Functional clustering of whole brain white matter fibers

Large numbers of fibers produced by fiber tractography are often grouped into bundles with anatomical interpretations. Traditional clustering methods usually generate bundles with spatial anatomic coherences only. To associate bundles with function, some studies incorporate functional connectivity of grey matter to guide clustering on the premise that fibers provide the basis of information transmission for cortex. However, functional properties along fiber tracts were ignored by these methods. Considering several recent studies showing that BOLD (Blood-Oxygen-Level Dependent) signals of white matter contain functional information of axonal fibers, this work is motivated to demonstrate that whole brain white matter fibers can be clustered with integration of functional and structural information they contain.

We proposed a novel algorithm based on Gaussian mixture model and expectation maximization to achieve optimal bundling with both structural and functional coherences. The functional coherence between two fibers is defined as the average correlation in BOLD signal between corresponding points. Whole brain fibers under resting state and sensory stimulation conditions were used to demonstrate the effectiveness of the proposed technique.

Our in vivo experiments show the robustness of proposed algorithm and influences of weights between structure and function, and repeatability of reconstructed major bundles across individuals.

In contrast to traditional methods, the proposed clustering method can achieve structurally more compact bundles, which are specifically related to evoking function.

The proposed concept and framework can be used to identify functional pathways and their structural features under specific function loading.