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Psychosis risk subtypes and white matter integrity: exploring subgroup trajectories

CORDIS · observation · Publication date unknown

and environmental risk profiles by employing advanced clustering approaches. I will assess whether stratifying participants into these more similar subgroups enhances the effectiveness of supervised machine learning models that utilize WM microstructure and baseline clinical features to predict traditional one-year clinical outcomes. Yet, traditional retrospective assessments to evaluate clinical outcomes are prone to memory and assessor bias. Hence, I will further re-contact a subset of individuals up to eight years after psychosis onset to collect traditional and smartphone-based assessments to determine whether my models predictive accuracy is robust when evaluating longer-term and real-life outcomes. During this project, I will build expertise in diffusion-weighted imaging, unsupervised machine learning, and smartphone-based assessments, mentored by Prof. Pasternak (Harvard Medical School) and Prof. Koutsouleris (Ludwig-Maximilian-University). These skills will bolster my academic profile as a neuroscientific psychologist and facilitate my growth as an independent research grou

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recordType

award

status

SIGNED

region

EU

value

220966.8

unit

EUR