Developmental trajectories of neuroanatomical alterations associated with the 16p11.2 Copy Number Variations

Most of human genome is present in two copies (maternal and paternal). However, segments of the genome can be deleted or duplicated, and many of these genomic variations (known as Copy Number Variants) are associated with psychiatric disorders. 16p11.2 copy number variants (breakpoint 4-5) confer high risk for neurodevelopmental disorders and are associated with structural brain alterations of large effect-size. Methods used in previous studies were unable to investigate the onset of these alterations and whether they evolve with age. In this study, we aim at characterizing age-related effects of 16p11.2 copy number variants by analyzing a group with a broad age range including younger individuals. A large normative developmental dataset was used to accurately adjust for effects of age. We normalized volumes of segmented brain regions as well as volumes of each voxel defined by tensor-based morphometry. Results show that the total intracranial volumes, the global gray and white matter volumes are respectively higher and lower in deletion and duplication carriers compared to control subjects at 4.5 years of age. These differences remain stable through childhood, adolescence and adulthood until 23 years of age (range: 0.5 to 1.0 Z-score). Voxel-based results are consistent with previous findings in 16p11.2 copy number variant carriers, including increased volume in the calcarine cortex and insula in deletions, compared to controls, with an inverse effect in duplication carriers (1.0 Z-score). All large effect-size voxel-based differences are present at 4.5 years and seem to remain stable until the age of 23. Our results highlight the stability of a neuroimaging endophenotype over 2 decades during which neurodevelopmental symptoms evolve at a rapid pace.

[1]  N Hadjikhani,et al.  The 16p11.2 locus modulates brain structures common to autism, schizophrenia and obesity , 2014, Molecular Psychiatry.

[2]  D. Louis Collins,et al.  Unbiased average age-appropriate atlases for pediatric studies , 2011, NeuroImage.

[3]  D. Louis Collins,et al.  Automatic 3‐D model‐based neuroanatomical segmentation , 1995 .

[4]  J. Piven,et al.  Early brain overgrowth in autism associated with an increase in cortical surface area before age 2 years. , 2011, Archives of general psychiatry.

[5]  Joshua M. Korn,et al.  Association between microdeletion and microduplication at 16p11.2 and autism. , 2008, The New England journal of medicine.

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

[7]  M. Hallett Human Brain Function , 1998, Trends in Neurosciences.

[8]  Wei Cheng,et al.  Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects , 2016, Nature Genetics.

[9]  D. Louis Collins,et al.  BEaST: Brain extraction based on nonlocal segmentation technique , 2012, NeuroImage.

[10]  Jennifer Fedor,et al.  Cortical and subcortical brain morphometry differences between patients with autism spectrum disorders (ASD) and healthy individuals across the lifespan: results from the ENIGMA-ASD working group , 2017 .

[11]  Vladimir S Fonov,et al.  Onset of multiple sclerosis before adulthood leads to failure of age-expected brain growth , 2014, Neurology.

[12]  J. Rosenfeld,et al.  Defining the Effect of the 16p11.2 Duplication on Cognition, Behavior, and Medical Comorbidities. , 2016, JAMA psychiatry.

[13]  R. Malenka,et al.  Behavioral abnormalities and circuit defects in the basal ganglia of a mouse model of 16p11.2 deletion syndrome. , 2014, Cell reports.

[14]  Meritxell Bach Cuadra,et al.  Deviant trajectories of cortical maturation in 22q11.2 deletion syndrome (22q11DS): A cross-sectional and longitudinal study , 2009, Schizophrenia Research.

[15]  Abraham Z. Snyder,et al.  Opposing Brain Differences in 16p11.2 Deletion and Duplication Carriers , 2014, The Journal of Neuroscience.

[16]  D. Collins,et al.  Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space , 1994, Journal of computer assisted tomography.

[17]  Alan C. Evans,et al.  The NIH MRI study of normal brain development , 2006, NeuroImage.

[18]  D H Geschwind,et al.  Using large clinical data sets to infer pathogenicity for rare copy number variants in autism cohorts , 2012, Molecular Psychiatry.

[19]  The Simons,et al.  Simons Variation in Individuals Project (Simons VIP): A Genetics-First Approach to Studying Autism Spectrum and Related Neurodevelopmental Disorders , 2012, Neuron.

[20]  J. Sebat,et al.  Spatiotemporal 16p11.2 Protein Network Implicates Cortical Late Mid-Fetal Brain Development and KCTD13-Cul3-RhoA Pathway in Psychiatric Diseases , 2015, Neuron.

[21]  Jared A. Nielsen,et al.  Quantifying the Effects of 16p11.2 Copy Number Variants on Brain Structure: A Multisite Genetic-First Study , 2018, Biological Psychiatry.

[22]  Martin Styner,et al.  Trajectories of early brain volume development in fragile X syndrome and autism. , 2012, Journal of the American Academy of Child and Adolescent Psychiatry.

[23]  Eveline A. Crone,et al.  Structural brain development between childhood and adulthood: Convergence across four longitudinal samples , 2016, NeuroImage.

[24]  Alan C. Evans,et al.  Longitudinal neuroanatomical changes determined by deformation-based morphometry in a mouse model of Alzheimer's disease , 2008, NeuroImage.

[25]  Guido Gerig,et al.  The Emergence of Network Inefficiencies in Infants With Autism Spectrum Disorder , 2017, Biological Psychiatry.

[26]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[27]  D. Louis Collins,et al.  A new method for structural volume analysis of longitudinal brain MRI data and its application in studying the growth trajectories of anatomical brain structures in childhood , 2013, NeuroImage.

[28]  Alan C. Evans,et al.  Brain development during childhood and adolescence: a longitudinal MRI study , 1999, Nature Neuroscience.

[29]  Paul M. Thompson,et al.  Large-scale mapping of cortical alterations in 22q11.2 deletion syndrome: Convergence with idiopathic psychosis and effects of deletion size , 2018, Molecular Psychiatry.

[30]  D. Louis Collins,et al.  Regional brain atrophy in children with multiple sclerosis , 2011, NeuroImage.

[31]  Alan C. Evans,et al.  Early brain development in infants at high risk for autism spectrum disorder , 2017, Nature.