ArticleImaging neuroscience (Cambridge, Mass.)2024
Non-Gaussian normative modelling with hierarchical Bayesian regression.
Article in Imaging neuroscience (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Latent representations of early brain development: A multivariate normative model of brain structure and behaviour.Developmental cognitive neuroscience · 2026Article
- ComBat-Predict Enhances Generalizability of Neuroimaging Models to New Sites.Human brain mapping · 2026Article
- Cerebellar growth is associated with domain-specific cerebral maturation and socio-linguistic behavior.Nature communications · 2026Article
- Normative modeling of MEG brain oscillations across the human lifespan.Communications biology · 2026Article
- Unified Multi-Cohort Harmonisation and Normative Modelling of Neuroimaging Data via Hierarchical GAMLSS.bioRxiv : the preprint server for biology · 2026Article
- Article
- Structural similarity networks reveal brain vulnerability in dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- The normative modelling framework for traumatic brain injury.Brain : a journal of neurology · 2025Review
- Understanding individual neurodegenerative progression in Parkinson's disease through normative modelling.Scientific reports · 2025Article
- Lifespan Normative Modeling of Brain Microstructure.bioRxiv : the preprint server for biology · 2025Article
- Unraveling Microstructural and Macrostructural Brain Age Dynamics in Multiple Sclerosis.Neurology(R) neuroimmunology & neuroinflammation · 2025Article
- ComBatLS: A Location- and Scale-Preserving Method for Multi-Site Image Harmonization.Human brain mapping · 2025Article
- Smooth Normative Brain Mapping of Three-Dimensional Morphometry Imaging Data Using Skew-Normal Regression.Human brain mapping · 2025Article
- Spectral normative modeling of brain structure.medRxiv : the preprint server for health sciences · 2025Article
- Learning Latent Profiles via Cognitive Growth Charting in Psychosis: Design and Rationale for the PRECOGNITION Project.Schizophrenia bulletin open · 2025Article
- Normative trajectories of RImaging neuroscience (Cambridge, Mass.) · 2025Article
- Lifespan Normative Models of White Matter Fractional Anisotropy: Applications to Early Psychosis.bioRxiv : the preprint server for biology · 2024Article
- ComBatLS: A location- and scale-preserving method for multi-site image harmonization.bioRxiv : the preprint server for biology · 2024Article
- Population-wide cerebellar growth models of children and adolescents.Nature communications · 2024Article
- Estimating cortical thickness trajectories in children across different scanners using transfer learning from normative models.Human brain mapping · 2024Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Normative modelling is an emerging technique for parsing heterogeneity in clinical cohorts. This can be implemented in practice using hierarchical Bayesian regression, which provides an elegant probabilistic solution to handle site variation in a federated learning framework. However, applications of this method to date have employed a Gaussian assumption, which may be restrictive in some applications. We have extended the hierarchical Bayesian regression framework to flexibly model non-Gaussian data with heteroskdastic skewness and kurtosis. To this end, we employ a flexible distribution from the sinh-arcsinh (SHASH) family, and introduce a novel reparameterisation and a Markov chain Monte Carlo sampling approach to perform inference in this model. Using a large neuroimaging dataset collected at 82 different sites, we show that the results achieved with this extension are equivalent or better than a warped Bayesian linear regression baseline model on most datasets, while providing better control over the parameters governing the shape of distributions that the approach is able to model. We also demonstrate that the attained flexibility is useful for accurately modelling highly nonlinear relationships between aging and imaging derived phenotypes, which shows that the extension is important for pushing the field of normative modelling forward. All methods described here are available in the open-source pcntoolkit.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.