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Properties of the brain's structural networks can be analyzed by applying fiber-tracking techniques and network analysis to diffusion MRI. Here we applied exploratory factor analysis (EFA) to anatomical connectivity matrices, to identify brain networks whose properties predicted higher-order cognitive function. Using diffusion MRI scans from 104 healthy young adults, we computed connectivity matrices based on deterministic and probabilistic tractography (with the FACT and Hough transform methods). Both sets of matrices were submitted to factor analysis, to identify sub-networks relevant for predicting cognitive function. The Kaiser-Meyer-Olkin measure and Bartlett's sphericity test were used to recover latent factors from the connectivity matrices, and only the Hough method yielded factorable outputs. Factor scores were related to fluid, crystallized, and spatial intelligence, and processing speed. Middle temporal and lateral prefrontal connectivity measures predicted all cognitive scores, except spatial intelligence. Cognitive performance was not predictable from global connectivity measures, which depended on the tractography method.

We present a new flow-based method for modeling brain structural connectivity. The method uses a modified maximum-flow algorithm that is robust to noise in the diffusion data and guided by biologically viable pathways and structure of the brain. A flow network is first created using a lattice graph by connecting all lattice points (voxel centers) to all their neighbors by edges. Edge weights are based on the orientation distribution function (ODF) value in the direction of the edge. The maximum-flow is computed based on this flow graph using the flow or the capacity between each region of interest (ROI) pair by following the connected tractography fibers projected onto the flow graph edges. Network measures such as global efficiency, transitivity, path length, mean degree, density, modularity, small world, and assortativity are computed from the flow connectivity matrix. We applied our method to diffusion-weighted images (DWIs) from 110 subjects (28 normal elderly, 56 with early and 11 with late mild cognitive impairment, and 15 with AD) and segmented co-registered anatomical MRIs into cortical regions. Experimental results showed better performance compared to the standard fiber-counting methods when distinguishing Alzheimer's disease from normal aging.

Diffusion imaging and brain connectivity analyses can monitor white matter deterioration, revealing how neural pathways break down in aging and Alzheimer's disease (AD). Here we tested how AD disrupts the 'rich club' effect - a network property found in the normal brain - where high-degree nodes in the connectivity network are more heavily interconnected with each other than expected by chance. We analyzed 3-Tesla whole-brain diffusion-weighted images (DWI) from 66 subjects (22 AD/44 normal elderly). We performed whole-brain tractography based on the orientation distribution functions. Connectivity matrices were compiled, representing the proportion of detected fibers interconnecting 68 cortical regions. As expected, AD patients had a lower nodal degree (average number of connections) in cortical regions implicated in the disease. Unexpectedly, the normalized rich club coefficient was higher in AD. AD disrupts cortical networks by removing connections; when these networks are thresholded, organizational properties are disrupted leading to additional new biomarkers of AD.