diff --git a/wholeBrain_main.md b/wholeBrain_main.md index 3c97596..1ecf7e1 100644 --- a/wholeBrain_main.md +++ b/wholeBrain_main.md @@ -73,29 +73,23 @@ The strongest correlations at each age typically occurred at symmetrical regions ![ **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 within cortical modules during network development (Fig. 5a). +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). -Indeed, 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 marginally over the course of development. Both of these measures were significantly different than that of equivalent random networks at each age group. 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. - -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 devleopment. 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. +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 -We have described for the first time how neural population activity constitutes discrete spatial and temporal activations among developing cortical areas in vivo. In contrast to classical in vitro imaging studies (Yuste, Katz) +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) -Cortical activity was coordinated with motor behavioral state in an areal dependent fashion. +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 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. -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 experimetns), 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. +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. -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, our functional mesoscale imaging technique should be a useful tool for assessing potentially altered functional connectivity 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