Literature DB >> 27639358

Diffusion kurtosis metrics as biomarkers of microstructural development: A comparative study of a group of children and a group of adults.

Farida Grinberg1, Ivan I Maximov2, Ezequiel Farrher2, Irene Neuner3, Laura Amort4, Heike Thönneßen5, Eileen Oberwelland6, Kerstin Konrad7, N Jon Shah8.   

Abstract

The most common modality of diffusion MRI used in the ageing and development studies is diffusion tensor imaging (DTI) providing two key measures, fractional anisotropy and mean diffusivity. Here, we investigated diffusional changes occurring between childhood (average age 10.3 years) and mitddle adult age (average age 54.3 years) with the help of diffusion kurtosis imaging (DKI), a recent novel extension of DTI that provides additional metrics quantifying non-Gaussianity of water diffusion in brain tissue. We performed voxelwise statistical between-group comparison of diffusion tensor and kurtosis tensor metrics using two methods, namely, the tract-based spatial statistics (TBSS) and the atlas-based regional data analysis. For the latter, fractional anisotropy, mean diffusivity, mean diffusion kurtosis, and other scalar diffusion tensor and kurtosis tensor parameters were evaluated for white matter fibres provided by the Johns-Hopkins-University Atlas in the FSL toolkit (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases). Within the same age group, all evaluated parameters varied depending on the anatomical region. TBSS analysis showed that changes in kurtosis tensor parameters beyond adolescence are more widespread along the skeleton in comparison to the changes of the diffusion tensor metrics. The regional data analysis demonstrated considerably larger between-group changes of the diffusion kurtosis metrics than of diffusion tensor metrics in all investigated regions. The effect size of the parametric changes between childhood and middle adulthood was quantified using Cohen's d. We used Cohen's d related to mean diffusion kurtosis to examine heterogeneous maturation of various fibres. The largest changes of this parameter (interpreted as reflecting the lowest level of maturation by the age of children group) were observed in the association fibres, cingulum (gyrus) and cingulum (hippocampus) followed by superior longitudinal fasciculus and inferior longitudinal fasciculus. The smallest changes were observed in the commissural fibres, forceps major and forceps minor. In conclusion, our data suggest that DKI is sensitive to developmental changes in local microstructure and environment, and is particularly powerful to unravel developmental differences in major association fibres, such as the cingulum and superior longitudinal fasciculus.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Brain; Development; Diffusion kurtosis imaging; MRI; Microstructure; Non-Gaussian Diffusion

Mesh:

Substances:

Year:  2016        PMID: 27639358     DOI: 10.1016/j.neuroimage.2016.08.033

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


  18 in total

1.  Miniature pig magnetic resonance spectroscopy model of normal adolescent brain development.

Authors:  Meghann C Ryan; Peter Kochunov; Paul M Sherman; Laura M Rowland; S Andrea Wijtenburg; Ashley Acheson; L Elliot Hong; John Sladky; Stephen McGuire
Journal:  J Neurosci Methods       Date:  2018-08-09       Impact factor: 2.390

2.  MK-curve - Characterizing the relation between mean kurtosis and alterations in the diffusion MRI signal.

Authors:  Fan Zhang; Lipeng Ning; Lauren J O'Donnell; Ofer Pasternak
Journal:  Neuroimage       Date:  2019-04-10       Impact factor: 6.556

3.  Miniature pig model of human adolescent brain white matter development.

Authors:  Meghann C Ryan; Paul Sherman; Laura M Rowland; S Andrea Wijtenburg; Ashley Acheson; Els Fieremans; Jelle Veraart; Dmitry S Novikov; L Elliot Hong; John Sladky; P Dana Peralta; Peter Kochunov; Stephen A McGuire
Journal:  J Neurosci Methods       Date:  2017-12-24       Impact factor: 2.390

4.  Effects of SYN1Q555X mutation on cortical gray matter microstructure.

Authors:  Jean-François Cabana; Guillaume Gilbert; Laurent Létourneau-Guillon; Dima Safi; Isabelle Rouleau; Patrick Cossette; Dang Khoa Nguyen
Journal:  Hum Brain Mapp       Date:  2018-04-19       Impact factor: 5.038

5.  A comparative study of the superior longitudinal fasciculus subdivisions between neonates and young adults.

Authors:  Wenjia Liang; Qiaowen Yu; Wenjun Wang; Thijs Dhollander; Emmanuel Suluba; Zhuoran Li; Feifei Xu; Yang Hu; Yuchun Tang; Shuwei Liu
Journal:  Brain Struct Funct       Date:  2022-09-17       Impact factor: 3.748

6.  Future Directions for Examination of Brain Networks in Neurodevelopmental Disorders.

Authors:  Lucina Q Uddin; Katherine H Karlsgodt
Journal:  J Clin Child Adolesc Psychol       Date:  2018-04-10

7.  Association of White Matter With Core Cognitive Deficits in Patients With Schizophrenia.

Authors:  Peter Kochunov; Thomas R Coyle; Laura M Rowland; Neda Jahanshad; Paul M Thompson; Sinead Kelly; Xiaoming Du; Hemalatha Sampath; Heather Bruce; Joshua Chiappelli; Meghann Ryan; Feven Fisseha; Anya Savransky; Bhim Adhikari; Shuo Chen; Sara A Paciga; Christopher D Whelan; Zhiyong Xie; Craig L Hyde; Xing Chen; Christian R Schubert; Patricio O'Donnell; L Elliot Hong
Journal:  JAMA Psychiatry       Date:  2017-09-01       Impact factor: 21.596

8.  Comparison of cumulant expansion and q-space imaging estimates for diffusional kurtosis in brain.

Authors:  Vaibhav Mohanty; Emilie T McKinnon; Joseph A Helpern; Jens H Jensen
Journal:  Magn Reson Imaging       Date:  2018-01-03       Impact factor: 2.546

9.  Microstructural white matter alterations in Alzheimer's disease and amnestic mild cognitive impairment and its diagnostic value based on diffusion kurtosis imaging: a tract-based spatial statistics study.

Authors:  Tongtong Li; Yu Zhang; Xiuwei Fu; Xianchang Zhang; Yuan Luo; Hongyan Ni
Journal:  Brain Imaging Behav       Date:  2021-04-24       Impact factor: 3.978

10.  Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM) in big data analysis: U.K. Biobank 18,608 example.

Authors:  Ivan I Maximov; Dennis van der Meer; Ann-Marie G de Lange; Tobias Kaufmann; Alexey Shadrin; Oleksandr Frei; Thomas Wolfers; Lars T Westlye
Journal:  Hum Brain Mapp       Date:  2021-03-31       Impact factor: 5.038

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.