Suzie Lavoie1,2, Kelly Allott1,2, Paul Amminger1,2, Cali Bartholomeusz1,2, Maximus Berger1,2, Michael Breakspear3, Anjali K Henders4, Rico Lee5, Ashleigh Lin6, Patrick McGorry1,2, Simon Rice1,2, Lianne Schmaal1,2, Stephen J Wood1,2,7. 1. Orygen, The National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia. 2. Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia. 3. University of Newcastle, Newcastle, NSW, Australia. 4. Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia. 5. Brain and Mental Health Research Hub, Monash University, Clayton, VIC, Australia. 6. Telethon Kids Institute, The University of Western Australia, Perth, WA, Australia. 7. School of Psychology, University of Birmingham, Birmingham, UK.
Abstract
OBJECTIVE: The current international trend is to create large datasets with existing data and/or deposit newly collected data into repositories accessible to the scientific community. These practices lead to more efficient data sharing, better detection of small effects, modelling of confounders, establishment of sample generalizability and identification of differences between any given disorders. In Australia, to facilitate such data-sharing and collaborative opportunities, the Neurobiology in Youth Mental Health Partnership was created. This initiative brings together specialised researchers from around Australia to work towards a better understanding of the cross-diagnostic neurobiology of youth mental health and the translation of this knowledge into clinical practice. One of the mandates of the partnership was to develop a protocol for harmonised prospective collection of data across research centres in the field of youth mental health in order to create large datasets. METHODS: Four key research modalities were identified: clinical assessments, brain imaging, neurocognitive assessment and collection of blood samples. This paper presents the consensus set of assessments/data collection that has been selected by experts in each domain. CONCLUSION: The use of this core set of data will facilitate the pooling of psychopathological and neurobiological data into large datasets allowing researchers to tackle important questions requiring very large numbers. The aspiration of this transdiagnostic approach is a better understanding of the mechanisms underlying mental illnesses.
OBJECTIVE: The current international trend is to create large datasets with existing data and/or deposit newly collected data into repositories accessible to the scientific community. These practices lead to more efficient data sharing, better detection of small effects, modelling of confounders, establishment of sample generalizability and identification of differences between any given disorders. In Australia, to facilitate such data-sharing and collaborative opportunities, the Neurobiology in Youth Mental Health Partnership was created. This initiative brings together specialised researchers from around Australia to work towards a better understanding of the cross-diagnostic neurobiology of youth mental health and the translation of this knowledge into clinical practice. One of the mandates of the partnership was to develop a protocol for harmonised prospective collection of data across research centres in the field of youth mental health in order to create large datasets. METHODS: Four key research modalities were identified: clinical assessments, brain imaging, neurocognitive assessment and collection of blood samples. This paper presents the consensus set of assessments/data collection that has been selected by experts in each domain. CONCLUSION: The use of this core set of data will facilitate the pooling of psychopathological and neurobiological data into large datasets allowing researchers to tackle important questions requiring very large numbers. The aspiration of this transdiagnostic approach is a better understanding of the mechanisms underlying mental illnesses.
Keywords:
Harmonisation; data sharing; neurocognition; neuroimaging; transdiagnostic
Authors: S M Cotton; M Berk; A Watson; S Wood; K Allott; C F Bartholomeusz; C C Bortolasci; K Walder; B O'Donoghue; O M Dean; A Chanen; G P Amminger; P D McGorry; A Burnside; J Uren; A Ratheesh; S Dodd Journal: Trials Date: 2019-11-28 Impact factor: 2.279