What is NODDI and what is its role in Parkinson’s assessment?

Dendrites and axons, known as neuronal processes or neurites, constitute the basic cellular units of the brain’s computational circuits. Dendrite function is reflected by their branching complexity (hence density). Cortical areas with simple and complex dendritic structures are involved in the early and late stages, respectively, of information processing [1]. Neurite structure dynamically changes with development [2] and aging [3], and degenerates in various neurological disorders including Alzheimer’s disease [4] and amyotrophic lateral sclerosis [5]. Dendritic spine loss and shortening in the substantia nigra (SN) and striatum are reported in Parkinson’s disease (PD), a representative neurodegenerative disorder [6]. Hence these changes, which may be the primary causes of motor symptoms in PD, are thought to reflect the PD disease state. Magnetic resonance imaging (MRI) is an excellent tool for noninvasive in vivo brain assessment. However, the spatial resolution with conventional MRI is at best a few millimeters, while microscopic nerve tissue evaluation in the brain requires submicron scale resolution. Therefore, diffusion MRI (dMRI) was developed to resolve the micron-scale displacement of water molecules diffusing in tissue and to evaluate neural tissue microstructure. Water molecule displacement in dMRI-targeted tissues is strongly influenced by the direction, permeability, and number of barriers (cell membranes and myelin sheaths) in the tissue, and by differences in intracellular proteins and organelles in neurons, dendrites, axons, neurofilaments, and microtubules. Thus, dMRI can indirectly infer the microstructures of these barriers and organelles [7]. Although many dMRI methods are proposed, the most representative models are diffusion tensor imaging (DTI) [8], diffusional kurtosis imaging (DKI) [9], and neurite orientation dispersion and density imaging (NODDI) [10]. DTI, which is widely utilized in clinical and research settings, has two major limitations. First, the assumption in DTI that water molecule diffusion follows a Gaussian distribution may be inappropriate because water molecule diffusion in actual living tissue is restricted by cell membranes and organelles, thereby showing a non-Gaussian distribution of displacement. Second, interpreting DTI parameter estimations, e.g. fractional anisotropy (FA), which reflects neuronal density, fiber orientation dispersion (OD), axonal diameter, and myelination degree, is difficult. Because FA changes are nonspecific, there are limits for using this parameter to infer pathological changes [11]. DKI was proposed to quantify the non-Gaussian distribution of water molecules in complicated structures where diffusion is restricted by cell membranes and organelles [9], and made it possible to quantify structural complexity. DKI parameters were at least as sensitive as DTI parameters, if not more so, to microstructural changes; however, DKI parameter changes are difficult to interpret because they are nonspecific. Lately, NODDI was proposed to overcome these limitations (of the Gaussian distribution model assumed in DTI and the nonspecific changes in DTI and DKI parameters). NODDI assumes a three-compartment biophysical tissue model including intracellular, extracellular, and cerebrospinal fluids in a single voxel, which enables the inference and quantification of the direction and structure of neurites (axons and dendrites) by the orientation-dispersed cylinder model and the Watson distribution. Representative NODDI parameters include the intracellular volume fraction (Vic), indicating neurite density, and the OD index indicating the neurite OD. For reference, we indicate the current NODDI imaging conditions at our facility. Diffusion-weighted data for NODDI were acquired with a spin-echo planar imaging sequence along 32 isotropic diffusion gradient directions. For each direction, diffusion-weighted data were acquired with three b values (0, 1000, and 2,000 s/mm). Sequence parameters were as EXPERT REVIEW OF NEUROTHERAPEUTICS, 2016 VOL. 16, NO. 3, 241–243 http://dx.doi. org/10.1586/14737175.2016. 1142876