Mapping topographic structure in white matter pathways with level set trees.
Fiber tractography on diffusion imaging data offers rich potential for describing white matter pathways in the human brain, but characterizing the spatial organization in these large and complex data sets remains a challenge. We show that level set trees--which provide a concise representation of the hierarchical mode structure of probability density functions--offer a statistically-principled framework for visualizing and analyzing topography in fiber streamlines. Using diffusion spectrum imaging data collected on neurologically healthy controls (N = 30), we mapped white matter pathways from the cortex into the striatum using a deterministic tractography algorithm that estimates fiber bundles as dimensionless streamlines. Level set trees were used for interactive exploration of patterns in the endpoint distributions of the mapped fiber pathways and an efficient segmentation of the pathways that had empirical accuracy comparable to standard nonparametric clustering techniques. We show that level set trees can also be generalized to model pseudo-density functions in order to analyze a broader array of data types, including entire fiber streamlines. Finally, resampling methods show the reliability of the level set tree as a descriptive measure of topographic structure, illustrating its potential as a statistical descriptor in brain imaging analysis. These results highlight the broad applicability of level set trees for visualizing and analyzing high-dimensional data like fiber tractography output.
Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
Brian P
BP
Kent
Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
Alessandro
A
Rinaldo
Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
Fang-Cheng
FC
Yeh
Department of Psychology and Center for the Neural Basis of Computation, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
Timothy
T
Verstynen
D000328:Adult; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D016000:Cluster Analysis; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009413:Nerve Fibers, Myelinated / Q000502:physiology; D009434:Neural Pathways / Q000033:anatomy & histology / Q000502:physiology; D011336:Probability; D055815:Young Adult