13 research outputs found
Brain-age prediction:Systematic evaluation of site effects, and sample age range and size
Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5â90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8â80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9â25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5â40 and 40â90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.</p
Brainâage prediction: systematic evaluation of site effects, and sample age range and size
Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brainâage) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brainâage has highlighted the need for robust and publicly available brainâage models preâtrained on data from large samples of healthy individuals. To address this need we have previously released a developmental brainâage model. Here we expand this work to develop, empirically validate, and disseminate a preâtrained brainâage model to cover most of the human lifespan. To achieve this, we selected the bestâperforming model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brainâage prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5â90 years; 53.59% female). The preâtrained models were tested for crossâdataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8â80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9â25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two ageâbins (5â40 and 40â90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brainâage prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an openâscience, webâbased platform for individualized neuroimaging metrics
Brainâage prediction:Systematic evaluation of site effects, and sample age range and size
Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5â90âyears; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8â80âyears; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9â25âyears; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5â40 and 40â90âyears) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.<br/
Subcortical volumes across the lifespan: Data from 18,605 healthy individuals aged 3â90 years
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 MetaâAnalysis (ENIGMA) Consortium to examine ageârelated trajectories inferred from crossâsectional 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 3â90âyears. 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 interâindividual 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 ageârelated morphometric patterns
Cortical thickness across the lifespan: Data from 17,075 healthy individuals aged 3-90 years
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 largeâscale studies. In response, we used crossâsectional data from 17,075 individuals aged 3â90âyears from the Enhancing Neuroimaging Genetics through MetaâAnalysis (ENIGMA) Consortium to infer ageârelated 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 metaâanalysis and oneâway 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
Jim Kalnin : Stream
Holubizky provides a time line of the germination and development of Kalninâs exhibition. Writing in a personal, auto-biographical style, he addresses the major issues of Kalninâs work, particularly the artistâs âregionalism,â since the artist is based in Kelowna. The author states that a personal approach like his own is best suited to comment on art. Kalnin also contributes an auto-biographical sketch (4 p.). 6 bibl. ref
D'Est en Ouest = West in East
Catalogue documenting a two-point exchange carried out in two exhibitions bringing together three Montreal artists in Kelowna and Vernon (British Columbia), and six artists from the Kelowna area in Montreal. Curators Théoret and Macklem each present their own project in a short text (in French and English), followed by artists' statements and more specific commentaries on each work (both in the original language only). Biographical notes on curators and artists, including additional statements by the latter
Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain : Algorithm Benchmarking and Model Optimization
We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain ()
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Brainâage prediction: Systematic evaluation of site effects, and sample age range and size
Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90âyears; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80âyears; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25âyears; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90âyears) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics
Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization
We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain ( https://centilebrain.org/ ).</p