Development of the brain's functional network architecture.
- Authors
- Vogel, Alecia C; Power, Jonathan D; Petersen, Steven E; Schlaggar, Bradley L
- Year
- 2010
- Journal
- Neuropsychology review
- PMID
- 20976563
- DOI
- 10.1007/s11065-010-9145-7
- PMCID
- PMC3811138
A full understanding of the development of the brain's functional network architecture requires not only an understanding of developmental changes in neural processing in individual brain regions but also an understanding of changes in inter-regional interactions. Resting state functional connectivity MRI (rs-fcMRI) is increasingly being used to study functional interactions between brain regions in both adults and children. We briefly review methods used to study functional interactions and networks with rs-fcMRI and how these methods have been used to define developmental changes in network functional connectivity. The developmental rs-fcMRI studies to date have found two general properties. First, regional interactions change from being predominately anatomically local in children to interactions spanning longer cortical distances in young adults. Second, this developmental change in functional connectivity occurs, in general, via mechanisms of segregation of local regions and integration of distant regions into disparate subnetworks.
rs-fcMRI signal. a rs-fcMRI timecourses from left and right anterior insula/frontal operculum (aI/fO) regions, showing the high correlation or rs-fcMRI “connectivity” found between homotopic regions. b Left aI/fO seed map: the seed map uses the same type of correlations depicted in (a), but instead of determining the correlation between only the left and right aI/fO regions, the seed map shows all voxels with rs-fcMRI timecourses significantly correlated with the left aI/fO
rs-fcMRI analyses using a region matrix approach to network definition. In contrast to a seed map analysis, this approach finds the relationships between a group of functionally or anatomically defined regions. The rs-fcMRI timecourse is extracted from each region, and the timecourse from each region is correlated with each other region to form a matrix. The correlation matrix can then be thresholded to define any correlation above a given value as an edge or connection, which can either be depicted visually (see example in bottom left) or entered into a community detection algorithm (see example on top right). The matrix, in that it includes nodes and edges, constitutes a network in the graph theoretical sense
Development of community structure from local to distributed communities via segregation and integration
Functional brain maturation curve: 238 individual measures of brain maturity are shown as open circles (115 females in red), plotted by chronological age on the x-axis and rs-fcMRI brain maturation index on the y-axis. The data was fit to curves using information criteria analyses and form a non-linear shape typical of many growth curves. The maturation curves for two separate algorithms are shown in solid gray and black lines, while the 95% prediction limits are shown in the dashed lines. Figure from Dosenbach et al. 2010
Direct comparison of region-pair correlations between children and adults. a Connections that get significantly stronger with age (shown in red) are between generally nearby regions. Connections that get significantly weaker with age (shown in blue) are between generally distant regions. Note that regions from both hemispheres are reflected onto a single surface, with the left hemisphere regions displayed in a darker yellow. (Figure adapted from Fair et al. 2007). b Plot of difference between adult and child correlation values by Euclidean distance for each of the pairwise connections shown in panel a. The mean distance for correlations greater in children than adults (red regions) is significantly shorter than the mean distance for correlations greater in adults than children. (Figure adapted from Fair et al. 2007)
Development of rs-fcMRI correlations via functional segregation and integration occurs differentially in functionally distinct regions. a Location of anterior cingulate regions. S1, S3, S5, S7, and I9 are used in the developmental analysis. b Plots reflect the number of voxels significantly correlated with the seed region (S1, S3, S5, S7, and I9) in bins of 20 mm Euclidean distance from the original seed. Significant differences between age groups are denoted with an asterisk. While the S1 region, related to motor control, shows no developmental effects, the S3 region related to attentional control shows decreased correlations with nearby voxels and the S5, S7 and I9 regions related to conflict monitoring, social processing, and emotional regulation, repectively, show both decreased correlations with nearby voxels and increased correlations with distant voxels. (Figure adapted from Kelly et al. 2009)
| # | Section | Preview |
|---|---|---|
| 0 | Resting State Functional Connectivity MRI Signal, Brain Networks, and Common Analysis Techniques — Resting State Functional Connectivity MRI (rs-fcMRI) Signal | fMRI studies generally report differences in the brain's BOLD response to various task conditions… |
| 1 | Resting State Functional Connectivity MRI Signal, Brain Networks, and Common Analysis Techniques — Resting State Functional Connectivity MRI (rs-fcMRI) Signal | Further research has shown that not only do motor regions show correlated resting state timecourses,… |
| 2 | Resting State Functional Connectivity MRI Signal, Brain Networks, and Common Analysis Techniques — Resting State Functional Connectivity MRI (rs-fcMRI) Signal | An important aspect of these correlations is that they appear to be strongest between functionally… |
| 3 | Resting State Functional Connectivity MRI Signal, Brain Networks, and Common Analysis Techniques — What is a Brain Network? | Having established a method to measure functional relationships within the brain, one must decide… |
| 4 | Resting State Functional Connectivity MRI Signal, Brain Networks, and Common Analysis Techniques — What is a Brain Network? | The brain, of course, is also a network. With perfect knowledge, one could define a brain network… |
| 5 | Resting State Functional Connectivity MRI Signal, Brain Networks, and Common Analysis Techniques — What is a Brain Network? | fMRI-based techniques can only deliver data at a macroscopic view of this network, since fMRI… |
| 6 | Resting State Functional Connectivity MRI Signal, Brain Networks, and Common Analysis Techniques — What is a Brain Network? | of interest obtained from fMRI studies (e.g., Dosenbach et al. 2007; Fair et al. 2009). While… |
| 7 | Resting State Functional Connectivity MRI Signal, Brain Networks, and Common Analysis Techniques — What is a Brain Network? | Though some authors study rs-fcMRI networks from the graph theoretic perspective, the word… |
| 8 | Resting State Functional Connectivity MRI Signal, Brain Networks, and Common Analysis Techniques — What is a Brain Network? | One exception to this rule, however, needs to be made: the “networks” (e.g., dorsal attention… |
| 9 | Resting State Functional Connectivity MRI Signal, Brain Networks, and Common Analysis Techniques — Common Analysis Techniques for rs-fcMRI Data | Methods used to study rs-fcMRI relationships have varied greatly. Some papers reviewed here have… |
| 10 | Resting State Functional Connectivity MRI Signal, Brain Networks, and Common Analysis Techniques — Independent and Principal Components Analysis | Though none of the studies reviewed here have defined relationships with independent or principal… |
| 11 | Resting State Functional Connectivity MRI Signal, Brain Networks, and Common Analysis Techniques — Seed Map Analyses | The earliest rs-fcMRI studies (Biswal et al. 1995; Greicius et al. 2003; Fox et al. 2005) used seed… |
| 12 | Resting State Functional Connectivity MRI Signal, Brain Networks, and Common Analysis Techniques — Region Matrix Analyses | In contrast to the seed map approach, which finds voxels related to only a single seed, a region or… |
| 13 | Resting State Functional Connectivity MRI Signal, Brain Networks, and Common Analysis Techniques — Region Matrix Analyses | the matrix will have a value. In an attempt to study biologically significant functional… |
| 14 | Resting State Functional Connectivity MRI Signal, Brain Networks, and Common Analysis Techniques — Graph Theoretic Analyses of Region Matrices: Communities and Small-World Properties | In the course of this review, we will encounter several studies in which graph theoretic tools are… |
| 15 | Resting State Functional Connectivity MRI Signal, Brain Networks, and Common Analysis Techniques — Graph Theoretic Analyses of Region Matrices: Communities and Small-World Properties | Many networks can be viewed as being composed of sub-networks. For example, a person's social… |
| 16 | Resting State Functional Connectivity MRI Signal, Brain Networks, and Common Analysis Techniques — Graph Theoretic Analyses of Region Matrices: Communities and Small-World Properties | In addition to providing the basis for dividing networks of nodes into communities, graph theory can… |
| 17 | Resting State Functional Connectivity MRI Signal, Brain Networks, and Common Analysis Techniques — Graph Theoretic Analyses of Region Matrices: Communities and Small-World Properties | of edges. These networks have high clustering coefficients because each node is well-connected to… |
| 18 | Resting State Functional Connectivity MRI Signal, Brain Networks, and Common Analysis Techniques — Graph Theoretic Analyses of Region Matrices: Communities and Small-World Properties | or poorly networks are suited to efficient processing. Note that here we are using efficiency to… |
| 19 | Resting State Functional Connectivity MRI Signal, Brain Networks, and Common Analysis Techniques — Graph Theoretic Analyses of Region Matrices: Communities and Small-World Properties | More comprehensive reviews of region matrix analysis techniques in the study of brain connectivity,… |
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