Not all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted means

Neurite orientation dispersion and density imaging (NODDI) estimates microstructural properties of brain tissue relating to the organisation and processing capacity of neurites, which are essential elements for neuronal communication. Descriptive statistics of NODDI tissue metrics are commonly analy...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	C.S. Parker, T. Veale, M. Bocchetta, C.F. Slattery, I.B. Malone, D.L. Thomas, J.M. Schott, D.M. Cash, H. Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Diffusion MRI Microstructure imaging Region-of-interest Arithmetic mean Tissue-weighted mean Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/96b87f50aff549fb9a6097058077ec26
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:96b87f50aff549fb9a6097058077ec26
record_format	dspace
spelling	oai:doaj.org-article:96b87f50aff549fb9a6097058077ec262021-12-04T04:33:17ZNot all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted means1095-957210.1016/j.neuroimage.2021.118749https://doaj.org/article/96b87f50aff549fb9a6097058077ec262021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1053811921010211https://doaj.org/toc/1095-9572Neurite orientation dispersion and density imaging (NODDI) estimates microstructural properties of brain tissue relating to the organisation and processing capacity of neurites, which are essential elements for neuronal communication. Descriptive statistics of NODDI tissue metrics are commonly analyzed in regions-of-interest (ROI) to identify brain-phenotype associations. Here, the conventional method to calculate the ROI mean weights all voxels equally. However, this produces biased estimates in the presence of CSF partial volume. This study introduces the tissue-weighted mean, which calculates the mean NODDI metric across the tissue within an ROI, utilising the tissue fraction estimate from NODDI to reduce estimation bias. We demonstrate the proposed mean in a study of white matter abnormalities in young onset Alzheimer's disease (YOAD). Results show the conventional mean induces significant bias that correlates with CSF partial volume, primarily affecting periventricular regions and more so in YOAD subjects than in healthy controls. Due to the differential extent of bias between healthy controls and YOAD subjects, the conventional mean under- or over-estimated the effect size for group differences in many ROIs. This demonstrates the importance of using the correct estimation procedure when inferring group differences in studies where the extent of CSF partial volume differs between groups. These findings are robust across different acquisition and processing conditions. Bias persists in ROIs at higher image resolution, as demonstrated using data obtained from the third phase of the Alzheimer's disease neuroimaging initiative (ADNI); and when performing ROI analysis in template space. This suggests that conventional ROI means of NODDI metrics are biased estimates under most contemporary experimental conditions, the correction of which requires the proposed tissue-weighted mean. The tissue-weighted mean produces accurate estimates of ROI means and group differences when ROIs contain voxels with CSF partial volume. In addition to NODDI, the technique can be applied to other multi-compartment models that account for CSF partial volume, such as the free water elimination method. We expect the technique to help generate new insights into normal and abnormal variation in tissue microstructure of regions typically confounded by CSF partial volume, such as those in individuals with larger ventricles due to atrophy associated with neurodegenerative disease.C.S. ParkerT. VealeM. BocchettaC.F. SlatteryI.B. MaloneD.L. ThomasJ.M. SchottD.M. CashH. ZhangElsevierarticleDiffusion MRIMicrostructure imagingRegion-of-interestArithmetic meanTissue-weighted meanNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENNeuroImage, Vol 245, Iss , Pp 118749- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Diffusion MRI Microstructure imaging Region-of-interest Arithmetic mean Tissue-weighted mean Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Diffusion MRI Microstructure imaging Region-of-interest Arithmetic mean Tissue-weighted mean Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 C.S. Parker T. Veale M. Bocchetta C.F. Slattery I.B. Malone D.L. Thomas J.M. Schott D.M. Cash H. Zhang Not all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted means
description	Neurite orientation dispersion and density imaging (NODDI) estimates microstructural properties of brain tissue relating to the organisation and processing capacity of neurites, which are essential elements for neuronal communication. Descriptive statistics of NODDI tissue metrics are commonly analyzed in regions-of-interest (ROI) to identify brain-phenotype associations. Here, the conventional method to calculate the ROI mean weights all voxels equally. However, this produces biased estimates in the presence of CSF partial volume. This study introduces the tissue-weighted mean, which calculates the mean NODDI metric across the tissue within an ROI, utilising the tissue fraction estimate from NODDI to reduce estimation bias. We demonstrate the proposed mean in a study of white matter abnormalities in young onset Alzheimer's disease (YOAD). Results show the conventional mean induces significant bias that correlates with CSF partial volume, primarily affecting periventricular regions and more so in YOAD subjects than in healthy controls. Due to the differential extent of bias between healthy controls and YOAD subjects, the conventional mean under- or over-estimated the effect size for group differences in many ROIs. This demonstrates the importance of using the correct estimation procedure when inferring group differences in studies where the extent of CSF partial volume differs between groups. These findings are robust across different acquisition and processing conditions. Bias persists in ROIs at higher image resolution, as demonstrated using data obtained from the third phase of the Alzheimer's disease neuroimaging initiative (ADNI); and when performing ROI analysis in template space. This suggests that conventional ROI means of NODDI metrics are biased estimates under most contemporary experimental conditions, the correction of which requires the proposed tissue-weighted mean. The tissue-weighted mean produces accurate estimates of ROI means and group differences when ROIs contain voxels with CSF partial volume. In addition to NODDI, the technique can be applied to other multi-compartment models that account for CSF partial volume, such as the free water elimination method. We expect the technique to help generate new insights into normal and abnormal variation in tissue microstructure of regions typically confounded by CSF partial volume, such as those in individuals with larger ventricles due to atrophy associated with neurodegenerative disease.
format	article
author	C.S. Parker T. Veale M. Bocchetta C.F. Slattery I.B. Malone D.L. Thomas J.M. Schott D.M. Cash H. Zhang
author_facet	C.S. Parker T. Veale M. Bocchetta C.F. Slattery I.B. Malone D.L. Thomas J.M. Schott D.M. Cash H. Zhang
author_sort	C.S. Parker
title	Not all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted means
title_short	Not all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted means
title_full	Not all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted means
title_fullStr	Not all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted means
title_full_unstemmed	Not all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted means
title_sort	not all voxels are created equal: reducing estimation bias in regional noddi metrics using tissue-weighted means
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/96b87f50aff549fb9a6097058077ec26
work_keys_str_mv	AT csparker notallvoxelsarecreatedequalreducingestimationbiasinregionalnoddimetricsusingtissueweightedmeans AT tveale notallvoxelsarecreatedequalreducingestimationbiasinregionalnoddimetricsusingtissueweightedmeans AT mbocchetta notallvoxelsarecreatedequalreducingestimationbiasinregionalnoddimetricsusingtissueweightedmeans AT cfslattery notallvoxelsarecreatedequalreducingestimationbiasinregionalnoddimetricsusingtissueweightedmeans AT ibmalone notallvoxelsarecreatedequalreducingestimationbiasinregionalnoddimetricsusingtissueweightedmeans AT dlthomas notallvoxelsarecreatedequalreducingestimationbiasinregionalnoddimetricsusingtissueweightedmeans AT jmschott notallvoxelsarecreatedequalreducingestimationbiasinregionalnoddimetricsusingtissueweightedmeans AT dmcash notallvoxelsarecreatedequalreducingestimationbiasinregionalnoddimetricsusingtissueweightedmeans AT hzhang notallvoxelsarecreatedequalreducingestimationbiasinregionalnoddimetricsusingtissueweightedmeans
_version_	1718372983104339968

Not all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted means

Ejemplares similares