ICA-fNORM: Spatial normalization of fMRI data using intrinsic group-ICA networks

Siddharth Khullar, Andrew M. Michael, Nathan D. Cahill, Kent A. Kiehl, Godfrey Pearlson, StefiA Baum, Vince Daniel Calhoun

Research output: Contribution to journalArticle

Abstract

A common pre-processing challenge associated with group level fMRI analysis is spatial registration of multiple subjects to a standard space. Spatial normalization, using a reference image such as the Montreal Neurological Institute brain template, is the most common technique currently in use to achieve spatial congruence across multiple subjects. This method corrects for global shape differences preserving regional asymmetries, but does not account for functional differences. We propose a novel approach to co-register task-based fMRI data using resting state group-ICA networks. We posit that these intrinsic networks (INs) can provide to the spatial normalization process with important information about how each individual's brain is organized functionally. The algorithm is initiated by the extraction of single subject representations of INs using group level independent component analysis (ICA) on resting state fMRI data. In this proof-of-concept work two of the robust, commonly identified, networks are chosen as functional templates. As an estimation step, the relevant INs are utilized to derive a set of normalization parameters for each subject. Finally, the normalization parameters are applied individually to a different set of fMRI data acquired while the subjects performed an auditory oddball task. These normalization parameters, although derived using rest data, generalize successfully to data obtained with a cognitive paradigm for each subject. The improvement in results is verified using two widely applied fMRI analysis methods: the general linear model and ICA. Resulting activation patterns from each analysis method show significant improvements in terms of detection sensitivity and statistical significance at the group level. The results presented in this article provide initial evidence to show that common functional domains from the resting state brain may be used to improve the group statistics of task-fMRI data.

Original languageEnglish (US)
Article number93
JournalFrontiers in Systems Neuroscience
Issue numberNOVEMBER 2011
DOIs
StatePublished - Nov 17 2011
Externally publishedYes

Fingerprint

Spatial Analysis
Magnetic Resonance Imaging
Brain
Linear Models

Keywords

  • fMRI
  • Functional re-alignment
  • ICA
  • Inter-subject co-registration
  • Oddball paradigm
  • Resting state networks
  • Spatial normalization
  • Study-specific template

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Cellular and Molecular Neuroscience
  • Cognitive Neuroscience
  • Developmental Neuroscience

Cite this

Khullar, S., Michael, A. M., Cahill, N. D., Kiehl, K. A., Pearlson, G., Baum, S., & Calhoun, V. D. (2011). ICA-fNORM: Spatial normalization of fMRI data using intrinsic group-ICA networks. Frontiers in Systems Neuroscience, (NOVEMBER 2011), [93]. https://doi.org/10.3389/fnsys.2011.00093

ICA-fNORM : Spatial normalization of fMRI data using intrinsic group-ICA networks. / Khullar, Siddharth; Michael, Andrew M.; Cahill, Nathan D.; Kiehl, Kent A.; Pearlson, Godfrey; Baum, StefiA; Calhoun, Vince Daniel.

In: Frontiers in Systems Neuroscience, No. NOVEMBER 2011, 93, 17.11.2011.

Research output: Contribution to journalArticle

Khullar S, Michael AM, Cahill ND, Kiehl KA, Pearlson G, Baum S et al. ICA-fNORM: Spatial normalization of fMRI data using intrinsic group-ICA networks. Frontiers in Systems Neuroscience. 2011 Nov 17;(NOVEMBER 2011). 93. https://doi.org/10.3389/fnsys.2011.00093
Khullar, Siddharth ; Michael, Andrew M. ; Cahill, Nathan D. ; Kiehl, Kent A. ; Pearlson, Godfrey ; Baum, StefiA ; Calhoun, Vince Daniel. / ICA-fNORM : Spatial normalization of fMRI data using intrinsic group-ICA networks. In: Frontiers in Systems Neuroscience. 2011 ; No. NOVEMBER 2011.
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