Kurt G Schilling1,2, Yurui Gao1,2, Muwei Li1,3, Tung-Lin Wu1,2, Justin Blaber4, Bennett A Landman1,2,3,4, Adam W Anderson1,2,3, Zhaohua Ding1,3,4, John C Gore1,2,3. 1. Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee. 2. Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee. 3. Radiology and Radiological Sciences, Vanderbilt University, Nashville, Tennessee. 4. Department of Electrical Engineering, Vanderbilt University, Nashville, Tennessee.
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.
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.
Authors: Van J Wedeen; Patric Hagmann; Wen-Yih Isaac Tseng; Timothy G Reese; Robert M Weisskoff Journal: Magn Reson Med Date: 2005-12 Impact factor: 4.668
Authors: Zhaohua Ding; Yali Huang; Stephen K Bailey; Yurui Gao; Laurie E Cutting; Baxter P Rogers; Allen T Newton; John C Gore Journal: Proc Natl Acad Sci U S A Date: 2017-12-27 Impact factor: 11.205
Authors: Kurt G Schilling; Alessandro Daducci; Klaus Maier-Hein; Cyril Poupon; Jean-Christophe Houde; Vishwesh Nath; Adam W Anderson; Bennett A Landman; Maxime Descoteaux Journal: Magn Reson Imaging Date: 2018-11-29 Impact factor: 2.546
Authors: Kurt G Schilling; Vishwesh Nath; Colin Hansen; Prasanna Parvathaneni; Justin Blaber; Yurui Gao; Peter Neher; Dogu Baran Aydogan; Yonggang Shi; Mario Ocampo-Pineda; Simona Schiavi; Alessandro Daducci; Gabriel Girard; Muhamed Barakovic; Jonathan Rafael-Patino; David Romascano; Gaëtan Rensonnet; Marco Pizzolato; Alice Bates; Elda Fischi; Jean-Philippe Thiran; Erick J Canales-Rodríguez; Chao Huang; Hongtu Zhu; Liming Zhong; Ryan Cabeen; Arthur W Toga; Francois Rheault; Guillaume Theaud; Jean-Christophe Houde; Jasmeen Sidhu; Maxime Chamberland; Carl-Fredrik Westin; Tim B Dyrby; Ragini Verma; Yogesh Rathi; M Okan Irfanoglu; Cibu Thomas; Carlo Pierpaoli; Maxime Descoteaux; Adam W Anderson; Bennett A Landman Journal: Neuroimage Date: 2018-10-11 Impact factor: 6.556
Authors: John C Gore; Muwei Li; Yurui Gao; Tung-Lin Wu; Kurt G Schilling; Yali Huang; Arabinda Mishra; Allen T Newton; Baxter P Rogers; Li Min Chen; Adam W Anderson; Zhaohua Ding Journal: Magn Reson Imaging Date: 2019-07-31 Impact factor: 2.546
Authors: David Abramian; Martin Larsson; Anders Eklund; Iman Aganj; Carl-Fredrik Westin; Hamid Behjat Journal: Neuroimage Date: 2021-05-14 Impact factor: 6.556