title: Think global, act local; projectome estimation with BlueMatter.
abstract: Estimating the complete set of white matter fascicles (the projectome) from diffusion data requires evaluating an enormous number of potential pathways; consequently, most algorithms use computationally efficient greedy methods to search for pathways. The limitation of this approach is that critical global parameters--such as data prediction error and white matter volume conservation--are not taken into account. We describe BlueMatter, a parallel algorithm for global projectome evaluation, which uniquely accounts for global prediction error and volume conservation. Leveraging the BlueGene/L supercomputing architecture, BlueMatter explores a massive database of 180 billion candidate fascicles. The candidates are derived from several sources, including atlases and multiple tractography algorithms. Using BlueMatter we created the highest resolution, volume-conserved projectome of the human brain.
authors.affiliation: IBM Almaden Reserach Center, Almaden, USA. anthony.sherbondy@stanford.edu
authors.forename: Anthony J
authors.initials: AJ
authors.lastname: Sherbondy
authors.forename: Robert F
authors.initials: RF
authors.lastname: Dougherty
authors.forename: Rajagopal
authors.initials: R
authors.lastname: Ananthanarayanan
authors.forename: Dharmendra S
authors.initials: DS
authors.lastname: Modha
authors.forename: Brian A
authors.initials: BA
authors.lastname: Wandell
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000166:cytology*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique