General objectives - The objective is to employ the expertise present at CIMeC to apply big data approaches to translational research relating to clinical populations. The objective is to link CIMeC to the interests of local clinical researchers and derive targeted approaches to address questions of interest to these clinical researchers using, predominantly, resting state and machine learning approaches. The end goal is to attain reliable measures that have value in a diagnostic setting.
These may include but are certainly not limited to:
- investigate different subtypes of disorders of interest to other research groups focusing on specific neural hypothesis about aetiology rather than talking a ‘black-box; approach;
- similar indices as they relate to symptomology and clinical intervention, as they might be useful to track individual patients’ clinical progression.
Overall vision - This is a multistage project initiating with projects designed to allow expertise acquisition and extending to translational implementations in successive years:
- a preliminary goal is to augment and extend CIMeCs expertise in differentiating between populations and identifying individual differences using machine learning and big data approaches;
- contemporaneously, the objective is to gently promote this project with researchers at the APSS and CERiN and to encourage the process of collecting resting state data within relevant ongoing research projects;
- following this an appraisal of the viability of this project. A positive outcome will lead to a scaling up of the project and collaborations between CIMeC and the APSS.
Project contact person: Scott Fairhall