title: An integrated framework for high angular resolution diffusion imaging-based investigation of structural connectivity.
abstract: Structural connectivity models hold great promise for expanding what is known about the ways information travels throughout the brain. The physiologic interpretability of structural connectivity models depends heavily on how the connections between regions are quantified. This article presents an integrated structural connectivity framework designed around such an interpretation. The framework provides three measures to characterize the structural connectivity of a subject: (1) the structural connectivity matrix describing the proportion of connections between pairs of nodes, (2) the nodal connection distribution (nCD) characterizing the proportion of connections that terminate in each node, and (3) the connection density image, which presents the density of connections as they traverse through white matter (WM). Individually, each possesses different information concerning the structural connectivity of the individual and could potentially be useful for a variety of tasks, ranging from characterizing and localizing group differences to identifying novel parcellations of the cortex. The efficiency of the proposed framework allows the determination of large structural connectivity networks, consisting of many small nodal regions, providing a more detailed description of a subject's connectivity. The nCD provides a gray matter contrast that can potentially aid in investigating local cytoarchitecture and connectivity. Similarly, the connection density images offer insight into the WM pathways, potentially identifying focal differences that affect a number of pathways. The reliability of these measures was established through a test/retest paradigm performed on nine subjects, while the utility of the method was evaluated through its applications to 20 diffusion datasets acquired from typically developing adolescents.
authors.affiliation: Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA. lbloy@seas.upenn.edu
authors.forename: Luke
authors.initials: L
authors.lastname: Bloy
authors.forename: Madhura
authors.initials: M
authors.lastname: Ingalhalikar
authors.forename: Nematollah K
authors.initials: NK
authors.lastname: Batmanghelich
authors.forename: Robert T
authors.initials: RT
authors.lastname: Schultz
authors.forename: Timothy P L
authors.initials: TP
authors.lastname: Roberts
authors.forename: Ragini
authors.initials: R
authors.lastname: Verma
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology; D056324:Diffusion Tensor Imaging; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D009412:Nerve Fibers / Q000502:physiology; D009415:Nerve Net / Q000033:anatomy & histology* / Q000502:physiology; D009434:Neural Pathways / Q000502:physiology; D015203:Reproducibility of Results