[HTML][HTML] Warped Bayesian linear regression for normative modelling of big data
Normative modelling is becoming more popular in neuroimaging due to its ability to make
predictions of deviation from a normal trajectory at the level of individual participants. It …
predictions of deviation from a normal trajectory at the level of individual participants. It …
Hierarchical bayesian regression for multi-site normative modeling of neuroimaging data
Clinical neuroimaging has recently witnessed explosive growth in data availability which
brings studying heterogeneity in clinical cohorts to the spotlight. Normative modeling is an …
brings studying heterogeneity in clinical cohorts to the spotlight. Normative modeling is an …
Closing the life-cycle of normative modeling using federated hierarchical Bayesian regression
Clinical neuroimaging data availability has grown substantially in the last decade, providing
the potential for studying heterogeneity in clinical cohorts on a previously unprecedented …
the potential for studying heterogeneity in clinical cohorts on a previously unprecedented …
Bayesian analysis of neuroimaging data in FSL
Typically in neuroimaging we are looking to extract some pertinent information from
imperfect, noisy images of the brain. This might be the inference of percent changes in blood …
imperfect, noisy images of the brain. This might be the inference of percent changes in blood …
Using normative modelling to detect disease progression in mild cognitive impairment and Alzheimer's disease in a cross-sectional multi-cohort study
Normative modelling is an emerging method for quantifying how individuals deviate from the
healthy populational pattern. Several machine learning models have been implemented to …
healthy populational pattern. Several machine learning models have been implemented to …
Normative modeling of neuroimaging data using generalized additive models of location scale and shape
Normative modeling aims to quantify the degree to which an individual's brain deviates from
a reference sample with respect to one or more variables, which can be used as a potential …
a reference sample with respect to one or more variables, which can be used as a potential …
Bayesian model selection for group studies
Bayesian model selection (BMS) is a powerful method for determining the most likely among
a set of competing hypotheses about the mechanisms that generated observed data. BMS …
a set of competing hypotheses about the mechanisms that generated observed data. BMS …
[HTML][HTML] Accommodating site variation in neuroimaging data using normative and hierarchical Bayesian models
The potential of normative modeling to make individualized predictions from neuroimaging
data has enabled inferences that go beyond the case-control approach. However, site …
data has enabled inferences that go beyond the case-control approach. However, site …
Handling multiplicity in neuroimaging through Bayesian lenses with multilevel modeling
Here we address the current issues of inefficiency and over-penalization in the massively
univariate approach followed by the correction for multiple testing, and propose a more …
univariate approach followed by the correction for multiple testing, and propose a more …
Charting brain growth and aging at high spatial precision
Defining reference models for population variation, and the ability to study individual
deviations is essential for understanding inter-individual variability and its relation to the …
deviations is essential for understanding inter-individual variability and its relation to the …