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Sökning: WFRF:(Gutman BA)

  • Resultat 1-7 av 7
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1.
  • Grasby, KL, et al. (författare)
  • The genetic architecture of the human cerebral cortex
  • 2020
  • Ingår i: Science (New York, N.Y.). - : American Association for the Advancement of Science (AAAS). - 1095-9203 .- 0036-8075. ; 367:6484, s. 1340-
  • Tidskriftsartikel (refereegranskat)
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2.
  • Hibar, Derrek P., et al. (författare)
  • Novel genetic loci associated with hippocampal volume
  • 2017
  • Ingår i: Nature Communications. - 2041-1723 .- 2041-1723. ; 8
  • Tidskriftsartikel (refereegranskat)abstract
    • The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (r(g) = -0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness.
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3.
  • Petrov, Dmitry, et al. (författare)
  • Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
  • 2017
  • Ingår i: Machine learning in medical imaging. MLMI (Workshop). ; 10541, s. 371-378
  • Tidskriftsartikel (refereegranskat)abstract
    • As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.
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  • Resultat 1-7 av 7

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