| 11. |
|
|
| 12. |
- Bas-Hoogendam, Janna Marie, et al.
(författare)
-
ENIGMA-anxiety working group : Rationale for and organization of large-scale neuroimaging studies of anxiety disorders
- 2022
-
Ingår i: Human Brain Mapping. - : Wiley. - 1065-9471 .- 1097-0193. ; 43:1, s. 83-112
-
Forskningsöversikt (refereegranskat)abstract
- Anxiety disorders are highly prevalent and disabling but seem particularly tractable to investigation with translational neuroscience methodologies. Neuroimaging has informed our understanding of the neurobiology of anxiety disorders, but research has been limited by small sample sizes and low statistical power, as well as heterogenous imaging methodology. The ENIGMA-Anxiety Working Group has brought together researchers from around the world, in a harmonized and coordinated effort to address these challenges and generate more robust and reproducible findings. This paper elaborates on the concepts and methods informing the work of the working group to date, and describes the initial approach of the four subgroups studying generalized anxiety disorder, panic disorder, social anxiety disorder, and specific phobia. At present, the ENIGMA-Anxiety database contains information about more than 100 unique samples, from 16 countries and 59 institutes. Future directions include examining additional imaging modalities, integrating imaging and genetic data, and collaborating with other ENIGMA working groups. The ENIGMA consortium creates synergy at the intersection of global mental health and clinical neuroscience, and the ENIGMA-Anxiety Working Group extends the promise of this approach to neuroimaging research on anxiety disorders.
|
|
| 13. |
- Chye, Yann, et al.
(författare)
-
Subcortical surface morphometry in substance dependence : An ENIGMA addiction working group study
- 2020
-
Ingår i: Addiction Biology. - : WILEY. - 1355-6215 .- 1369-1600. ; 25:6
-
Tidskriftsartikel (refereegranskat)abstract
- While imaging studies have demonstrated volumetric differences in subcortical structures associated with dependence on various abused substances, findings to date have not been wholly consistent. Moreover, most studies have not compared brain morphology across those dependent on different substances of abuse to identify substance-specific and substance-general dependence effects. By pooling large multinational datasets from 33 imaging sites, this study examined subcortical surface morphology in 1628 nondependent controls and 2277 individuals with dependence on alcohol, nicotine, cocaine, methamphetamine, and/or cannabis. Subcortical structures were defined by FreeSurfer segmentation and converted to a mesh surface to extract two vertex-level metrics-the radial distance (RD) of the structure surface from a medial curve and the log of the Jacobian determinant (JD)-that, respectively, describe local thickness and surface area dilation/contraction. Mega-analyses were performed on measures of RD and JD to test for the main effect of substance dependence, controlling for age, sex, intracranial volume, and imaging site. Widespread differences between dependent users and nondependent controls were found across subcortical structures, driven primarily by users dependent on alcohol. Alcohol dependence was associated with localized lower RD and JD across most structures, with the strongest effects in the hippocampus, thalamus, putamen, and amygdala. Meanwhile, nicotine use was associated with greater RD and JD relative to nonsmokers in multiple regions, with the strongest effects in the bilateral hippocampus and right nucleus accumbens. By demonstrating subcortical morphological differences unique to alcohol and nicotine use, rather than dependence across all substances, results suggest substance-specific relationships with subcortical brain structures.
|
|
| 14. |
- 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). - Cham : Springer International Publishing. ; 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.
|
|
| 15. |
- Wolfensberger, Saskia P, et al.
(författare)
-
Quantification of the neurokinin 1 receptor ligand [¹¹C]R116301
- 2011
-
Ingår i: Nuclear medicine communications. - 0143-3636 .- 1473-5628. ; 32:10, s. 896-902
-
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.
|
|
