title: Probabilistic anatomical connectivity derived from the microscopic persistent angular structure of cerebral tissue.
abstract: Recently developed methods to extract the persistent angular structure (PAS) of axonal fibre bundles from diffusion-weighted magnetic resonance imaging (MRI) data are applied to drive probabilistic fibre tracking, designed to provide estimates of anatomical cerebral connectivity. The behaviour of the PAS function in the presence of realistic data noise is modelled for a range of single and multiple fibre configurations. This allows probability density functions (PDFs) to be generated that are parametrized according to the anisotropy of individual fibre populations. The PDFs are incorporated in a probabilistic fibre-tracking method to allow the estimation of whole-brain maps of anatomical connection probability. These methods are applied in two exemplar experiments in the corticospinal tract to show that it is possible to connect the entire primary motor cortex (M1) when tracing from the cerebral peduncles, and that the reverse experiment of tracking from M1 successfully identifies high probability connection via the pyramidal tracts. Using the extracted PAS in probabilistic fibre tracking allows higher specificity and sensitivity than previously reported fibre tracking using diffusion-weighted MRI in the corticospinal tract.
authors.affiliation: Imaging Science and Biomedical Engineering, University of Manchester, Stopford Building, Oxford Road, Manchester M13 9PT, UK. geoff.parker@manchester.ac.uk
authors.forename: Geoffrey J M
authors.initials: GJ
authors.lastname: Parker
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Alexander
mesh_terms: D016880:Anisotropy; D001369:Axons / Q000502:physiology*; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D011336:Probability; D012680:Sensitivity and Specificity