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- Fouche, JP, et al.
(författare)
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Cortical thickness in obsessive-compulsive disorder: multisite mega-analysis of 780 brain scans from six centres
- 2017
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Ingår i: The British journal of psychiatry : the journal of mental science. - : Royal College of Psychiatrists. - 1472-1465. ; 210:1, s. 67-74
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Tidskriftsartikel (refereegranskat)abstract
- There is accumulating evidence for the role of fronto-striatal and associated circuits in obsessive–compulsive disorder (OCD) but limited and conflicting data on alterations in cortical thickness.AimsTo investigate alterations in cortical thickness and subcortical volume in OCD.MethodIn total, 412 patients with OCD and 368 healthy adults underwent magnetic resonance imaging scans. Between-group analysis of covariance of cortical thickness and subcortical volumes was performed and regression analyses undertaken.ResultsSignificantly decreased cortical thickness was found in the OCD group compared with controls in the superior and inferior frontal, precentral, posterior cingulate, middle temporal, inferior parietal and precuneus gyri. There was also a group x age interaction in the parietal cortex, with increased thinning with age in the OCD group relative to controls.ConclusionsOur findings are partially consistent with earlier work, suggesting that group differences in grey matter volume and cortical thickness could relate to the same underlying pathology of OCD. They partially support a frontostriatal model of OCD, but also suggest that limbic, temporal and parietal regions play a role in the pathophysiology of the disorder. The group x age interaction effects may be the result of altered neuroplasticity.
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- Ge, R, et al.
(författare)
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Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization
- 2023
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Ingår i: bioRxiv : the preprint server for biology. - : Cold Spring Harbor Laboratory.
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background: Normative modeling is a statistical approach to quantify the degree to which a particular individual-level measure deviates from the pattern observed in a normative reference population. When applied to human brain morphometric measures it has the potential to inform about the significance of normative deviations for health and disease. Normative models can be implemented using a variety of algorithms that have not been systematically appraised. Methods: To address this gap, eight algorithms were compared in terms of performance and computational efficiency using brain regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) collated from 87 international MRI datasets. Performance was assessed with the mean absolute error (MAE) and computational efficiency was inferred from central processing unit (CPU) time. The algorithms evaluated were Ordinary Least Squares Regression (OLSR), Bayesian Linear Regression (BLR), Generalized Additive Models for Location, Scale, and Shape (GAMLSS), Parametric Lambda, Mu, Sigma (LMS), Gaussian Process Regression (GPR), Warped Bayesian Linear Regression (WBLG), Hierarchical Bayesian Regression (HBR), and Multivariable Fractional Polynomial Regression (MFPR). Model optimization involved testing nine covariate combinations pertaining to acquisition features, parcellation software versions, and global neuroimaging measures (i.e., total intracranial volume, mean cortical thickness, and mean cortical surface area). Findings: Statistical comparisons across models at PFDR<0.05 indicated that the MFPR-derived sex- and region-specific models with nonlinear polynomials for age and linear effects of global measures had superior predictive accuracy; the range of the MAE of the models of regional subcortical volumes was 70-520 mm3 and the corresponding ranges for regional cortical thickness and regional cortical surface area were 0.09-0.26 mm and 24-560 mm2, respectively. The MFPR-derived models were also computationally more efficient with a CPU time below one second compared to a range of 2 seconds to 60 minutes for the other algorithms. The performance of all sex- and region-specific MFPR models plateaued at sample sizes exceeding 3,000 and showed comparable MAEs across distinct 10-year age-bins covering the human lifespan. Interpretation: These results provide an empirically benchmarked framework for normative modeling of brain morphometry that is useful for interpreting prior literature and supporting future study designs. The model and tools described here are freely available through CentileBrain (https://centilebrain.org/), a user-friendly web platform.
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