James Stanis INI USC - NIH Brain initiative 2019 submission

Diffusion MRI is a powerful tool for mapping the microstructure and connectivity of the human brain. It entails the combination of MRI scanning with time-varying magnetic field gradients to characterize spatial patterns of water molecule diffusion, which are reflected by signal attenuation due to phase dispersion in the gradient direction. Given the image data from the scanner, computational techniques can be used to model or summarize the diffusion signals to obtain image parameter maps that reflect underlying tissue microstructure properties. Beyond these quantitative maps, we can also use diffusion MRI tractography to explore the large-scale organization of the brain. Tractography uses diffusion MRI estimates of fiber orientations to reconstruct geometric models of the large scale groupings of axons, known as fiber bundles, that connect cortical and subcortical brain areas. Due to their unique capabilities for mapping brain structure in vivo, such computational diffusion MRI approaches have been widely adopted; however, large scale data collection efforts, such as the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) Study, have created a need for reliable and automated approaches that can be readily applied to thousands of scans. Researchers at the Laboratory of Neuro Imaging (LONI) in the USC Stevens Neuroimaging and Informatics Institute have been developing scalable tools for modeling fiber bundles using diffusion MRI and applying them to better understand how the structure of the human brain changes in health and disease. Ryan Cabeen, a postdoctoral researcher at LONI, and institute director Arthur W. Toga have developed an approach that uses a statistical brain atlas to automatically extract an array of fiber bundle models and quantitative metrics from individual diffusion MRI datasets. These methods are part of the group’s neuroimaging software package, the Quantitative Imaging Toolkit (QIT), which provides a framework for image analysis, data exploration, and visualization. The tools are also supported by the LONI Pipeline, which enables their use in a grid computing environment to process large datasets in parallel. The video, created by scientific animator Jim Stanis, shows selected pathways in the atlas they created, including the fornix, cingulum bundle, optic radiation, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, arcuate fasciculus, corpus callosum, cortico-spinal tract, thalamic radiation (somatosensory, motor, and frontal subdivisions), and superior longitudinal fasciculus (subdivisions I, II, and III). The bundle coloring signifies directionality, with left-right fibers labeled red, front-back fibers labeled green, and vertically oriented fibers labeled blue. These bundle models were made by first averaging a large group of high-quality diffusion MRI datasets from the Human Connectome Project and then manually segmenting each bundle based on neuroanatomical references. These atlas models can then be used to obtain analogous bundles and metrics in the individual brain scans. Researchers at LONI are using these approaches to study how white matter changes throughout development, aging, neurodegeneration, and a variety of other neurological disorders. Cabeen RP, Bastin ME, Laidlaw DH (2016). Kernel regression estimation of fiber orientation mixtures in diffusion MRI. Neuroimage, 127, 158-172. Cabeen RP, Laidlaw DH, Toga AW (2018) Quantitative Imaging Toolkit: Software for Interactive 3D Visualization, Data Exploration, and Computational Analysis of Neuroimaging Datasets, Proceedings of the Joint Annual Meeting ISMRM-ESMRMB. Paris, France, 2854 Cabeen RP, Toga AW (2019) A Fully Atlas-driven Framework for Bundle-specific Tractography with Multi-compartment Diffusion MRI. 25th Annual Meeting of the Organization for Human Brain Mapping (OHBM)