Literature DB >> 29033222

Processing of structural neuroimaging data in young children: Bridging the gap between current practice and state-of-the-art methods.

Thanh Vân Phan1, Dirk Smeets2, Joel B Talcott3, Maaike Vandermosten4.   

Abstract

The structure of the brain is subject to very rapid developmental changes during early childhood. Pediatric studies based on Magnetic Resonance Imaging (MRI) over this age range have recently become more frequent, with the advantage of providing in vivo and non-invasive high-resolution images of the developing brain, toward understanding typical and atypical trajectories. However, it has also been demonstrated that application of currently standard MRI processing methods that have been developed with datasets from adults may not be appropriate for use with pediatric datasets. In this review, we examine the approaches currently used in MRI studies involving young children, including an overview of the rationale for new MRI processing methods that have been designed specifically for pediatric investigations. These methods are mainly related to the use of age-specific or 4D brain atlases, improved methods for quantifying and optimizing image quality, and provision for registration of developmental data obtained with longitudinal designs. The overall goal is to raise awareness of the existence of these methods and the possibilities for implementing them in developmental neuroimaging studies.
Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  Brain atlas; Children; Longitudinal analysis; Neural development; Neuroimaging methods; Structural MRI

Mesh:

Year:  2017        PMID: 29033222     DOI: 10.1016/j.dcn.2017.08.009

Source DB:  PubMed          Journal:  Dev Cogn Neurosci        ISSN: 1878-9293            Impact factor:   6.464


  18 in total

1.  Decreased Cortical Thickness in the Anterior Cingulate Cortex in Adults with Autism.

Authors:  Charles Laidi; Jennifer Boisgontier; Amicie de Pierrefeu; Edouard Duchesnay; Sevan Hotier; Marc-Antoine d'Albis; Richard Delorme; Federico Bolognani; Christian Czech; Céline Bouquet; Anouck Amestoy; Julie Petit; Štefan Holiga; Juergen Dukart; Alexandru Gaman; Elie Toledano; Myriam Ly-Le Moal; Isabelle Scheid; Marion Leboyer; Josselin Houenou
Journal:  J Autism Dev Disord       Date:  2019-04

2.  A preliminary examination of brain morphometry in youth with Down syndrome with and without parent-reported sleep difficulties.

Authors:  Nancy Raitano Lee; Megan Perez; Taralee Hamner; Elizabeth Adeyemi; Liv S Clasen
Journal:  Res Dev Disabil       Date:  2020-02-24

3.  Family Income, Cumulative Risk Exposure, and White Matter Structure in Middle Childhood.

Authors:  Alexander J Dufford; Pilyoung Kim
Journal:  Front Hum Neurosci       Date:  2017-11-13       Impact factor: 3.169

4.  Functional brain network characteristics are associated with epilepsy severity in childhood absence epilepsy.

Authors:  Gerhard S Drenthen; Floor Fasen; Eric L A Fonseca Wald; Walter H Backes; Albert P Aldenkamp; R Jeroen Vermeulen; Mariette Debeij-van Hall; Jos Hendriksen; Sylvia Klinkenberg; Jacobus F A Jansen
Journal:  Neuroimage Clin       Date:  2020-04-23       Impact factor: 4.881

5.  Mapping the neuroanatomical impact of very preterm birth across childhood.

Authors:  Marlee M Vandewouw; Julia M Young; Sarah I Mossad; Julie Sato; Hilary A E Whyte; Manohar M Shroff; Margot J Taylor
Journal:  Hum Brain Mapp       Date:  2019-11-05       Impact factor: 5.038

6.  Photogrammetry-based stereoscopic optode registration method for functional near-infrared spectroscopy.

Authors:  Xiao-Su Hu; Neelima Wagley; Akemi Tsutsumi Rioboo; Alexandre DaSilva; Ioulia Kovelman
Journal:  J Biomed Opt       Date:  2020-09       Impact factor: 3.170

7.  Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age.

Authors:  Nicola Amoroso; Marianna La Rocca; Loredana Bellantuono; Domenico Diacono; Annarita Fanizzi; Eufemia Lella; Angela Lombardi; Tommaso Maggipinto; Alfonso Monaco; Sabina Tangaro; Roberto Bellotti
Journal:  Front Aging Neurosci       Date:  2019-05-22       Impact factor: 5.750

8.  Evaluation of methods for volumetric analysis of pediatric brain data: The childmetrix pipeline versus adult-based approaches.

Authors:  Thanh Vân Phan; Diana M Sima; Caroline Beelen; Jolijn Vanderauwera; Dirk Smeets; Maaike Vandermosten
Journal:  Neuroimage Clin       Date:  2018-05-23       Impact factor: 4.881

9.  Volumetric gray matter measures of amygdala and accumbens in childhood overweight/obesity.

Authors:  Gabor Perlaki; Denes Molnar; Paul A M Smeets; Wolfgang Ahrens; Maike Wolters; Gabriele Eiben; Lauren Lissner; Peter Erhard; Floor van Meer; Manfred Herrmann; Jozsef Janszky; Gergely Orsi
Journal:  PLoS One       Date:  2018-10-18       Impact factor: 3.240

10.  Deep learning-based automatic tumor burden assessment of pediatric high-grade gliomas, medulloblastomas, and other leptomeningeal seeding tumors.

Authors:  Jian Peng; Daniel D Kim; Jay B Patel; Xiaowei Zeng; Jiaer Huang; Ken Chang; Xinping Xun; Chen Zhang; John Sollee; Jing Wu; Deepa J Dalal; Xue Feng; Hao Zhou; Chengzhang Zhu; Beiji Zou; Ke Jin; Patrick Y Wen; Jerrold L Boxerman; Katherine E Warren; Tina Y Poussaint; Lisa J States; Jayashree Kalpathy-Cramer; Li Yang; Raymond Y Huang; Harrison X Bai
Journal:  Neuro Oncol       Date:  2022-02-01       Impact factor: 13.029

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.