Common brain disorders are associated with heritable patterns of apparent aging of the brain

Common risk factors for psychiatric and other brain disorders are likely to converge on biological pathways influencing the development and maintenance of brain structure and function across life. Using structural MRI data from 45,615 individuals aged 3-96 years, we demonstrate distinct patterns of apparent brain aging in several brain disorders and reveal genetic pleiotropy between apparent brain aging in healthy individuals and common brain disorders.

[1]  S. Karterud,et al.  The symptom and function dimensions of the Global Assessment of Functioning (GAF) scale. , 2012, Comprehensive psychiatry.

[2]  Nick C Fox,et al.  Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease , 2013, Nature Genetics.

[3]  Christine Ecker,et al.  Neuroimaging in autism spectrum disorder: brain structure and function across the lifespan , 2015, The Lancet Neurology.

[4]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[5]  Wesley K. Thompson,et al.  A Nonlinear Simulation Framework Supports Adjusting for Age When Analyzing BrainAGE , 2018, bioRxiv.

[6]  Giovanni Montana,et al.  Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker , 2016, NeuroImage.

[7]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[8]  Warren W. Kretzschmar,et al.  Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression , 2017, Nature Genetics.

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

[10]  V. Bansal,et al.  Genome-wide association study results for educational attainment aid in identifying genetic heterogeneity of schizophrenia , 2018, Nature Communications.

[11]  T. Lumley,et al.  gplots: Various R Programming Tools for Plotting Data , 2015 .

[12]  Stuart J. Ritchie,et al.  Brain age predicts mortality , 2017, Molecular Psychiatry.

[13]  Terry L. Jernigan,et al.  Cerebral structure on MRI, part II: Specific changes in Alzheimer's and Huntington's diseases , 1991, Biological Psychiatry.

[14]  Gary King,et al.  MatchIt: Nonparametric Preprocessing for Parametric Causal Inference , 2011 .

[15]  M. Daly,et al.  An Atlas of Genetic Correlations across Human Diseases and Traits , 2015, Nature Genetics.

[16]  Daniel H. Geschwind,et al.  Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders , 2015, Nature Reviews Genetics.

[17]  M. McCarthy,et al.  Improved detection of common variants associated with schizophrenia by leveraging pleiotropy with cardiovascular-disease risk factors. , 2013, American journal of human genetics.

[18]  Jesper Andersson,et al.  A multi-modal parcellation of human cerebral cortex , 2016, Nature.

[19]  M. Daly,et al.  LD Score regression distinguishes confounding from polygenicity in genome-wide association studies , 2014, Nature Genetics.

[20]  T. Insel,et al.  Brain disorders? Precisely , 2015, Science.

[21]  J. Cole,et al.  Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers , 2017, Trends in Neurosciences.

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

[23]  E. Bullmore,et al.  Anatomy of bipolar disorder and schizophrenia: A meta-analysis , 2010, Schizophrenia Research.

[24]  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.

[25]  F. Cunningham,et al.  The Ensembl Variant Effect Predictor , 2016, Genome Biology.

[26]  Jakob Grove,et al.  Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder , 2017, bioRxiv.

[27]  I. Melle,et al.  Mapping the Heterogeneous Phenotype of Schizophrenia and Bipolar Disorder Using Normative Models , 2018, JAMA psychiatry.

[28]  Vikram Patel,et al.  No health without mental health , 2007, The Lancet.

[29]  Stefan Klöppel,et al.  Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: Exploring the influence of various parameters , 2010, NeuroImage.

[30]  John P. Rice,et al.  Identification of common genetic risk variants for autism spectrum disorder , 2019, Nature Genetics.

[31]  M. Dylan Tisdall,et al.  Quantitative assessment of structural image quality , 2018, NeuroImage.

[32]  S. Kay,et al.  The positive and negative syndrome scale (PANSS) for schizophrenia. , 1987, Schizophrenia bulletin.

[33]  Jesper Andersson,et al.  Valid conjunction inference with the minimum statistic , 2005, NeuroImage.

[34]  C. Spencer,et al.  Biological Insights From 108 Schizophrenia-Associated Genetic Loci , 2014, Nature.

[35]  J. Kurtzke.  Rating neurologic impairment in multiple sclerosis , 1983, Neurology.

[36]  Wolfgang Viechtbauer,et al.  Conducting Meta-Analyses in R with the metafor Package , 2010 .

[37]  Y Wang,et al.  Genetic pleiotropy between multiple sclerosis and schizophrenia but not bipolar disorder: differential involvement of immune-related gene loci , 2014, Molecular Psychiatry.

[38]  Thomas E. Nichols,et al.  Statistical Challenges in “Big Data” Human Neuroimaging , 2018, Neuron.

[39]  I. Cuthill,et al.  Effect size, confidence interval and statistical significance: a practical guide for biologists , 2007, Biological reviews of the Cambridge Philosophical Society.

[40]  Alicia R. Martin,et al.  Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder , 2018, Nature Genetics.

[41]  Oscar Marín,et al.  Developmental timing and critical windows for the treatment of psychiatric disorders , 2016, Nature Medicine.

[42]  Mark E Bastin,et al.  Sex Differences in the Adult Human Brain: Evidence from 5216 UK Biobank Participants , 2017, bioRxiv.

[43]  P. Donnelly,et al.  The UK Biobank resource with deep phenotyping and genomic data , 2018, Nature.