Neurite Orientation Dispersion and Density Imaging (NODDI)
NODDI in QSIRecon
QSIRecon supports NODDI reconstruction using the AMICO package: see init_amico_noddi_fit_wf().
This is accessible in a reconstruction specification by using a node with action: fit_noddi and software: AMICO.
Also see NODDI.
NODDI Foundational Papers
Multi-Compartment Modeling: NODDI is a three-compartment diffusion MRI model that separates intra-neurite, extra-neurite, and CSF water signals. This yields more specific microstructural indices than DTI - notably the intracellular volume fraction (ICVF) as a proxy for neurite density, and the orientation dispersion index (ODI) for neurite orientation variability (Zhang et al., 2012). These metrics disentangle factors contributing to diffusion anisotropy that were conflated in DTI measures like FA (Zhang et al., 2012).
Estimation Speed: A major practical advance was the AMICO algorithm (Daducci et al. 2015), which reformulated NODDI fitting as a linear inverse problem. AMICO achieves a 1000× speedup in fitting time with minimal loss of accuracy (Daducci et al., 2015). This enabled large studies and clinical workflows to include NODDI analyses, cementing NODDI’s popularity. Notably, AMICO does not alter NODDI’s metrics; it accelerates their computation.
Partial Volume Correction: A known issue is that CSF partial-volume can lead to underestimation of neurite density in voxels near ventricles or cortex. In 2021, Parker et al. (2021) introduced tissue-fraction-modulated ICVF and ODI, scaling NODDI metrics by the tissue signal fraction (1 - ISOVF). This adjustment was shown to remove artifactual group differences that were driven by differing CSF contamination rather than true tissue changes (Parker et al., 2021). Tissue modulated maps are produced by default in QSIRecon.
NODDI Studies Across the Lifespan
Early Development (Infancy): NODDI studies in infants show dramatic microstructural maturation within the first years of life. For example, neurite density (ICVF) in major tracts increases steeply from birth to age 3 (Jelescu et al., 2015), reflecting rapid axonal growth and myelination. This is accompanied by increasing restriction of diffusion in the extra-cellular space (consistent with growing fibers and tighter packing).
Childhood and Adolescence: Neurite density continues to rise through childhood and adolescence, though the rate slows with age. Multiple studies (e.g. Genc et al. (2017), Mah et al. (2017)) found strong positive correlations between age and ICVF/NDI in children. By contrast, orientation dispersion (ODI) remains relatively stable during childhood, indicating that fiber organization (coherence) does not markedly increase after early childhood in most regions (Mah et al., 2017). The net effect is that white matter FA increases in youth are chiefly driven by increasing neurite density (more and thicker axons with more myelin), rather than fibers becoming straighter or more aligned. Children with higher NDI tend to have higher FA even if ODI is unchanged, meaning microstructural density is a key determinant of developmental differences (Mah et al., 2017).
Late Adolescence to Early Adulthood: Neurite density in many tracts appears to plateau by the early 20s, reaching peak or near-peak values, while ODI might begin to creep upward. Chang and Mukherjee (2015) noted that NDI followed a logarithmic growth curve that leveled off in the 20s-30s, whereas ODI began an exponential upward trend in the 30s. This implies that during the transition to adulthood, continued incremental myelination is eventually counteracted by emerging microscopic disorganization (potentially reflecting early branching/pruning or cumulative minor damage), which foreshadows the patterns seen in later aging.
Mid-Life and Healthy Aging: In mid-to-late adulthood, NODDI reveals progressive loss of neurite density and increasing fiber dispersion. Large-scale data from the UK Biobank showed that older age is associated with lower ICVF and higher ODI across virtually all white matter tracts (Lawrence et al., 2021). Notably, these microstructural changes can be detected even in healthy adults with no disease, reflecting normative brain aging. Age-related ODI increases often accelerate in the later decades, consistent with accumulating structural disintegration. Lawrence et al. (2021) also found that CSF fraction (ISOVF) increases with age, indicating expanding extracellular space.
Clinical and Cognitive Relevance: The lifespan changes in NODDI metrics align with known windows of brain plasticity and decline. The steep NDI increase in childhood corresponds to learning and cognitive development, whereas rising ODI in late life correlates with cognitive slowing and increased white matter vulnerability. Importantly, NDI has been found to be a better predictor of chronological age than standard DTI measures in youth (Genc et al., 2017).
NODDI Methodological Warnings and Caveats
Model Assumptions and Biases: NODDI relies on several fixed model assumption. Notably that all intra-axonal and extra-axonal water shares the same diffusion coefficient (typically d‖ = 1.7 µm²/ms) and that fibers in a voxel have a single average orientation dispersion (Watson distribution). In reality, these assumptions are often violated: gray matter neurites have slower diffusion, pathology can alter compartment diffusivities, and multiple fiber populations can exist. As a result, NODDI parameter estimates may be biased if these conditions aren’t met (Guerrero et al., 2019). For example, using the default d‖ in cortical gray matter can lead to overestimation of ICVF (since true diffusion is slower). Investigators have to adjust this value for different tissues or accept some bias (Guerrero et al., 2019). Simplifying assumptions are necessary to keep NODDI practical, but users should understand they introduce systematic errors in certain contexts.
Degeneracy and Parameter Coupling: A fundamental challenge with multi-compartment models like NODDI is that different parameter combinations can produce very similar diffusion signals. There is a trade-off between neurite density and dispersion, for instance: a voxel with fewer, well-aligned axons can have a similar diffusion profile to one with more axons that are highly dispersed. This can lead to degenerate solutions where the fitting algorithm might converge on one of several “equivalent” parameter sets (Jelescu and Budde, 2017).
Interpretational Specificity: While NODDI’s indices are more directly linked to microstructure than DTI’s, they are not one-to-one with histology. ICVF, for example, is often called “neurite density index,” but it doesn’t strictly equal axon count or volume fraction in a straightforward way. It’s influenced by dendrites in gray matter, by glial processes, and by whether axons are myelinated or not. ODI is likewise a proxy for fiber orientation dispersion, but it can be increased by diverse scenarios (actual fanning of fibers, beading/swelling of axons, or mixture of fiber orientations). Moreover, pathology can confound these metrics: e.g., inflammation adds isotropic water which lowers ICVF and raises ISOVF, mimicking axonal loss. NODDI is best used to compare groups or conditions, rather than to obtain exact “neurite counts”.
References
Ileana O. Jelescu and Matthew D. Budde. Design and validation of diffusion MRI models of white matter. Frontiers in Physics, 5:61, 2017. doi:10.3389/fphy.2017.00061.
Hui Zhang, Torben Schneider, Claudia A Wheeler-Kingshott, and Daniel C Alexander. Noddi: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage, 61(4):1000–1016, 2012. doi:10.1016/j.neuroimage.2012.03.072.
Alessandro Daducci, Erick J Canales-Rodríguez, Hui Zhang, Tim B Dyrby, Daniel C Alexander, and Jean-Philippe Thiran. Accelerated microstructure imaging via convex optimization (amico) from diffusion mri data. NeuroImage, 105:32–44, 2015. doi:10.1016/j.neuroimage.2014.10.026.
C.S. Parker, T. Veale, M. Bocchetta, C.F. Slattery, I.B. Malone, D.L. Thomas, J.M. Schott, D.M. Cash, and H. Zhang. Not all voxels are created equal: reducing estimation bias in regional noddi metrics using tissue-weighted means. NeuroImage, 245:118749, 2021. URL: https://www.sciencedirect.com/science/article/pii/S1053811921010211, doi:https://doi.org/10.1016/j.neuroimage.2021.118749.
Ileana O. Jelescu, Jelle Veraart, Vitria Adisetiyo, Sol Milla, Dmitry S. Novikov, and Els Fieremans. One diffusion acquisition and different white matter models: how does microstructure change in human early development based on wmti and noddi? NeuroImage, 107:242–256, 2015. URL: https://doi.org/10.1016/j.neuroimage.2014.12.009, doi:10.1016/j.neuroimage.2014.12.009.
Sami Genc, Charles B. Malpas, Scott K. Holland, Richard Beare, and Timothy J. Silk. Neurite density index is sensitive to age related differences in the developing brain. NeuroImage, 148:373–380, 2017. URL: https://doi.org/10.1016/j.neuroimage.2017.01.023, doi:10.1016/j.neuroimage.2017.01.023.
Alyssa A. N. Mah, Bryce L. Geeraert, and Catherine Lebel. Detailing neuroanatomical development in late childhood and early adolescence using noddi. PLOS ONE, 12(8):e0182340, August 2017. URL: https://doi.org/10.1371/journal.pone.0182340, doi:10.1371/journal.pone.0182340.
Yishin Chang and Pratik Mukherjee. Noddi-plos-one-chang et al 2015. 2015. URL: https://doi.org/10.7272/Q6D798BD, doi:10.7272/Q6D798BD.
Kate E. Lawrence, Layal Nabulsi, Vijay Santhalingam, and et al. Age and sex effects on advanced white matter microstructure measures in 15,628 older adults: a uk biobank study. Brain Imaging and Behavior, 15:2813–2823, 2021. URL: https://doi.org/10.1007/s11682-021-00548-y, doi:10.1007/s11682-021-00548-y.