Literature DB >> 31077476

Discriminating between neurofibromatosis-1 and typically developing children by means of multimodal MRI and multivariate analyses.

Federico Nemmi1, Fabien Cignetti2,3,4, Christine Assaiante2,3, Stephanie Maziero1,5, Fredrique Audic6, Patrice Péran1, Yves Chaix1.   

Abstract

Neurofibromatosis Type 1 leads to brain anomalies involving both gray and white matter. The extent and granularity of these anomalies, together with their possible impact on brain activity, is still unknown. In this multicentric cross-sectional study we submitted a sample of 42 typically developing and 38 neurofibromatosis-1 children to a multimodal MRI assessment including T1, diffusion weighted and resting state functional sequences. We used a pipeline involving several features selection steps coupled with multivariate statistical analysis (supporting vector machine) to discriminate between the two groups while having interpretable models. We used MRI indexes measuring macro (gray matter volume) and microstructural (fractional anisotropy, mean diffusivity) characteristics of the brain, as well as indexes of brain activity (fractional amplitude of low frequency fluctuations) and connectivity (local and global correlation) at rest. We found that structural indexes could discriminate between the two groups, with the mean diffusivity leading to performance as high as the combination of all structural indexes combined (accuracy = 0.86), while functional indexes had worse performances. The MRI signature of NF1 brain pathology is a combination of gray and white matter abnormalities, as measured with gray matter volume, fractional anisotropy, and mean diffusivity.
© 2019 Wiley Periodicals, Inc.

Entities:  

Keywords:  brain pathology; features selection; multimodal MRI; multivariate analysis; neurofibromatosis type 1

Mesh:

Year:  2019        PMID: 31077476      PMCID: PMC6865722          DOI: 10.1002/hbm.24612

Source DB:  PubMed          Journal:  Hum Brain Mapp        ISSN: 1065-9471            Impact factor:   5.038


  60 in total

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Authors:  Jennifer J Vogel; Clint A Bowers; David S Vogel
Journal:  Brain Cogn       Date:  2003-07       Impact factor: 2.310

2.  Support vector machines for temporal classification of block design fMRI data.

Authors:  Stephen LaConte; Stephen Strother; Vladimir Cherkassky; Jon Anderson; Xiaoping Hu
Journal:  Neuroimage       Date:  2005-03-24       Impact factor: 6.556

3.  An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF.

Authors:  Qi-Hong Zou; Chao-Zhe Zhu; Yihong Yang; Xi-Nian Zuo; Xiang-Yu Long; Qing-Jiu Cao; Yu-Feng Wang; Yu-Feng Zang
Journal:  J Neurosci Methods       Date:  2008-04-22       Impact factor: 2.390

4.  MR imaging of the corpus callosum in pediatric patients with neurofibromatosis type 1.

Authors:  E C Dubovsky; T N Booth; G Vezina; C A Samango-Sprouse; K M Palmer; C O Brasseux
Journal:  AJNR Am J Neuroradiol       Date:  2001-01       Impact factor: 3.825

5.  Discriminating between neurofibromatosis-1 and typically developing children by means of multimodal MRI and multivariate analyses.

Authors:  Federico Nemmi; Fabien Cignetti; Christine Assaiante; Stephanie Maziero; Fredrique Audic; Patrice Péran; Yves Chaix
Journal:  Hum Brain Mapp       Date:  2019-05-11       Impact factor: 5.038

6.  Unidentified bright objects in neurofibromatosis type 1: conventional MRI in the follow-up and correlation of microstructural lesions on diffusion tensor images.

Authors:  José Roberto Lopes Ferraz-Filho; Antônio José da Rocha; Marcos Pontes Muniz; Antônio Soares Souza; Eny Maria Goloni-Bertollo; Erika Cristina Pavarino-Bertelli
Journal:  Eur J Paediatr Neurol       Date:  2011-11-15       Impact factor: 3.140

7.  Gene selection from microarray data for cancer classification--a machine learning approach.

Authors:  Yu Wang; Igor V Tetko; Mark A Hall; Eibe Frank; Axel Facius; Klaus F X Mayer; Hans W Mewes
Journal:  Comput Biol Chem       Date:  2005-02       Impact factor: 2.877

8.  Quantitative morphology of the corpus callosum in children with neurofibromatosis and attention-deficit hyperactivity disorder.

Authors:  A E Kayl; B D Moore; J M Slopis; E F Jackson; N E Leeds
Journal:  J Child Neurol       Date:  2000-02       Impact factor: 1.987

9.  Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data.

Authors:  Xing Meng; Rongtao Jiang; Dongdong Lin; Juan Bustillo; Thomas Jones; Jiayu Chen; Qingbao Yu; Yuhui Du; Yu Zhang; Tianzi Jiang; Jing Sui; Vince D Calhoun
Journal:  Neuroimage       Date:  2016-05-10       Impact factor: 6.556

10.  White matter microstructure of patients with neurofibromatosis type 1 and its relation to inhibitory control.

Authors:  M Koini; S A R B Rombouts; I M Veer; M A Van Buchem; S C J Huijbregts
Journal:  Brain Imaging Behav       Date:  2017-12       Impact factor: 3.978

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  3 in total

1.  Discriminating between neurofibromatosis-1 and typically developing children by means of multimodal MRI and multivariate analyses.

Authors:  Federico Nemmi; Fabien Cignetti; Christine Assaiante; Stephanie Maziero; Fredrique Audic; Patrice Péran; Yves Chaix
Journal:  Hum Brain Mapp       Date:  2019-05-11       Impact factor: 5.038

2.  Atypical connectivity in the cortico-striatal network in NF1 children and its relationship with procedural perceptual-motor learning and motor skills.

Authors:  Eloïse Baudou; Federico Nemmi; Patrice Peran; Fabien Cignetti; Melody Blais; Stéphanie Maziero; Jessica Tallet; Yves Chaix
Journal:  J Neurodev Disord       Date:  2022-03-01       Impact factor: 4.025

3.  Brain-age estimation accuracy is significantly increased using multishell free-water reconstruction.

Authors:  Federico Nemmi; Mathilde Levardon; Patrice Péran
Journal:  Hum Brain Mapp       Date:  2022-02-10       Impact factor: 5.038

  3 in total

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