Display Settings:

Items per page
We are sorry, but NCBI web applications do not support your browser and may not function properly. More information

Results: 6

1.
Figure 6

Figure 6. From: The development of Human Functional Brain Networks.

Node degree (A,C) and betweenness centrality (B,D) z-scores of voxels in voxelwise graphs in infants (A,B) and adults (C,D). Calculations were performed on adjacency matrices that were thresholded at 0.3, and then binarized. Note the congruence between the two measures within children or adults, but the evident differences between children and adult patterns. Figure adapted from (Fransson et al., 2010).

Jonathan D Power, et al. Neuron. ;67(5):735-748.
2.
Figure 4

Figure 4. From: The development of Human Functional Brain Networks.

The 39-node task control graphs of Fair et al, 2007. Fronto-parietal nodes are colored in yellow, and cingulo-opercular nodes are colored in black. Graphs are portrayed in a pseudo-anatomical layout at 10% edge density. (A-C) show graph structures in children ages 7–9 years, adolescents ages 10–15 years, and adults ages 19–31 years, respectively. Note the shifting associations of aPFC and dACC/msFC nodes, and the emergence of greater frontal-parietal functional connectivity over development. ROIs and node memberships were defined in (Dosenbach et al., 2006) and (Dosenbach et al., 2007). Figure modified from (Fair et al., 2007a).

Jonathan D Power, et al. Neuron. ;67(5):735-748.
3.
Figure 1

Figure 1. From: The development of Human Functional Brain Networks.

Group resting-state components in 12 sedated preterm infants. Each row shows coronal, sagittal, and axial view of components thresholded at P>0.5 (alternative-hypothesis threshold for activation vs null). (A) primary visual cortex,(B) bilateral somatosensory and motor cortex, (C) bilateral temporal/inferior parietal cortex encompassing primary auditory cortex, (D) posterior lateral and midline parts of parietal cortex and lateral cerebellum, (E) medial and lateral anterior prefrontal cortex. Figure modified from (Fransson et al., 2007).

Jonathan D Power, et al. Neuron. ;67(5):735-748.
4.
Figure 2

Figure 2. From: The development of Human Functional Brain Networks.

Pseudo-anatomical layouts of a network of 13 DMN ROIs in (A) children 7–9 years old and (B) adults 21–31 years old. Connection widths indicate the strength of correlation between ROI timecourses, and only correlations of r > 0.15 are shown. (C) The results of a two-tailed t-test of children and adult correlations corrected for multiple comparisons at P < 0.05. Correlations that increased with age are shown in blue, and those that decreased with age are shown in red. Insets show LOWESS curves of several individual connections over development. ROIs were defined from (Fox et al., 2005). Figure modified from (Fair et al., 2008).

Jonathan D Power, et al. Neuron. ;67(5):735-748.
5.
Figure 5

Figure 5. From: The development of Human Functional Brain Networks.

The 34-node graphs of task-control, default, and error-related (cerebellar) ROIs of Fair et al, 2009. Graphs are presented in spring embedded layouts at average ages 9, 13, and 25 years moving from left to right. Node centers are colored by pre-defined functional system membership (i.e. all red nodes represent published DMN coordinates), and node rims are colored by anatomical location within the brain (e.g. all blue rims indicate nodes in frontal cortex). The blue clouds (A) highlight the location of frontal nodes across development, and the red clouds (B) highlight the location of pre-defined default mode regions over development. ROIs and node memberships were defined in (Dosenbach et al., 2007; Dosenbach et al., 2006; Fair et al., 2007a; Fox et al., 2005). Figure modified from (Fair et al., 2009).

Jonathan D Power, et al. Neuron. ;67(5):735-748.
6.
Figure 3

Figure 3. From: The development of Human Functional Brain Networks.

(A) The adjacency matrix of a model 22-node graph. For any two nodes, the value of the edge between them (here a 1 or 0), is found at the intersection of the respective row and column (or vice versa) of each node. For example, matrix entry (2,3) shows a value of 1, which is the edge between node 2 and node 3. (B) A spring-embedded layout of the graph, where nodes are circles, and edges are lines between the nodes. (C) A list of several node properties. We illustrate several principles with this figure: the degree of node 20 is 3, because it has 3 edges (to nodes 5, 16, and 22). These nodes are the neighbors of node 20. The clustering coefficient of this node is 0/3, since the neighbors share no edges, out of 3 possible edges. In contrast, the clustering coefficient of node 5 is 1/3. The minimum path length is the number of edges that must be crossed to travel between two nodes. In the case of nodes 2 and 18 (red arrows), the minimum path length is 4 (2-3-6-22-18). The characteristic path length and average clustering coefficient are simply the average of all minimum path lengths and clustering coefficients across the network. The values of this network, in comparison to random and regular graphs, indicate a small-world structure. The betweenness centrality of a node is (proportional to) the fraction of all shortest paths in the network that run through the node. The value for node 1 is 0, whereas the values for nodes 6 and 22 are among the highest in the network, since paths from green to blue nodes almost always use these nodes. High degree and betweenness centrality values are often used to identify hubs, but these values do not necessarily correlate with each other, and must be used with caution, as illustrated by comparing the properties of node 2 with node 18 (values circled in red). Node 2 has a relatively high betweenness centrality, despite the fact that it is evidently not playing a “central” role in the network. This “discrepancy” is because all shortest paths to node 1 must traverse node 2, inflating its betweenness centrality. Contrast the network position of node 2 with that of node 18, which has a much higher degree but only slightly higher betweenness centrality, or node 9, which has high degree but quite low betweenness centrality. Community structure is visually evident in this layout, and modularity-optimizing algorithms obtain the partition indicated by node colorings, which yields a “modularity” of 0.54. Modularity values above 0.3 are typically thought to indicate strong community structure.

Jonathan D Power, et al. Neuron. ;67(5):735-748.

Display Settings:

Items per page

Supplemental Content

Recent activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...
Write to the Help Desk