Dataset Information

Accuracy and reliability of diffusion imaging models.

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.

SUBMITTER: Seider NA

PROVIDER: S-EPMC9841915 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Accuracy and reliability of diffusion imaging models.

Seider Nicole A NA Adeyemo Babatunde B Miller Ryland R Newbold Dillan J DJ Hampton Jacqueline M JM Scheidter Kristen M KM Rutlin Jerrel J Laumann Timothy O TO Roland Jarod L JL Montez David F DF Van Andrew N AN Zheng Annie A Marek Scott S Kay Benjamin P BP Bretthorst G Larry GL Schlaggar Bradley L BL Greene Deanna J DJ Wang Yong Y Petersen Steven E SE Barch Deanna M DM Gordon Evan M EM Snyder Abraham Z AZ Shimony Joshua S JS Dosenbach Nico U F NUF

NeuroImage 20220323

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 ...[more]

PMID: 35339687

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Dataset Information

Accuracy and reliability of diffusion imaging models.

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Accuracy and reliability of diffusion imaging models.

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