Reprint of: Mapping connectivity in the developing brain

Abstract

Recently, there has been a wealth of research into structural and functional brain connectivity, and how they change over development. While we are far from a complete understanding, these studies have yielded important insights into human brain development. There is an ever growing variety of methods for assessing connectivity, each with its own advantages. Here we review research on the development of structural and/or functional brain connectivity in both typically developing subjects and subjects with neurodevelopmental disorders. Space limitations preclude an exhaustive review of brain connectivity across all developmental disorders, so we review a representative selection of recent findings on brain connectivity in autism, Fragile X, 22q11.2 deletion syndrome, Williams syndrome, Turner syndrome, and ADHD. Major strides have been made in understanding the developmental trajectory of the human connectome, offering insight into characteristic features of brain development and biological processes involved in developmental brain disorders. We also discuss some common themes, including hemispheric specialization - or asymmetry - and sex differences. We conclude by discussing some promising future directions in connectomics, including the merger of imaging and genetics, and a deeper investigation of the relationships between structural and functional connectivity.

Keywords: 22q11.2 DS; ADHD; Autism; Brain connectivity; DTI; Development; Fragile X; HARDI; Turner syndrome; Williams syndrome; rs-fMRI.

Fig. 1

Decreases in regional gray matter volume, in normal children, between age 5 and age 20. As a general principle of development, cortical regions that are concerned with more low-level, primary functions – such as vision and sensation – mature more quickly than the regions subserving higher order cognition. Here the loss of gray matter volume is thought to be due to greater myelination of the cortex, rather than solely due to synaptic and dendritic pruning. Vascular and glial changes many also play a role. Reprinted with permission from Gogtay et al. (2004).

Fig. 2

Diffusion tensor imaging and tractography. In whole brain tractography, a set of diffusion weighted images (left) are collected to show how rapidly water is diffusing in a range of different directions. By sampling a large number of directions, a diffusion function (little crosses in the middle panel) can be reconstructed—the peaks in this function tend to point along axons and major tracts. Tract tracing algorithms can sew together the paths of maximal diffusion into curves and fiber bundles. The right panel shows the set of recovered fibers—red, green, and blue colors show the directions of the fibers. These can be grouped into meaningful anatomical bundles and their integrity and connectivity can be assessed. Adapted with permission from Aganj et al. (2011).

Fig. 3

Methods for graph theoretical analyses of structural connectivity, computed from diffusion imaging data. To compute connectivity maps, a set of cortical regions is defined on a standard anatomical MRI scan. The tracing of fiber tracts in diffusion images can be used to create a vast set of fiber connections in the brain. These are then assigned to different regions of interest. The matrix on the far right can be used to count how many of the fibers pass through any pair of regions; it can also store information on their integrity or other biological parameters. Reprinted with permission from Jahanshad et al. (2011).

Fig. 4

Regions that show age-related effects in regional efficiency in 95 subjects 19–85 years old. Red indicates increases in efficiency with age, blue indicates decreases in efficiency with age. Reprinted with permission from Gong et al. (2009). See Gong et al. (2009) supplemental material for abbreviations of cortical regions.

Fig. 5

Age-related increases and decreases in nodal degree and edge fiber density in 439 subjects aged 12–30. This study is based on HARDI, a form of diffusion imaging that can be used to recover anatomical connections. Colors correspond to the fiber density, with red indicating greater values and blue indicating smaller values. The diameter of nodes corresponds to their degree. Reprinted with permission from Dennis et al. (2013).

Fig. 6

Maturation of the connection between the PCC (posterior cingulate cortex) and mPFC (medial prefrontal cortex), two main hubs of the DMN (default mode network) between 18 7–9 year olds and 15 19–22 year olds. DTI tractography depicting developmental effects in fibers between PCC and mPFC. Bar graph showing significant difference in fiber density (p ≪ 0.0001, indicated by **). Reprinted with permission from Supekar et al. (2010).

Fig. 7

Differences in the connectivity of the DMN between children (7–9 y) and adults (21–31 y). Graph visualization of DMN regions in children and adults generated by correlating the time series’ of 13 regions, including hubs of the DMN. In children (7–9 years old), DMN regions are sparsely connected, while they appear highly integrated in adults (21–31 years old). Reprinted with permission from Fair et al. (2008).

Fig. 8

Spatial renderings of components corresponding to the default (red), left executive (pink), right executive (green) and salience networks (blue) generated from group ICA analysis of 65 children aged 9–15 years. Reprinted with permission from Thomason et al. (2011).

Fig. 9

Maturation from “local” organization to “distributed” organization, as measured by functional graph theoretical measures. Frontal regions are highlighted in blue in A; notice they are closely connected in children and less so in adults. The DMN, a collection of anatomically distributed regions, is highlighted in red in B. Notice they are segregated in children and highly integrated in adults. Reprinted with permission from Fair et al. (2009).