, and frequency range size is calculated from wavelet significance. C) The Allen Brain atlas map31 is additionally used for anatomical reference. D) The final manually assigned region, with associated labels. E) Domain area and eccentricity by region. Population analysis of distribution of spatial characteristics in individual domains within defined regions across multiple recordings (n=16 videos; from 8 different animals). F) Example overlay of one domain map on another from the same animal. Individual domain or region overlap is calculated using the Jaccard index (intersect / union). Population analysis of the Jaccard index for domain (G) and region (H) overlap comparisons. Maps are generated from a different recording on the same animal, a littermate, a non-littermate, or a randomly generated voronoi map. Significance is calculated using a two-way ANOVA, followed by post-hoc t-test analysis with Holm-Sidak correction. Retrosplenial: R; Higher order visual: V+; Auditory: A; Somataosensory Seondary: Ss; Somataosensory Core: Sc; Somataosensory Barrel: Sb; Somatosensory other: S; Motor medial: Mm; Motor lateral: Ml; Olfactory: O
+k
 </figcaption></figure>
 
 Maps generated from different recordings from the same animal were found to be highly overlapping, and hence more similar (Fig. 7G, top; p < 0.001). There was no significant difference in comparisons between littermates vs non littermates. Non-littermate map comparisons were significantly more similar to each other than to voronoi maps (p < 0.001). 
@@ -406,10 +470,14 @@ Statistical significance was calculated using OLS models from statsmodel.formula
 
 [^Cohen:1968]: Cohen LB, Keynes RD, Hille B. Light scattering and birefringence changes during nerve activity. Nature. (1968). 218:438–41. pmid:5649693
 
+[^Salzberg:1977]: Salzberg BM, Grinvald A, Cohen LB, Davila HV, Ross WN. Optical recording of neuronal activity in an invertebrate central nervous system: Simultaneous monitoring of several neurons. J Neurophysiol. (1977). 40:1281–91. pmid:925730
+
 [^Grinvald:1986]: Grinvald A, Lieke E, Frostig RD, Gilbert CD, Wiesel TN. Functional architecture of cortex revealed by optical imaging of intrinsic signals. Nature. (1986). 324:361–4. pmid:3785405
 
 [^Grinvald:2004]: Grinvald A, Hildesheim R. VSDI: A new era in functional imaging of cortical dynamics. Nat Rev Neurosci. (2004). 5:874–85. pmid:15496865
 
+[^Ackman:2012]: Ackman JB, Burbridge TJ, Crair MC. Retinal waves coordinate patterned activity throughout the developing visual system. Nature. (2012). 490:219–25. doi:10.1038/nature11529 pmid:23060192
+
 [^Tsien:1989]: Tsien RY. Fluorescent probes of cell signaling. Annu Rev Neurosci. (1989). 12:227–53. pmid:2648950
 
 [^Chen:2013a]: Chen T-W, Wardill TJ, Sun Y, Pulver SR, Renninger SL, Baohan A, et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature. (2013). 499:295–300. doi:10.1038/nature12354 pmid:23868258