Normative Modelling in Neuroimaging: A Practical Guide for Researchers
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917026040119296 |
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| author | Alyas, Nida Horsley, Jonathan Little, Bethany Taylor, Peter N. Wang, Yujiang Leiberg, Karoline |
| author_facet | Alyas, Nida Horsley, Jonathan Little, Bethany Taylor, Peter N. Wang, Yujiang Leiberg, Karoline |
| contents | Normative modelling is an increasingly common statistical technique in neuroimaging that estimates population-level benchmarks in brain structure. It enables the quantification of individual deviations from expected distributions whilst accounting for biological and technical covariates without requiring large, matched control groups. This makes it a powerful alternative to traditional case-control studies for identifying brain structural alterations associated with pathology. Despite the availability of numerous modelling approaches and several toolboxes with pre-trained models, the distinct strengths and limitations of normative modelling make it difficult to determine how and when to implement them appropriately. This review offers practical guidance and outlines statistical considerations for clinical researchers using normative modelling in neuroimaging. Through a worked example using clinical epilepsy data, we outline considerations for responsible implementation of pre-trained normative models, to support their broad and rigorous adoption in neuroimaging research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07237 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Normative Modelling in Neuroimaging: A Practical Guide for Researchers Alyas, Nida Horsley, Jonathan Little, Bethany Taylor, Peter N. Wang, Yujiang Leiberg, Karoline Neurons and Cognition Image and Video Processing Normative modelling is an increasingly common statistical technique in neuroimaging that estimates population-level benchmarks in brain structure. It enables the quantification of individual deviations from expected distributions whilst accounting for biological and technical covariates without requiring large, matched control groups. This makes it a powerful alternative to traditional case-control studies for identifying brain structural alterations associated with pathology. Despite the availability of numerous modelling approaches and several toolboxes with pre-trained models, the distinct strengths and limitations of normative modelling make it difficult to determine how and when to implement them appropriately. This review offers practical guidance and outlines statistical considerations for clinical researchers using normative modelling in neuroimaging. Through a worked example using clinical epilepsy data, we outline considerations for responsible implementation of pre-trained normative models, to support their broad and rigorous adoption in neuroimaging research. |
| title | Normative Modelling in Neuroimaging: A Practical Guide for Researchers |
| topic | Neurons and Cognition Image and Video Processing |
| url | https://arxiv.org/abs/2509.07237 |