Reducing inter-subject anatomical variation: Effect of normalization method on sensitivity of functional magnetic resonance imaging data analysis in auditory cortex and the superior temporal region

Conventional group analysis of functional MRI (fMRI) data usually involves spatial alignment of anatomy across participants by registering every brain image to an anatomical reference image. Due to the high degree of inter-subject anatomical variability, a low-resolution average anatomical model is typically used as the target template, and/or smoothing kernels are applied to the fMRI data to increase the overlap among subjects' image data. However, such smoothing can make it difficult to resolve small regions such as subregions of auditory cortex when anatomical morphology varies among subjects. Here, we use data from an auditory fMRI study to show that using a high-dimensional registration technique (HAMMER) results in an enhanced functional signal-to-noise ratio (fSNR) for functional data analysis within auditory regions, with more localized activation patterns. The technique is validated against DARTEL, a high-dimensional diffeomorphic registration, as well as against commonly used low-dimensional normalization techniques such as the techniques provided with SPM2 (cosine basis functions) and SPM5 (unified segmentation) software packages. We also systematically examine how spatial resolution of the template image and spatial smoothing of the functional data affect the results. Only the high-dimensional technique (HAMMER) appears to be able to capitalize on the excellent anatomical resolution of a single-subject reference template, and, as expected, smoothing increased fSNR, but at the cost of spatial resolution. In general, results demonstrate significant improvement in fSNR using HAMMER compared to analysis after normalization using DARTEL, or conventional normalization such as cosine basis function and unified segmentation in SPM, with more precisely localized activation foci, at least for activation in the region of auditory cortex.

[1]  Alan C. Evans,et al.  Enhancement of MR Images Using Registration for Signal Averaging , 1998, Journal of Computer Assisted Tomography.

[2]  Jeffrey R. Binder,et al.  Volumetric vs. surface-based alignment for localization of auditory cortex activation , 2005, NeuroImage.

[3]  Stephen C. Strother,et al.  Impact of inter-subject image registration on group analysis of fMRI data , 2004 .

[4]  R. Woods,et al.  Mathematical/computational challenges in creating deformable and probabilistic atlases of the human brain , 2000, Human brain mapping.

[5]  Mark Slifstein,et al.  Effect of Spatial Smoothing on t-Maps: Arguments for Going Back from t-Maps to Masked Contrast Images , 2006, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[6]  E. William Yund,et al.  Local landmark-based mapping of human auditory cortex , 2004, NeuroImage.

[7]  Alan C. Evans,et al.  Quantifying variability in the planum temporale: a probability map. , 1999, Cerebral cortex.

[8]  Michael A Yassa,et al.  A quantitative evaluation of cross-participant registration techniques for MRI studies of the medial temporal lobe , 2009, NeuroImage.

[9]  Jonas Svensson,et al.  Investigation of spatial resolution, partial volume effects and smoothing in functional MRI using artificial 3D time series , 2008, NeuroImage.

[10]  John Ashburner,et al.  A fast diffeomorphic image registration algorithm , 2007, NeuroImage.

[11]  Dinggang Shen,et al.  HAMMER: hierarchical attribute matching mechanism for elastic registration , 2002, IEEE Transactions on Medical Imaging.

[12]  D. Louis Collins,et al.  Retrospective evaluation of intersubject brain registration , 2003, IEEE Transactions on Medical Imaging.

[13]  C. Stark,et al.  Pattern Separation in the Human Hippocampal CA3 and Dentate Gyrus , 2008, Science.

[14]  L C Maas,et al.  Post-registration spatial filtering to reduce noise in functional MRI data sets. , 1999, Magnetic resonance imaging.

[15]  Can Ceritoglu,et al.  Increasing the power of functional maps of the medial temporal lobe by using large deformation diffeomorphic metric mapping. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[16]  J C Mazziotta,et al.  Automated image registration: II. Intersubject validation of linear and nonlinear models. , 1998, Journal of computer assisted tomography.

[17]  M. Raichle,et al.  A Stereotactic Method of Anatomical Localization for Positron Emission Tomography , 1985, Journal of computer assisted tomography.

[18]  Arno Klein,et al.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.

[19]  Karl J. Friston,et al.  Analysis of fMRI Time-Series Revisited—Again , 1995, NeuroImage.

[20]  Karl J. Friston,et al.  Fully three-dimensional nonlinear spatial normalisation: A new approach , 1996, NeuroImage.

[21]  C. Davatzikos Spatial normalization of 3D brain images using deformable models. , 1996, Journal of computer assisted tomography.

[22]  J M Maisog,et al.  An efficient method for correcting the edge artifact due to smoothing , 1998, Human brain mapping.

[23]  Babak A. Ardekani,et al.  Quantitative comparison of algorithms for inter-subject registration of 3D volumetric brain MRI scans , 2005, Journal of Neuroscience Methods.

[24]  Xenophon Papademetris,et al.  Spatial resolution, signal-to-noise ratio, and smoothing in multi-subject functional MRI studies , 2006, NeuroImage.

[25]  J Mazziotta,et al.  A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). , 2001, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[26]  Craig K. Jones,et al.  High‐resolution fMRI investigation of the medial temporal lobe , 2007, Human brain mapping.

[27]  Kevin G. Munhall,et al.  Functional Overlap between Regions Involved in Speech Perception and in Monitoring One's Own Voice during Speech Production , 2010, Journal of Cognitive Neuroscience.

[28]  Alan C. Evans,et al.  Interhemispheric anatomical differences in human primary auditory cortex: probabilistic mapping and volume measurement from magnetic resonance scans. , 1996, Cerebral cortex.

[29]  P. Hluštík,et al.  Effects of spatial smoothing on fMRI group inferences. , 2008, Magnetic resonance imaging.

[30]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.

[31]  Karl J. Friston,et al.  Analysis of fMRI Time-Series Revisited , 1995, NeuroImage.

[32]  Karl J. Friston,et al.  Unified segmentation , 2005, NeuroImage.

[33]  A. Dale,et al.  High‐resolution intersubject averaging and a coordinate system for the cortical surface , 1999, Human brain mapping.

[34]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[35]  Minjie Wu,et al.  Quantitative comparison of AIR, SPM, and the fully deformable model for atlas‐based segmentation of functional and structural MR images , 2006, Human brain mapping.

[36]  Jean-Philippe Thiran,et al.  Local landmark-based registration for fMRI group studies of nonprimary auditory cortex , 2009, NeuroImage.

[37]  Nasser Kehtarnavaz,et al.  Brain Functional Localization: A Survey of Image Registration Techniques , 2007, IEEE Transactions on Medical Imaging.

[38]  J. Ashburner,et al.  Nonlinear spatial normalization using basis functions , 1999, Human brain mapping.

[39]  James C. Gee,et al.  Effect of spatial normalization on analysis of functional data , 1997, Medical Imaging.