Meeting Banner
Abstract #3643

Assembling the Large-Scale Human Connectome: How Do We Partition the Brain?

Etay Ziv1, Olga Tymofiyeva1, Christopher P. Hess1, Donna M. Ferriero2, A James Barkovich1, Duan Xu1

1Department of Radiology & Biomedical Imaging, UCSF, San Francisco, CA, United States; 2Department of Pediatrics, UCSF, San Francisco, CA, United States

Structural connectivity networks derived from diffusion MRI vary with choice of brain parcellation. In adults, parcellation and subsequent assembly of the large-scale human connectome relies heavily on brain atlases. However, in the context of the rapidly developing pediatric brain, such approaches introduce problematic biases. Here we present a network-driven brain parcellation that does not rely on brain atlases and propose a method to define the optimal number and size of nodes.

Keywords

acquisition adding address adjacency adults allow anatomic anatomical anatomy approaches assembling assembly assuming assumption atlases babies begins better biases born brain brains broad broadly choice cohort component components connected connections connectivity consistent construct context cortical define defined defines dense dependence depth derived deterministic developing deviate differs diffusion directly driven encephalopathy entire entries enumerated equal evidence evolving example excite extracted facilitate fiber finest giant good grants heavily human identifying illustrates inherent injury isolated largest leads made mapped maps matrices matrix methodology month months network networks neurological node nodes onto optimal optimum outcome part particularly partition partitioned partitioning pediatric pediatrics population presume prior problematic propose proposed radiology rapidly recombining reconstruction recorded recursive relies rely representative represents rest review scale scanned scanner search spanning sphere standardized starts steps still streamline structural structurally subject subsequent supported surface template tensor term toolkit tract transient tree typical ultimately unbiased undertaken unknown useful variable vary whole wise zonal