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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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- Andreoli, L., et al.
(författare)
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COVID-19 VACCINE SAFETY DURING PREGNANCY AND BREASTFEEDING IN WOMEN WITH AUTOIMMUNE DISEASES : RESULTS FROM THE COVAD STUDY
- 2023
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Ingår i: Annals of the Rheumatic Diseases. - : HighWire Press. - 0003-4967 .- 1468-2060. ; 82:Suppl. 1, s. 56-57
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Background: COVID-19 vaccine hesitancy among pregnant and breastfeeding women with autoimmune diseases (AID) is often attributed to the fear of adverse events (AE) and disease flares (DF). No data are available regarding COVID-19 vaccine safety in this population.Objectives: We aimed at describing delayed-onset (>7 days) vaccine-related AE (minor and major), DF, and related AID treatment modifications from the COVID-19 Vaccination in Autoimmune Diseases (COVAD) study.Methods: Among complete responses from 9201 participants as of June 21, 2022, 6787 (73.8%) were women. Six subgroups were identified upon diagnosis of AID vs healthy controls (HC) and their pregnancy/breastfeeding status at the time of any dose of vaccine (Figure 1).Results: Forty pregnant and 52 breastfeeding AID patients were identified and their vaccination rates (at least one dose) was 100% and 96.2%, respectively (Table 1). Overall AE, minor AE, and major AE were reported significantly more frequently by pregnant than non-pregnant patients (45% vs. 26%, p=0.01; 40% vs. 25.9%, p=0.03; 17.5% vs. 4.6%, p<0.01), but no difference was found in comparison with pregnant HC. No difference was observed between breastfeeding patients and HC. Post-vaccination DF were reported by 17.5% of pregnant and 20% of breastfeeding patients, and by 18% of age- and disease-matched control patients (n=2315). All DF in pregnant/breastfeeding patients were managed with glucocorticoids and a fifth of them required initiation or change in immunosuppressive treatment.Conclusion: This study provides the first insights into the safety of COVID-19 vaccination during the antenatal period in women with AID. While AEs were more commonly reported by pregnant patients with AID, these were no higher than among pregnant healthy controls without AID. These observations are reassuring, likely to strengthen physician-patient communication and overcome hesitancy as the benefits for the mother and fetus by passive immunization are likely to overweigh the potential risks of AE and DF.Reference: [1]Fazal ZZ, et al; COVAD Study Group. COVAD survey 2 long-term outcomes: unmet need and protocol. Rheumatol Int 2022; 42:2151-2158.
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