TY - JOUR
T1 - Accuracy and reliability of diffusion imaging models
AU - Seider, Nicole A.
AU - Adeyemo, Babatunde
AU - Miller, Ryland
AU - Newbold, Dillan J.
AU - Hampton, Jacqueline M.
AU - Scheidter, Kristen M.
AU - Rutlin, Jerrel
AU - Laumann, Timothy O.
AU - Roland, Jarod L.
AU - Montez, David F.
AU - Van, Andrew N.
AU - Zheng, Annie
AU - Marek, Scott
AU - Kay, Benjamin P.
AU - Bretthorst, G.
AU - Schlaggar, Bradley L.
AU - Greene, Deanna
AU - Wang, Yong
AU - Petersen, Steven E.
AU - Barch, Deanna M.
AU - Gordon, Evan M.
AU - Snyder, Abraham Z.
AU - Shimony, Joshua S.
AU - Dosenbach, Nico U.F.
N1 - Publisher Copyright:
© 2022
PY - 2022/7/1
Y1 - 2022/7/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85127097041&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2022.119138
DO - 10.1016/j.neuroimage.2022.119138
M3 - Article
C2 - 35339687
AN - SCOPUS:85127097041
SN - 1053-8119
VL - 254
JO - NeuroImage
JF - NeuroImage
M1 - 119138
ER -