re-init

* main.md contains latest from
brmullen, scweiser gdoc vers <https://docs.google.com/document/d/1VU3gaRKq3z3dN8ZNvDmwCUXAtIp4xkUgb4xXx5mhGvM>
  - that version was in turn a merged version from the biorxiv
    manuscripts contained in the methods-paper.git and ML-paper.git
    repos from 2020-2021
* jba added comments.md

comments.md

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+* 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
+* [ ] 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.

main.md

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+---
+author: James B. Ackman  
+date: 2022-04-19T15:32:50-07:00  
+tags: paper, draft, manuscript, literature, research, #results, retinal waves, spontaneous activity, development, calcium domains  
+---
+
+# Data-driven filtration and segmentation of mesoscale neural dynamics 
+
+**Authors and Affiliated Institutions:**  
+Sydney C. Weiser¹†, Brian R. Mullen¹†, Desiderio Ascencio², & James B. Ackman¹  
+
+†these authors contributed equally to this work  
+¹Department of Molecular, Cell, and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA, USA  
+²Department of Psychology, University of California Santa Cruz, Santa Cruz, CA, USA  
+
+Correspondence and requests for materials should be addressed to: james.ackman@gmail.com  
+
+
+
+
+# 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. 
+
+
+# Introduction
+
+An understanding of 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 8,9.
+Optical techniques have long been used to monitor the functional dynamics in sets of neuronal elements ranging from isolated crustacean nerve fibers1 to whole regions of mammalian cerebral cortex 2,3. Imaging calcium flux with calcium sensors 4,5 allows monitoring of neural activity across the entire neocortex with high enough spatiotemporal resolution to identify sub-areal networks of the neocortex 6,7. These techniques have the potential to map group function at unprecedented resolution and scale across the neocortical sheet, in awake behaving mice, however the determination of neural signals from calcium imaging sessions is challenging due to numerous confounding signal sources.  
+Wide-field imaging of neuronal calcium flux offers mesoscale observation of cortical neural dynamics, providing a view of supracellular group organization between microscale (cell) and macroscale (tissue lobe) investigations– however it is affected by issues common to topical imaging recordings. Body or facial movements can create large fluctuations in autofluorescence of the brain and blood vessels, which produce significant artifacts in the data. Vascular artifacts are commonly seen due to vasodynamics and the resulting changes in blood flow to meet the energy demands of surrounding tissue. Fluid exchange between vascular and neural tissue causes cortical hemodynamics, which results in region specific changes of optical properties among cerebral lobes 10. Further, during the experimental preparation the skull is typically fixed to a specific location, however slight brain movements occur within the cranium that influence the recordings. Any optical property differences that originate from the experimental preparation may be highlighted in the dataset as signal due to changes in tissue contrast. 
+
+Eigendecompositions can be used to identify and filter components of signal,11–13 and present a flexible method of filtering that is not hardware dependent, and can be applied to any video dataset regardless of the recording hardware or parameters. Independent Component Analysis (ICA) 14 has been previously applied to fMRI and EEG data with varying success; identifying intrinsic connectivity networks, rather than identification of individual areas, and artifacts that represent large-scale effects, rather than spatially localized effects 15–18. We hypothesize that this is due to the lower density of spatial sampling in fMRI and EEG data. Wide-field calcium imaging provides a unique combination of spatially and temporally resolved dynamics across the cortical surface, with scale ranging from complex activation patterns in high-order circuits, to discrete activations hundreds of micrometers in diameter, to whole cortical lobe activity patterns 6,7. Researchers have recorded wide field calcium dynamics at frame rates ranging from 5-100Hz 6,19,20. In addition, spatial resolution varies between different researchers’ setups, but is typically in the range of 256x256 to 512x512 pixels (0.06 to 0.2 megapixels) for the entire cortical surface, and is often further spatially reduced for processing 6,19,21. Selection of resolution is often dependent on the video observer’s perceived quality of the data or available computational resources, rather than a quantified comparison of signal content. 
+
+It is common to use sensory stimulation to identify specific regions in the neocortex, and align a reference map based on the location of these defined regions 21–23. Even if these maps are reliable for the location of primary sensory areas, they often lack specificity for higher order areas, or even completely lack sub-regional divisions. This is especially true in areas with a high degree of interconnectedness, with overlapping functionality, such as motor cortex 24.  Moreover, there is evidence that the shape and location of higher order regions can vary from subject to subject 25,26. Improper map alignment or misinformed regional boundaries can lead to a loss in dynamic range between signals across a regional border. Thus, to extract the most information from a recorded dataset, the level of parcellation must reflect the quality and sources present within the data. Thus a flexible data-driven method is necessary that respects functional boundaries of the cortex and is sensitive to age, genotype and individual variation. 
+
+Here we present an ICA-based workflow that isolates and filters artifacts from calcium imaging videos, with principled exploration of each component to identify each signal source necessary to reduce the contamination resulting from these physiological dynamics. Independent component analysis (ICA) is a nonparametric unsupervised machine learning technique that we utilize to identify each signal source in densely sampled (5.5 million pixels per frame) calcium imaging videos based on their spatially co-activating pixels and temporal properties. The global mean time course was initially subtracted and stored, thereby allowing ICA to decompose each signal distinct from global effects. The decomposition results in hundreds of neural source components per hemisphere that are distinctly de-mixed from artifact source signals. Our concurrent analysis of control wide-field imaging data corroborates the identification of artifact signal sources and gives insight into the structure of neuronal calcium dynamics across neocortex.
+
+Further, we explore the resolution-dependent effect on ICA quality of signal extraction, and find a quantified increase in ICA signal separation for collecting wide-field calcium imaging at mesoscale resolution. Using neural components, we additionally generate data-driven maps that are specific to functional borders from individual animals. We use these maps to extract time series from functional regions of the cortex, and show that this method for time series extraction produces a reduced set of time series while optimally representing the underlying signal and variation from the original dataset. Together, these methods provide a set of optimized techniques for enhanced filtering, segmentation, and time series extraction for wide-field calcium imaging videos.