Kurtosis analysis of neural diffusion organization

• Published in: NeuroImage (Orlando, Fla.) Vol. 106; pp. 391 - 403
• Main Authors: Hui, Edward S., Russell Glenn, G., Helpern, Joseph A., Jensen, Jens H.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.02.2015; Elsevier Limited
• Subjects: Adult; Alzheimer's disease; Attention deficit hyperactivity disorder; Autism; Axons - physiology; Brain - anatomy & histology; Brain - physiology; Computer Simulation; Data Interpretation, Statistical; Diffusion; Diffusion Magnetic Resonance Imaging - methods; Diffusion Tensor Imaging - methods; Estimates; Humans; Hyperactivity; Kurtosis; Male; Microstructure; Middle Aged; Models, Neurological; Models, Statistical; MRI; Statistical Distributions; Tissue model; Water - metabolism; White Matter - physiology; Kurtosis; Diffusion; Microstructure; MRI; Tissue model
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2014.11.015

A computational framework is presented for relating the kurtosis tensor for water diffusion in brain to tissue models of brain microstructure. The tissue models are assumed to be comprised of non-exchanging compartments that may be associated with various microstructural spaces separated by cell membranes. Within each compartment the water diffusion is regarded as Gaussian, although the diffusion for the full system would typically be non-Gaussian. The model parameters are determined so as to minimize the Frobenius norm of the difference between the measured kurtosis tensor and the model kurtosis tensor. This framework, referred to as kurtosis analysis of neural diffusion organization (KANDO), may be used to help provide a biophysical interpretation to the information provided by the kurtosis tensor. In addition, KANDO combined with diffusional kurtosis imaging can furnish a practical approach for developing candidate biomarkers for neuropathologies that involve alterations in tissue microstructure. KANDO is illustrated for simple tissue models of white and gray matter using data obtained from healthy human subjects. [Display omitted] •We present a tissue modeling method compatible with diffusional kurtosis imaging.•The method relates the kurtosis tensor to brain microstructure.•The method accommodates a variety of specific brain tissue models.•The method is illustrated for human data and with simulations.