Assessing age-related gray matter decline with voxel-based morphometry depends significantly on segmentation and normalization procedures

Healthy ageing coincides with a progressive decline of brain gray matter (GM) ultimately affecting the entire brain. For a long time, manual delineation-based volumetry within predefined regions of interest (ROI) has been the gold standard for assessing such degeneration. Voxel-Based Morphometry (VBM) offers an automated alternative approach that, however, relies critically on the segmentation and spatial normalization of a large collection of images from different subjects. This can be achieved via different algorithms, with SPM5/SPM8, DARTEL of SPM8 and FSL tools (FAST, FNIRT) being three of the most frequently used. We complemented these voxel based measurements with a ROI based approach, whereby the ROIs are defined by transforms of an atlas (containing different tissue probability maps as well as predefined anatomic labels) to the individual subject images in order to obtain volumetric information at the level of the whole brain or within separate ROIs. Comparing GM decline between 21 young subjects (mean age 23) and 18 elderly (mean age 66) revealed that volumetric measurements differed significantly between methods. The unified segmentation/normalization of SPM5/SPM8 revealed the largest age-related differences and DARTEL the smallest, with FSL being more similar to the DARTEL approach. Method specific differences were substantial after segmentation and most pronounced for the cortical structures in close vicinity to major sulci and fissures. Our findings suggest that algorithms that provide only limited degrees of freedom for local deformations (such as the unified segmentation and normalization of SPM5/SPM8) tend to overestimate between-group differences in VBM results when compared to methods providing more flexible warping. This difference seems to be most pronounced if the anatomy of one of the groups deviates from custom templates, a finding that is of particular importance when results are compared across studies using different VBM methods.

[1]  Karl J. Friston,et al.  Diffeomorphic registration using geodesic shooting and Gauss–Newton optimisation , 2011, NeuroImage.

[2]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[3]  L. Lagae,et al.  Atypical Neuropsychological Profile in a Boy with 22q11.2 Deletion Syndrome Keywords: , 2005, Child neuropsychology : a journal on normal and abnormal development in childhood and adolescence.

[4]  Leanne M Williams,et al.  Preservation of limbic and paralimbic structures in aging , 2005, Human brain mapping.

[5]  Koenraad Van Leemput,et al.  Automated model-based bias field correction of MR images of the brain , 1999, IEEE Transactions on Medical Imaging.

[6]  N. Roberts,et al.  Voxel‐based morphometry of temporal lobe epilepsy: An introduction and review of the literature , 2008, Epilepsia.

[7]  Q. Mu,et al.  A quantitative MR study of the hippocampal formation, the amygdala, and the temporal horn of the lateral ventricle in healthy subjects 40 to 90 years of age. , 1999, AJNR. American journal of neuroradiology.

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

[9]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[10]  A. Dale,et al.  Effects of age on volumes of cortex, white matter and subcortical structures , 2005, Neurobiology of Aging.

[11]  Anne-Catherine Bachoud-Lévi,et al.  Distribution of grey matter atrophy in Huntington’s disease patients: A combined ROI-based and voxel-based morphometric study , 2006, NeuroImage.

[12]  Fabrice Crivello,et al.  Age- and sex-related effects on the neuroanatomy of healthy elderly , 2005, NeuroImage.

[13]  Peter J. Scambler,et al.  22q11.2 deletion syndrome. , 2015, Nature reviews. Disease primers.

[14]  N. Raz,et al.  Differential Aging of the Brain: Patterns, Cognitive Correlates and Modifiers , 2022 .

[15]  Sm Smith,et al.  What happens when nine different groups analyze the same DT-MRI data set using voxel-based methods? , 2007 .

[16]  Anders M. Dale,et al.  Consistent neuroanatomical age-related volume differences across multiple samples , 2011, Neurobiology of Aging.

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

[18]  Arthur F. Kramer,et al.  Age-related differences in regional brain volumes: A comparison of optimized voxel-based morphometry to manual volumetry , 2009, Neurobiology of Aging.

[19]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[20]  H. Yamasue,et al.  Age-related changes in regional brain volume evaluated by atlas-based method , 2010, Neuroradiology.

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

[22]  Paul Suetens,et al.  An Information Theoretic Approach for Non-rigid Image Registration Using Voxel Class Probabilities , 2003, MICCAI.

[23]  Alan C. Evans,et al.  A voxel-based morphometric study to determine individual differences in gray matter density associated with age and cognitive change over time. , 2004, Cerebral cortex.

[24]  Stephen M. Smith,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[25]  C. Fennema-Notestine,et al.  Effects of age on tissues and regions of the cerebrum and cerebellum , 2001, Neurobiology of Aging.

[26]  Jean-Marc Constans,et al.  Voxel-based mapping of brain gray matter volume and glucose metabolism profiles in normal aging , 2009, Neurobiology of Aging.

[27]  Daniel Rueckert,et al.  Non-rigid registration using free-form deformations , 2015 .

[28]  J. Kaye,et al.  Brain volume preserved in healthy elderly through the eleventh decade , 1998, Neurology.

[29]  James R. MacFall,et al.  Aging, gender, and the elderly adult brain: An examination of analytical strategies , 2008, Neurobiology of Aging.

[30]  Alberto Beltramello,et al.  A comparison between the accuracy of voxel‐based morphometry and hippocampal volumetry in Alzheimer's disease , 2004, Journal of magnetic resonance imaging : JMRI.

[31]  Dzulkifli Mohamad,et al.  Segmentation of brain MR images , 2007 .

[32]  K. Van Laere,et al.  Brain perfusion SPECT: age- and sex-related effects correlated with voxel-based morphometric findings in healthy adults. , 2001, Radiology.

[33]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[34]  Abraham Z. Snyder,et al.  A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume , 2004, NeuroImage.

[35]  M. Jenkinson Non-linear registration aka Spatial normalisation , 2007 .

[36]  John S. Allen,et al.  Normal neuroanatomical variation due to age: The major lobes and a parcellation of the temporal region , 2005, Neurobiology of Aging.

[37]  Hanna Damasio,et al.  Methods for studying the aging brain: Volumetric analyses versus VBM , 2005, Neurobiology of Aging.

[38]  Karl J. Friston,et al.  A Voxel-Based Morphometric Study of Ageing in 465 Normal Adult Human Brains , 2001, NeuroImage.

[39]  Dirk Vandermeulen,et al.  Linear normalization of MR brain images in pediatric patients with periventricular leukomalacia , 2007, NeuroImage.

[40]  Khader M. Hasan,et al.  Improving the reliability of manual and automated methods for hippocampal and amygdala volume measurements , 2009, NeuroImage.

[41]  D Keeser,et al.  Functional and Structural MR Imaging in Neuropsychiatric Disorders, Part 2: Application in Schizophrenia and Autism , 2012, American Journal of Neuroradiology.

[42]  Jenny Caesar,et al.  Segmentation of the Brain from MR Images , 2005 .

[43]  R. C. Oldfield The assessment and analysis of handedness: the Edinburgh inventory. , 1971, Neuropsychologia.

[44]  D D Blatter,et al.  Quantitative volumetric analysis of brain MR: normative database spanning 5 decades of life. , 1995, AJNR. American journal of neuroradiology.

[45]  Karl J. Friston,et al.  Computing average shaped tissue probability templates , 2009, NeuroImage.

[46]  K. Skullerud Variations in the size of the human brain. Influence of age, sex, body length, body mass index, alcoholism, Alzheimer changes, and cerebral atherosclerosis. , 1985, Acta neurologica Scandinavica. Supplementum.

[47]  Stephen M. Smith,et al.  Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.

[48]  Faith M. Gunning-Dixon,et al.  Aging, sexual dimorphism, and hemispheric asymmetry of the cerebral cortex: replicability of regional differences in volume , 2004, Neurobiology of Aging.

[49]  John Duncan,et al.  Implementation and application of a brain template for multiple volumes of interest , 2002, Human brain mapping.

[50]  Faith M. Gunning-Dixon,et al.  Differential aging of the human striatum: longitudinal evidence. , 2003, AJNR. American journal of neuroradiology.

[51]  Karl J. Friston,et al.  Why Voxel-Based Morphometry Should Be Used , 2001, NeuroImage.

[52]  L. Lemieux,et al.  Statistical neuroanatomy of the human inferior frontal gyrus and probabilistic atlas in a standard stereotaxic space , 2007, Human brain mapping.

[53]  Terry L. Jernigan,et al.  Changes in volume with age—consistency and interpretation of observed effects , 2005, Neurobiology of Aging.

[54]  Jens C. Pruessner,et al.  Regional Frontal Cortical Volumes Decrease Differentially in Aging: An MRI Study to Compare Volumetric Approaches and Voxel-Based Morphometry , 2002, NeuroImage.

[55]  Chris I. Baker,et al.  Teaching an adult brain new tricks: A critical review of evidence for training-dependent structural plasticity in humans , 2013, NeuroImage.

[56]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[57]  S. Resnick,et al.  Longitudinal Magnetic Resonance Imaging Studies of Older Adults: A Shrinking Brain , 2003, The Journal of Neuroscience.

[58]  Robert Turner,et al.  Voxel-based cortical thickness measurements in MRI , 2008, NeuroImage.

[59]  D. Keeser,et al.  Functional and Structural MR Imaging in Neuropsychiatric Disorders, Part 1: Imaging Techniques and Their Application in Mild Cognitive Impairment and Alzheimer Disease , 2012, American Journal of Neuroradiology.

[60]  Karl J. Friston,et al.  Voxel-Based Morphometry—The Methods , 2000, NeuroImage.

[61]  G. Busatto,et al.  Neurostructural predictors of Alzheimer's disease: A meta-analysis of VBM studies , 2011, Neurobiology of Aging.

[62]  S. Resnick,et al.  Vulnerability of the Orbitofrontal Cortex to Age‐Associated Structural and Functional Brain Changes , 2007, Annals of the New York Academy of Sciences.

[63]  Bogdan Draganski,et al.  Neuroplasticity: Changes in grey matter induced by training , 2004, Nature.

[64]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[65]  Paul Suetens,et al.  A Viscous Fluid Model for Multimodal Non-rigid Image Registration Using Mutual Information , 2002, MICCAI.

[66]  Qian Wang,et al.  Construction and Validation of Mean Shape Atlas Templates for Atlas-Based Brain Image Segmentation , 2005, IPMI.

[67]  E. Tolosa,et al.  MRI and cognitive impairment in Parkinson's disease , 2009, Movement disorders : official journal of the Movement Disorder Society.

[68]  J. Townsend,et al.  Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers. , 2000, Radiology.

[69]  Paul Suetens,et al.  A Unified Framework for Atlas Based Brain Image Segmentation and Registration , 2006, WBIR.

[70]  Karl J. Friston,et al.  Spatial Normalization using Basis Functions , 2003 .

[71]  A. Dale,et al.  High consistency of regional cortical thinning in aging across multiple samples. , 2009, Cerebral cortex.

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

[73]  Jonathan E. Peelle,et al.  Adjusting for global effects in voxel-based morphometry: Gray matter decline in normal aging , 2012, NeuroImage.

[74]  Brigitte Landeau,et al.  Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: A longitudinal MRI study , 2005, NeuroImage.

[75]  Christos Davatzikos,et al.  Why voxel-based morphometric analysis should be used with great caution when characterizing group differences , 2004, NeuroImage.

[76]  Timothy Edward John Behrens,et al.  Training induces changes in white matter architecture , 2009, Nature Neuroscience.

[77]  E. Ringelstein,et al.  Ultrasound contrast enhancing agents in neurosonology: principles, methods,future possibilities , 2000, Acta neurologica Scandinavica.

[78]  Guy B. Williams,et al.  Development of an MRI rating scale for multiple brain regions: comparison with volumetrics and with voxel-based morphometry , 2009, Neuroradiology.

[79]  Karl J. Friston,et al.  Automatic Differentiation of Anatomical Patterns in the Human Brain: Validation with Studies of Degenerative Dementias , 2002, NeuroImage.

[80]  L. Jäncke,et al.  Brain structural trajectories over the adult lifespan , 2012, Human brain mapping.

[81]  Fred L. Bookstein,et al.  “Voxel-Based Morphometry” Should Not Be Used with Imperfectly Registered Images , 2001, NeuroImage.

[82]  Stefanie Brassen,et al.  Combining voxel-based morphometry and diffusion tensor imaging to detect age-related brain changes , 2006, Neuroreport.

[83]  Adam G. Thomas,et al.  Functional but not structural changes associated with learning: An exploration of longitudinal Voxel-Based Morphometry (VBM) , 2009, NeuroImage.

[84]  V. Calhoun,et al.  Voxel-based morphometry versus region of interest: a comparison of two methods for analyzing gray matter differences in schizophrenia , 2005, Schizophrenia Research.