English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  NODDI-DTI: Estimating neurite orientation and dispersion parameters from a diffusion tensor in healthy white matter

Edwards, L., Pine, K., Ellerbrock, I., Weiskopf, N., & Mohammadi, S. (2017). NODDI-DTI: Estimating neurite orientation and dispersion parameters from a diffusion tensor in healthy white matter. Frontiers in Neuroscience, 11: 720. doi:10.3389/fnins.2017.00720.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002E-A23C-A Version Permalink: http://hdl.handle.net/21.11116/0000-0003-ABA4-3
Genre: Journal Article

Files

show Files
hide Files
:
Edwards_Pine_2017.pdf (Publisher version), 14MB
Name:
Edwards_Pine_2017.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Edwards, Luke1, 2, Author              
Pine, Kerrin1, 2, Author              
Ellerbrock, Isabel3, Author
Weiskopf, Nikolaus1, 2, Author              
Mohammadi, Siawoosh1, 2, 3, Author
Affiliations:
1Department Neurophysics (Weiskopf), MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_2205649              
2Wellcome Trust Centre for Neuroimaging, University College London, United Kingdom, ou_persistent22              
3Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Germany, ou_persistent22              

Content

show
hide
Free keywords: Diffusion MRI; NODDI; DTI; Axonal density; Orientation dispersion
 Abstract: The NODDI-DTI signal model is a modification of the NODDI signal model that formally allows interpretation of standard single-shell DTI data in terms of biophysical parameters in healthy human white matter (WM). The NODDI-DTI signal model contains no CSF compartment, restricting application to voxels without CSF partial-volume contamination. This modification allowed derivation of analytical relations between parameters representing axon density and dispersion, and DTI invariants (MD and FA) from the NODDI-DTI signal model. These relations formally allow extraction of biophysical parameters from DTI data. NODDI-DTI parameters were estimated by applying the proposed analytical relations to DTI parameters estimated from the first shell of data, and compared to parameters estimated by fitting the NODDI-DTI model to both shells of data (reference dataset) in the WM of 14 in vivo diffusion datasets recorded with two different protocols, and in simulated data. The first two datasets were also fit to the NODDI-DTI model using only the first shell (as for DTI) of data. NODDI-DTI parameters estimated from DTI, and NODDI-DTI parameters estimated by fitting the model to the first shell of data gave similar errors compared to two-shell NODDI-DTI estimates. The simulations showed the NODDI-DTI method to be more noise-robust than the two-shell fitting procedure. The NODDI-DTI method gave unphysical parameter estimates in a small percentage of voxels, reflecting voxelwise DTI estimation error or NODDI-DTI model invalidity. In the course of evaluating the NODDI-DTI model, it was found that diffusional kurtosis strongly biased DTI-based MD values, and so, making assumptions based on healthy WM, a novel heuristic correction requiring only DTI data was derived and used to mitigate this bias. Since validations were only performed on healthy WM, application to grey matter or pathological WM would require further validation. Our results demonstrate NODDI-DTI to be a promising model and technique to interpret restricted datasets acquired for DTI analysis in healthy white matter with greater biophysical specificity, though its limitations must be borne in mind.

Details

show
hide
Language(s): eng - English
 Dates: 2017-06-122017-12-112017-12-20
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.3389/fnins.2017.00720
PMID: 29326546
PMC: PMC5742359
Other: eCollection 2017
 Degree: -

Event

show

Legal Case

show

Project information

show hide
Project name : Non-invasive in vivo histology in health and disease using Magnetic Resonance Imaging (MRI) / HMRI
Grant ID : 616905
Funding program : FP7 (ERC-2013-CoG)
Funding organization : European Commission (EC)
Project name : NEURON-Verbund hMRTofScl: Entschlüsselung der pathophysiologischen Prozesse induziert durch eine Querschnittslähmung: Anwendung von MRT basierter in vivo und ex vivo Histologie / Era-Net NEURON
Grant ID : 01EW1711B
Funding program : ERA-NET NEURON Call "Entwicklungsstörungen im Nervensystem" (ERA-NET NEURON JTC2016)
Funding organization : German Federal Ministry of Education and Research (BMBF)
Project name : Mesoscopic characterization of human white-matter: A computational in-vivo MRI framework / MWMI
Grant ID : 658589
Funding program : Horizon 2020
Funding organization : European Commission (EC)
Project name : -
Grant ID : 0915/Z/10/Z
Funding program : -
Funding organization : Wellcome Trust
Project name : -
Grant ID : 2397/4-1
Funding program : -
Funding organization : Deutsche Forschungsgemeinschaft (DFG)
Project name : -
Grant ID : -
Funding program : Forschungsforderungsfond der Medizinischen Fakultat (FFM) Postdoctoral Fellowship
Funding organization : University Medical Center Hamburg-Eppendorf

Source 1

show
hide
Title: Frontiers in Neuroscience
  Other : Front Neurosci
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Lausanne, Switzerland : Frontiers Research Foundation
Pages: - Volume / Issue: 11 Sequence Number: 720 Start / End Page: - Identifier: ISSN: 1662-4548
ISSN: 1662-453X
CoNE: https://pure.mpg.de/cone/journals/resource/1662-4548