fix Dec 2014 merge confict

wholeBrain_main.md

@@ -72,7 +72,6 @@ We monitored animal movements simultaneously with cortical activity during our f
 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.
 
 
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 <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>
 
@@ -111,7 +110,6 @@ There are several advantages of our fMOI approach for assessing cortical activit
 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.
 
 
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 # Methods Summary
 
 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.
@@ -147,6 +145,7 @@ Rx-Cre:GCaMP3 (Ai38) or SNAP25-GCaMP6 (Ai103) mice aged between postnatal day 2
 
 **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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 # References
 
 [^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
@@ -184,6 +183,7 @@ Rx-Cre:GCaMP3 (Ai38) or SNAP25-GCaMP6 (Ai103) mice aged between postnatal day 2
 [^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
 
 [^Clauset:2004]: Clauset A, Newman MEJ, and Moore C. Finding community structure in very large networks. ** (2004).
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 [^Khazipov:2004]: Khazipov R, Khalilov I, Tyzio R, Morozova E, Ben-Ari Y, and Holmes GL. Developmental changes in GABAergic actions and seizure susceptibility in the rat hippocampus. *Eur J Neurosci* (2004). 19(3):590-600.
 
 [^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.
@@ -205,6 +205,7 @@ Rx-Cre:GCaMP3 (Ai38) or SNAP25-GCaMP6 (Ai103) mice aged between postnatal day 2
 [^Spitzer:2006]: Spitzer NC. Electrical activity in early neuronal development. *Nature* (2006). 444(7120):707-12. PMID:17151658
 
 [^Bonacich:2007]: Bonacich P. Some unique properties of eigenvector centrality. *Social Networks* (2007). 29:555--564.
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 [^Tolonen:2007]: Tolonen M, Palva JM, Andersson S, and Vanhatalo S. Development of the spontaneous activity transients and ongoing cortical activity in human preterm babies. *Neuroscience* (2007). 145(3):997-1006. doi:10.1016/j.neuroscience.2006.12.070 PMID:17307296
 
 [^Fransson:2007]: Fransson, Skiöld, Horsch, Nordell, Blennow, Lagercrantz, and Aden. Resting-state networks in the infant brain. *Proc Natl Acad Sci U S A* (2007). 104(39):15531--15536.