Polygenic architecture of human neuroanatomical diversity, bioRxiv, 2019-03-29

AbstractWe analysed the genomic architecture of neuroanatomical diversity using magnetic resonance imaging and SNP data from &gt; 20,000 individuals. Our results replicate previous findings of a strong polygenic architecture of neuroanatomical diversity. SNPs captured from 40% to 54% of the variance in the volume of different brain regions. We observed a large correlation between chromosome length and the amount of phenotypic variance captured, r∼0.64 on average, suggesting that at a global scale causal variants are homogeneously distributed across the genome. At a more local scale, SNPs within genes (∼51%) captured ∼1.5-times more genetic variance than the rest; and SNPs with low minor allele frequency (MAF) captured significantly less variance than those with higher MAF the 40% of SNPs with MAF&lt;5% captured less than one fourth of the genetic variance. We also observed extensive pleiotropy across regions, with an average genetic correlation of rG∼0.45. Across regions, genetic correlations were in general similar to phenotypic correlations. By contrast, genetic correlations were larger than phenotypic correlations for the leftright volumes of the same region, and indistinguishable from 1. Additionally, the differences in leftright volumes were not heritable, underlining the role of environmental causes in the variability of brain asymmetry. Our analysis code is available at <jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsgithub.comneuroanatomygenomic-architecture>httpsgithub.comneuroanatomygenomic-architecture<jatsext-link>.

biorxiv neuroscience 0-100-users 2019

Variations in Structural MRI Quality Impact Measures of Brain Anatomy Relations with Age and Other Sociodemographic Variables, bioRxiv, 2019-03-20

AbstractIn-scanner head movements can introduce artifacts to MRI images and increase errors in brain-behavior studies. The magnitude of in-scanner head movements varies widely across developmental and clinical samples, making it increasingly difficult to parse out “true signal” from motion related noise. Yet, the quantification of structural imaging quality is typically limited to subjective visual assessments andor proxy measures of motion. It is, however, unknown how direct measures of image quality relate to developmental and behavioral variables, as well as measures of brain morphometrics. To begin to answer this question, we leverage a multi-site dataset of structural MRI images, which includes a range of children and adolescents with varying degrees of psychopathology. We first find that a composite of structural image quality relates to important developmental and behavioral variables (e.g., IQ; clinical diagnoses). Additionally, we demonstrate that even among T1-weighted images which pass visual inspection, variations in image quality impact volumetric derivations of regional gray matter. Image quality was associated with wide-spread variations in gray matter, including in portions of the frontal, parietal, and temporal lobes, as well as the cerebellum. Further, our image quality composite partially mediated the relationship between age and total gray matter volume, explaining 23% of this relationship. Collectively, the effects underscore the need for volumetric studies to model or mitigate the effect of image quality when investigating brain-behavior relations.

biorxiv neuroscience 0-100-users 2019

 

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