A controlled comparison of thickness, volume and surface areas from multiple cortical parcellation packages.
Atlas
Brain
Cingulate gyrus
Cortical parcellation
Grey matter
Superior frontal gyrus
Supramarginal gyrus
Surface area
Thickness
Volume
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
28 Jan 2019
28 Jan 2019
Historique:
received:
15
07
2018
accepted:
04
01
2019
entrez:
30
1
2019
pubmed:
30
1
2019
medline:
26
2
2019
Statut:
epublish
Résumé
Cortical parcellation is an essential neuroimaging tool for identifying and characterizing morphometric and connectivity brain changes occurring with age and disease. A variety of software packages have been developed for parcellating the brain's cortical surface into a variable number of regions but interpackage differences can undermine reproducibility. Using a ground truth dataset (Edinburgh_NIH10), we investigated such differences for grey matter thickness (GM Corresponding gyral definitions and morphometry approaches were not identical across the packages. As expected, there were differences in the bordering landmarks of each gyrus as well as in the manner in which variability was addressed. Rostral and caudal SFG and SMG boundaries differed, and in the event of a double CG occurrence, its upper fold was not always addressed. This led to a knock-on effect that was visible at the neighbouring gyri (e.g., knock-on effect at the SFG following CG definition) as well as gyral morphometric measurements of the affected gyri. Statistical analysis showed that the most consistent approaches were FreeSurfer's Desikan-Killiany-Tourville (DKT) protocol for GM Given the significance and implications that a parcellation protocol will have on the classification, and sometimes treatment, of subjects, it is essential to select the protocol which accurately represents their regions of interest and corresponding morphometrics, while embracing cortical variability.
Sections du résumé
BACKGROUND
BACKGROUND
Cortical parcellation is an essential neuroimaging tool for identifying and characterizing morphometric and connectivity brain changes occurring with age and disease. A variety of software packages have been developed for parcellating the brain's cortical surface into a variable number of regions but interpackage differences can undermine reproducibility. Using a ground truth dataset (Edinburgh_NIH10), we investigated such differences for grey matter thickness (GM
RESULTS
RESULTS
Corresponding gyral definitions and morphometry approaches were not identical across the packages. As expected, there were differences in the bordering landmarks of each gyrus as well as in the manner in which variability was addressed. Rostral and caudal SFG and SMG boundaries differed, and in the event of a double CG occurrence, its upper fold was not always addressed. This led to a knock-on effect that was visible at the neighbouring gyri (e.g., knock-on effect at the SFG following CG definition) as well as gyral morphometric measurements of the affected gyri. Statistical analysis showed that the most consistent approaches were FreeSurfer's Desikan-Killiany-Tourville (DKT) protocol for GM
CONCLUSIONS
CONCLUSIONS
Given the significance and implications that a parcellation protocol will have on the classification, and sometimes treatment, of subjects, it is essential to select the protocol which accurately represents their regions of interest and corresponding morphometrics, while embracing cortical variability.
Identifiants
pubmed: 30691385
doi: 10.1186/s12859-019-2609-8
pii: 10.1186/s12859-019-2609-8
pmc: PMC6348615
doi:
Types de publication
Comparative Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
55Subventions
Organisme : NIBIB NIH HHS
ID : R01 EB004155
Pays : United States
Organisme : NIH HHS
ID : R01 EB004155
Pays : United States
Références
Brain Inform. 2017 Jun;4(2):107-121
pubmed: 28054317
Neurology. 2009 Mar 24;72(12):1048-55
pubmed: 19109536
Neuroimage. 2018 Apr 15;170:348-364
pubmed: 28279814
Cereb Cortex. 2006 Dec;16(12):1701-8
pubmed: 16400164
Front Neurosci. 2012 Dec 05;6:171
pubmed: 23227001
Sci Data. 2019 Jan 29;6:190001
pubmed: 30694228
Neurology. 2002 Jan 22;58(2):198-208
pubmed: 11805245
Schizophr Bull. 2017 May 1;43(3):673-680
pubmed: 27169464
Neuroinformatics. 2012 Jan;10(1):81-96
pubmed: 21698393
Cereb Cortex. 2000 May;10(5):464-72
pubmed: 10847596
Neuroimage. 2016 Jul 1;134:617-629
pubmed: 27103141
PLoS One. 2012;7(6):e38234
pubmed: 22675527
J Neurosci. 2017 Mar 22;37(12):3402-3412
pubmed: 28242797
Front Aging Neurosci. 2017 Sep 26;9:309
pubmed: 29085292
Neurobiol Aging. 2012 Mar;33(3):617.e1-9
pubmed: 20739099
Cereb Cortex. 2007 Jul;17(7):1550-60
pubmed: 16945978
Cereb Cortex. 1999 Mar;9(2):151-60
pubmed: 10220227
Neuroimage. 2010 Oct 1;52(4):1215-23
pubmed: 20441796
Neuroimage. 1999 Feb;9(2):179-94
pubmed: 9931268
Eur J Neurosci. 2018 Mar;47(5):399-416
pubmed: 29359873
PLoS One. 2017 Jan 27;12(1):e0170875
pubmed: 28129351
Proc Natl Acad Sci U S A. 2000 Sep 26;97(20):11050-5
pubmed: 10984517
J Vis Exp. 2012 Jan 02;(59):e3417
pubmed: 22230945
Front Aging Neurosci. 2017 Dec 11;9:412
pubmed: 29321739
Sci Data. 2016 Jun 21;3:160044
pubmed: 27326542
Neuroimage. 2006 Jul 1;31(3):968-80
pubmed: 16530430
Cereb Cortex. 2013 Nov;23(11):2521-30
pubmed: 22892423
Neurobiol Aging. 2005 Jan;26(1):37-44
pubmed: 15585344
Hum Brain Mapp. 2015 Sep;36(9):3472-85
pubmed: 26033168
Brain Struct Funct. 2018 Jan;223(1):509-518
pubmed: 28879544
Med Image Anal. 2002 Jun;6(2):129-42
pubmed: 12045000
Neuroimage. 2013 Jan 15;65:511-21
pubmed: 23036450
Neuroimage. 2010 Feb 1;49(3):2479-93
pubmed: 19796696
Mol Autism. 2016 Jan 25;7:11
pubmed: 26816612
Brain. 2016 Jan;139(Pt 1):115-26
pubmed: 26637488
Neuroinformatics. 2014 Oct;12(4):535-42
pubmed: 24789776
Neuroimage. 1999 Feb;9(2):195-207
pubmed: 9931269