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Cited by 345Advanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsFunctional Brain Connectivity Studies

Multi-compartment microscopic diffusion imaging

Open access
Kaden, Enrico, Kelm, Nathaniel D., Carson, Robert P., Does, Mark D., Alexander, Daniel C.·2016·NeuroImage
DOI 10.1016/j.neuroimage.2016.06.002PMC PMC5517363
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Abstract

This paper introduces a multi-compartment model for microscopic diffusion anisotropy imaging. The aim is to estimate microscopic features specific to the intra- and extra-neurite compartments in nervous tissue unconfounded by the effects of fibre crossings and orientation dispersion, which are ubiquitous in the brain. The proposed MRI method is based on the Spherical Mean Technique (SMT), which factors out the neurite orientation distribution and thus provides direct estimates of the microscopic tissue structure. This technique can be immediately used in the clinic for the assessment of various neurological conditions, as it requires only a widely available off-the-shelf sequence with two b-shells and high-angular gradient resolution achievable within clinically feasible scan times. To demonstrate the developed method, we use high-quality diffusion data acquired with a bespoke scanner system from the Human Connectome Project. This study establishes the normative values of the new biomarkers for a large cohort of healthy young adults, which may then support clinical diagnostics in patients. Moreover, we show that the microscopic diffusion indices offer direct sensitivity to pathological tissue alterations, exemplified in a preclinical animal model of Tuberous Sclerosis Complex (TSC), a genetic multi-organ disorder which impacts brain microstructure and hence may lead to neurological manifestations such as autism, epilepsy and developmental delay.

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  • NODDI may overestimate the free-water content in cerebral white matter.

    “As NODDI depicts a single and fixed intrinsic diffusivity over the whole brain, which may hold the potential to overestimate the free-water content in the cerebral white matter (Kaden et al. 2016).”

    From Neurite orientation dispersion and density imaging of mouse brain microstructure (2019) · cites this paper as Kaden et al. 2016

  • The NODDI model assumes the neurite orientation distribution using a single Watson distribution without accounting for fiber crossings.

    “In addition, NODDI model assumes the neurite orientation distribution using a single Watson distribution without accounting for fiber crossings, the current results may be improved with multiple Bingham distributions or spherical mean technique (SMT) (Kaden et al. 2016; Tariq et al. 2016).”

    From Neurite orientation dispersion and density imaging of mouse brain microstructure (2019) · cites this paper as Kaden et al. 2016

  • NODDI depicts a single and fixed intrinsic diffusivity over the whole brain.

    “As NODDI depicts a single and fixed intrinsic diffusivity over the whole brain, which may hold the potential to overestimate the free-water content in the cerebral white matter (Kaden et al. 2016).”

    From Neurite orientation dispersion and density imaging of mouse brain microstructure (2019) · cites this paper as Kaden et al. 2016

  • Advanced diffusion MRI models (Spherical Mean Technique, NODDI, diffusion basis spectrum imaging) are becoming increasingly popular compared to tensor-based metrics such as FA, but are vastly under-represented in the validation literature.

    “advanced models, such as the Spherical Mean Technique (Kaden et al., 2016), neurite orientation dispersion and density imaging (NODDI; Zhang et al. (2012)), and diffusion basis spectrum imaging (Wang et al., 2011), are becoming increasingly popular compared to tensor-based metrics such as FA, but are vastly under-represented in the validation literature (Alexander, Dyrby, Nilsson, 2019, Caspers, A”

    From Can MRI measure myelin? Systematic review, qualitative assessment, and meta-analysis of studies validating microstructural imaging with myelin histology (2021) · cites this paper as Kaden et al., 2016

  • Recent years have seen a marked proliferation of biophysical models with the explicit aim of clinically feasible data acquisition.

    “Recent years have seen a marked proliferation of biophysical models with the explicit aim of clinically feasible data acquisition (single-diffusion encoding, b-values up to 2000-3000 s/mm 2 , diffusion time 20-100 ms) (Fieremans et al., 2011;Hansen et al., 2017;Kaden et al., 2016;Zhang et al., 2012).”

    From NODDI in clinical research (2020) · cites this paper as Kaden et al., 2016

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Cited by

Sorted by citations

Authors
Kaden, E., Kelm, N. D., Carson, R. P., Does, M. D., & Alexander, D. C.
Affiliations
  • University College London (GB)
  • Vanderbilt University (US)
Corresponding author
Kaden, Enrico
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2016-10
Volume
139
Pages
346-359
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2016.06.002
PMC
PMC5517363
OpenAlex
W2413955537
Cited by
345
References
10
Citations by year
2026: 18 · 2025: 35 · 2024: 42 · 2023: 35 · 2022: 37 · 2021: 43 · 2020: 47 · 2019: 35 · 2018: 28 · 2017: 21 · 2016: 4
FWCI
25.47
Type
journal article
Language
en
Open access
Licence
Locations
  • NeuroImage (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion, cc-by)
  • PubMed Central (acceptedVersion, cc-by)
  • UCL Discovery (University College London) (submittedVersion)
Funders
  • EPSRC (EP/G007748/1, EP/M020533/1, EP/N018702/1, H2020 634541-2)
  • NIH (R01 EB001744, 5K08 NS050484, T32 EB014841, S10 RR029523)
  • 16 NIH Institutes and Centers
  • NIH Blueprint for Neuroscience Research
  • McDonnell Center for Systems Neuroscience at Washington University
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords
orientation dispersion, diffusion MRI biomarkers, reference values, tuberous sclerosis complex, brain microstructure, human connectome
MeSH terms
Adult, Animals, Brain, Brain Mapping, Disease Models, Animal, Female, Humans, Image Processing, Computer-Assisted, Male, Signal Processing, Computer-Assisted, Tuberous Sclerosis, Neurites, Anisotropy, Diffusion Magnetic Resonance Imaging

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