Functional Tractography of White Matter by High Angular Resolution Functional-correlation Imaging (HARFI)
Kurt G. Schilling, Yurui Gao, Muwei Li, Tung-Lin Wu, Justin Blaber, Bennett A Landman, Adam W Anderson, Zhaohua Ding, John C Gore. “Functional Tractography of White Matter by High Angular Resolution Functional-correlation Imaging (HARFI)”. Magn Reson Med. 2018;00:1-14. https://doi.org/10.1002/mrm.27512.
Full text: https://www.ncbi.nlm.nih.gov/pubmed/30277272
https://onlinelibrary.wiley.com/doi/full/10.1002/mrm.27512
Abstract
Purpose
Functional magnetic resonance imaging with BOLD contrast is widely used for detecting brain activity in the cortex. Recently, several studies have described anisotropic correlations of resting‐state BOLD signals between voxels in white matter (WM). These local WM correlations have been modeled as functional‐correlation tensors, are largely consistent with underlying WM fiber orientations derived from diffusion MRI, and appear to change during functional activity. However, functional‐correlation tensors have several limitations. The use of only nearest‐neighbor voxels makes functional‐correlation tensors sensitive to noise. Furthermore, adjacent voxels tend to have higher correlations than diagonal voxels, resulting in orientation‐related biases. Finally, the tensor model restricts functional correlations to an ellipsoidal bipolar‐symmetric shape, and precludes the ability to detect complex functional orientation distributions (FODs).
Methods
We introduce high‐angular‐resolution functional‐correlation imaging (HARFI) to address these limitations. In the same way that high‐angular‐resolution diffusion imaging (HARDI) techniques provide more information than diffusion tensors, we show that the HARFI model is capable of characterizing complex FODs expected to be present in WM.
Results
We demonstrate that the unique radial and angular sampling strategy eliminates orientation biases present in tensor models. We further show that HARFI FODs are able to reconstruct known WM pathways. Finally, we show that HARFI allows asymmetric “bending” and “fanning” distributions, and propose asymmetric and functional indices which may increase fiber tracking specificity, or highlight boundaries between functional regions.
Conclusions
The results suggest the HARFI model could be a robust, new way to evaluate anisotropic BOLD signal changes in WM.
Keywords:
functional connectivity; functional magnetic resonance imaging; functional pathways; high angular resolution
