Table 2.

Data processing steps

Processing	Program	Steps and descriptions
Diffusion tensor imaging	TORTOISE	1. Axialization of images (similar to MIPAV) to optimize alignment without warping or changing intensity parameters, by calculating an affine alignment to the UNC neonate structural template.
		2. DIFFPREP: Distortion corrections for participant motion, eddy currents and basic echo-planar imaging (EPI) distortions separately on each anterior-posterior and posterior-anterior encoded image.
		3. DR-BUDDI: Merging encoded sets and further EPI distortion corrections.
	AFNI	4. Post processing and diffusion tensor parameter fitting.
Resting state	AFNI	1. Stabilizing magnetic field by removing first four EPI volumes per scan.
		2. Exclusion of outlier signal intensities per voxel using 3dDespike.
		3. Motion correction by rigid-body alignment of each EPI to the third volume, and resampling of data to 2.5 mm in three spatial dimensions.
		4. Intermediate anatomical registration to T2 images to derive displacement factors and final registration to UNC neonate atlas.
		5. Spatial smoothing using 5 mm full width at half-maximum (FWHM).
		6. Registration of individual resting state images to UNC neonate atlas, and utilization of the 90 regions as masks to extract time series data.
Graph theoretical analysis	GAT	1. Creation of small-world networks.
		2. Threshholding of association matrices at a range of network densities.
		3. Extraction of clustering coefficient that provides an indication of local segregation of networks i.e. mean connectivity among nodes.
		3. Extraction of characteristic path length that provides an indication of network integration i.e. mean shortest path length between nodes.
		4. Creation of random networks with regions and edges comparable to that of the actual brain network, to evaluate the clustering coefficient and characteristic path length, and determine network arrangement.
	BCT	5. Estimation of nodal betweenness centrality that determines all shortest path lengths of connections between local regions.
		6. Identification of hubs based on nodal betweenness centrality output.
		6. Nonparametric permutation testing (1000 permutations) to investigate group differences. Comparison of small-world index, clustering coefficient and characteristic path length by group across a range of densities (0.1 to 0.4); and regional network measures e.g. nodal betweenness centrality at minimum density (0.1).

AFNI, Analysis of Functional NeuroImages; BCT, Brain Connectivity Toolbox; GAT, Graph Theoretical Analysis Toolbox; MIPAV, Medical Image Processing, Analysis, and Visualization; TORTOISE, Tolerably Obsessive Registration and Tensor Optimization Indolent Software Ensemble; UNC, University of California