rev abstr

comments.md

@@ -5,6 +5,8 @@
 * 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
 
+<!-- figure 2D: What is the value of the mean neural component curve when it levels off? Maybe around 1000 s or 16.6 min? -->
+
 <!-- * 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? -->
 
@@ -99,6 +101,17 @@
 
 ## 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
+
+
 * [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
@@ -110,10 +123,23 @@
 * [ ] Check phrase 'detected regions' as in 'We additionally quantified whether detected regions...'
 
 * [ ] Fix Figure S6 "Nueral"
+* [ ] rw "considered collecting spatial samples higher than our current resolution"
 
 
 ## 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 
+
 * [ ] 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