Literature DB >> 31734272

Single-subject manual independent component analysis and resting state fMRI connectivity outcomes in patients with juvenile absence epilepsy.

Nicholas Parsons1, Stephen C Bowden2, Simon Vogrin3, Wendyl J D'Souza2.   

Abstract

The quality of fMRI data impacts functional connectivity measures and consequently, the decisions that clinicians and researchers make regarding functional connectivity interpretation. The present study used resting state fMRI to investigate resting state network connectivity in a sample of patients with Juvenile Absence Epilepsy. Single-subject manual independent component analysis was used in two levels, whereby all noise components were removed, and cerebrospinal fluid pulsation components only were isolated and removed. Improved temporal signal to noise ratios and functional connectivity metrics were observed in each of the cleaning levels for both epilepsy and control cohorts. Results showed full, single-subject manual independent component analysis reduced the number of functional connectivity correlations and increased the strength of these correlations. Similar effects were also observed for the cerebrospinal fluid pulsation only cleaned data relative to the uncleaned, and fully cleaned data. Single-subject manual independent component analysis coupled with short TR multiband acquisition can significantly improve the validity of findings derived from fMRI data sets.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Epilepsy; Functional MRI; ICA; Independent component analysis; Juvenile absence epilepsy; Multiband; Resting state; fMRI

Year:  2019        PMID: 31734272     DOI: 10.1016/j.mri.2019.11.012

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  2 in total

Review 1.  A systematic review of resting-state functional connectivity in obesity: Refining current neurobiological frameworks and methodological considerations moving forward.

Authors:  Nicholas Parsons; Trevor Steward; Rebecca Clohesy; Hannes Almgren; Leonie Duehlmeyer
Journal:  Rev Endocr Metab Disord       Date:  2021-06-23       Impact factor: 9.306

2.  Brain disorder prediction with dynamic multivariate spatio-temporal features: Application to Alzheimer's disease and autism spectrum disorder.

Authors:  Jianping Qiao; Rong Wang; Hongjia Liu; Guangrun Xu; Zhishun Wang
Journal:  Front Aging Neurosci       Date:  2022-08-30       Impact factor: 5.702

  2 in total

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