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# Methods # Methods
**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. **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 Ai38 floxed GCaMP3 reporter mice (JAX no. 014538)58 crossed with Rx-Cre[^Swindell:2006] or SNAP25-GCaMP6 (Ai103) transgenic mice aged 2–13 days (P2-P13) after birth (P0) were used.
**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. **Transgenic mice generation.** The Snap25 locus was targeted with homologous recombination to insert LSL-F2A-GFP at the endogenous stop codon, using a targeting vector containing the following components: 5’ arm – F3 – last ~300bp of intron 7 – exon 8 up to the endogenous stop codon – loxP – stop codons – PGK polyA – loxP – F2A-EGFP – WPRE – bGH polyA – AttB – pPGK – neomycin-resistant gene – PGKpA – F5 – mRNA splice acceptor – domain 2 from the hygromycin-resistant gene – SV40 polyA – AttP – 3’ arm. Targeting constructs were generated using a combination of molecular cloning, gene synthesis (GenScript, Piscataway, US) and Red/ET recombineering (Gene Bridges, Heidelberg, DE). The 129S6B6F1 ES cell line, G4, was used for the gene targeting. Correctly targeted neomycin resistant clones were identified by PCR, and then confirmed by Southern blot. One ES clone that gave high percentage chimeras following blastocyst injection was used in a Flp recombinase-mediated cassette exchange (RMCE) transfection to switch the LSL-F2A-GFP expression unit for a T2A-GCaMP6s expression unit. The replacement vector included: F3 – last ~250bp of intron 7 – exon 8 up to the endogenous stop codon – T2A-GCaMP6s – WPRE – bGH polyA – AttB – pPGK – domain 1 from the hygromycin-resistant gene – mRNA splice donor – F5. Following co-transfection with a pCAG-Flpe plasmid, hygromycin resistant colonies were screened for correct 5’ and 3’ junctions and for lack of the original vector sequence. Correct clones were used in blastocyst injections to obtain germline transmission. Resulting mice were crossed to the Rosa26-PhiC31 mice (JAX Stock # 007743) to delete the pPGK-hygro selection marker cassette (in between the AttB and AttP sites), and then backcrossed to C57BL/6J mice and maintained in C57BL/6J congenic background.
**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. **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 and the mouse was surrounded by a cotton ball nest. 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 red LED (Radioshack) and photodiode sampled at 25 kHz with a Power1401 (Cambridge Electronic Design) were positioned to monitor respiratory rate and limb/body movements.
**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. **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 EGFP 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, 10 x 12 mm, or 20 x 24 mm were acquired at a rate of 5 or 10 frames per second (200 ms or 100 ms frame period). Each recording consisted of a single, continuously acquired movie for 10 min.
**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 < 0.05 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. **Calcium signal detection.** Image processing and calcium signal detection was performed using custom software (available at [http://github.com/ackman678/wholeBrainDX](http://github.com/ackman678/wholeBrainDX)) written in MATLAB (The 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 array: *A = (Ft - F0)/F0*. A background estimate was calculated and subtracted from every frame of A 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, T was computed using Otsu’s method on the set of histogram of pixel intensities in A that corresponded to the set of pixels at the 99th percentile from the Sobel gradient transformation of A. Calcium domain signals were automatically segmented as contiguously connected components in space and time from the binary mask array, *A > T*. Components located outside the cortical hemisphere boundaries or having an area < 50 pixels or a duration of 1 frame were ignored.
**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. **Statistical analysis.** Data sets were analyzed and plotted using custom routines implemented in MATLAB (The Mathworks, Natick, MA) and in R (The R Project for Statistical Computing, http://www.r-project.org) with the ggplot2 plotting library (http://ggplot2.org). Distribution means were compared using two-sample Student’s t-tests unless otherwise noted (Wilcoxon Rank Sum test was used for small, non-normally distributed data sets) or using ANOVA followed by Tukey’s HSD post-hoc test 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 unless otherwise noted. All boxplots report the median, the 25th and 75th percentiles as lower and upper box hinges (1st and 3rd quartiles), the data range as lower and upper whiskers (lowest and highest data values within 1.5 * IQR of the lower and upper hinge, where IQR is the difference between the 3rd and 1st quartiles), and outliers as individual gray points (data values beyond the whisker range).
**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* > 0.15 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 greedy modularity optimization algorithm [#Newman:2004][#Clauset:2004] to perform hierarchical clustering using the igraph network analysis software library [#Csardi:2013]. **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 from A within each domain were taken as the mean and maximum domain amplitudes. After functional parcellation of the cortical hemipsheres (**Fig. 1**; [Supplementary Fig. 1](SupplementaryFig1.pnf)), domains were assigned areal membership by intersection of the domain centroid with a cortical area’s pixel mask. The number of individual domains per recording within a hemisphere or cortical area was taken to be domain frequency.
[#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 **Wave motion analysis.** Optical flow was computed using the Lucas-Kanade method on the binary movie array from all the segmented calcium domain masks for a recording. The motion magnitude, *R* was the velocity vector sum for each domain. The wave motion index for each domain was calculated as *R^2^/D^2^*, where *D* was the domain diameter.
[#Clauset:2004]: Clauset, MEJ Newman, C Moore: Finding community structure in very large networks, http://arxiv.org/pdf/cond-mat/0408187v2.pdf **Motor movement analysis.** A binary movie array from all the segmented calcium domain masks for a recording was intersected with masks representing different cortical area parcellations. The total number of active pixels per frame expressed as a fraction of the total number of possible pixels per frame for each cortical area gave active pixel fraction time courses for each cortical area in each recording. Movement signals acquired with the photodiode were bandpass filtered using an 8-order, 1-20 Hz pass band elliptic filter, and then rectified and downsampled to the movie frame rate to give a movement time course signal that corresponded to displacements of the limbs and body excluding those from respiration. Cross-correlations between combinations of cortical active fraction time courses and the movement signal time course were computed, and the Pearson’s correlation coefficient at the zeroth time lag was obtained.
[#Csardi:2013]: Csardi G. igraph, The network analysis package. http://igraph.org **Hemisphere correlation analysis.** A binary movie array from all the segmented calcium domain masks for a recording was intersected with masks representing the cortical hemispheres. The total number of active pixels per frame expressed as a fraction of the total number of possible pixels per frame for each cortical area gave active pixel fraction time courses for each hemisphere in each recording. The mean, normalized medial-lateral and anterior-posterior positions for all active pixels within each hemisphere during coactive frames gave spatial center of mass timecourses for each recording. Pearson’s correlation coefficient was calculated between the active pixel fraction time courses and the activity center of mass time courses to give the temporal and spatial correlation for each movie.
**Functional connectivity analysis.** A binary movie array from all the segmented calcium domain masks for a recording was intersected with masks representing different cortical area parcellations. The total number of active pixels per frame expressed as a fraction of the total number of possible pixels per frame for each cortical area gave active pixel fraction time courses for each cortical area in each recording. Correlation matrices were calculated for each recording by computing pairwise Pearson’s correlation coefficients, *r*, from the matrix containing the cortical active pixel fraction time courses. The mean correlation matrix for each age group was computed and then the binarized correlation matrix at *r* > 0.15 (the maximum *r* value where the 3 largest communities were connected in the graphs at all age groups) 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*.
**Network analysis.** Graph theoretical analyses were performed using the igraph network analysis software library (http://igraph.org). Community structure was detected within each functional association matrix using a greedy optimization algorithm that maximizes the graph modularity score to perform hierarchical clustering[^Newman:2004][^Clauset:2004], where the modularity score measures the fraction of edges within modules for a graph partition compared with that of a randomized equivalent network. Network graphs were plotted using an anatomical layout or using a force-directed graph layout[^Fruchterman:1991] with nodes colored by module membership and edges connecting nodes reflecting the edge weight, *r*. Node degree was the number of connections that link a vertex to the rest of the network. The average path length, *L* of a graph was the mean of the shortest paths (fewest number of edges) between all pairs of nodes. The random average path length, *L~r~* was the mean of the shortest paths in a set of 1000 equivalent random networks that had the same degree sequence as the original graph. The local clustering coefficient was the ratio of the triangles connected to the node and the triples centered on the node, measuring the probability that two neighbors of a node are also connected. The global clustering coefficient, *C* was the ratio of the triangles and connected triples in the graph. The random global clustering coefficient, *C~r~* was the mean of the clustering coefficients in a set of 1000 equivalent random networks that had the same degree sequence as the original graph. The small-world index was calculated as the ratio of the normalized clustering coefficient (*C/C~r~*) and the normalized path length (*L/L~r~*), where a small-world index > 1 indicates a small-world network organization[^Humphries:2008][^Heuvel:2014]. Node strength was the column sums in the weighted adjacency matrix. Betweenness centrality scores corresponded to the fraction of all shortest paths that pass through a node[^Brandes:2001]. Eigenvector centrality scores were the values of the first eigenvector of the association matrix[^Bonacich:2007][^Lohmann:2010], reflecting for each node the sum of direct and indirect connections of every length in a network.

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Author: James B. Ackman ---
Date: 2014-06-05 00:38:46 author: James B. Ackman
Tags: paper, draft, manuscript, literature, research, #results, retinal waves, spontaneous activity, development, calcium domains date: 2014-06-05 00:38:46
tags: paper, draft, manuscript, literature, research, #results, retinal waves, spontaneous activity, development, calcium domains
---
# Structured dynamics of neural activity across developing neocortex # Structured dynamics of neural activity across developing neocortex
**Authors and Affiliated Institutions:** **Authors and Affiliated Institutions:**
James B. Ackman¹, Hongkui Zeng², and Michael C. Crair¹ James B. Ackman¹†, Hongkui Zeng², and Michael C. Crair¹
¹Department of Neurobiology, Yale University School of Medicine, New Haven CT, 06510 ¹Department of Neurobiology, Yale University School of Medicine, New Haven CT, 06510
²Allen Institute for Brain Science, Seattle, Washington 98103, USA ²Allen Institute for Brain Science, Seattle, Washington 98103, USA
†Present address: Department of Molecular, Cell, and Developmental Biology, University of California, Santa Cruz, Santa Cruz, California, 95064, USA
Correspondence and requests for materials should be addressed to: jackman@ucsc.edu or michael.crair@yale.edu
# Summary # Abstract
The cerebral cortex exhibits spontaneous and sensory evoked patterns of activity during development that are vital for the activity-dependent formation and refinement of neural circuits. Identifying the source and flow of these activity patterns locally and globally is vital to understanding self-organization in the developing brain. Here we use whole brain transcranial optical imaging to show that the dynamical patterns of neuronal activity in developing mouse neocortex consists of spatially discrete domains that are coordinated in an age, region, and state- dependent fashion. Ongoing cortical activity displayed mirror-symmetric activation patterns across the cerebral hemispheres and showed characteristic network architectures that were shaped during development, with frontal-parietal areas functionally connected to occipital regions regions through cingulate and motor cortex. This study provides the first broad description of population activity in the developing neocortex at a scope and scale that bridges the microscopic and macroscopic spatiotemporal resolutions provided by traditional neurophysiological or functional neuroimaging techniques. Mesoscale maps of cortical population dynamics within animal models will be crucial for future efforts to understand and treat neurodevelopmental disorders. The cerebral cortex exhibits spontaneous and sensory evoked patterns of activity during early development that is vital for the formation and refinement of neural circuits. Identifying the source and flow of this activity locally and globally is critical for understanding principles guiding self-organization in the developing brain. Here we use whole brain transcranial optical imaging at high spatial and temporal resolution to demonstrate that dynamical patterns of neuronal activity in developing mouse neocortex consist of spatially discrete domains that are coordinated in an age, areal, and behavior- dependent fashion. Ongoing cortical activity displays mirror-symmetric activation patterns across the cerebral hemispheres and stereotyped network architectures that are shaped during development, with parietal-sensorimotor subnetworks functionally connected to occipital regions through frontal-medial cortical areas. This study provides the first broad description of population activity in the developing neocortex at a scope and scale that bridges the microscopic and macroscopic spatiotemporal resolutions provided by traditional neurophysiological and functional neuroimaging techniques. Mesoscale maps of cortical population dynamics within animal models will be crucial for future efforts to understand and treat neurodevelopmental disorders.
<!-- Furthermore, ongoing activity was regulated by physiological state with cortical regions exhibiting areal dependent coordination of activity with motor behavior differentially during the course of development. -->
# Introduction # Introduction
Brain development requires neural activity for establishing proper circuit structure and function [#Katz:1996]. Fetal movements, prenatal electroencephalographic oscillations [#Vanhatalo:2005][#Tolonen:2007], and sensitivity to disruptions in periphreal inputs affecting neurotransmission all underscore the presence and importance of neural activity in the developing brain. Indeed, embryonic limb movements in species ranging from chick to human are thought to be initiated by spontaneous motor neuron activity in the spinal cord and thought to be crucial for activity-dependent development of motor synapses [#Sanes:1999][#Petersson:2003][#Marder:2005]. In the visual system spontaneous waves of activity originating eye, 'retinal waves', have long been studied as a model for activity-dependent circuit development before the start of sensory experience [#Ackman:2014]. However it is only recently that we have begun to appreciate the actual nature of persistent neural activity patterns as they exist in the developing brain in vivo. For example, sensori-motor feedback associated with spontaneous movement generated by spinal motor neurons triggers synchronized 'spindle-burst' potentials among cells in somatosensory cortex [#Khazipov:2004a][#Yang:2009] before the start of locomotion and tactile behavior. Correlated bursts of activity occur among neurons in the developing rat hippocampus in vivo [#Leinekugel:2002][#Mohns:2008]. Spontaneous retinal waves drive patterned activation of circuits throughout the visual system before the onset of vision [#Ackman:2012][#Colonnese:2010] and provide spatiotemporal information suitable for organizing connections within and between different visual areas. However, a comprehensive account of the dynamical patterns of persistent activity across the developing neocortex in vivo has not been undertaken, largely because a method to assess neural activity between cortical areas simultaneously and non-invasively has not been available. Neuronal activity is required for the proper morphologic and functional development of the vertebrate brain[^Spitzer:2006][^Blankenship:2010]. Early in gestation, calcium transients associated with activity modulate neuronal proliferation, migration, differentiation, and neurotransmitter specification[^Spitzer:2006][^Spitzer:2012]. In mid-gestation, spontaneous motor neuron activity and associated embryonic limb movements shape neuromuscular junction formation and the self-organization of spinal cord circuitry in a wide variety of species[^Sanes:1999][^Petersson:2003][^Marder:2005]. Still later in development, spontaneous and sensory driven activity are vital for the formation and refinement of neural circuits in the spinal cord, brainstem and cortex[^Katz:1996][^Blankenship:2010][^Ackman:2014]. In humans, disrupting neurotransmission during fetal development results in severe brain malformations, epilepsy and cognitive disorders, underscoring the importance of activity for normal brain development[^Meador:2013][^Christensen:2013].
It is only recently, however, that a better appreciation is emerging of the complex spatial and temporal patterns of persistent neural activity that exist in the perinatal brain in vivo. For example, sensory-motor feedback associated with spontaneous movement generated by spinal motor neurons triggers synchronized ‘spindle-burst’ potentials among cells in somatosensory cortex before the start of locomotion and tactile behavior[^Khazipov:2004a][^Yang:2009]. EEG recordings in humans also demonstrate spindle-burst oscillations and slow activity transients in the somatosensory and occipital cortices before birth[^Vanhatalo:2005][^Tolonen:2007]. Correlated bursts of activity in the developing rat hippocampus in vivo[^Leinekugel:2002][^Mohns:2008], and spontaneous retinal waves that drive patterned activation of circuits throughout the visual system before the onset of vision[^Ackman:2012][^Colonnese:2010] are further examples of complex spatiotemporal patterned activity in the neonatal CNS in vivo. However, because we lack methods to assess neuronal activity at adequate temporal and spatial resolution, a holistic account of the dynamical patterns of persistent activity across the neocortex throughout neonatal development in vivo has never been undertaken. Here, we use genetically encoded calcium indicators and transcranial imaging to examine the emergence and pattern of spontaneous activity throughout the neonatal mouse neocortex at unprecedented spatial and temporal resolution in vivo. This work provides the first report on the dynamical nature of cortical activity across the developing cerebral hemispheres and its distinct and integrative features among brain regions during formation of cortical network architecture.
# Results # Results
## Ongoing activity in developing neocortex is characterized by discrete domains ## Ongoing activity in developing neocortex is characterized by discrete domains
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 at macroscopic scale (millimeters) and with mesoscopic spatial and temporal resolution (10s of microns and 100s of milliseconds). We performed our recordings in three age groups throughout the first two postnatal weeks during which the mouse brain attains >90% of its adult weight [#Kobayashi:1963]: P2-P5, P8-P9, and P12-13. We performed transcranial optical recordings from mice expressing the genetically encoded calcium reporter GCaMP (GCaMP3 or GCaMP6) in cortical neurons to assess neuronal population activity patterns at macroscopic scale (across the entire neocortex) with mesoscopic spatial (10s of microns) and temporal resolution (100s of milliseconds). We performed this functional mesoscopic optical imaging (fMOI) in three age groups through the first two postnatal weeks, during which the mouse brain attains >90% of its adult weight [^Kobayashi:1963]: P2-P5 (N = 6 mice), P8-P9 (N = 4), and P12–13 (N = 5).
The neocortex exhibits a characteristic modular organization across the cortical surface such that vertical arrays of cells concerned with specific sensory features are grouped together as columns in a topographic fashion [#Mountcastle:1997]. Most evidence suggests that cortical columns range from 300-600µm diameter, even between species whose brain volumes differ by a factor of 10^3 [#Mountcastle:1997]. Functional mesoscale optical imaging (fMOI) revealed that supracellular cortical activity patterns were characterized by discrete domains of activation (Fig. 1a-c) ([Supplementary Movie 1](../wholeBrain_blob/SupplementaryMovie-P3gcamp3.mov)). These activity domains ranged from 250 - 976 µm in diameter and 0.4 - 2.6 s in duration <!--(10-90th percentiles)-->(Fig. 1e-h) (Table 1). The duration of cortical 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). 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). The frequency with which cortical domain activations occurred 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. i,j) (Table 1) ([Supplementary Movie 2](../wholeBrain_blob/SupplementaryMovie-P8gcamp3.mov)). The neocortex exhibits a characteristic modular organization across the cortical surface in which vertical arrays of cells, derived from the same or nearby embryonic precursors, are grouped together as functional columns[^Mountcastle:1997]. Cortical columns range from 300–600 µm in diameter, even across species whose brain volume differs by greater than a factor of one-thousand[^Mountcastle:1997]. fMOI revealed that supracellular cortical activity patterns were characterized by discontinuous, discrete domains (**Fig. 1a-c**) [Supplementary Movie 1](SupplementaryMovie1.mp4). These domains were active from 0.4 - 2.6 s at a time (10-90th percentiles) and ranged from 250 - 976 µm in diameter (**Fig. 1e-h**) (**Table 1**). The duration of cortical domain activity was not significantly affected by age (F = 0.933, p = 0.428) (P2–5, N = 15653; P8–9, N = 70189; P12–13, N = 120214 domains) (**Fig. 1e,f**), but the diameter (F = 25.788, p = 0.000188), (**Fig. 1g,h**), and the frequency of cortical domain activations increased significantly with age (F = 29.562, p = 8.86e–12) (P2–5, N = 22; P8–9, N = 30; P12–13, N = 38 movies/hemi) (Fig. i,j) (Table 1) ([Supplementary Movie 2](SupplementaryMovie2.mp4),[Supplementary Movie 3](SupplementaryMovie3.mp4)). None of these measures differed significantly between the two hemispheres (duration: F = 0.017, p = 0.900; diameter: F = 0.192, p = 0.671808; frequency: F = 0.012, p = 0.911).
Its intriguing that we found the size of cortical domains to be centered on this range at early ages, because this is in agreement with previous work showing that population activity in neonatal rat barrel cortex maps onto ontogenetic modules centered on each barrel column [#Yang:2012a] and barrels are an archetypical model for columnar cortical function in rodent. Indeed, we found a cortical area in primary somatosensory cortex at P2-5 where cortical domain activations group into rows and individual modules that match primary barrel cortex structure (Fig. 1c) (Supplementary Fig. of zoomed maxproj image). This indicates that early activity in developing cortical areas can be matched to the size the functional columns thought to be the fundamental processing unit of the cerebral cortex. It’s notable that the size of the active cortical domains is in close agreement with previous work showing that population activity in neonatal rat barrel cortex is synchronized among local groups of neurons and maps onto ontogenetic modules centered on each barrel column[^Bureau:2004][^Golshani:2009][^Yang:2012a]. Indeed, we found a cortical area in primary somatosensory cortex at P2–5 where cortical domain activations grouped into rows and individual modules that match primary barrel cortex structure (**Fig. 1c**) ([Supplementary Fig. 1](SupplementaryFig1.pdf)). Since barrels are an archetypal model for cortical columns in the rodent, these results indicate that early domain activity across the developing cortex is related to the functional columns thought to be a fundamental processing unit of the cerebral cortex.
![ **Figure 1.** Calcium domains throughout neonatal mouse neocortex. **a** Experimental schematic. **b** Left panel: Single image frame showing calcium domains in both hemispheres at postnatal day 3 (P3) and the mask of detected domain signals. Middle and right panels: Time projection map from a raw dF/F movie segment and the corresponding map from automatically detected domain masks. Notice the individual domains of activity in the area of barrel cortex (arrow) **c** Centroid positions for segmented domain masks from a 10 min recording. Points are overlaid on a reference map of primary sensory areas determined by thalamocortical inputs (red outlines). Notice rows of whisker barrels are evident in the structure of domain centroid positions (arrow). **d** Functional activity map at P3. Based on pixel activation frequency from all detected domains in a single 10 min recording. Map is overlaid on cortical areal parcellations. Notice localized maxima and minima of functional activity between areas that approximate known anatomical cortical area boundaries and the mirroring of map structure bilaterally. **e** Mean domain duration maps from 3 SNAP25-Ai103 mice. **f** Histograms showing domain durations distributions in the P2-5, P8-9, and P12-13 age groups and by cortical hemisphere (L, R). **g** Mean domain diameter maps from same 3 mice in e. **h** Histograms showing the distributions of domain diameters. **i** Mean domain frequency maps from same 3 mice in e. **j** Boxplot distributions of hemispheric domain frequencies.](Figure1.png) <figure><img src="Figure1.png" /><figcaption>Figure 1. Calcium domains throughout neonatal mouse neocortex.
a, Experimental schematic. b, Left panel: Single image frame showing calcium domains in both hemispheres at postnatal day 3 (P3) and the mask of detected domain signals. Middle and right panels: Time projection map from a raw dF/F movie segment and the corresponding map from automatically detected domain masks. Notice the individual domains of activity in the area of barrel cortex (arrow) c, Centroid positions for segmented domain masks from a 10 min recording. Points are overlaid on a reference map of primary sensory areas determined by thalamocortical inputs (red outlines). Notice rows of whisker barrels are evident in the structure of domain centroid positions (arrow). d, Functional activity map at P3. Based on pixel activation frequency from all detected domains in a single 10 min recording. Map is overlaid on cortical areal parcellations. Notice localized maxima and minima of functional activity between areas that approximate known anatomical cortical area boundaries and the mirroring of map structure bilaterally. e, Mean domain duration maps from 3 SNAP25-Ai103 mice. f, Histograms showing domain durations distributions in the P2–5, P8–9, and P12–13 age groups and by cortical hemisphere (L, R). g, Mean domain diameter maps from same 3 mice in e. h, Histograms showing the distributions of domain diameters. i, Mean domain frequency maps from same 3 mice in e. j, Boxplot distributions of hemispheric domain frequencies.</figcaption></figure>
Table 1: Domain statistics
| | duration (s) | diameter (µm) | frequency (hemisphere-min^-1) | | | duration (s) | diameter (µm) | frequency (hemisphere-min^-1) |
| ------ | ------------ | ----------------- | ----------------------------- | | ------ | ------------ | ----------------- | ----------------------------- |
@@ -39,197 +49,228 @@ The neocortex exhibits a characteristic modular organization across the cortical
| P8-9 | 0.6 (0.2) | 569.78 (220.56) | 98.35 (27.60) | | P8-9 | 0.6 (0.2) | 569.78 (220.56) | 98.35 (27.60) |
| P12-13 | 0.4 (0.2) | 1047.66 (367.60) | 147.80 (78.65) | | P12-13 | 0.4 (0.2) | 1047.66 (367.60) | 147.80 (78.65) |
| Notes: Values are reported as medians (median absolute deviation) |||| <small>Notes: Values are reported as medians (median absolute deviation)</small>
[ **Table 1: Domain statistics**]
## Cortical dynamics differs with area and age ## Cortical dynamics differs with area and age
We examined how the spatiotemporal properties of cortical domains vary among different cortical regions by parcellating the brain into distinct anatomical boundaries using reference coordinates from a mouse line that expressed a tdtomato reporter in thalamocortical afferents at P7 (Fig. 1c,d) (Supplementary Fig.). Patterns of thalamocortical axon terminals outline primary sensory cortical areas [#Lebrand:1996] during mouse postnatal development. We aligned these parcellations to the Allen brain mouse atlas and then scaled the resulting cortical area reference coordinates to match activity maps from each animal containing functional boundaries for barrel cortex and visual cortex where spontaneous retinal waves functionally map out developing visual areas [#Ackman:2012] (Fig. 1c-e,g,i). We examined how the spatiotemporal properties of cortical domains vary among different cortical areas by parcellating the brain into distinct anatomical regions using reference coordinates from a mouse line that expressed a tdtomato reporter in thalamocortical afferents at P7 (**Fig. 1c,d**, [Supplementary Fig. 1](SupplementaryFig.1.pdf)). Patterns of thalamocortical axon terminals outline primary sensory cortical areas[^Lebrand:1996] during mouse postnatal development. We aligned these parcellations to the Allen brain mouse atlas and then scaled the resulting cortical area reference coordinates to match activity maps from each animal containing functional boundaries for barrel cortex and visual cortex where spontaneous retinal waves functionally map out developing visual areas[^Ackman:2012] (**Fig. 1c-e,g,i**).
Cortical domain frequency among different regions scaled as a function of net cortical area and this association became stronger during the course of development (Fig. 2a). The most frequently active cortical regions at each age group when normalized to the amount of total amount of cortical space was the limb/trunk representations in somatosensory cortex (Fig. 1i, [SupplementaryFig-areal-stats.ai](../wholeBrain_blob/SupplementaryFig-areal-stats.ai)). In contrast to diameter and duration, the frequency and amplitude of cortical domain activity was remarkably uniform across areas at each age of development ([SupplementaryFig-areal-stats.ai](../wholeBrain_blob/SupplementaryFig-areal-stats.ai)) indicating a homeostatic regulation of global activity levels. The long tails in the domain duration and diameter distributions at P2-5 and P8-9 (Fig. 1f,h) were dominated by retinal wave driven cortical activity in V1 that lasted on the order of seconds to tens of seconds (Fig. 1e, Fig. 2b,c), but also by long lasting wave-like activations occurring in motor cortex (Fig. 1e, Fig. 2b,c). Indeed the cortical regions with the highest wave motion indices were V1 and M1 at P2-5, with V1 continuing to have the highest index at P8-9 and then dropping to mean motion idx level similar to other cortical regions at P12-13. The diameter of domain activation became larger among cortical regions during the second postnatal week including the S1-limb/body regions where at P13 a small subpopulation of events had mean diameters approaching that of the entire hemisphere and a higher wave motion index (Fig. 2c-f) (x% of all events, ~2/10min) ([Supplementary Movie 3](../wholeBrain_blob/SupplementaryMovie-P13gcamp6.mov)). These global population events synchronized activity across cortical areas and had centers of mass that were concentrated near the middle of each hemisphere in the S1-limb/body area. Cortical domain frequency among different regions scaled as a function of net cortical area, and this association became stronger during the course of development (**Fig. 2a**). The most frequently active cortical regions at each age group when normalized to the total amount of cortical space were the limb/trunk representations in somatosensory cortex (**Fig. 1i**, [Supplementary Fig. 2](SupplementaryFig.2.pdf)). In contrast to diameter and duration, the normalized frequency and amplitude of cortical domain activity was remarkably uniform across areas at each age of development ([Supplementary Fig. 2](SupplementaryFig.2.pdf)), suggesting a homeostatic regulation of global activity levels. The long tails in the domain duration and diameter distributions at P2–5 and P8–9 (**Fig. 1f,h**) were largely due to retinal wave driven cortical activity in V1 (P2–5, 37.3%; P8–9, 17.7% of all cortical domains > 5s) and wave-like activations in motor cortex (**Fig. 1e**, **Fig. 2b,c**) (P2–5, 23.7%; P8–9, 21.8% of all domains > 5s). In contrast, the next largest areal contribution to long-lasting cortical domains (durations > 5s), from V1L and barrel cortex, was considerably smaller (P2–5: V1L, 5.6%, barrel 1.2%; P8–9: V1L, 6.7%, barrel, 7.2%).
To further characterize domain activations across development and cortical regions, we computed a wave motion index, which varied significantly with age and brain area (age: F = 104.068, p = < 2e-16; area: F = 3.848, p = 1.88e-06, two-way ANOVA). Indeed, the cortical regions with the highest wave motion indices were V1 and M1 at P2–5, with V1 continuing to have the highest index at P8–9 and then dropping to a mean motion index level similar to other cortical regions at P12–13 (**Fig. 2d,e**). At P12-13, wave motion was much less common in all areas, but interestingly a small subpopulation of long-lasting activations (57.5 ± 11.4 s vs 0.4 ± 0.2 for all other domains) had a high wave motion index (0.055 ± 0.038 vs 1.006e-04 ± 8.606e-05), with diameters approaching that of the entire hemisphere (6488.14 ± 18.38 µm vs 1029.28 ± 367.6 µm), but occurring very infrequently (0.033% of all domains, 13.2% of all active pixels at P12–13, 0.1 events per hemisphere-min) (**Fig. 2b-e**) ([Supplementary Movie 4](SupplementaryMovie4.mp4)). These global population events at P12-13 synchronized activity across cortical areas and had centers of mass that were concentrated near the middle of each hemisphere in the S1-limb/body area (**Fig. 2d**). These results show that the spatiotemporal properties of ongoing cortical activity is regulated in an age and areal dependent fashion and that differences in dynamical activity patterns may reflect unique developmental requirements for functional circuits locally and globally among brain regions.
![ **Figure 2.** Spatiotemporal properties of cortical domains. **a** Domain frequency as function of cortical area size. **b** Scatterplots of domain diameter and duration. **c** Time projection maps of waves in motor cortex at P3, visual cortex at P5, and occipital-parietal-frontal cortex at P13. **d** Scatterplots of wave motion index as function of domain diameter. **e** Mean wave motion index over development.](Figure2.png) <figure><img src="Figure2.png" /><figcaption>Figure 2. Spatiotemporal properties of cortical domains.
a, Domain frequency as function of cortical area size. b, Scatterplots of domain diameter and duration. c, Time projection maps of waves in motor cortex at P3, visual cortex at P5, and occipital-parietal- frontal cortex at P13. d, Scatterplots of wave motion index as function of domain diameter. e, Mean wave motion index over development.</figcaption></figure>
## Cortical activity is coordinated with motor behavior ## Cortical activity is coordinated with motor behavior
Next we assessed mesoscale cortical activity patterns as a function of physiological state and motor behavior. It has previously been demonstrated that general anesthesia abolishes spontaneous retinal wave activity in visual system [#Ackman:2012] and spontaneous activity in entorhinal cortex [#Adelsberger:2005]. We found that during anesthesia induction, there is rapid (<60 s) knock down of cortical activity (Supplementary Movie) (Supplementary Fig) at all ages. While in neonates, no cortical activity was found during general anesthesia, at P12-13 within ~10-20min after general induction we found altered spontaneous patterns, with short duration, large diameter population activities synchronizing multiple cortical regions. (Supplementary Movie). The continued spinal motor activity during early anesthesia and the altered cortical activity patterns that ensue under anesthesia at P12-13 suggest a maturational dependence of isoflurane anesthesia on neural activity that affects brain regions differentially during development. We monitored animal movements simultaneously with cortical activity during our fMOI recordings to gain insight into the relationship between motor activity, behavioral state and cerebral cortical dynamics during development. General anesthesia is known to abolish spontaneous retinal wave activity in the visual system[^Ackman:2012][^Colonnese:2010] as well as spontaneous activity in entorhinal cortex[^Adelsberger:2005]. We found that activity across the entire cortex rapidly decreased upon the administration of isoflurane anesthesia (**Fig. 3b,c**) ([Supplementary Movie 5](SupplementaryMovie5.mp4)) at all ages tested (P2–5, N = 2; P8–9, N = 4; P12-13, N = 3 animals). In young neonates (P2–P9), little cortical activity was observed within 20 min of deep anesthesia onset (0.37 ± 0.22 domains/hemisphere-min), while at P12–13 there was altered spontaneous activity patterns, with short duration, large diameter activations synchronizing multiple cortical regions (1.43 ± 0.41 domains/hemisphere-min; p = 0.00821 Wilcoxon Rank Sum test vs P2–9) ([Supplementary Movie 6](SupplementaryMovie6.mp4)). Despite the rapid cessation of all cortical activity under anesthesia in young neonates, motor twitches continued (**Fig. 3b**), presumably through the autonomous activity of spinal motor neurons[^Blumberg:1994]. The continued spinal motor activity during early anesthesia and the altered cortical activity patterns that ensue under anesthesia at P12–13 suggests a maturational dependence of general anesthesia on network activity that affects brain regions differentially during development. Furthermore, spontaneous slow oscillations in corticothalamic networks begin during sleep and general anesthesia in rodent and cat near the end of the second postnatal week[^Jouvet-Mounier:1970][^Steriade:1993b][^Rochefort:2009]—therefore it is likely that persistent cortical activity under deep anesthesia at P12–13 reflects lasting recurrent excitatory connectivity characteristic of more mature corticothalamic networks.
We monitored motor movements simultaneously with cortical activity during our fMOI recordings to gain insight into the relationship between motor behavior output to cerebral cortical dynamics during development. The highest levels of synchronized cortical domain activity occurred during periods of relatively sparse motor behavior whereas the lowest levels of synchronized cortical activity occurred during periods of increased motor movement (Fig. 3c-e). Variation in the strength of correlation between cortical areas and the motor movement signal depended on both brain region (p < 2.2e-16, anova) and age (p = 1.627e-05, anova) (Fig 3c-f). Interestingly, the first age group in which motor cortex exhibited signficant positive correlation with motor movements was at P12-13 (r=0.06±0.02, p-value = 0.001449, t-test) (Fig. 3f). We hypothesized that just before eye opening around P11-P13 there will be a shift with significant zero lag or preceding correlation between motor cortex and the motor movement signals perhaps conincciding with teh begining fo ggoal directed behavior. Motor and state dependent behavior surprisingly complex, even in neonates. In the unanaesthetized animal, the relationship between motor movements and cortical activity varied depending on brain area (F = 33.7975, p < 2.2e–16) and age (F = 11.1671, p = 1.627e–05, two-way ANOVA) (P2–5, N = 22; P8–9, N = 30; P12–13, N = 38 movies) (**Fig 3d-f**). At all ages examined (P2-5, P8-9 and P12-13), activity in sensory cortex (S1-limb/body) was strongly correlated with movement, consistent with movement driven somatosensory self-stimulation[^Khazipov:2004]. At P2-5, activity in PPC was also strongly correlated with animal movement (Fig. 3e,f) but not at P12-13, suggesting the presence of transient input to PPC from the somatosensory periphery. Interestingly, the first age group in which activity in motor cortex exhibited significant positive correlation with motor movements was at P12–13 (r = 0.062 ± 0.019, p = 0.001449, t-test) (**Fig. 3e,f**). Before this (P2-5 and P8-9), motor cortex activity was not correlated with animal movements. These results indicate that spontaneous motor twitches, which are typical during perinatal development[^Gramsbergen:1970][^Narayanan:1971][^Vries:1982][^Petersson:2003][^Blumberg:2013], occur independent of motor cortex activity and anesthesia, but are powerful drivers of activity in somatosensory cortex in the unanaesthetized animal. Moreover, at around P12–13—a period of time when patterned vision, hearing, and locomotor exploration is beginning in mice— there is a shift towards positive preceding or zero lag correlation between frontal cortex activity and motor movements, perhaps coinciding with the beginning of goal directed behavior.
![ **Figure 3**. Cortical domain activity is state dependent. **a** Experimental schematic. Red light illumination measured with a photodiode (PD) was used to monitor motor movements. **b** Cortical activity (active pixel fraction by hemisphere) and motor movement signal after onset of isoflurane anesthesia at 205 s. **c** Time projection maps (40 s segments) at times indicated in recording from **b**. **d** Cortical activity and coincident motor activity signals. Gray shading indicates active and quiet motor periods determined by the half-rise and decay times from peak in the low pass filtered motor signal. Active pixel fraction traces for motor (M1,M2), somatosensory (HL,FL,T; barrel), and visual (V1,V2) cortex shown at bottom of panel. Red links show synchronized motor movements and brain activity with different cortical regions. **e** Single frame domain masks for times indicated in **d**. **f** Pixel activation frequency maps during quiet and active motor periods. **g** Cross-correlation functions between cortical regions and motor movement signals. Notice the general weak correlation between motor movement and all cortical activity signals (r = , p = ) and the high positive correlation between motor movement and S1-limb/body signals (r = 0.3019, p < 2.2e-16).](Figure3.png)
<figure><img src="Figure3.png" /><figcaption>Figure 3. Cortical domain activity is state dependent.
a, Experimental schematic. Red light illumination measured with a photodiode was used to monitor motor movements. b, Cortical activity (active pixel fraction by hemisphere) and motor movement signal after onset of isoflurane anesthesia at 205 s. c, Time projection maps (40 s segments) at times indicated in recording from b. d, Cortical activity and coincident motor movement activity signals. Active pixel fraction traces for motor (M1,M2), somatosensory (HL,FL,T; barrel), and visual (V1,V2) cortex shown at bottom of panel. e, Time projection map at P5 showing HL,FL,T and PPC activity coincident with motor movements. f, Mean cross-correlation functions between cortical regions and motor movement signals across all movies. Notice the high positive correlation between motor movement and S1-limb/body (HL,FL,T) signals at all ages. g, Boxplots of cortical activity and motor movement correlation at lag zero.</figcaption></figure>
## Cortical activity comprises distinct subnetworks ## Cortical activity comprises distinct subnetworks
The complex activity dynamics among nascent neocortical networks suggests that spatiotemporal correlations between areas may exist that provide information for development of intra- and inter-hemispheric connections. Indeed, recent work has suggested that neural activity is required for the migration of some interneuron subtypes (fishell work; ZJ. Huang work) and the development of callosal connections (kir2.1 eporation paper; olavarria work?). Since the timing of neural activity patterns is thought to be important for various aspects of circuit development it remains crucial to understand the correlational structure of ongoing cortical activity between brain regions. Thus to achieve a better understanding of the early activity patterns that may regulate interactions between cortical regions we first looked at correlation between the hemispheres. Cortical activity exhibited high temporal correlation between the hemispheres ([SupplementaryFig-symmetry.ai](../wholeBrain_blob/SupplementaryFig-symmetry.ai)). In addition 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. The complex dynamical activity apparent in our fMOI recordings of nascent neocortical networks may reflect the emergence of functional circuitry, and could play a fundamental role in the development of intra- and inter-hemispheric connectivity[^Suarez:2014]. To better understand the early activity patterns that may regulate cortical development and function, we examined activity correlations within and between the two hemispheres. Notably, mean activity in each hemisphere exhibited high temporal and spatial correlations across the midline (temporal, r = 0.59 ± 0.02; anterior-posterior, r = 0.43 ± 0.02; medial-lateral, r = 0.43 ± 0.02; N = 90 movies), with the strength of these inter-hemispheric correlations changing with age (temporal: F = 7.801, p = 0.000765; anterior-posterior: F = 14.63, p = 3.34e-06; medial-lateral: F = 3.663, p = 0.0297; one-way ANOVA) ([Supplementary Fig. 3](SupplementaryFig.3.pdf)). These correlations are readily apparent in still frames ([Supplementary Fig. 3](SupplementaryFig.3.pdf)) and movies (**Supplementary Movies 1-3**), which show frequent episodes of mirror symmetric activity between the two hemispheres across the midline.
We computed a correlation matrix for each recording based on the pixel active fraction timecourses for each pair of cortical parcellations. Community structure among cortical nodes in the pairwise association matrix at P12-13 was detected with hierarchical clustering based on optimization of the graph modularity score, which measures the number of edges that fall within groups minus the number expected by chance [#Newman:2004]. The resulting community dendrogram was used to order the mean correlation matrices for each age group (Fig. 4a) (N=). This revealed a non-random network organization and a grossly similar correlational structure present across development (Fig. 4a). There were 4 primary subnetworks detected at P12-13-- motor-S1-face (green), S1-body (red), medial--visual (blue), and auditory (purple). Indeed the 4 network modules detected at 12-13 display as clusters along the diagonal in the correlation matrix and these are apparent in the earlier age groups as well. However, functional correlation between cortical regions was highly dynamic across development, with both increases and decreases in correlation depending on brain area and age. Remarkably, detection of community structure in each age group independently showed similar vertex memberships in the 3 largest modules, with developmental switches in module membership occurring in PPC and M2, two brain regions known to integrate multimodal sensory and motor planning information (Fig. 4b). For a more detailed analysis of the activity patterns, we computed a pairwise correlation coefficient of activity between all cortical areal parcellations in the two hemispheres (**Fig. 4a**). The matrix of activity correlations amongst all areas within and between the two hemispheres reveals strong, modest, and even negative correlations between areas. To examine the community structure among the cortical areas or ‘nodes’ in the pairwise association matrix, we used a hierarchical clustering algorithm based on the optimization of a graph modularity score, which measures the number of edges that fall within groups minus the number expected by chance[^Newman:2004]. The resulting community dendrogram (at P12-13) was used to order the mean correlation matrix for all age groups (**Fig. 4a**) (P2–5, N = 22/6; P8–9, N = 30/4; P12–13, N = 38/5 movies/mice). This analysis revealed a network organization and a coarse correlational structure that was similar between animals and across development (**Fig. 4a**). There were 4 primary network modules detected at P12–13; motor-S1-face, S1-body, medial–visual, and auditory. Indeed the 4 network modules detected at P12–13 are obvious as clusters along the diagonal in the correlation matrix, which were also apparent in the earlier age groups. Remarkably, detection of community structure in each age group independently showed similar node memberships in the 3 largest modules, with developmental switches in module membership occurring for areas PPC and M2, two brain regions known to be integral for multimodal sensory processing and motor planning (**Fig. 4b**).
The strongest correlations at each age typically occurred at symmetrical regions between the hemispheres in RSA, M1, M2, and T (Fig. 4a,b). In primary sensory areas such as A1, V1, and S1-barrel, inter-hemispheric correlation was initially weak and became strong at P12-13. At P8-9, correlations among cortical regions became more spread out, with strong correlations becoming stronger and weak correlations becoming weaker. There was increased positive correlation within modules and stronger negative correlation between modules at P12-13. For example, visual regions exhibited increased negative correlation with the body-parietal subnetwork, A1 showed increased negative correlations with multiple areas, and motor-parietal regions exhibited increased intra-module correlations. Functional connectivity between cortical regions was highly dynamic across development, with both increases and decreases in correlation depending on brain area and age. The strongest correlations at each age typically occurred at symmetrical regions between the hemispheres in RSA, M1, M2, and T (**Fig. 4a,b**). In primary sensory areas such as A1, V1, and S1-barrel cortex, inter-hemispheric correlation was initially weak, but became strong at P12–13. At P8–9, correlations among cortical regions were more spread out relative to P2-5, with strong correlations becoming stronger and weak correlations becoming weaker. At P12–13, there were increased positive correlations among areas within modules and stronger negative correlations between modules. For example, visual regions exhibited increased negative correlations with the body-parietal subnetwork, A1 showed increased negative correlations with most regions, and motor-parietal areas exhibited increased intra-module correlations.
<figure><img src="Figure4.png" /><figcaption>Figure 4. Functional architecture of developing neocortex.
a, Group averaged correlation matrices of domain activity among cortical areas. Color map indicates Pearson’s r correlation coefficient values. Dendrogram and node order from community structure detected with hierarchical clustering in the P12–13 group. b, Map of cortical area associations for r > 0.15. Node colors represent cortical communities detected with hierarchical clustering within each age group. Edge width indicates the squared connection weight (r^2). Note both similarities in module membership and increased connection strength with age.</figcaption></figure>
We analyzed the topological properties of developing mouse cortical networks with graph theoretic measures used in fMRI studies of large-scale functional connectivity[^Bullmore:2009]. Graphs of mean functional connectivity illustrated decreased randomness and tighter clustering among cortical modules during network development (**Fig. 5a**). The average path length-- the mean shortest path between all node pairs-- changed little over the course of development (**Fig. 5c**) (F = 3.153, p = 0.04765, one-way ANOVA) and did not differ significantly from equivalent random networks at any age group. In contrast, the global clustering coefficient– reflecting how highly interconnected each node’s neighbors are– significantly increased during development (**Fig. 5c**) (F = 18.491, p = 2.033e-07, one-way ANOVA) and was significantly greater than that of equivalent random networks at all ages. The mean degree (number of edges per node) was higher at P12–13 (two-way ANOVA: F = 33.83, p = 5.81e-08; Tukey HSD: P2-5:P8-9, p = 0.0106535; P2-5:P12-13, p = 0.0000983, P8-9:P12-13, p = 0) ([Supplementary Fig. 4](SupplementaryFig.4.pdf)) and varied significantly by area (**Fig. 5b**) (F = 10.30, p = 3.51e-07). Mean node strength–the sum of all edge weights per node– was also significantly affected by both age and area (two-way ANOVA: age, F = 27.477, p = 3.87e-07; node, F = 8.957, p = 1.39e-06) and was highest at P12–13 (Tukey HSD: P2-5:P8-9, p = 0.0105873; P2-5:P12-13, p = 0.0007400, P8-9:P12-13, p = 0.0000002). The five cortical areas having the highest degree and node strength at all ages were M1, M2, PPC, V2M, and RSA (**Fig. 5b**).
We computed measures of node centrality to identify potential hubs in the cortical network. Betweenness centrality identifies important throughputs in a network by measuring the fraction of all shortest paths that pass through a node[^Bassett:2008]. Betweenness centrality varied significantly by node but not with age (node: F = 17.320, p < 2e-16; age: F = 0.869, p = 0.42; two-way ANOVA) (**Fig. 5d**). In contrast, eigenvector centrality—which is proportional to the sum of centralities for a node’s connections, giving larger scores to nodes that are linked to highly connected nodes—varied significantly by node but also increased with age (node: F = 88.29, p < 2e-16; age: F = 6.84, p < 2e-16; two-way ANOVA) (**Fig. 5d**). High scores in both centrality measures indicate hub nodes[^Bassett:2008], which included M1 and M2. Deviations from betweenness-eigenvector linearity can indicate whether a node is potentially more important as a network throughput (higher betweenness centrality) or as a driver node (higher eigenvector centrality). Interestingly, V1 and V2L shifted towards larger eigenvector centrality at P12-13, but maintained low betweenness centrality indicating that visual cortex may be positioned to have greater influence on network activity around the time of eye opening through action as a driver node. These results indicate that greater local connectivity together with higher connection strengths are key features in the development of global network architecture of the cerebral cortex. Furthermore, the connection of occipital to parietal-sensorimotor network modules through frontal-medial cortical hubs such as M2/cingulate, RSA, and posterior parietal cortex suggests that the developing mouse cerebral cortex shares key features of network topology observed in rat, primate, and human resting state brain networks.
<figure><img src="Figure5.png" /><figcaption>Figure 5. Dynamics of functional connectivity in developing neocortex.
a, Graph of functional connections for r > 0.15. Node colors represent cortical communities detected with hierarchical clustering within each age group. b, Boxplots of degree (number of links) and node strength (sum of connection weights) by cortical area. The distributions become increasingly ordered like the P12–13 group with age. c, Boxplots of clustering coefficient and average path length by recording. d, Scatterplots of mean network centrality scores by cortical area. Error bars are s.e.m.</figcaption></figure>
![ **Figure 4.** Functional architecture of developing neocortex. **a** Group averaged correlation matrices of domain activity among cortical areas. Colormap indicates Pearson's r correlation coefficient values. Dendrogram and node order from community structure detected with hierarchical clustering in the P12-13 group. **b** Map of cortical area associations for r > 0.15. Node colors represent cortical communities detected with clustering within each age group. Edge width indicates the squared connection weight (r^2). Note both similarities in module membership and increased connection strength with age.](Figure4.png)
We analysed the topological properties of developing mouse cortical networks with graph theoretic measures used in fMRI studies of large-scale functional connectivity [#Bullmore:2009]. Graphs of mean functional connectivity illustrated decreased randomness and tighter clustering among cortical modules during network development (Fig. 5a). The global clustering coefficient-- calculated as the mean of local clustering coefficients that reflect how highly interconnected each node's neighbors are-- significantly increased during development (Fig. 5c). In contrast, the average network diameter and shortest path changed little over the course of development. Both of these measures were significantly different than that of equivalent random networks at each age group. The mean degree (number of edges per node) was higher at P12-13 and the degree distribution shifted during development to match the node order seen at P12-13 (Fig. 5b) (Supplementary Fig graph metrics). The mean node strength--the sum of all edge weights per node-- was higher at P12-13 and the rank order of node strengths also changed to that of the P12-13 distribution during development. The 4 cortical areas having both the highest degree and node strength were M1, M2, PPC, and V2M (Fig. 5b).
Measures of node centrality were computed to identify potential network hubs. Betweeness centrality measures the fraction of all shortest paths in a network that pass through a node, therefore identifing important throughputs in the network. M1, M2, PPC, and RSA had the highest betweeness centrality scores, which generally decreased during development. Eigenvector centrality is proportional to the sum of centralities for a node's connections, giving high scores to nodes that are linked to many other highly connected nodes. High scores (near the top of the linear fit) indicated hub nodes which included M1 and M2. Deviations from betweeness-eigenvector linearity can indicate whether a node is potentially more important as a network throughput (higher betweeness) or a driver node (higher eigenvector centrality). These results indicate that greater local connectivity together with higher connection strengths are key features in development of global network architecture of the cerebral cortex. Furthermore, the developing mouse cerebral cortex shares features of network topology that are similar to those seen in resting states networks in human infant. Networks having both high clustering and short average path lengths are key features that distinguish small world networks from random or lattice based networks-- , the clustering coefficient and average path length in the mean graphs from each age group. While the average path length decreased only slightly over the course of development, the clustering coefficient increased significantly with age.
![ **Figure 5.** Dynamics of functional connectivity in developing neocortex. **a** Graph of functional connections for r > 0.15. Node colors represent cortical communities detected with clustering within each age group. **b** Boxplots of degree (number of links) and node strength (sum of connection weights) by cortical area. The distributions become increasingly ordered like the P12-13 group with age. **c** Boxplots of clustering coefficient and average path length by recording. **d** Scatterplots of mean network centrality scores by cortical area.](Figure5.png)
# Conclusions # Conclusions
We have provided the large-scale account of spatially discrete neural population activity among the developing cortical hemispheres in vivo. In contrast to classical in vitro imaging studies (Yuste, Katz) We have provided the first account of neuronal population activity at high spatial and temporal resolution across the developing cortex in vivo. Characteristic features of brain activity varied with age and with cortical region. At early postnatal ages (P2-5), activity in some cortical areas was wave-like, particularly the primary visual (V1) and motor (M1) cortices, consistent with earlier reports of waves in V1 originating in the retina[^Ackman:2014]. Wave-like activity persisted in V1 until P8-9, but elsewhere the activity domains were generally more static and became more frequent with age. In contrast to classical in vitro calcium imaging work[^Yuste:1992], but similar to more recent functional imaging and electrophysiology studies in rodent[^Bureau:2004][^Golshani:2009][^Yang:2012a], ongoing cortical domain activity in the neonatal barrel cortex was associated with functional columns. Interestingly, domain activity throughout the neocortex increased in diameter by a factor of 2-3x during the course of the second postnatal week– an amount greater than the 1.5x increase in linear growth along the medial-lateral or anterior-posterior surface of the mouse necortex from P3 to P13. Notably, there was spontaneous domain activity in primary auditory cortex (A1) at all ages studied, even before the end of the second postnatal week when hearing begins in mice. This indicates the presence of spontaneous activity in the auditory system, likely originating in the cochlea[^Tritsch:2010], that may coordinate patterned activity in the developing auditory system, much as spontaneous retinal waves do in the visual system before onset of vision[^Ackman:2012].
We found that cortical activity was coordinated with motor behavioral state in an areal dependent fashion. This is consistent with reports of twitch activated LFPs called spindle bursts in S1 and M1 during development (Khazipov 2004; Yang & luhmann, Luhman JNS 2014). The high spatial resolution of our fMOI recordings provide the first evidence that Consistent with reports of twitch-activated local field potentials (‘spindle-bursts’) in S1 and M1 during development[^Khazipov:2004][^Yang:2009][^An:2014], cortical activity was coordinated with motor movements depending upon area and age. The high spatial resolution of our fMOI recordings showed that transient cortical domain activity was positively associated with motor movements primarily in somatosensory cortex and posterior parietal cortex (PPC) in young neonates. Throughout somatosensory cortex, activity was closely correlated with limb, body, and facial movements that generate sensory stimulation[^Khazipov:2004]. Remarkably, activity in motor cortex was not associated with general limb and body movements until P12-13, presumably because movement in young neonates is initiated in the brainstem and spinal cord, not motor cortex[^Blumberg:1994][^Petersson:2003][^Khazipov:2004], suggesting that ongoing activity in frontal-motor cortex might be linked to the development of higher order associative connections within the cerebral cortex. The PPC is a multimodal sensorimotor processing region important for perceptual decision making, attention, and movement planning in primates and memory-dependent spatial navigation and decision tasks in rodents[^Whitlock:2008][^Harvey:2012]. The PPC receives input from sensory cortical areas, association thalamic nuclei (LD, LP), and retrosplenial cortex (RSA) and may influence motor planning in frontal cortex via its major axonal output to prefrontal cortex, secondary motor cortex (M2), striatum, superior colliculus, and LD/LP[^Harvey:2012]. Our observation of an age dependent correlation between motor movements and the PPC may reflect progressive coordination of activity in PPC with sensorimotor inputs from cortex and thalamus, regulating varying aspects of associational connectivity at different developmental stages before the start of memory directed spatial navigation.
Cortical activity exhibited symmetrical spatial and temporal activations across the hemispheres. This is consistent with the stronger interhemsipheric resting state connectivity between homotopic cortical regions in the human infant. Increased intra-hemispheric connectivity and modularity is thought to be key features of functional connectivity development in large scale brain networks. Our fMOI recordings permitted a global network analysis of activity at unprecedented temporal and spatial scale across the cortex. Since the current work focused on recordings of ongoing cortical activity in the absence of directed sensory stimulation or task engagement, our results are comparable to studies of functional connectivity using resting state fMRI[^Smith:2013] in humans or animal models. Cortical activity exhibited spatial and temporal features that were consistent between animals and across ages. Most strikingly, much of the activity was symmetrical across the two hemispheres, similar to the inter-hemispheric resting state connectivity between homotopic cortical regions observed in human fetal[^Thomason:2013] or infant[^Fransson:2007] resting state fMRI. Large scale brain networks share features of ‘small-world’ network organization[^Watts:1998], such as short average path lengths (efficient global information transfer like a random network) and high clustering among neighboring nodes (robustness to random error like a lattice network)[^Bullmore:2009], along with the presence of high network modularity (presence of community subnetworks) and hubs (nodes having high degree of connections and centrality within or between communities)[^Fair:2009][^Heuvel:2013]. Our network analysis showed that the developing functional network in rodent cortex has a short average path length with high clustering and a significant small-world index[^Heuvel:2014] ([Supplementary Fig. 4](SupplementaryFig4.pdf)), consistent with reports of small-world properties in human resting state networks at birth[^Fransson:2011][^Heuvel:2014]. We found that the developing mouse brain network is comprised of 3-5 distinct functional modules among cortical areas, reminiscent of the 4 functional networks in neocortex observed with resting state fMRI in the fetal, infant, and adult human brain[^Thomason:2014][^Heuvel:2014][^Salvador:2005]. Furthermore, our data revealed several hub areas, including secondary motor cortex (M2, cingulate, mPFC), posterior parietal cortex (PPC), and retrosplenial cortex (RSA), that are remarkably consistent with homolgous cortical hub regions identified in resting state fMRI networks in human[^Buckner:2009][^Heuvel:2013] (mPFC, anterior cingulate, lateral parietal cortex, posterior cingulate cortex) and adult anesthetised rat[^Lu:2012] (mPFC, cingulate, PPC, RSA). Some hub regions, such as M2 and PPC, shifted module memberships during development, perhaps signaling their importance as associative areas and connector hubs between communities, which is comparable to developmental changes in module membership in human resting state fMRI cortical networks[^Fair:2009][^Thomason:2014]. The increasing strength of intra- and inter-hemispheric connections with age is consistent with greater long-range connection strength between disparate brain regions during brain development[^Fair:2009][^Heuvel:2013]. This work shows that properties of functional network architecture emerge and may be shaped very early in cortical development. Given that pathophysiological changes in functional connectivity of large scale brain networks is thought to be a key feature of a number of neurological disorders, including autism and schizophrenia[^Menon:2011][^Rubinov:2013], extension of these fMOI methods to genetic and environmental disease models could help in understanding altered network dynamics that underlie neurodevelopmental disorders.
Developing cerebral networks comprised distinct functional modules among cortical areas. This indicated that functional network identities may be shaped early in development. The inter-module dynamics we found may have important relevance for cortical plasticity seen after sensory deprivation or traumatic injury early in development-- such as that seen in enucleated monkeys (H. kennedy experiments), children born blind or deaf, or epilepsy patients undergoing hemispherectomies. Given that altered inter- and intra- hemispheric functional connectivity is thought to be relevant in autism and schizophrenia extension of the work performed in this study to mouse models for these neurological diseases will be interesting.
There are several advantages of our fMOI approach for assessing cortical activity dynamics in animal models compared with other techniques for recording population activity in the brain. One is the combination of high temporal and spatial resolution relative to techniques like functional magnetic resonance imaging (fMRI), intrinsic signal imaging or traditional EEG and electrode recordings. A second advantage is that fMOI provides a relatively non-invasive method of recording ongoing neuronal population activity in mice transcranially, without confounds associated with anesthesia, compared with traditional electrode recordings, voltage-sensitive dye-, intrinsic signal-, or two-photon imaging based recordings. Another benefit is that the calcium dynamics imaged with fMOI provide a direct readout of ongoing neuronal activity, and signals can be localized by select expression of the genetic calcium indicator in distinct cell populations. Furthermore, the isolation of functional response from movement artifacts is relatively simple with fMOI in comparison to many population recording methods. Finally, the spatial scale of the fMOI approach surpasses that of traditional techniques, with nearly complete coverage of the neocortex, enabling simultaneous recording of numerous sensory-motor areas throughout the hemispheres. Given the notable parallels we’ve demonstrated here between the functional development of mouse and human cortical network architectures together with the broad scope and high spatiotemporal resolution of fMOI for direct recordings of neuronal activity, extension of this work may prove useful in understanding the nature of fMRI connectivity and its link to ongoing brain activity patterns.
This study demonstrates that ongoing activity in developing cortex is not random – it is coordinated in space and time across the entire neocortex. These structured whole brain activity patterns may play key roles in the activity-dependent development of local and global circuit connectivity throughout the nervous system. Moreover, fMOI provides a new, non-invasive method for studying functional connectivity in animal models, and our results demonstrate how the functional cortical network architecture in developing mouse shares common fundamental features with human resting state networks. The simplicity and robustness of functional mesoscale optical imaging will likely be key to integrative assessments of altered brain activity dynamics in animal models for neurological disorders.
This study demonstrates that ongoing activity in developing cortex is not random -- it is coordinated in space and time between hemispheric networks in the neocortex. Furthermore, functional mesoscale optical imaging will be useful in assessing potentially altered functional connectivity dynamics in animal models for neurological disorders.
# Methods Summary # Methods Summary
Anesthetized Rx-Cre:GCaMP3 or SNAP25-GCaMP6 mice between postnatal day 2 to 13 (P2-P13) were were prepared for transcranial optical imaging. Calcium imaging was performed in vivo using wide-field epifluoresence microsopy using a DC-Hg2+ lamp, 1x macro objective, and pco.edge sCMOS camera after a 1 hour recovery period from general anesthesia. Automated image segmentation and calcium event detection was performed using custom MATLAB routines. Rx-Cre:GCaMP3 (Ai38) or SNAP25-GCaMP6 (Ai103) mice aged between postnatal day 2 to 13 (P2-P13) were prepared for transcranial optical imaging as described previously[^Ackman:2012]. Calcium imaging was performed in vivo using wide-field epifluoresence microscopy with a 1x macro objective and a pco.edge sCMOS camera after a 1-hour recovery period from general anesthesia. Automated image segmentation and calcium event detection was performed using custom, freely available MATLAB routines.
**Full methods** and any associated references are available in the online version of the paper at www.nature.com/nature **Acknowledgements** We thank Y. Zhang for technical support. We thank C. Messinger for help with initial SERT-Cre:tdTomatof/f histology. We would like to thank Emily Finn, Todd Constable, and Daeyeol Lee for valuable comments on the manuscript. This work was supported by NIH Grants RR19895, RR029676–01 for the Yale University Biomedical High Performance Computing Center and NIH grants P30 EY000785, R01 EY015788 to M.C.C. M.C.C. also thanks the family of William Ziegler III for their support.
**Supplementary Information** is linked to the online version of the paper at www.nature.com/nature. **Author Contributions** J.B.A. and M.C.C. designed the experiments. J.B.A. performed in vivo imaging experiments, wrote the image processing and data analysis routines, and analyzed the recordings. H.Z. created the GCaMP3 and GCaMP6 mouse lines. J.B.A. and M.C.C. wrote the manuscript.
**Acknowledgements** We thank Y. Zhang for technical support. We would like to thank members of the Crair lab for valuable comments on the manuscript. This work was supported by NIH Grants RR19895, RR029676-01 for the Yale University Biomedical High Performance Computing Center and NIH grants P30 EY000785, R01 EY015788 to M.C.C. M.C.C. also thanks the family of William Ziegler III for their support.
**Author Contributions** J.B.A. and M.C.C. designed the experiments. J.B.A. performed in vivo imaging experiments, wrote the image processing and data analysis code, and analyzed the recordings. H.Z. created the GCaMP3 and GCaMP6 mouse lines. J.B.A. and M.C.C. wrote the manuscript.
**Author Information** Reprints and permissions information is available at www.nature.com/reprints. The authors declare no competing financial interests. Correspondence and requests for materials should be addressed to J.B.A. (james.ackman@gmail.com) or M.C.C. (michael.crair@yale.edu).
# Methods
<<[methods.txt] **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 Ai38 floxed GCaMP3 reporter mice (JAX no. 014538)58 crossed with Rx-Cre[^Swindell:2006] or SNAP25-GCaMP6 (Ai103) transgenic mice aged 2–13 days (P2-P13) after birth (P0) were used.
<<[references.txt]
**Transgenic mice generation.** The Snap25 locus was targeted with homologous recombination to insert LSL-F2A-GFP at the endogenous stop codon, using a targeting vector containing the following components: 5’ arm – F3 – last ~300bp of intron 7 – exon 8 up to the endogenous stop codon – loxP – stop codons – PGK polyA – loxP – F2A-EGFP – WPRE – bGH polyA – AttB – pPGK – neomycin-resistant gene – PGKpA – F5 – mRNA splice acceptor – domain 2 from the hygromycin-resistant gene – SV40 polyA – AttP – 3’ arm. Targeting constructs were generated using a combination of molecular cloning, gene synthesis (GenScript, Piscataway, US) and Red/ET recombineering (Gene Bridges, Heidelberg, DE). The 129S6B6F1 ES cell line, G4, was used for the gene targeting. Correctly targeted neomycin resistant clones were identified by PCR, and then confirmed by Southern blot. One ES clone that gave high percentage chimeras following blastocyst injection was used in a Flp recombinase-mediated cassette exchange (RMCE) transfection to switch the LSL-F2A-GFP expression unit for a T2A-GCaMP6s expression unit. The replacement vector included: F3 – last ~250bp of intron 7 – exon 8 up to the endogenous stop codon – T2A-GCaMP6s – WPRE – bGH polyA – AttB – pPGK – domain 1 from the hygromycin-resistant gene – mRNA splice donor – F5. Following co-transfection with a pCAG-Flpe plasmid, hygromycin resistant colonies were screened for correct 5’ and 3’ junctions and for lack of the original vector sequence. Correct clones were used in blastocyst injections to obtain germline transmission. Resulting mice were crossed to the Rosa26-PhiC31 mice (JAX Stock # 007743) to delete the pPGK-hygro selection marker cassette (in between the AttB and AttP sites), and then backcrossed to C57BL/6J mice and maintained in C57BL/6J congenic background.
<!-- # Metadata --> **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 and the mouse was surrounded by a cotton ball nest. 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 red LED (Radioshack) and photodiode sampled at 25 kHz with a Power1401 (Cambridge Electronic Design) were positioned to monitor respiratory rate and limb/body movements.
<!--Figure 1 metadata **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 EGFP 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, 10 x 12 mm, or 20 x 24 mm were acquired at a rate of 5 or 10 frames per second (200 ms or 100 ms frame period). Each recording consisted of a single, continuously acquired movie for 10 min.
* neonate_ms_fig.png
* binary masks: Screen_Shot_2013-03-29_at_12.06.25_PM_crop.png, ..._crop1.png, ..._crop2.png
* 133.6s timeColorMap projection: 120518_07-fr698-1365-20140710-222230.tif
* Segmentation mask of 133.6s timeColorMap projection: 120518_07-fr698-1365-20140715-230320-cadj.png
* parcellation map
* update 2013-10-03 11:07:29:
* 120518_07_2013-09-11-225029_d2rImageCoords20130930-144657.ai
* created using area coords: 120518_07_2013-09-11-225029_d2rImageCoords20130930-144657.eps
* 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
* contourplot of 20 levels 120518_07_connComponents_BkgndSubtr-60px_noWatershed-20130327-151022_d2r_20130930-124942.eps
* 120518_07_connComponents_BkgndSubtr-60px_noWatershed-20130327-151022_d2r_20130930-124950_eps.png
* [x] add a domain centroid size/duration map similar to: ![](../figures/Screen_Shot_2013-04-03_at_8.42.49_AM.png)
* ![](../figures/Screen_Shot_2013-04-03_at_10.04.36_AM.png)
* domainFreq map: 20140613-084032_ActivityMapFigRawProj-domainFreqRdBu.eps, P5mapDomainFreq-crop.tif, P8mapDomainFreq-crop.tif, P13mapDomainFreq-crop.tif
* domainFreq boxplot:
* domainDur map: 20140613-082954_ActivityMapFigRawProj-domainDurRdBu.eps, P5mapDur-crop.tif, P8mapDur-crop.tif, P13mapDur-crop.tif
* domainDur cdf:
* domainDiam map: 20140613-083156_ActivityMapFigRawProj-domainDiamRdBu.eps, P5mapDiam-crop.tif, P8mapDiam-crop.tif, P13mapDiam-crop.tif
-->
<!-- Figure 2 metadata **Calcium signal detection.** Image processing and calcium signal detection was performed using custom software (available at [http://github.com/ackman678/wholeBrainDX](http://github.com/ackman678/wholeBrainDX)) written in MATLAB (The 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 array: *A = (Ft - F0)/F0*. A background estimate was calculated and subtracted from every frame of A 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, T was computed using Otsu’s method on the set of histogram of pixel intensities in A that corresponded to the set of pixels at the 99th percentile from the Sobel gradient transformation of A. Calcium domain signals were automatically segmented as contiguously connected components in space and time from the binary mask array, *A > T*. Components located outside the cortical hemisphere boundaries or having an area < 50 pixels or a duration of 1 frame were ignored.
* Scatterplot mean freq: 140704-143614-freq_min-scatter.pdf
* Scatterplot domain duration-diameter timeline: 140702-141423-durDiam-scatter-img.pdf
* P3 M1 wave time projection, lomag: 140218_13-fr2564-2601-20140716-144024.tif
* P5 V1 wave time projection, himag: 140328_10-fr2016-2153-20140710-092344.tif
* P13 global wave time projection, lomag: ~~140509_07-fr1725-2004-20140717-172658.tif~~
* P13 global wave time projection, lomag: 140509_22-fr484-609-20140725-155542.tif
* wave motion index scatter: 140702-152109-rhoDiam-scatter.png
* Wave motion index timeline: 140702-162029-waveMotionIdx-jitterTimeline.pdf
-->
<!--Figure 3 metadata **Statistical analysis.** Data sets were analyzed and plotted using custom routines implemented in MATLAB (The Mathworks, Natick, MA) and in R (The R Project for Statistical Computing, http://www.r-project.org) with the ggplot2 plotting library (http://ggplot2.org). Distribution means were compared using two-sample Student’s t-tests unless otherwise noted (Wilcoxon Rank Sum test was used for small, non-normally distributed data sets) or using ANOVA followed by Tukey’s HSD post-hoc test 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 unless otherwise noted. All boxplots report the median, the 25th and 75th percentiles as lower and upper box hinges (1st and 3rd quartiles), the data range as lower and upper whiskers (lowest and highest data values within 1.5 * IQR of the lower and upper hinge, where IQR is the difference between the 3rd and 1st quartiles), and outliers as individual gray points (data values beyond the whisker range).
* P8 isoflurane active px fraction and motor movement signal traces: 140331_11_20140425-171130_d2ractvFraction20140425-171354-20140425-171355.ai
* P8 isoflurane time color projection sequence: 140331_11-fr775-975-20140710-163609.tif, 140331_11-fr1800-2000-20140710-162448.tif, 140331_11-fr2800-3000-20140710-162206.tif **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 from A within each domain were taken as the mean and maximum domain amplitudes. After functional parcellation of the cortical hemipsheres (**Fig. 1**; [Supplementary Fig. 1](SupplementaryFig1.pnf)), domains were assigned areal membership by intersection of the domain centroid with a cortical area’s pixel mask. The number of individual domains per recording within a hemisphere or cortical area was taken to be domain frequency.
* Cortical and photodiode signals and moving averages color coded at diff lags: 120518_07_2013-09-11-225029_d2r_motorSignalFiltFilt_fig.eps **Wave motion analysis.** Optical flow was computed using the Lucas-Kanade method on the binary movie array from all the segmented calcium domain masks for a recording. The motion magnitude, *R* was the velocity vector sum for each domain. The wave motion index for each domain was calculated as *R^2^/D^2^*, where *D* was the domain diameter.
* Pixel activation frequency map projections **Motor movement analysis.** A binary movie array from all the segmented calcium domain masks for a recording was intersected with masks representing different cortical area parcellations. The total number of active pixels per frame expressed as a fraction of the total number of possible pixels per frame for each cortical area gave active pixel fraction time courses for each cortical area in each recording. Movement signals acquired with the photodiode were bandpass filtered using an 8-order, 1-20 Hz pass band elliptic filter, and then rectified and downsampled to the movie frame rate to give a movement time course signal that corresponded to displacements of the limbs and body excluding those from respiration. Cross-correlations between combinations of cortical active fraction time courses and the movement signal time course were computed, and the Pearson’s correlation coefficient at the zeroth time lag was obtained.
* quiet motor state: 120518_07_connComponents_BkgndSubtr-60px_noWatershed-20130327-151022_d2rActivityMapFigContour20131016-163945-20131023_MotorQuiet_eps.png
* active motor state: 120518_07_connComponents_BkgndSubtr-60px_noWatershed-20130327-151022_d2rActivityMapFigContour20131016-163945-20131023_MotorActive_eps.png
* xcorr traces for cortical activity and motor activity: 120518_07_20140604-151545_d2rmotorSignalXCorr20140604-152316.eps **Hemisphere correlation analysis.** A binary movie array from all the segmented calcium domain masks for a recording was intersected with masks representing the cortical hemispheres. The total number of active pixels per frame expressed as a fraction of the total number of possible pixels per frame for each cortical area gave active pixel fraction time courses for each hemisphere in each recording. The mean, normalized medial-lateral and anterior-posterior positions for all active pixels within each hemisphere during coactive frames gave spatial center of mass timecourses for each recording. Pearson’s correlation coefficient was calculated between the active pixel fraction time courses and the activity center of mass time courses to give the temporal and spatial correlation for each movie.
120518_07_20140604-151545_d2rmotorSignalXCorr20140604-152321.eps
* xcorr lags matrix for cortical activity and motor activity: motorXcorrLags_20140602-161719.eps **Functional connectivity analysis.** A binary movie array from all the segmented calcium domain masks for a recording was intersected with masks representing different cortical area parcellations. The total number of active pixels per frame expressed as a fraction of the total number of possible pixels per frame for each cortical area gave active pixel fraction time courses for each cortical area in each recording. Correlation matrices were calculated for each recording by computing pairwise Pearson’s correlation coefficients, *r*, from the matrix containing the cortical active pixel fraction time courses. The mean correlation matrix for each age group was computed and then the binarized correlation matrix at *r* > 0.15 (the maximum *r* value where the 3 largest communities were connected in the graphs at all age groups) 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*.
-->
<!--Figure 4 metadata **Network analysis.** Graph theoretical analyses were performed using the igraph network analysis software library (http://igraph.org). Community structure was detected within each functional association matrix using a greedy optimization algorithm that maximizes the graph modularity score to perform hierarchical clustering[^Newman:2004][^Clauset:2004], where the modularity score measures the fraction of edges within modules for a graph partition compared with that of a randomized equivalent network. Network graphs were plotted using an anatomical layout or using a force-directed graph layout[^Fruchterman:1991] with nodes colored by module membership and edges connecting nodes reflecting the edge weight, *r*. Node degree was the number of connections that link a vertex to the rest of the network. The average path length, *L* of a graph was the mean of the shortest paths (fewest number of edges) between all pairs of nodes. The random average path length, *L~r~* was the mean of the shortest paths in a set of 1000 equivalent random networks that had the same degree sequence as the original graph. The local clustering coefficient was the ratio of the triangles connected to the node and the triples centered on the node, measuring the probability that two neighbors of a node are also connected. The global clustering coefficient, *C* was the ratio of the triangles and connected triples in the graph. The random global clustering coefficient, *C~r~* was the mean of the clustering coefficients in a set of 1000 equivalent random networks that had the same degree sequence as the original graph. The small-world index was calculated as the ratio of the normalized clustering coefficient (*C/C~r~*) and the normalized path length (*L/L~r~*), where a small-world index > 1 indicates a small-world network organization[^Humphries:2008][^Heuvel:2014]. Node strength was the column sums in the weighted adjacency matrix. Betweenness centrality scores corresponded to the fraction of all shortest paths that pass through a node[^Brandes:2001]. Eigenvector centrality scores were the values of the first eigenvector of the association matrix[^Bonacich:2007][^Lohmann:2010], reflecting for each node the sum of direct and indirect connections of every length in a network.
* corr matrix: MeanCorrMatrix-age-2014-06-02.ai, (140602-113013-age.g-groupCorrMatrix.pdf, 140602-112947-dendr-ageP12-13.pdf)
* 140808-093608-graph-squaredEdgeScale-auto.pdf
~~* corr graph spatial layout: 140602-100049-P12-13_0.15.pdf ~~
-->
<!--Figure 5 metadata # References
* 140807-121720-graph-squaredEdgeScale.pdf
~~* corr graph force layout: 140602-101610-P12-13_0.15.pdf~~
~~140806-174912-modularity-boxplot.pdf~~ [^Kobayashi:1963]: Kobayashi T. Brain-to-body ratios and time of maturation of the mouse brain. *Am J Physiol* (1963). 204:343-6. PMID:14033949
~~140806-174917-diam-boxplot.pdf~~
140806-174920-clusterCoeffGlobal-boxplot.pdf
140806-174915-pathLength-boxplot.pdf
* 140804-111208-eigCentr-scatterplot.pdf, r>0.15 [^Gramsbergen:1970]: Gramsbergen A, Schwartze P, and Prechtl HF. The postnatal development of behavioral states in the rat. *Dev Psychobiol* (1970). 3(4):267-80. PMID:5527425
~~* 140804-075217-degree-hist.pdf, binwidth=2, r>0.15~~
--> [^Jouvet-Mounier:1970]: Jouvet-Mounier D, Astic L, and Lacote D. Ontogenesis of the states of sleep in rat, cat, and guinea pig during the first postnatal month. *Dev Psychobiol* (1970). 2(4):216-39. doi:10.1002/dev.420020407 PMID:5527153
[^Narayanan:1971]: Narayanan CH, Fox MW, and Hamburger V. Prenatal development of spontaneous and evoked activity in the rat (Rattus norvegicus albinus). *Behaviour* (1971). 40(1):100-34. PMID:5157515
<!-- Supplementary Movies [^Vries:1982]: de Vries JI, Visser GH, and Prechtl HF. The emergence of fetal behaviour. I. Qualitative aspects. *Early Hum Dev* (1982). 7(4):301-22. PMID:7169027
* supplementaryMovie-P3gcamp3.mov: wholeBrain-shortAlpha-lomed.mov, (120518_07.tif) 10 s long playback, 30fps, = 300fr = 60 s real time [^Fruchterman:1991]: Fruchterman TMJ and Reingold EM. Graph Drawing by Force-directed Placement. *Software - Practice and Experience* (1991). 21(11):1129-1164.
* supplementaryMovie-P8gcamp3.mov: 131208_06_std_lomed-all.mov, 6 s long playback, 30fps, 6s*30fps+3fr = 184fr = 36.8 s real time
* supplementaryMovie-P13gcamp6.mov: 140509_22_fr484-735-lo-all-trans.mov, 8 s long playback, 30fps, 8*30+11fr=. 50.2 s real time.
-->
<!-- Supplementary Figs [^Yuste:1992]: Yuste R, Peinado A, and Katz LC. Neuronal domains in developing neocortex. *Science* (1992). 257(5070):665-9.
* supplementaryFig-domain-stats.ai:
* supplementaryFig-areal-stats.ai: [^Steriade:1993b]: Steriade M, Nunez A, and Amzica F. Intracellular analysis of relations between the slow (< 1 Hz) neocortical oscillation and other sleep rhythms of the electroencephalogram. *J Neurosci* (1993). 13(8):3266--3283. PMID:8340807
* 140702-204319-freq_min-boxplot.pdf
* 140703-083800-freq_min-boxplot.pdf
* 140606-091326-diamnodeT-boxplot.png
* 140606-091332-MeanIntensitynodeT-boxplot.png
* 140606-091338-MaxIntensitynodeT-boxplot.png
* 140606-091345-Duration_snodeT-boxplot.png
* 140606-143236-pairwisetPvalMatrix.pdf
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* Temporal correlation of activity between the hemispheres and preceding motor activation:
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* hemisphere active fraction traces: Screen_Shot_2013-04-08_at_8.47.19_AM.png
* activefraction hemis AP and ML all: ![](../figures/Screen_Shot_2013-04-23_at_8.45.18_AM.png) | 120518_07_connComponents_BkgndSubtr-60px_noWatershed-20130327-151022_d2ractiveFractionPixelLocaCorr20130423-094506.eps [^Petersson:2003]: Petersson P, Waldenström A, Fåhraeus C, and Schouenborg J. Spontaneous muscle twitches during sleep guide spinal self-organization. *Nature* (2003). 424(6944):72-5. PMID:12840761
* activefraction hemis AP and ML segment: ![](../figures/Screen_Shot_2013-04-23_at_8.46.27_AM.png)
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* 2500fr lags: 120518_07_connComponents_BkgndSubtr-60px_noWatershed-20130327-151022activeFraction20130408-143100.eps [^Bureau:2004]: Bureau I, Shepherd GMG, and Svoboda K. Precise development of functional and anatomical columns in the neocortex. *Neuron* (2004). 42(5):789-801.
* 250fr lags: 120518_07_connComponents_BkgndSubtr-60px_noWatershed-20130327-151022activeFraction20130408-151655.eps
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