| 11. |
- Petrov, Dmitry, et al.
(författare)
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Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
- 2017
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Ingår i: Machine learning in medical imaging. MLMI (Workshop). - Cham : Springer International Publishing. ; 10541, s. 371-378
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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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| 12. |
- Satizabal, Claudia L., et al.
(författare)
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Genetic architecture of subcortical brain structures in 38,851 individuals
- 2019
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Ingår i: Nature Genetics. - : Nature Publishing Group. - 1061-4036 .- 1546-1718. ; 51:11, s. 1624-
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Tidskriftsartikel (refereegranskat)abstract
- Subcortical brain structures are integral to motion, consciousness, emotions and learning. We identified common genetic variation related to the volumes of the nucleus accumbens, amygdala, brainstem, caudate nucleus, globus pallidus, putamen and thalamus, using genome-wide association analyses in almost 40,000 individuals from CHARGE, ENIGMA and UK Biobank. We show that variability in subcortical volumes is heritable, and identify 48 significantly associated loci (40 novel at the time of analysis). Annotation of these loci by utilizing gene expression, methylation and neuropathological data identified 199 genes putatively implicated in neurodevelopment, synaptic signaling, axonal transport, apoptosis, inflammation/infection and susceptibility to neurological disorders. This set of genes is significantly enriched for Drosophila orthologs associated with neurodevelopmental phenotypes, suggesting evolutionarily conserved mechanisms. Our findings uncover novel biology and potential drug targets underlying brain development and disease.
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| 13. |
- Thompson, Paul M., et al.
(författare)
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The ENIGMA Consortium : large-scale collaborative analyses of neuroimaging and genetic data
- 2014
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Ingår i: BRAIN IMAGING BEHAV. - : Springer Science and Business Media LLC. - 1931-7557 .- 1931-7565. ; 8:2, s. 153-182
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Tidskriftsartikel (refereegranskat)abstract
- The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.
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| 14. |
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| 15. |
- Wierenga, Lara M., et al.
(författare)
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Greater male than female variability in regional brain structure across the lifespan
- 2022
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Ingår i: Human Brain Mapping. - : John Wiley & Sons. - 1065-9471 .- 1097-0193. ; 43:1, s. 470-499
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Tidskriftsartikel (refereegranskat)abstract
- For many traits, males show greater variability than females, with possible implications for understanding sex differences in health and disease. Here, the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Consortium presents the largest-ever mega-analysis of sex differences in variability of brain structure, based on international data spanning nine decades of life. Subcortical volumes, cortical surface area and cortical thickness were assessed in MRI data of 16,683 healthy individuals 1-90 years old (47% females). We observed significant patterns of greater male than female between-subject variance for all subcortical volumetric measures, all cortical surface area measures, and 60% of cortical thickness measures. This pattern was stable across the lifespan for 50% of the subcortical structures, 70% of the regional area measures, and nearly all regions for thickness. Our findings that these sex differences are present in childhood implicate early life genetic or gene-environment interaction mechanisms. The findings highlight the importance of individual differences within the sexes, that may underpin sex-specific vulnerability to disorders.
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| 16. |
- Wolfensberger, Saskia P, et al.
(författare)
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Quantification of the neurokinin 1 receptor ligand [¹¹C]R116301
- 2011
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Ingår i: Nuclear medicine communications. - 0143-3636 .- 1473-5628. ; 32:10, s. 896-902
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Tidskriftsartikel (refereegranskat)abstract
- PURPOSE:Neurokinin 1 (NK1) receptors have been implicated in depression, anxiety, and pain perception. Recently, it was shown that, in the human brain, a specific NK1 receptor-related signal was obtained with the novel radioligand, [¹¹C]R116301, using positron emission tomography. The purpose of this study was to evaluate various methods for quantifying specific [¹¹C]R116301 binding.METHODS:Two dynamic 90-min [¹¹C]R116301 scans, separated by 5 h, were performed in 11 healthy volunteers. In three patients, the second scan was performed after an oral blocking dose of 125 mg of aprepitant, whereas in the other eight, no intervention was performed (test-retest). Whole striatum was used as the tissue of interest, as it has the highest density of NK1 receptors. Cerebellum was used as the reference tissue.RESULTS:Reference tissue models were stable with the simplified reference tissue model (SRTM) performing best. Average (± standard deviation) SRTM-derived mean nondisplaceable binding potential (BP(ND)) of all (first) baseline scans was 0.64±0.31 (n=11), which reduced to -0.01±0.03 (n=3) after aprepitant administration. Test-retest results showed low variability (14.0±10.7%) and excellent reliability, as indicated by the intraclass correlation coefficient (0.93). The ratio of standardized uptake values of striatum and cerebellum minus 1, an approximation of BP(ND), showed very low variability (6.2±3.1%) with excellent reliability (intraclass correlation coefficient=0.98), and correlated well with SRTM-derived BP(ND) (R²=0.96).CONCLUSION:SRTM is the model of choice for quantifying [¹¹C]R116301 binding. Semiquantitative tissue ratios hold promise for routine clinical applications.
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