Monotonic non-linear transformations as a tool to investigate age-related effects on brain white matter integrity: A Box–Cox investigation

Non-linear effects of age on white matter integrity are ubiquitous in the brain and indicate that these effects are more pronounced in certain brain regions at specific ages. Box-Cox analysis is a technique to increase the log-likelihood of linear relationships between variables by means of monotonic non-linear transformations. Here we employ Box-Cox transformations to flexibly and parsimoniously determine the degree of non-linearity of age-related effects on white matter integrity by means of model comparisons using a voxel-wise approach. Analysis of white matter integrity in a sample of adults between 20 and 89years of age (n=88) revealed that considerable portions of the white matter in the corpus callosum, cerebellum, pallidum, brainstem, superior occipito-frontal fascicle and optic radiation show non-linear effects of age. Global analyses revealed an increase in the average non-linearity from fractional anisotropy to radial diffusivity, axial diffusivity, and mean diffusivity. These results suggest that Box-Cox transformations are a useful and flexible tool to investigate more complex non-linear effects of age on white matter integrity and extend the functionality of the Box-Cox analysis in neuroimaging.

[1]  Simon B. Eickhoff,et al.  A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data , 2005, NeuroImage.

[2]  Osamu Abe,et al.  Aging in the CNS: Comparison of gray/white matter volume and diffusion tensor data , 2008, Neurobiology of Aging.

[3]  Torsten Rohlfing,et al.  Variation in longitudinal trajectories of regional brain volumes of healthy men and women (ages 10 to 85years) measured with atlas-based parcellation of MRI , 2013, NeuroImage.

[4]  K. Lim,et al.  Age‐related decline in brain white matter anisotropy measured with spatially corrected echo‐planar diffusion tensor imaging , 2000, Magnetic resonance in medicine.

[5]  Jung-Lung Hsu,et al.  Gender differences and age-related white matter changes of the human brain: A diffusion tensor imaging study , 2008, NeuroImage.

[6]  Peter Seres,et al.  Selective effects of aging on brain white matter microstructure: A diffusion tensor imaging tractography study , 2010, NeuroImage.

[7]  D. Ruppert.  Robust Statistics: The Approach Based on Influence Functions , 1987 .

[8]  Y. Benjamini,et al.  THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .

[9]  Ameer Pasha Hosseinbor,et al.  Characterization of Cerebral White Matter Properties Using Quantitative Magnetic Resonance Imaging Stains , 2011, Brain Connect..

[10]  Anders M. Dale,et al.  When does brain aging accelerate? Dangers of quadratic fits in cross-sectional studies , 2010, NeuroImage.

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

[12]  C. Lebel,et al.  Diffusion tensor imaging of white matter tract evolution over the lifespan , 2012, NeuroImage.

[13]  H. Johansen-Berg,et al.  Accelerated Changes in White Matter Microstructure during Aging: A Longitudinal Diffusion Tensor Imaging Study , 2014, The Journal of Neuroscience.

[14]  Baoci Shan,et al.  Age-related white matter degradation rule of normal human brain: the evidence from diffusion tensor magnetic resonance imaging. , 2014, Chinese medical journal.

[15]  M. Filippi,et al.  Microstructural changes and atrophy in brain white matter tracts with aging , 2012, Neurobiology of Aging.

[16]  Matthew C. Keller,et al.  Increased sensitivity in neuroimaging analyses using robust regression , 2005, NeuroImage.

[17]  Simon B. Eickhoff,et al.  Testing anatomically specified hypotheses in functional imaging using cytoarchitectonic maps , 2006, NeuroImage.

[18]  Volker Schmid,et al.  Quantitative Analysis of Dynamic Contrast-Enhanced and Diffusion-Weighted Magnetic Resonance Imaging for Oncology in R , 2011 .

[19]  Jie Peng,et al.  Diffusion tensor smoothing through weighted Karcher means. , 2013, Electronic journal of statistics.

[20]  R. Sakia.  The Box-Cox transformation technique: a review , 1992 .

[21]  Brian D. Ripley,et al.  Modern Applied Statistics with S Fourth edition , 2002 .

[22]  M S Buchsbaum,et al.  Regional and global changes in cerebral diffusion with normal aging. , 2001, AJNR. American journal of neuroradiology.

[23]  Simon B. Eickhoff,et al.  Assignment of functional activations to probabilistic cytoarchitectonic areas revisited , 2007, NeuroImage.

[24]  Sterling C. Johnson,et al.  White Matter in Aging and Cognition: A Cross-Sectional Study of Microstructure in Adults Aged Eighteen to Eighty-Three , 2010, Developmental neuropsychology.

[25]  Jun Chen,et al.  Impact of DTI smoothing on the study of brain aging , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[27]  A. Pfefferbaum,et al.  Quantitative fiber tracking of lateral and interhemispheric white matter systems in normal aging: Relations to timed performance , 2010, Neurobiology of Aging.

[28]  Michael J. Martinez,et al.  Bias between MNI and Talairach coordinates analyzed using the ICBM‐152 brain template , 2007, Human brain mapping.

[29]  Charles DeCarli,et al.  Structural Imaging Measures of Brain Aging , 2014, Neuropsychology Review.

[30]  A. Dale,et al.  Age-related alterations in white matter microstructure measured by diffusion tensor imaging , 2005, Neurobiology of Aging.

[31]  Christina Gloeckner,et al.  Modern Applied Statistics With S , 2003 .

[32]  David R. Anderson,et al.  Model Selection and Inference: A Practical Information-Theoretic Approach , 2001 .

[33]  Joseph G. Ibrahim,et al.  Bayesian spatial transformation models with applications in neuroimaging data. , 2013, Biometrics.

[34]  Derek K. Jones,et al.  The effect of filter size on VBM analyses of DT-MRI data , 2005, NeuroImage.

[35]  Edith V. Sullivan,et al.  Equivalent disruption of regional white matter microstructure in ageing healthy men and women , 2001, Neuroreport.

[36]  Gerard R. Ridgway,et al.  Individualized Gaussian process-based prediction and detection of local and global gray matter abnormalities in elderly subjects , 2014, NeuroImage.

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

[38]  V. Vieland,et al.  Statistical Evidence: A Likelihood Paradigm , 1998 .

[39]  N. Gong,et al.  Aging in deep gray matter and white matter revealed by diffusional kurtosis imaging , 2014, Neurobiology of Aging.

[40]  D. Selkoe.  Alzheimer's disease. , 2011, Cold Spring Harbor perspectives in biology.

[41]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[42]  A. Dale,et al.  Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry. , 2010, Cerebral cortex.

[43]  Jung-Lung Hsu,et al.  Microstructural white matter changes in normal aging: A diffusion tensor imaging study with higher-order polynomial regression models , 2010, NeuroImage.

[44]  Sinisa Pajevic.  Statistical Issues in Diffusion Tensor MRI , 2010 .

[45]  Sterling C. Johnson,et al.  Midlife measurements of white matter microstructure predict subsequent regional white matter atrophy in healthy adults , 2014, Human brain mapping.

[46]  Christian Beaulieu,et al.  Age-related regional variations of the corpus callosum identified by diffusion tensor tractography , 2010, NeuroImage.

[47]  Y. Assaf,et al.  Diffusion Tensor Imaging (DTI)-based White Matter Mapping in Brain Research: A Review , 2007, Journal of Molecular Neuroscience.

[48]  D. Cox,et al.  An Analysis of Transformations , 1964 .

[49]  Volker Schmid,et al.  Working with the DICOM and NIfTI Data Standards in R , 2011 .

[50]  S. Folstein,et al.  "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. , 1975, Journal of psychiatric research.