Abstract
Diffusion MRI has evolved towards an important clinical diagnostic and research tool. Though clinical routine is using mainly diffusion weighted and tensor imaging approaches, Q-ball imaging and diffusion spectrum imaging techniques have become more widely available. They are frequently used in research-oriented investigations in particular those aiming at measuring brain network connectivity. In this work, we aim at assessing the dependency of connectivity measurements on various diffusion encoding schemes in combination with appropriate data modeling. We process and compare the structural connection matrices computed from several diffusion encoding schemes, including diffusion tensor imaging, q-ball imaging and high angular resolution schemes, such as diffusion spectrum imaging with a publically available processing pipeline for data reconstruction, tracking and visualization of diffusion MR imaging. The results indicate that the high angular resolution schemes maximize the number of obtained connections when applying identical processing strategies to the different diffusion schemes. Compared to the conventional diffusion tensor imaging, the added connectivity is mainly found for pathways in the 50–100mm range, corresponding to neighboring association fibers and long-range associative, striatal and commissural fiber pathways. The analysis of the major associative fiber tracts of the brain reveals striking differences between the applied diffusion schemes. More complex data modeling techniques (beyond tensor model) are recommended 1) if the tracts of interest run through large fiber crossings such as the centrum semi-ovale, or 2) if non-dominant fiber populations, e.g. the neighboring association fibers are the subject of investigation. An important finding of the study is that since the ground truth sensitivity and specificity is not known, the comparability between results arising from different strategies in data reconstruction and/or tracking becomes implausible to understand.
Highlights
Over recent years, there has been a growing interest in investigating the connectivity profile of the entire brain, referred to by the scientific community as the human connectome [1,2]
We evaluate the connectivity produced by each diffusion encoding scheme using the number of computed connections, i.e. the number of links between a pair of regions of interest (ROIs) given by at least one fiber
We use structural connection matrices produced by tractography to assess the performance of various diffusion encoding schemes
Summary
There has been a growing interest in investigating the connectivity profile of the entire brain, referred to by the scientific community as the human connectome [1,2]. By allowing in vivo imaging of the brain’s major fiber pathways, diffusion MR tractography [3,4,5,6] has turned out to be a promising technique to map the connectome at the millimeter scale. Several groups have independently proposed to build structural connection matrices from diffusion MR tractography using various diffusion acquisition protocols and models [7,8,9,10]. Diffusion Tensor Imaging (DTI) is a frequently-used method to model the diffusion data in order to obtain orientational information. This method maps the orientation of fibers by fitting a second-order symmetric tensor on the diffusion data [11]. Behrens et al concluded that one third of white matter voxels may be affected by this problem [13], and later work by Jeurissen et al reported finding two or more fiber orientations in 90% of white matter voxels [14]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
