Literature DB >> 16431137

Variable precision registration via wavelets: optimal spatial scales for inter-subject registration of functional MRI.

J Suckling1, C Long, C Triantafyllou, M Brammer, E Bullmore.   

Abstract

The detection of significantly activated brain regions in multi-subject functional magnetic resonance imaging (fMRI) studies almost invariably entails the coregistration of individual subjects' data in a standard space. Here, we investigate how sensitivity to detect loci of generic activation in such studies may be conditioned by the precision of anatomical registration. We describe a novel algorithm, implemented in the wavelet domain, for inhomogeneous deformation of individual images to match a template. The algorithm matches anatomical features in a coarse-to-fine fashion, first minimising a cost function in terms of relatively coarse spatial features and then proceeding iteratively to match the images in terms of progressively more detailed anatomical features. Applying the method to data acquired from two groups of 12 healthy volunteers (with mean age 27 and 70 years, respectively), during performance of a paired associate learning task, we show that geometrical overlap between template and individual images is monotonically improved, compared to an affine transform, by additional inhomogeneous deformations informed by more detailed features. Likewise, sensitivity to detect activated voxels can be substantially improved, by a factor of 4 or more, if wavelet-mediated deformations informed by medium-sized anatomical features are applied in addition to a preliminary affine transform. However, sensitivity to detect activated voxels was reduced by "over-registering" data or matching anatomical features at the finest scales of the wavelet transform. The benefits of variable precision registration are particularly salient for data acquired in older subjects, which showed evidence of greater inter-subject anatomic variability and generally required more extensive local deformation to achieve a satisfactory match to the template image. We conclude that major benefits in sensitivity to detect functional activation in multi-subject fMRI studies can be attained with an inhomogeneous deformation applied over appropriate spatial scales.

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Year:  2006        PMID: 16431137     DOI: 10.1016/j.neuroimage.2005.11.032

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  6 in total

1.  The relationship between regional and inter-regional functional connectivity deficits in schizophrenia.

Authors:  Andrew Zalesky; Alex Fornito; Gary F Egan; Christos Pantelis; Edward T Bullmore
Journal:  Hum Brain Mapp       Date:  2011-09-16       Impact factor: 5.038

2.  Functional connectivity and brain networks in schizophrenia.

Authors:  Mary-Ellen Lynall; Danielle S Bassett; Robert Kerwin; Peter J McKenna; Manfred Kitzbichler; Ulrich Muller; Ed Bullmore
Journal:  J Neurosci       Date:  2010-07-14       Impact factor: 6.167

Review 3.  Group comparisons: imaging the aging brain.

Authors:  Gregory R Samanez-Larkin; Mark D'Esposito
Journal:  Soc Cogn Affect Neurosci       Date:  2008-09       Impact factor: 3.436

4.  Topological isomorphisms of human brain and financial market networks.

Authors:  Petra E Vértes; Ruth M Nicol; Sandra C Chapman; Nicholas W Watkins; Duncan A Robertson; Edward T Bullmore
Journal:  Front Syst Neurosci       Date:  2011-09-15

5.  Brain Networks Reveal the Effects of Antipsychotic Drugs on Schizophrenia Patients and Controls.

Authors:  Emma K Towlson; Petra E Vértes; Ulrich Müller-Sedgwick; Sebastian E Ahnert
Journal:  Front Psychiatry       Date:  2019-09-12       Impact factor: 4.157

6.  Are power calculations useful? A multicentre neuroimaging study.

Authors:  John Suckling; Julian Henty; Christine Ecker; Sean C Deoni; Michael V Lombardo; Simon Baron-Cohen; Peter Jezzard; Anna Barnes; Bhismadev Chakrabarti; Cinly Ooi; Meng-Chuan Lai; Steven C Williams; Declan G M Murphy; Edward Bullmore
Journal:  Hum Brain Mapp       Date:  2014-02-19       Impact factor: 5.038

  6 in total

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