title: Diffusion tensor fiber tracking of human brain connectivity: aquisition methods, reliability analysis and biological results.
abstract: We present a description, biological results and a reliability analysis for the method of diffusion tensor tracking (DTT) of white matter fiber pathways. In DTT, diffusion-tensor MRI (DT-MRI) data are collected and processed to visualize the line trajectories of fiber bundles within white matter (WM) pathways of living humans. A detailed description of the data acquisition is given. Technical aspects and experimental results are illustrated for the geniculo-calcarine tract with broad projections to visual cortex, occipital and parietal U-fibers, and the temporo-calcarine ventral pathway. To better understand sources of error and to optimize the method, accuracy and precision were analyzed by computer simulations. In the simulations, noisy DT-MRI data were computed that would be obtained for a WM pathway having a helical trajectory passing through gray matter. The error vector between the real and ideal track was computed, and random errors accumulated with the square root of track length consistent with a random-walk process. Random error was most dependent on signal-to-noise ratio, followed by number of averages, pathway anisotropy and voxel size, in decreasing order. Systematic error only occurred for a few conditions, and was most dependent on the stepping algorithm, anisotropy of the surrounding tissue, and non-equal voxel dimensions. Both random and systematic errors were typically below the voxel dimension. Other effects such as track rebound and track recovery also depended on experimental conditions. The methods, biological results and error analysis herein may improve the understanding and optimization of DTT for use in various applications in neuroscience and medicine.
authors.affiliation: Mallinckrodt Institute of Radiology, Washington University School of Medicine, 4525 Scott Avenue, St Louis, MO 63110, USA. nlori@npg.wustl.edu
authors.forename: N F
authors.initials: NF
authors.lastname: Lori
authors.forename: E
authors.initials: E
authors.lastname: Akbudak
authors.forename: J S
authors.initials: JS
authors.lastname: Shimony
authors.forename: T S
authors.initials: TS
authors.lastname: Cull
authors.forename: A Z
authors.initials: AZ
authors.lastname: Snyder
authors.forename: R K
authors.initials: RK
authors.lastname: Guillory
authors.forename: T E
authors.initials: TE
authors.lastname: Conturo
mesh_terms: D000328:Adult; D000465:Algorithms; D003198:Computer Simulation; D005829:Geniculate Bodies / Q000166:cytology; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D015233:Models, Statistical; D009434:Neural Pathways / Q000166:cytology*; D009778:Occipital Lobe / Q000166:cytology; D011786:Quality Control; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013269:Stochastic Processes