Importance of realignment parameters in fMRI data analysis

Raheel Zafar, Aamir Saeed Malik, Nidal Kamel, Sarat C. Dass

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Functional magnetic resonance imaging (fMRI) is one of the finest modality to measure brain activity. Two main steps in the analysis of fMRI data are pre-processing and the statistical analysis. Pre-processing is equally an important part because it takes raw data from the scanner and prepares it for the statistical analysis. This study first explains the realignment during preprocessing and then the importance of realignment parameters (one of nuisance parameters) in General Linear model (GLM). Nuisance regressors are used to reduce noise only and are effect of no interest. In this study, it is concluded that realignment parameters have a significant effect in the model estimation because the results are improved with these parameters especially when large head movement is found during data acquisition.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)
PublisherIEEE
Pages546-550
Number of pages5
ISBN (Electronic)9781479989966
DOIs
Publication statusPublished - 25 Feb 2016
Event4th IEEE International Conference on Signal and Image Processing Applications 2015 - Kuala Lumpur, Malaysia
Duration: 19 Oct 201521 Oct 2015

Conference

Conference4th IEEE International Conference on Signal and Image Processing Applications 2015
Abbreviated titleICSIPA 2015
CountryMalaysia
CityKuala Lumpur
Period19/10/1521/10/15

Keywords

  • fMRI
  • GLM
  • Nuisance regressors
  • Realignment

ASJC Scopus subject areas

  • Computer Science Applications
  • Signal Processing

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  • Cite this

    Zafar, R., Malik, A. S., Kamel, N., & Dass, S. C. (2016). Importance of realignment parameters in fMRI data analysis. In 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) (pp. 546-550). IEEE. https://doi.org/10.1109/ICSIPA.2015.7412251