Quantile rank maps: A new tool for understanding individual brain development

We propose a novel method for neurodevelopmental brain mapping that displays how an individual's values for a quantity of interest compare with age-specific norms. By estimating smoothly age-varying distributions at a set of brain regions of interest, we derive age-dependent region-wise quantile ranks for a given individual, which can be presented in the form of a brain map. Such quantile rank maps could potentially be used for clinical screening. Bootstrap-based confidence intervals are proposed for the quantile rank estimates. We also propose a recalibrated Kolmogorov-Smirnov test for detecting group differences in the age-varying distribution. This test is shown to be more robust to model misspecification than a linear regression-based test. The proposed methods are applied to brain imaging data from the Nathan Kline Institute Rockland Sample and from the Autism Brain Imaging Data Exchange (ABIDE) sample.

[1]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[2]  Rabi Bhattacharya,et al.  Stochastic processes with applications , 1990 .

[3]  Jonathan D. Power,et al.  Prediction of Individual Brain Maturity Using fMRI , 2010, Science.

[4]  B. Silverman,et al.  Nonparametric Regression and Generalized Linear Models: A roughness penalty approach , 1993 .

[5]  D. Cox.  Nonparametric Regression and Generalized Linear Models: A roughness penalty approach , 1993 .

[6]  T J Cole,et al.  Smoothing reference centile curves: the LMS method and penalized likelihood. , 1992, Statistics in medicine.

[7]  J. Morris,et al.  The Cortical Signature of Alzheimer's Disease: Regionally Specific Cortical Thinning Relates to Symptom Severity in Very Mild to Mild AD Dementia and is Detectable in Asymptomatic Amyloid-Positive Individuals , 2008, Cerebral cortex.

[8]  Eileen Luders,et al.  Brain maturation: Predicting individual BrainAGE in children and adolescents using structural MRI , 2012, NeuroImage.

[9]  Nathalie Boddaert,et al.  Autism, the superior temporal sulcus and social perception , 2006, Trends in Neurosciences.

[10]  Ying Han,et al.  Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: A resting-state fMRI study , 2011, NeuroImage.

[11]  P. Lachenbruch.  Mathematical Statistics, 2nd Edition , 1972 .

[12]  Paul H. C. Eilers,et al.  Flexible smoothing with B-splines and penalties , 1996 .

[13]  Pin T. Ng,et al.  Quantile smoothing splines , 1994 .

[14]  John H. Gilmore,et al.  Regional characterization of longitudinal DT-MRI to study white matter maturation of the early developing brain , 2013, NeuroImage.

[15]  M. Mesulam,et al.  From sensation to cognition. , 1998, Brain : a journal of neurology.

[16]  Anders M. Dale,et al.  Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature , 2010, NeuroImage.

[17]  Daniel P. Kennedy,et al.  The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism , 2013, Molecular Psychiatry.

[19]  A M Dale,et al.  Measuring the thickness of the human cerebral cortex from magnetic resonance images. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[20]  W. Thompson,et al.  Design considerations for characterizing psychiatric trajectories across the lifespan: application to effects of APOE-ε4 on cerebral cortical thickness in Alzheimer's disease. , 2011, The American journal of psychiatry.

[21]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[22]  Margaret D. King,et al.  The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry , 2012, Front. Neurosci..

[23]  Vijay K. Venkatraman,et al.  Neuroanatomical Assessment of Biological Maturity , 2012, Current Biology.

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

[25]  Thomas F. Nugent,et al.  Dynamic mapping of human cortical development during childhood through early adulthood. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[26]  N. Makris,et al.  Gyri of the human neocortex: an MRI-based analysis of volume and variance. , 1998, Cerebral cortex.

[27]  R. Rigby,et al.  Generalized additive models for location, scale and shape , 2005 .

[28]  Wesley K. Thompson,et al.  Modeling Change in the Brain , 2016 .

[29]  Alan C. Evans,et al.  Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation , 2007, Proceedings of the National Academy of Sciences.

[30]  R. Rigby,et al.  Automatic smoothing parameter selection in GAMLSS with an application to centile estimation , 2014, Statistical methods in medical research.

[31]  Maarten Mennes,et al.  Massively Parallel Nonparametric Regression, With an Application to Developmental Brain Mapping , 2014, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

[32]  Thomas R Insel,et al.  Mental disorders in childhood: shifting the focus from behavioral symptoms to neurodevelopmental trajectories. , 2014, JAMA.

[33]  Chaozhe Zhu,et al.  An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF , 2008, Journal of Neuroscience Methods.

[34]  J. Rapoport,et al.  Abnormal Cortical Growth in Schizophrenia Targets Normative Modules of Synchronized Development , 2014, Biological Psychiatry.

[35]  Catie Chang,et al.  Connectivity trajectory across lifespan differentiates the precuneus from the default network , 2014, NeuroImage.

[36]  B. Leventhal,et al.  The Autism Diagnostic Observation Schedule—Generic: A Standard Measure of Social and Communication Deficits Associated with the Spectrum of Autism , 2000, Journal of autism and developmental disorders.

[37]  Yong He,et al.  Brain gray matter deficits at 33-year follow-up in adults with attention-deficit/hyperactivity disorder established in childhood. , 2011, Archives of general psychiatry.

[38]  Xi-Nian Zuo,et al.  A Connectome Computation System for discovery science of brain , 2015 .

[39]  Philip T Reiss,et al.  The International Journal of Biostatistics Smoothness Selection for Penalized Quantile Regression Splines , 2012 .

[40]  Alan C. Evans,et al.  Longitudinal mapping of cortical thickness and clinical outcome in children and adolescents with attention-deficit/hyperactivity disorder. , 2006, Archives of general psychiatry.

[41]  Efstathios D. Gennatas,et al.  Impact of puberty on the evolution of cerebral perfusion during adolescence , 2014, Proceedings of the National Academy of Sciences.

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

[43]  Yong He,et al.  BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics , 2013, PloS one.

[44]  Xi-Nian Zuo,et al.  Amplitude of low-frequency oscillations in schizophrenia: A resting state fMRI study , 2010, Schizophrenia Research.

[45]  Kosha Ruparel,et al.  Neurocognitive growth charting in psychosis spectrum youths. , 2014, JAMA psychiatry.