| 1. |
|
|
| 2. |
- Ge, R, et al.
(author)
-
Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization
- 2023
-
In: bioRxiv : the preprint server for biology. - : Cold Spring Harbor Laboratory.
-
Journal article (other academic/artistic)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.
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
- Bethlehem, RAI, et al.
(author)
-
Brain charts for the human lifespan
- 2022
-
In: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 604:79057906, s. 525-
-
Journal article (peer-reviewed)abstract
- Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data (http://www.brainchart.io/). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.
|
|
| 6. |
|
|
| 7. |
- Dima, Danai, et al.
(author)
-
Subcortical volumes across the lifespan : Data from 18,605 healthy individuals aged 3-90 years.
- 2022
-
In: Human Brain Mapping. - : Wiley. - 1065-9471 .- 1097-0193. ; 43:1, s. 452-469
-
Journal article (peer-reviewed)abstract
- Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3-90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns.
|
|
| 8. |
- Frangou, Sophia, et al.
(author)
-
Cortical thickness across the lifespan : Data from 17,075 healthy individuals aged 3-90 years
- 2022
-
In: Human Brain Mapping. - : John Wiley & Sons. - 1065-9471 .- 1097-0193. ; 43:1, s. 431-451
-
Journal article (peer-reviewed)abstract
- Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3-90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes.
|
|
| 9. |
- Wierenga, Lara M., et al.
(author)
-
Greater male than female variability in regional brain structure across the lifespan
- 2022
-
In: Human Brain Mapping. - : John Wiley & Sons. - 1065-9471 .- 1097-0193. ; 43:1, s. 470-499
-
Journal article (peer-reviewed)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.
|
|
| 10. |
- Singh, G., et al.
(author)
-
Revealing asymmetrical dust distribution in the inner regions of HD 141569
- 2021
-
In: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 653
-
Journal article (peer-reviewed)abstract
- Context. The combination of high-contrast imaging with spectroscopy and polarimetry offers a pathway to studying the grain distribution and properties of debris disks in exquisite detail. Here, we focus on the case of a gas-rich debris disk around HD 141569A, which features a multiple-ring morphology first identified with SPHERE in the near-infrared.Aims. We obtained polarimetric differential imaging with SPHERE in the H-band to compare the scattering properties of the innermost ring at 44 au with former observations in total intensity with the same instrument. In polarimetric imaging, we observed that the intensity of the ring peaks in the south-east, mostly in the forward direction, whereas in total intensity imaging, the ring is detected only at the south. This noticeable characteristic suggests a non-uniform dust density in the ring. With these two sets of images, we aim to study the distribution of the dust to solve for the actual dust distribution.Methods. We implemented a density function varying azimuthally along the ring and generated synthetic images both in polarimetry and in total intensity, which are then compared to the actual data. The search for the best-fit model was performed both with a grid-based and an MCMC approach. Using the outcome of this modelization, we further measured the polarized scattering phase function for the observed scattering angle between 33 degrees and 147 degrees as well as the spectral reflectance of the southern part of the ring between 0.98 and 2.1 mu m. We tentatively derived the grain properties by comparing these quantities with MCFOST models and assuming Mie scattering.Results. We find that the dust density peaks in the south-west at an azimuthal angle of 220 degrees similar to 238 degrees with a rather broad width of 61 degrees similar to 127 degrees. The difference in the intensity distributions observed in polarimetry and total intensity is the result of this particular morphology. Although there are still uncertainties that remain in the determination of the anisotropic scattering factor, the implementation of an azimuthal density variation to fit the data proved to be robust. Upon elaborating on the origin of this dust density distribution, we conclude that it could be the result of a massive collision when we account for the effect of the high gas mass that is present in the system on the dynamics of grains. In terms of grain composition, our preliminary interpretation indicates a mixture of porous sub-micron sized astro-silicate and carbonaceous grains.Conclusions. The SPHERE observations have allowed, for the first time, for meaningful constraints to be placed on the dust distribution beyond the standard picture of a uniform ring-like debris disk. However, future studies with a multiwavelength approach and additional detailed modeling would be required to better characterize the grain properties in the HD 141569 system.
|
|
