RT Journal Article
SR Electronic
T1 The Convergence of Maturational Change and Structural Covariance in Human Cortical Networks
JF The Journal of Neuroscience
JO J. Neurosci.
FD Society for Neuroscience
SP 2889
OP 2899
DO 10.1523/JNEUROSCI.3554-12.2013
VO 33
IS 7
A1 Aaron Alexander-Bloch
A1 Armin Raznahan
A1 Ed Bullmore
A1 Jay Giedd
YR 2013
UL http://www.jneurosci.org/content/33/7/2889.abstract
AB Large-scale covariance of cortical thickness or volume in distributed brain regions has been consistently reported by human neuroimaging studies. The mechanism of this population covariance of regional cortical anatomy has been hypothetically related to synchronized maturational changes in anatomically connected neuronal populations. Brain regions that grow together, i.e., increase or decrease in volume at the same rate over the course of years in the same individual, are thus expected to demonstrate strong structural covariance or anatomical connectivity across individuals. To test this prediction, we used a structural MRI dataset on healthy young people (N = 108; aged 9–22 years at enrollment), comprising 3–6 longitudinal scans on each participant over 6–12 years of follow-up. At each of 360 regional nodes, and for each participant, we estimated the following: (1) the cortical thickness in the median scan and (2) the linear rate of change in cortical thickness over years of serial scanning. We constructed structural and maturational association matrices and networks from these measurements. Both structural and maturational networks shared similar global and nodal topological properties, as well as mesoscopic features including a modular community structure, a relatively small number of highly connected hub regions, and a bias toward short distance connections. Using resting-state functional magnetic resonance imaging data on a subset of the sample (N = 32), we also demonstrated that functional connectivity and network organization was somewhat predictable by structural/maturational networks but demonstrated a stronger bias toward short distance connections and greater topological segregation. Brain structural covariance networks are likely to reflect synchronized developmental change in distributed cortical regions.