Accuracy and reliability of diffusion imaging models
Year: 2022
Type: article
Abstract: Diffusion imaging aims to non-invasively characterize the anatomy and integrity of the brain's white matter fibers. We evaluated the accuracy and reliability of commonly used diffusion imaging methods as a function of data quantity and analysis method, using both simulations and highly sampled individual-specific data (927-1442 diffusion weighted images [DWIs] per individual). Diffusion imaging methods that allow for crossing fibers (FSL's BedpostX [BPX], DSI Studio's Constant Solid Angle Q-Ball Imaging [CSA-QBI], MRtrix3's Constrained Spherical Deconvolution [CSD]) estimated excess fibers when insufficient data were present and/or when the data did not match the model priors. To reduce such overfitting, we developed a novel Bayesian Multi-tensor Model-selection (BaMM) method and applied it to the popular ball-and-stick model used in BedpostX within the FSL software package. BaMM was robust to overfitting and showed high reliability and the relatively best crossing-fiber accuracy with increasing amounts of diffusion data. Thus, sufficient data and an overfitting resistant analysis method enhance precision diffusion imaging. For potential clinical applications of diffusion imaging, such as neurosurgical planning and deep brain stimulation (DBS), the quantities of data required to achieve diffusion imaging reliability are lower than those needed for functional MRI.
Source: NeuroImage
Authors: Nicole A. Seider, Babatunde Adeyemo, Ryland Miller, Dillan J. Newbold, Jacqueline M. Hampton, Kristen M. Scheidter, Jerrel Rutlin, Timothy O. Laumann, Jarod L. Roland, David F. Montez, Andrew N. Van, Annie Zheng, Scott Marek, Benjamin P. Kay, G. Larry Bretthorst, Bradley L. Schlaggar, Deanna J. Greene, Yong Wang, Steven E. Petersen, Deanna M. Barch, Evan M. Gordon, Abraham Z. Snyder, Joshua S. Shimony, Nico U.F. Dosenbach
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,450
Funders: U.S. Department of Veterans Affairs, March of Dimes Foundation, James S. McDonnell Foundation, BrightFocus Foundation, Child Neurology Foundation, Hope Center for Neurological Disorders, Jacobs Foundation, National Institutes of Health, Intellectual and Developmental Disabilities Research Center, McDonnell Center for Systems Neuroscience, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, Research and Development, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Intellectual and Developmental Disabilities Research Center, Washington University School of Medicine in St. Louis
Awards: G1192882743, G1271896228, G1322185500, G2081160309, G2178218197, G240218576, G2589791453, G2598171177, G2659291318, G3693353605, G3802757361, PET-MRI Imaging of White Matter Damages and Inflammation in AD, G4831634465, G5465968614, G5640978069, Identification of outcome-based sub-populations using deep phenotyping and precision functional mapping across ADHD and ASD, G674518674, G7342150704, Neurodevelopmental profiles related to transdiagnostic and disorder-specific symptoms of anxiety and depression, G7737936661, NINDS CENTER CORE FOR BRAIN IMAGING, G8871309443, G8962763581, G9000001503, G9000050007, G9000052733, G9000060609, G9000159794, Washington University Psychiatry Residency Research Education Program, G9000172771, G9000220761, G9000239813, G9000245528, G9000262551, G9000267206, G9000317393, G9000319168, G9000351954, Developmental Neuroscience and Child Psychopathology
OpenAlex ID: W4221007243