{"paper":{"title":"A Bayesian Longitudinal Spatial Normative Model for Individualized Brain Deviation Mapping","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Bayesian longitudinal spatial model reduces brain deviation map reconstruction error by jointly capturing temporal and spatial dependencies.","cross_cats":["math.ST","stat.AP","stat.TH"],"primary_cat":"stat.ME","authors_text":"J. T. Korley","submitted_at":"2026-05-14T08:36:45Z","abstract_excerpt":"Normative modeling enables individualized characterization of structural brain deviations by evaluating subjects against a reference population rather than a group average. Most existing implementations treat brain regions independently and remain cross-sectional, despite the availability of repeated neuroimaging measurements and the well-documented spatial organization of neuroanatomical variation. We propose a Bayesian longitudinal spatial normative model that jointly captures within-subject temporal dependence and spatially structured subject-specific deviations within a unified hierarchica"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Across six simulation scenarios and OASIS-3 structural MRI data, the proposed Bayesian longitudinal spatial normative model reduced deviation-map reconstruction error relative to independent cross-sectional and longitudinal non-spatial benchmarks, with RMSE reductions of 54% and 45% respectively in the real data application.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The model assumes that subject-specific deviations can be adequately represented as a latent spatial process whose posterior can be computed under the chosen hierarchical Bayesian specification, with the spatial dependence structure correctly specified for the neuroanatomical data.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A new Bayesian model jointly models longitudinal and spatial dependencies in brain MRI to produce individualized deviation maps with substantially lower error than independent or non-spatial alternatives.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Bayesian longitudinal spatial model reduces brain deviation map reconstruction error by jointly capturing temporal and spatial dependencies.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"4b17194122d0c5fbd242e99c308dd95442a0aa3150ebc47d2206b76f2c66b2b4"},"source":{"id":"2605.14565","kind":"arxiv","version":1},"verdict":{"id":"ec3c4b9f-3c29-40fe-b41e-9c92c334dfb8","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T01:39:58.229404Z","strongest_claim":"Across six simulation scenarios and OASIS-3 structural MRI data, the proposed Bayesian longitudinal spatial normative model reduced deviation-map reconstruction error relative to independent cross-sectional and longitudinal non-spatial benchmarks, with RMSE reductions of 54% and 45% respectively in the real data application.","one_line_summary":"A new Bayesian model jointly models longitudinal and spatial dependencies in brain MRI to produce individualized deviation maps with substantially lower error than independent or non-spatial alternatives.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The model assumes that subject-specific deviations can be adequately represented as a latent spatial process whose posterior can be computed under the chosen hierarchical Bayesian specification, with the spatial dependence structure correctly specified for the neuroanatomical data.","pith_extraction_headline":"Bayesian longitudinal spatial model reduces brain deviation map reconstruction error by jointly capturing temporal and spatial dependencies."},"references":{"count":13,"sample":[{"doi":"","year":null,"title":"Ifτ 2 u = 0, the model reduces to a longitudinal non-spatial normative model","work_id":"6b7fa367-73ed-447c-9038-a5a8aaca1b26","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Ifσ 2 b = 0, dependence across regions is induced only through the spatial deviation process","work_id":"fca3f70f-2d54-4f8a-a64c-2759a5da4317","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Ifτ 2 u = 0,σ 2 b = 0, andT i = 1for all subjects, the model reduces to an independent cross-sectional regional model. 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