Combining multi-site MRI datasets increases statistical power and model generalisability but may be hindered by variability between sites. Harmonisation methods aim to remove potentially confounding variance while preserving biologically meaningful signals. However, this can be challenging, as each T1-weighted image reflects both scanner properties (e.g., field strength, sequence parameters) and individual biological characteristics (e.g., age, sex, ethno-cultural background, and pathology). Two image-based (HACA3, IGUANe) and two feature-based (neuroHarmonize, neuroCombat) harmonisation methods were assessed using T1-weighted brain imaging data from the Psy-ShareD database; 564 participants (295 schizophrenia, 269 controls) from seven studies acquired across 5 sites from the Psy-ShareD database. We trained several models to classify sites, schizophrenia diagnosis, age, and symptom levels. Site-classification accuracy was high for unharmonised data (90.1%) and for HACA3 (92.2%), slightly reduced with IGUANe (86.6%), and near chance for feature-based methods (4.2% neuroHarmonize; 1.8% neuroCombat), indicating effective bias removal. We fitted several models predicting biological signals including diagnosis, age, and symptom levels across different harmonisation methods. In most cases, classification with harmonised data performed at least as well as with unharmonised data. Generally, feature-based methods best remove site-related variance, but image-based approaches remain a promising avenue for preserving individual biological differences. This work provides practical guidance for selecting harmonisation strategies in multi-site psychiatric neuroimaging, depending on whether the priority is bias reduction or preservation of subject-level variability. ### Competing Interest Statement PA has been funded by FrieslandCampina and Nedra, GKM consults for Ieso Digital Health. RU reports consultancy from Vitaris and Springer Healthcare unrelated to the current work. ### Funding Statement This work was funded by the United Kingdom Medical Research Council grant number MR/X010651/1 and delivered through the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre (BRC). This work was supported in part by the Japan Agency for Medical Research and Development (AMED) Grant Number JP24wm0625302. All research at the Department of Psychiatry in the University of Cambridge is supported by the NIHR Cambridge Biomedical Research Centre (NIHR203312) and the NIHR Applied Research Collaboration East of England. RU is supported by the NIHR Oxford Health Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. A list of funders and acknowledgements for Psy-ShareD datasets can be found at https://psyshared.com/Team.html ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: London - Dulwich Research Ethics Committee Title of the Research Database: Psychosis MRI Shared Data Resource (Psy- ShareD), V1 REC reference: 25/LO/0184 IRAS project ID: 352347 I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data used in the study is available via the Psychsis MRI ShareD Data Resource (psyshared.com)
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