title: An automated strategy for the delineation and parcellation of commissural pathways suitable for clinical populations utilising high angular resolution diffusion imaging tractography.
abstract: There is a growing interest in understanding alterations to the interhemispheric transfer of information as a result of brain injury and neurological disease. To facilitate research, we have developed a fully automated method for the accurate extraction of commissural pathways (corpus callosum (CC) and anterior commissure (AC)) and functional parcellation of the CC using a high angular resolution diffusion imaging (HARDI) based probabilistic tractography approach that is applicable to clinical populations. The CC was divided into 33 functional divisions based on its connections to cortical parcellations derived from individual structural images in 8 healthy participants. Probabilistic CC population maps acquired at two different b-values (1000 s mm(-2) and 3000 s mm(-2)) are presented. Topography of the CC was consistent with histology reports. We show that HARDI data acquired at a higher b-value reveals more callosal-temporal connections than low b-value data. With respect to intra-subject precision, data acquired using a higher b-value show superior reproducibility of the delineated CC area on the midsagittal plane (MSP), as well as the total number of callosal streamlines and the number of clustered callosal streamlines. The AC was delineated in all 8 participants using high b-value HARDI tractography. Cortical projections of the AC were analysed and are in agreement with known anatomy. We conclude that, while data acquired at a lower b-value may be used, this is associated with a loss in quality, both in the delineation of commissural pathways and, potentially, the reproducibility of results over time.
authors.affiliation: Centre for Magnetic Resonance, The University of Queensland, Brisbane, Australia.
authors.forename: Kerstin
authors.initials: K
authors.lastname: Pannek
authors.forename: Jane L
authors.initials: JL
authors.lastname: Mathias
authors.forename: Erin D
authors.initials: ED
authors.lastname: Bigler
authors.forename: Greg
authors.initials: G
authors.lastname: Brown
authors.forename: Jamie D
authors.initials: JD
authors.lastname: Taylor
authors.forename: Stephen
authors.initials: S
authors.lastname: Rose
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001331:Automation; D001921:Brain / Q000033:anatomy & histology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009434:Neural Pathways / Q000033:anatomy & histology; D011336:Probability; D015203:Reproducibility of Results; D013997:Time Factors