The ability to use variability in structural connectivity to explain variability in clinical outcomes would be a critical validation of connectomics, and would help push the field forward.
Towards that end, Tymofiyeva et al used DTI to map the structural connectivity of a clinical cohort of six-month old infants with perinatal hypoxic ischemic encephalopathy.
Since there is no well-established atlas for the rapidly changing brain at such an early stage of development, the authors relied on two unbiased parcellation techniques, illustrated here:
colors arbitrarily refer to distinct brain regions; a = partitioned spherically, b = partitioned along linear dimensions; doi:10.1371/journal.pone.0031029
They then used these parcellation schemes to compute adjacency matrices for each baby. Here are representative matrices for each of the above parcellation schemes:
binary adjacency matrices where a connection (denoted in white) were called if there was a fiber tract with end points located in both regions; c = partitioned spherically, d = partitioned along linear dimensions ; doi:10.1371/journal.pone.0031029
The authors then attempted to correlate a neuromotor score of the infants with the brain network’s degree of clustering. For this, they chose to test 1) the average shortest path length between any two nodes, and 2) the average clustering coefficient.
By the authors’ own admission, a larger sample size and a longitudinal design would be ideal to make inferences from this sort of study. In the scatterplots they presented, they found one positive correlation below the arbitrary threshold of p=0.05, but the effect seems to be mediated in no small part by one outlier.
Still, this is an innovative approach and has great potential. One thing that might be interesting would be to take a more unbiased approach to the data analysis. That is, instead of choosing the network summary statistics to test a priori, use some sort of ensemble learning method to let the data tell you how best to predict the neuromotor scores on the basis of the adjacency matrices.
Reference
Tymofiyeva O, Hess CP, Ziv E, Tian N, Bonifacio SL, et al. (2012) Towards the “Baby Connectome”: Mapping the Structural Connectivity of the Newborn Brain. PLoS ONE 7(2): e31029. doi:10.1371/journal.pone.0031029
Casanova R, et al. 2012 Combining Graph and Machine Learning Methods to Analyze Differences in Functional Connectivity Across Sex. doi: 10.2174/1874440001206010001.
