{"doi":"10.1101/2025.09.14.25335702","title":"Normative Modelling of Brain Volume in Multiple Sclerosis","abstract":"Abstract Background and Objectives Interpretation of brain atrophy in multiple sclerosis (MS) relies on group-level research and lacks individualized reference standards. Normative modelling can enable patient-level assessments of regional brain volumes relative to population expectations. Methods We constructed age-, sex-, and intracranial volume–adjusted normative models of regional cortical and subcortical FreeSurfer-estimated brain volumes and applied to data from a concluded clinical trial and routine hospital examinations to derive regional deviation Z-scores and counts of critical deviations (Z < –1.96). Associations with disability (Expanded Disability Status Scale [EDSS]), cognitive performance (Paced Auditory Serial Addition Test [PASAT]), and fatigue (Fatigue Severity Scale [FSS]) were examined cross-sectionally and longitudinally using fixed and random effects models. A deviation-based stratification rule was evaluated for disability progression and relapse risk using survival analyses. Results Models were trained on 62,444 MRI datasets from healthy individuals across the lifespan (50.8% females, age range 6.0-90.1) and applied to 953 longitudinal MRI scans from 362 people with MS (mean age = 38.8±9.7, 70.5% females, follow-up up to 12 years). People with MS exhibited a higher number of critical deviations than matched controls (incidence rate ratio 2.70, 95% CI 2.21–3.30), most prominently in the thalamus (approximately 25% of patients). A higher number of deviations was associated with higher disability (EDSS) indicated by a cross-sectional (β=0.24, 95% CI 0.14–0.34) and longitudinal main effect (β=0.07, 95% CI 0.02–0.13). Lower-than-reference volumes in the thalamus, hippocampus, and putamen were consistently associated with higher disability cross-sectionally (β standardized = –0.17 to –0.23) and over time (β standardized = –0.14 to –0.18). Deviation-based risk stratification identified patients with modestly higher disability trajectories (β=0.13, 95% CI 0.03–0.24). Discussion Normative modelling reveals a heterogeneous morphometric deviation profile in MS, centred on deep grey matter structures and associated with disability accumulation. These findings support the use of population-referenced MRI metrics for individual-level phenotyping in MS and warrant validation in independent cohorts.","journal":"medRxiv","year":2025,"id":574883,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.5175,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1482511,"name":"Ingrid Anne Lie","orcid":"0000-0002-9948-2411","position":1,"is_corresponding":false},{"id":1482512,"name":"Kristin Wesnes","orcid":"0000-0002-0918-8367","position":2,"is_corresponding":false},{"id":252034,"name":"Eric Westman","orcid":"0000-0002-3115-2977","position":3,"is_corresponding":false},{"id":355770,"name":"Thomas Espeseth","orcid":"0000-0001-9076-6365","position":4,"is_corresponding":false},{"id":52934,"name":"Ole A. Andreassen","orcid":"0000-0002-4461-3568","position":5,"is_corresponding":false},{"id":246433,"name":"Lars T. Westlye","orcid":"0000-0001-8644-956X","position":6,"is_corresponding":false},{"id":1482513,"name":"Stig Wergeland","orcid":"0000-0002-7645-7686","position":7,"is_corresponding":false},{"id":95004,"name":"Hanne F. Harbo","orcid":"0000-0002-6047-0122","position":8,"is_corresponding":false},{"id":1482514,"name":"Gro Owren Nygaard","orcid":"0000-0002-4913-8567","position":9,"is_corresponding":false},{"id":1482515,"name":"Kjell‐Morten Myhr","orcid":"0000-0002-0980-510X","position":10,"is_corresponding":false},{"id":355771,"name":"Einar August Høgestøl","orcid":"0000-0001-8446-2111","position":11,"is_corresponding":false},{"id":1482516,"name":"Øivind Torkildsen","orcid":"0000-0001-5294-2866","position":12,"is_corresponding":false},{"id":265450,"name":"the Alzheimer’s Disease Neuroimaging Initiative","orcid":null,"position":13,"is_corresponding":false},{"id":49424,"name":"Max Korbmacher","orcid":"0000-0002-8113-2560","position":0,"is_corresponding":true}],"reference_count":28,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:57:44.572630Z","pmid":"41001457","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}