Literature DB >> 25583609

Analysis of the contribution of experimental bias, experimental noise, and inter-subject biological variability on the assessment of developmental trajectories in diffusion MRI studies of the brain.

Neda Sadeghi1, Amritha Nayak2, Lindsay Walker3, M Okan Irfanoglu4, Paul S Albert5, Carlo Pierpaoli6.   

Abstract

Metrics derived from the diffusion tensor, such as fractional anisotropy (FA) and mean diffusivity (MD) have been used in many studies of postnatal brain development. A common finding of previous studies is that these tensor-derived measures vary widely even in healthy populations. This variability can be due to inherent inter-individual biological differences as well as experimental noise. Moreover, when comparing different studies, additional variability can be introduced by different acquisition protocols. In this study we examined scans of 61 individuals (aged 4-22 years) from the NIH MRI study of normal brain development. Two scans were collected with different protocols (low and high resolution). Our goal was to separate the contributions of biological variability and experimental noise to the overall measured variance, as well as to assess potential systematic effects related to the use of different protocols. We analyzed FA and MD in seventeen regions of interest. We found that biological variability for both FA and MD varies widely across brain regions; biological variability is highest for FA in the lateral part of the splenium and body of the corpus callosum along with the cingulum and the superior longitudinal fasciculus, and for MD in the optic radiations and the lateral part of the splenium. These regions with high inter-individual biological variability are the most likely candidates for assessing genetic and environmental effects in the developing brain. With respect to protocol-related effects, the lower resolution acquisition resulted in higher MD and lower FA values for the majority of regions compared with the higher resolution protocol. However, the majority of the regions did not show any age-protocol interaction, indicating similar trajectories were obtained irrespective of the protocol used. Published by Elsevier Inc.

Entities:  

Keywords:  Brain development; DTI; Experimental variability; Mixed effects model

Mesh:

Year:  2015        PMID: 25583609      PMCID: PMC4350793          DOI: 10.1016/j.neuroimage.2014.12.084

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  40 in total

1.  Condition number as a measure of noise performance of diffusion tensor data acquisition schemes with MRI.

Authors:  S Skare; M Hedehus; M E Moseley; T Q Li
Journal:  J Magn Reson       Date:  2000-12       Impact factor: 2.229

2.  Replicability of diffusion tensor imaging measurements of fractional anisotropy and trace in brain.

Authors:  Adolf Pfefferbaum; Elfar Adalsteinsson; Edith V Sullivan
Journal:  J Magn Reson Imaging       Date:  2003-10       Impact factor: 4.813

3.  The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: a Monte Carlo study.

Authors:  Derek K Jones
Journal:  Magn Reson Med       Date:  2004-04       Impact factor: 4.668

4.  Atlas-based analysis of neurodevelopment from infancy to adulthood using diffusion tensor imaging and applications for automated abnormality detection.

Authors:  Andreia V Faria; Jiangyang Zhang; Kenichi Oishi; Xin Li; Hangyi Jiang; Kazi Akhter; Laurent Hermoye; Seung-Koo Lee; Alexander Hoon; Elaine Stashinko; Michael I Miller; Peter C M van Zijl; Susumu Mori
Journal:  Neuroimage       Date:  2010-04-24       Impact factor: 6.556

5.  Diffusion tensor MR imaging of the human brain.

Authors:  C Pierpaoli; P Jezzard; P J Basser; A Barnett; G Di Chiro
Journal:  Radiology       Date:  1996-12       Impact factor: 11.105

6.  Volumetric neuroimage analysis extensions for the MIPAV software package.

Authors:  Pierre-Louis Bazin; Jennifer L Cuzzocreo; Michael A Yassa; William Gandler; Matthew J McAuliffe; Susan S Bassett; Dzung L Pham
Journal:  J Neurosci Methods       Date:  2007-05-29       Impact factor: 2.390

7.  Effects of signal-to-noise ratio on the accuracy and reproducibility of diffusion tensor imaging-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5 T.

Authors:  Jonathan A D Farrell; Bennett A Landman; Craig K Jones; Seth A Smith; Jerry L Prince; Peter C M van Zijl; Susumu Mori
Journal:  J Magn Reson Imaging       Date:  2007-09       Impact factor: 4.813

8.  Normal brain maturation during childhood: developmental trends characterized with diffusion-tensor MR imaging.

Authors:  P Mukherjee; J H Miller; J S Shimony; T E Conturo; B C Lee; C R Almli; R C McKinstry
Journal:  Radiology       Date:  2001-11       Impact factor: 11.105

9.  Comprehensive approach for correction of motion and distortion in diffusion-weighted MRI.

Authors:  G K Rohde; A S Barnett; P J Basser; S Marenco; C Pierpaoli
Journal:  Magn Reson Med       Date:  2004-01       Impact factor: 4.668

10.  Identical, but not the same: intra-site and inter-site reproducibility of fractional anisotropy measures on two 3.0T scanners.

Authors:  Christian Vollmar; Jonathan O'Muircheartaigh; Gareth J Barker; Mark R Symms; Pamela Thompson; Veena Kumari; John S Duncan; Mark P Richardson; Matthias J Koepp
Journal:  Neuroimage       Date:  2010-03-23       Impact factor: 6.556

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  11 in total

1.  The diffusion tensor imaging (DTI) component of the NIH MRI study of normal brain development (PedsDTI).

Authors:  Lindsay Walker; Lin-Ching Chang; Amritha Nayak; M Okan Irfanoglu; Kelly N Botteron; James McCracken; Robert C McKinstry; Michael J Rivkin; Dah-Jyuu Wang; Judith Rumsey; Carlo Pierpaoli
Journal:  Neuroimage       Date:  2015-06-03       Impact factor: 6.556

2.  A registration method for improving quantitative assessment in probabilistic diffusion tractography.

Authors:  J L Waugh; J K Kuster; M L Makhlouf; J M Levenstein; T J Multhaupt-Buell; S K Warfield; N Sharma; A J Blood
Journal:  Neuroimage       Date:  2019-01-03       Impact factor: 6.556

3.  Differential White Matter Maturation from Birth to 8 Years of Age.

Authors:  Qinlin Yu; Yun Peng; Huiying Kang; Qinmu Peng; Minhui Ouyang; Michelle Slinger; Di Hu; Haochang Shou; Fang Fang; Hao Huang
Journal:  Cereb Cortex       Date:  2020-04-14       Impact factor: 5.357

4.  DR-TAMAS: Diffeomorphic Registration for Tensor Accurate Alignment of Anatomical Structures.

Authors:  M Okan Irfanoglu; Amritha Nayak; Jeffrey Jenkins; Elizabeth B Hutchinson; Neda Sadeghi; Cibu P Thomas; Carlo Pierpaoli
Journal:  Neuroimage       Date:  2016-02-28       Impact factor: 6.556

5.  Brain phenotyping in Moebius syndrome and other congenital facial weakness disorders by diffusion MRI morphometry.

Authors:  Neda Sadeghi; Elizabeth Hutchinson; Carol Van Ryzin; Edmond J FitzGibbon; John A Butman; Bryn D Webb; Flavia Facio; Brian P Brooks; Francis S Collins; Ethylin Wang Jabs; Elizabeth C Engle; Irini Manoli; Carlo Pierpaoli
Journal:  Brain Commun       Date:  2020-02-14

6.  Anisotropic finite element models for brain injury prediction: the sensitivity of axonal strain to white matter tract inter-subject variability.

Authors:  Chiara Giordano; Stefano Zappalà; Svein Kleiven
Journal:  Biomech Model Mechanobiol       Date:  2017-02-23

7.  White matter structural connectivity and episodic memory in early childhood.

Authors:  Chi T Ngo; Kylie H Alm; Athanasia Metoki; William Hampton; Tracy Riggins; Nora S Newcombe; Ingrid R Olson
Journal:  Dev Cogn Neurosci       Date:  2017-11-16       Impact factor: 6.464

8.  Reproducibility, reliability and variability of FA and MD in the older healthy population: A test-retest multiparametric analysis.

Authors:  Pedro A Luque Laguna; Anna J E Combes; Johannes Streffer; Steven Einstein; Maarten Timmers; Steve C R Williams; Flavio Dell'Acqua
Journal:  Neuroimage Clin       Date:  2020-01-25       Impact factor: 4.881

9.  Investigation of the effect of dietary intake of omega-3 polyunsaturated fatty acids on trauma-induced white matter injury with quantitative diffusion MRI in mice.

Authors:  Laura D Reyes; Thaddeus Haight; Abhishek Desai; Huazhen Chen; Asamoah Bosomtwi; Alexandru Korotcov; Bernard Dardzinski; Hee-Yong Kim; Carlo Pierpaoli
Journal:  J Neurosci Res       Date:  2020-08-25       Impact factor: 4.164

10.  Neurocircuitry of Deep Brain Stimulation for Obsessive-Compulsive Disorder as Revealed by Tractography: A Systematic Review.

Authors:  Eduardo Varjão Vieira; Paula Ricci Arantes; Clement Hamani; Ricardo Iglesio; Kleber Paiva Duarte; Manoel Jacobsen Teixeira; Euripedes C Miguel; Antonio Carlos Lopes; Fabio Godinho
Journal:  Front Psychiatry       Date:  2021-07-01       Impact factor: 4.157

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