Fig1
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methods.txt
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methods.txt
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**Animals.** Animal care and use was performed in compliance with the Yale IACUC, U. S. Department of Health and Human Services and Institution guidelines. Neonatal Rx-Cre:GCaMP3 f/f (Ai38, JAX no. 014538) or SNAP-25-GCaMP6 (Ai103) mice aged 2-13 days after birth (P2-P13) were used.
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**Surgical procedure for in vivo imaging.** Mice aged P2-P13 were deeply anesthetized with isoflurane (2.5%) in oxygen and then placed on a heating pad set to 35ºC via a isothermic temperature monitor (NPI TC-20, ALA Scientific). Local anesthesia was produced by subcutaneous injection (0.05 ml) of 1% Xylocaine (10 mg/ml lidocaine/0.01 mg/ml epinephrine, AstraZeneca) under the scalp. After removal of the scalp, steel head posts were fixed to the exposed skull using cyanoacrylate glue. A 1 hr recovery period in the dark under continuously delivered medical oxygen with isoflurane at 0% was allowed after surgical preparation. This recovery period was the typical minimum time required for spontaneous waves of activity to develop in the visual system after the cessation of deep anesthesia [#Ackman:2012].
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**Surgical procedure for in vivo imaging.** Mice aged P2-P13 were deeply anesthetized with isoflurane (2.5%) in oxygen and then placed on a heating pad set to 35ºC via a isothermic temperature monitor (NPI TC-20, ALA Scientific). Local anesthesia was produced by subcutaneous injection (0.05 ml) of 1% Xylocaine (10 mg/ml lidocaine/0.01 mg/ml epinephrine, AstraZeneca) under the scalp. After removal of the scalp, steel head posts were fixed to the exposed skull using cyanoacrylate glue. A 1 hr recovery period in the dark under continuously delivered medical oxygen with isoflurane at 0% was allowed after surgical preparation. This recovery period was the typical minimum time required for spontaneous waves of activity to develop in the visual system after the cessation of deep anesthesia [#Ackman:2012]. A photodiode and red LED were positioned to monitor respiratory rate and limb/body movements and the body was surrounded in a cotton nest.
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**Wide field calcium imaging.** A 16 bit CMOS camera (pco.edge, PCO) coupled to a Zeiss AxioZoom V16 Microscope with 1X Macro objective was used to image transcranial calcium dynamics. Epifluorescent illumination was provided by a DC stabilized Hg2+ light source (X-Cite, EXFO) through a filter cube set (Zeiss) with the minimum illumination intensity that gave detectable calcium signals using a exposure of 200 msec. Image frames corresponding to a field of view of 6 x 8 mm or 11 x 13 mm were acquired at a rate of 5 or 10Hz. Each recording consisted of a single, continuously acquired movie during a period of 10min.
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**Calcium signal detection.** Image processing and calcium signal detection was performed using custom software routines written in MATLAB (Mathworks, Natick, MA). The mean pixel intensity at each pixel location, F0 was subtracted and normalized to each frame, Ft of the movie to form a dF/F = (Ft - F0)/F0 array. A background estimate was calculated and subtracted from every frame with a top hat filtering algorithm using a disk shaped structuring element with radius of 620 µm. Each frame was smoothed with a Gaussian having a standard deviation of 56 µm and a signal intensity threshold was computed using Otsu's method on the histogram of pixel intensities at the 99th percentile from the Sobel gradient transformation of the image array. Calcium signals were automatically segmented as contiguously connected components in space and time using the binary mask for the array from the computed Otsu intensity threshold. Components having an area <50 pixels or a duration of <= 1 frame were discarded.
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**Calcium signal detection.** Image processing and calcium signal detection was performed using custom software routines written in MATLAB (Mathworks, Natick, MA). The mean pixel intensity at each pixel location, F0 was subtracted and normalized to each frame, Ft of the movie to form a dF/F = (Ft - F0)/F0 array. A background estimate was calculated and subtracted from every frame with a top hat filter using a disk shaped structuring element with radius of 620 µm. Each frame was smoothed with a Gaussian having a standard deviation of 56 µm and a signal intensity threshold was computed using Otsu's method on the histogram of pixel intensities at the 99th percentile from the Sobel gradient transformation of the image array. Calcium signals were automatically segmented as contiguously connected components in space and time using the binary mask for the array from the computed Otsu intensity threshold. Components having an area <50 pixels or a duration of 1 frame were ignored.
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**Data analysis.** Data sets were analyzed using custom routines written in MATLAB (The Mathworks, Natick, MA) and in R (The R Project for Statistical Computing, http://www.r-project.org). Distribution means were compared using two-sample Student's t-Tests or using ANOVA followed by pairwise-t-tests with Holm correction or Tukey's HSD when analyzing the effects of multiple grouping factors (p < 0.05 set as significance). Values are reported as means with the standard error of the mean or medians with the median absolute deviation.
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**Statistical analysis.** Data sets were analyzed using custom routines written in MATLAB (The Mathworks, Natick, MA) and in R (The R Project for Statistical Computing, http://www.r-project.org). Distribution means were compared using two-sample Student's t-Tests or using ANOVA followed by Tukey's HSD post-hoc test when analyzing the effects of multiple grouping factors (p < 0.01 set as significance). Values are reported as means with the 95% confidence interval or standard error of the mean or medians with the median absolute deviation.
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**Calcium domain analysis.** The mean width in the medial-lateral and height in the rostral-caudal dimensions of the bounding box fitted to each segmented calcium domain signal was taken to be the domain diameter. The number of contiguous frames (bounding box depth) for each segmented calcium domain was taken to be the domain duration. The mean and maximum pixel intensities within each domain were taken as the mean and maximum domain amplitudes. Domains were assigned areal membership by intersection of the domain centroid with a cortical ares's pixel mask. The number of individual domains per recording within a hemisphere or cortical area was taken to be domain frequency.
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**Functional correlation analysis.** A binary movie array from all the segmented calcium domain masks for a recording was intersected mask representing different cortical areas. The total number of active pixels per frame expressed as a fraction of possibly active pixels per frame for each cortical area gave active pixel fraction timecourses for each cortical area in each recording. Correlation matrices were calculated for each recording by computing pairwise Pearson's product moment correlation coefficents from the matrix containing the cortical active pixel fraction timecourses. The binarized correlation matrix at *r* > 0.1 was used to form an adjacency matrix with each node representing a cortical area and each edge representing an association between a pair of nodes at weight, *r*. Community structure was detected within each functional association matrix using a fast greedy modularity optimization algorithm [#Clauset:2004] to perform hierarchial clustering using the igraph network analysis software library [#Csardi:2013].
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[#Ackman:2012]: Ackman, J. B., Burbridge, T. J., and Crair, M. C. (2012). Retinal waves coordinate patterned activity throughout the developing visual system, Nature, 490(7419), 219-25
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[#Clauset:2004]: Clauset, MEJ Newman, C Moore: Finding community structure in very large networks, http://arxiv.org/pdf/cond-mat/0408187v2.pdf
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[#Csardi:2013]: Csardi G. igraph, The network analysis package. http://igraph.org
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@@ -24,46 +24,31 @@ Brain development requires neural activity and calcium dynamics for establishing
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We performed transcranial optical recordings from mice expressing the genetic calcium reporter GCaMP (GCaMP3 or GCaMP6) throughout cortical neurons to assess neural population activity patterns with high spatial resolution and at macroscopic scale. We performed our recordings in three age groups: P2-P5, P8-P9, and P12-13. Functional mesoscale optical imaging (fMOI) revealed that supracellular cortical activity patterns were characterized by discrete domains of activation (Fig. 1) [Supplementary Movie 1](../wholeBrain_blob/ackmanWholeBrainGcampP3.mov) during the first two postnatal weeks. These activity domains ranged from 200-800 µm in diameter (*Ns*, *fig*), with larger sized domains of activation in the visual cortex and motor cortex (*Ns*, *fig*). In the second postnatal week the size of cortical activity domains became larger in the frontal-motor and S1-limb/body regions [Supplementary Movie 2](../wholeBrain_blob/ackmanWholeBrainImaging-lo.mov).
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The neocortex exhibits a characteristic columnar organization by cortical macrocolumns tiled across the cortical surface that reflects a such that vertical arrays of cells concerned with specific sensory features are grouped together as columns [#Mountcastle:1997] in a topographic fashion . Most evidence to date suggests that cortical macrocolumns are 300-500µm diameter across species [mountcastle ref].
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The duration of domain activations was not significantly affected by age (F = 0.933, p = 0.428, r^2 = 0.00567) or by hemisphere (F = 0.017, p = 0.900) (P2-5, N = 15653; P8-9, N = 70189; P12-13, N = 120214 domains) (Fig. 1e,f).
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There was a significant effect of age on the diameter of cortical domain activations(F = 25.788, p = 0.000188, r^2 = 0.1277), but not hemisphere (F = 0.192, p = 0.671808) (Fig. 1g,h).
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There was a significant effect of age and brain region on the duration of cortical domain activations (age: 2 Df, F=20.8078 , p < 1.273e-09; region: Df 14, F=25.3941, < 2.2e-16, Anova). Mean cortical domain duration across all regions increased from 1.34 ± 0.04 s at P2-5 (N=327 region-movies), to 1.49 ± 0.03 at P8-9 (N = 450), and then decreased to 1.22 ± 0.03 at P12-13 (N=570) (P2-5:P8-9, p = 0.0001504; P2-5:P12-13, p = 0.0000583; P8-9:P12-13, p < 2e-16). cortical domain activations decreased over the course of development from , and this (*Ns*, *fig*) There were longer activations on the order of seconds to tens of seconds in visual cortex driven by retinal waves [#Ackman:2012]. Long lasting wave-like activations also occured in motor cortex P2-5 (Fig 2 montage).
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Cortical calcium domain frequency significantly increased with age (F = 29.562, p = 8.86e-12, r^2 = 0.2535) and did not differ significantly between the hemispheres (F = 0.012, p = 0.911) (P2-5, N = 22; P8-9, N = 30; P12-13, N = 38 movies/hemi) (Fig. 1i,j).
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The neocortex exhibits a characteristic columnar organization by cortical macrocolumns tiled across the cortical surface that reflects a such that vertical arrays of cells concerned with specific sensory features are grouped together as columns [#Mountcastle:1997] in a topographic fashion. Most evidence to date suggests that cortical macrocolumns are 300-800µm diameter across species [mountcastle ref].
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We parcellated the brain into distinct anatomical boundaries by using reference coordinates from a mouse line that expressed the tdtomato reporter in thalamocortical afferents. The expression can be used to parcellate out areal boundaries of primary sensory cortical areas (wong riley 1979). We matched these parcellations to a Allen brain atlas adult mouse reference image and than linearly scaled the remaining parcellations in our FOV on to the images of our recordings that contain fucntional boundaries (like in the domain centroid activation plot and in the normalized domain frequency plots).
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There were longer activations on the order of seconds to tens of seconds in visual cortex driven by retinal waves [#Ackman:2012]. Long lasting wave-like activations also occured in motor cortex P2-5 (Fig 2 montage).
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metric | mean | min | max | unit
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------------- | ----- | ---- | ------ | --------------------
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diameter | 396.0 | 22.7 | 2383.5 | µm
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duration | 0.6 | 0.2 | 14.6 | s
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frequency | 2.9 | | | domains/sec/hemisphere
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[**Table 1: Domain statistics**]
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| | duration (s) | diameter (µm) | frequency (hemisphere-min^-1) |
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| ------ | ------------ | ----------------- | ----------------------------- |
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| P2-5 | 0.8 ± 0.4 | 441.12 ± 147.04 | 30.90 ± 9.55 |
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| P8-9 | 0.6 ± 0.2 | 569.78 ± 220.56 | 98.35 ± 27.60 |
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| P12-13 | 0.4 ± 0.2 | 1047.66 ± 367.60 | 147.80 ± 78.65 |
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| Notes: Values are reported as medians ± median absolute deviation ||||
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[ **Table 1: Domain statistics**]
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## Cortical activity is mirrored between the hemispheres
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* Inter hemispheric functional connectivity, importance for autism, schizophrenia. Maybe an activity-dependent mechanism for commisural connectivity.
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* olavarria work, evidence for inter hemispheric activity dependence
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* [#Hanganu:2006], 30% of spindle bursts correlated across hemispheres
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To understand the patterns and how they interact we first looked at correlation between the hemispheres. Cortical activity exhibited high temporal correlation between the hemispheres () . In additon this activity was highly correlated in the spatial dimension. We found that activity was correlated in anterior-posterior and medial-lateral directions. It exhibited mirror symmetric and non-mirror symmetric patterns. For example epochs of time would exhibit high correlation in the medial-lateral dimension or in the rostral-caudal dimension. This strength of correlation temporally and spatially increased between the hemsipheres with a function of age.
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<!-- * Each hemisphere 'training' the other one in preparation for behaviorally relevant sensory-motor imitations '[[mirror_neurons]]' hypothesis? -->
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**Conclusions:** The two hemispheres seem to be mostly synchronized, though it’s possible the R hemispshere (which is also the slightly more ‘active’ hemisphere, see stats table below) leads the left by a bit. The asymmetric peak at –150–175frames is interesting. That would be about 30–35 sec.
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The big secondary peaks around ±30 sec is present in both autocorrs and xcorrs and is far above the random normal xcorr baseline (blue trace). In fact there is a periodicity seen in the autocorrs and the xcorrs where there is a dampening oscillation about on this interval! (See ideal dampening frequency in random sine wave example above). This corresponds to a 1/30sec == 0.033 Hz ultra-slow oscillation.
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Looking at the above plot showing lags from [–1000, 1000] frames which is ± 200 s, we can see about 5.5 cycles of this underlying dampening oscillation in both autocorr plots. This corresponds to (1000fr*0.2sec/fr)/5.5 => 36.36 sec/cycle => 0.0275 cycles/sec or ~0.03 Hz
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**Conclusions:** So the activity in both hemispheres at postnatal day 3 (P3) clearly exhibits significant spatial correlations in both in the medial-lateral and anterior-extent. This is consistent with and complementary to the fact that the active pixel fraction in each hemisphere exhibits a strong temporal correlation as I found earlier in this report [Temporal correlation of activity][]. The medial-lateral positional correlation is stronger than the anterior-posterior (higher *R* and lower *p* value). The total number of coactive frames is `numel(y1(~isnan(y1)&~isnan(y2)))` == **1114 frames**. This is accounts to **37.13%** of the movie or **222.8 s**. Cortex.L had 1635 actvFrames and cortex.R had 1677 actvFrames which means that each hemisphere was coactive with the other hemisphere 1114/1635 == **68.13%** and 1114/1677 == **66.43%** of the active time respectively.
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@@ -91,6 +76,31 @@ lenActvFraction>0 | fracCorr | timeCorr_s | fracCorrPos | timeCorrPos_s | fracCo
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## Cortical activity is mirrored between the hemispheres
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* Inter hemispheric functional connectivity, importance for autism, schizophrenia. Maybe an activity-dependent mechanism for commisural connectivity.
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* olavarria work, evidence for inter hemispheric activity dependence
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* [#Hanganu:2006], 30% of spindle bursts correlated across hemispheres
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To understand the patterns and how they interact we first looked at correlation between the hemispheres. Cortical activity exhibited high temporal correlation between the hemispheres () . In additon this activity was highly correlated in the spatial dimension. We found that activity was correlated in anterior-posterior and medial-lateral directions. It exhibited mirror symmetric and non-mirror symmetric patterns. For example epochs of time would exhibit high correlation in the medial-lateral dimension or in the rostral-caudal dimension. This strength of correlation temporally and spatially increased between the hemsipheres with a function of age.
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<!-- * Each hemisphere 'training' the other one in preparation for behaviorally relevant sensory-motor imitations '[[mirror_neurons]]' hypothesis? -->
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**Conclusions:** The two hemispheres seem to be mostly synchronized, though it’s possible the R hemispshere (which is also the slightly more ‘active’ hemisphere, see stats table below) leads the left by a bit. The asymmetric peak at –150–175frames is interesting. That would be about 30–35 sec.
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The big secondary peaks around ±30 sec is present in both autocorrs and xcorrs and is far above the random normal xcorr baseline (blue trace). In fact there is a periodicity seen in the autocorrs and the xcorrs where there is a dampening oscillation about on this interval! (See ideal dampening frequency in random sine wave example above). This corresponds to a 1/30sec == 0.033 Hz ultra-slow oscillation.
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Looking at the above plot showing lags from [–1000, 1000] frames which is ± 200 s, we can see about 5.5 cycles of this underlying dampening oscillation in both autocorr plots. This corresponds to (1000fr*0.2sec/fr)/5.5 => 36.36 sec/cycle => 0.0275 cycles/sec or ~0.03 Hz
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**Conclusions:** So the activity in both hemispheres at postnatal day 3 (P3) clearly exhibits significant spatial correlations in both in the medial-lateral and anterior-extent. This is consistent with and complementary to the fact that the active pixel fraction in each hemisphere exhibits a strong temporal correlation as I found earlier in this report [Temporal correlation of activity][]. The medial-lateral positional correlation is stronger than the anterior-posterior (higher *R* and lower *p* value). The total number of coactive frames is `numel(y1(~isnan(y1)&~isnan(y2)))` == **1114 frames**. This is accounts to **37.13%** of the movie or **222.8 s**. Cortex.L had 1635 actvFrames and cortex.R had 1677 actvFrames which means that each hemisphere was coactive with the other hemisphere 1114/1635 == **68.13%** and 1114/1677 == **66.43%** of the active time respectively.
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## Developing cortical activity consists of distinct subnetworks
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We then calculated a matrix of pearsons correlation coefficients based on the pixel active fraction timecourses for each pair of parcellations. The resulting assocaition matrix was run through a hierarchal clustering alogtithm to reveal functional modules of of activation. These functional modules typically consisted of 3 distinct subnetworks-- a frontal motor network, a posterior parietal network, a S1-body/limb network, and an auditory A1 network at P12.
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@@ -98,7 +108,7 @@ We then calculated a matrix of pearsons correlation coefficients based on the pi
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We found many similarities but some striking differences as a function of age.
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@@ -141,38 +151,22 @@ Anesthetized Rx-Cre:GCaMP3 or SNAP25-GCaMP6 mice between postnatal day 2 to 13 (
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<!--Figure 1 metadata
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* neonate_ms_fig.png
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* binary masks: Screen_Shot_2013-03-29_at_12.06.25_PM_crop.png, ..._crop1.png, ..._crop2.png
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* domain map: 120518_07_connComponents_BkgndSubtr-60px_noWatershed-20130327-151022_d2rdomainPatchesPlot20130912-111733.eps
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* parcellation map
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* update 2013-10-03 11:07:29:
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* 120518_07_2013-09-11-225029_d2rImageCoords20130930-144657.ai
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* created using area coords: 120518_07_2013-09-11-225029_d2rImageCoords20130930-144657.eps
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* 120518_07_parcellation_fig.tif: alpha overlay of brightfield image with Allen gray parcellation image and Sert-tdtomato images linearly scaled to fit V1 and S1-barrel reprsentations in functional image and domain centroid map
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* contourplot of 20 levels 120518_07_connComponents_BkgndSubtr-60px_noWatershed-20130327-151022_d2r_20130930-124942.eps
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* 120518_07_connComponents_BkgndSubtr-60px_noWatershed-20130327-151022_d2r_20130930-124950_eps.png
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* hists: 120518_07_connComponents_BkgndSubtr60px-20130327-163111domains20130402-151440-crop.png
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* [x] add a domain centroid size/duration map similar to: 
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* 
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* [x] add a domain centroid size/duration map similar to: 
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* 
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* domainFreq map:
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* domainFreq boxplot:
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* domainDur map: 20140613-082954_ActivityMapFigRawProj-domainDurRdBu.eps
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* domainDur cdf:
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-->
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<!--Figure 2 metadata
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* binary mask snapshots, cropped from screen shots in [[2013-04-19_analysis]]
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* Screen_Shot_2013-04-19_at_8.26.00_AM_fr1786.png
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* Screen_Shot_2013-04-19_at_8.27.49_AM_fr2134.png
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* Screen_Shot_2013-04-19_at_8.30.27_AM_fr759.png
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* Screen_Shot_2013-04-19_at_8.30.51_AM_fr373.png
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* Screen_Shot_2013-04-19_at_8.38.54_AM_fr177.png
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* Temporal correlation of activity between the hemispheres and preceding motor activation:
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* 
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* hemisphere active fraction traces: Screen_Shot_2013-04-08_at_8.47.19_AM.png
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* activefraction hemis AP and ML all:  | 120518_07_connComponents_BkgndSubtr-60px_noWatershed-20130327-151022_d2ractiveFractionPixelLocaCorr20130423-094506.eps
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* activefraction hemis AP and ML segment: 
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* activefraction hemis AP and ML segment: 
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### Cortical activity correlated between the hemispheres and is periodic
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* hemi auto and xcorr:
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* 2500fr lags: 120518_07_connComponents_BkgndSubtr-60px_noWatershed-20130327-151022activeFraction20130408-143100.eps
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* 250fr lags: 120518_07_connComponents_BkgndSubtr-60px_noWatershed-20130327-151022activeFraction20130408-151655.eps
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* 1500fr lags: 120518_07_2013-10-18_AllgoodactiveFraction20131023-145023.eps
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<!-- Figure 2 metadata
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-->
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<!--Figure 3 metadata
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@@ -191,8 +185,27 @@ Anesthetized Rx-Cre:GCaMP3 or SNAP25-GCaMP6 mice between postnatal day 2 to 13 (
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-->
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<!--Figure 4 metadata
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* binary mask snapshots, cropped from screen shots in [[2013-04-19_analysis]]
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* Screen_Shot_2013-04-19_at_8.26.00_AM_fr1786.png
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* Screen_Shot_2013-04-19_at_8.27.49_AM_fr2134.png
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* Screen_Shot_2013-04-19_at_8.30.27_AM_fr759.png
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* Screen_Shot_2013-04-19_at_8.30.51_AM_fr373.png
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* Screen_Shot_2013-04-19_at_8.38.54_AM_fr177.png
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* Temporal correlation of activity between the hemispheres and preceding motor activation:
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* 
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* hemisphere active fraction traces: Screen_Shot_2013-04-08_at_8.47.19_AM.png
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* activefraction hemis AP and ML all:  | 120518_07_connComponents_BkgndSubtr-60px_noWatershed-20130327-151022_d2ractiveFractionPixelLocaCorr20130423-094506.eps
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* activefraction hemis AP and ML segment: 
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* activefraction hemis AP and ML segment: 
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### Cortical activity correlated between the hemispheres and is periodic
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* hemi auto and xcorr:
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* 2500fr lags: 120518_07_connComponents_BkgndSubtr-60px_noWatershed-20130327-151022activeFraction20130408-143100.eps
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* 250fr lags: 120518_07_connComponents_BkgndSubtr-60px_noWatershed-20130327-151022activeFraction20130408-151655.eps
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* 1500fr lags: 120518_07_2013-10-18_AllgoodactiveFraction20131023-145023.eps
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-->
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<!--Figure 5 metadata
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* corr matrix: MeanCorrMatrix-age-2014-06-02.ai, (140602-113013-age.g-groupCorrMatrix.pdf, 140602-112947-dendr-ageP12-13.pdf)
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* corr graph force layout: 140602-101610-P12-13_0.15.pdf
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* corr graph spatial layout: 140602-100049-P12-13_0.15.pdf
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-->
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