Melbourne Ageing Study
About the dataset
Primary description: University of Melbourne cohorts
Participant details are included in previous papers (https://doi.org/10.1001/archpsyc.56.2.133). Participants were recruited from the Early Psychosis Prevention and Intervention Centre, were aged between 16 – 30 years, and were diagnosed with psychosis within 2 weeks of admission according to the DSM-III-R based on medical record review and either the Royal Park Multidiagnostic Instrument for Psychosis or the Structured Clinical Interview for DSM-III-R. All subjects were screened for comorbid medical and psychiatric conditions by clinical assessment and physical and neurological examination. Exclusion criteria were a history of significant head injury, seizures, neurologic diseases, impaired thyroid function, steroid use, or DSM-III-R criteria of alcohol or substance abuse or dependence. Control subjects with a personal or family history of psychiatric illness were excluded. Clinical information was obtained from patient interview and medical record review. First-episode patients were neuroleptic naïve prior to admission but had received antipsychotic medication prior to scanning. Medication doses for patients with chronic schizophrenia were calculated for the 30 days prior to scanning. FEP cases were followed-up at a 10-year follow-up point (Tables S1/S2).
The study is part of the PHENOM consortium. For more information, see: https://doi.org/10.1038/s41380-023-02069-0 and https://doi.org/10.1093/brain/awaa025. The PHENOM (Psychosis Heterogeneity Evaluated Via Dimensional Neuroimaging) consortium is an international consortium pooling MRI and clinical data across several institutions world-wide. The focus on the consortium is to study the use of machine learning and MRI as a means for deriving personalized imaging signatures of schizophrenia, and of predicting future clinical progression. An associated goal is to use state of the art weakly supervised machine learning methods to dissect neuroanatomical heterogeneity in psychosis. In addition to offering healthy control subjects at younger ages, this consortium helps establish imaging signatures of psychosis into NiChart.
The study is part of the PHENOM consortium. For more information, see: https://doi.org/10.1038/s41380-023-02069-0 and https://doi.org/10.1093/brain/awaa025. The PHENOM (Psychosis Heterogeneity Evaluated Via Dimensional Neuroimaging) consortium is an international consortium pooling MRI and clinical data across several institutions world-wide. The focus on the consortium is to study the use of machine learning and MRI as a means for deriving personalized imaging signatures of schizophrenia, and of predicting future clinical progression. An associated goal is to use state of the art weakly supervised machine learning methods to dissect neuroanatomical heterogeneity in psychosis. In addition to offering healthy control subjects at younger ages, this consortium helps establish imaging signatures of psychosis into NiChart.
| Study | PHENOM_Melbourne | |
|---|---|---|
| Data Descriptor Paper | https://doi.org/10.1001/archpsyc.56.2.133 | |
| DUA | Please contact Dr. Christos Davatzikos to obtain the DUA for the derived data and request an introduction to the study PI to obtain the DUA for the raw data. | |
| Country/Region | Australia | |
| Disease Tag | psychosis | |
| Age (mean ± SD) | 25.8 ± 9.3 | |
| Age Range | 13-57 | |
| % Female | 31.6 | |
| CUBIC Project | /cbica/projects/PsychAnalysis |
|
| PI for CUBIC Access Approval | Christos Davatzikos | |
| N Subjects | 328 | |
| N MR Sessions | 328 | |
|
DLMUSE
(v1.0.7)
(328/328 complete) |
/cbica/projects/PsychAnalysis/Pipelines/PHENOM_DLMUSE_2025/Melbourne/Results/Melbourne_DLMUSE_Volumes.csv
|
|
| Non-Imaging Data |
Contact Mathilde Antoniades to request access to the non-imaging data. /cbica/home/harmang/for_others/final_combined_istaging_withMUSE.csv
Data consolidated and harmonized in 2026. This study: subset with |
Funding
The PHENOM study is funded by NIA grant R01MH112070 and by the PRONIA project as funded by the European Union 7th Framework Program grant 602152. Other supporting funds are 5U01AG068057, 1U24AG074855, R01MH119219 and S10OD023495.