Summary
In many areas of medicine, biomarkers have revolutionised diagnosis and personalised treatment allocation (‘precision medicine’). Psychiatry lags behind: disorders are still diagnosed by symptoms and no biomarkers have been found. However, addressing this is a formidable task because of a lack of analysis tools to understand the complex disruptions of mental disorders at multiple levels –from neurobiology to behaviour– and to tackle their extreme heterogeneity at every level.
My vision is to provide a set of principled, next generation analysis tools to stratify mental disorders on the basis of biomarkers derived from population-scale neuroimaging and quantitative measures of behaviour from smartphone-based digital phenotyping.
I will build generative models to chart variation in brain organisation across massive neuroimaging samples from more than 40,000 individuals based on ‘brain growth charting’ methodology I have pioneered. This will provide a universal platform to understand shared and distinct mechanisms of mental disorders at the level of the individual, simulate clinical brain states and test putative interventions using synthetic data.
I will develop innovative machine learning tools to: (1) learn latent dynamics of digital phenotyping measures; (2) parse the dynamic interplay between brain systems and the behaviours they underpin; (3) integrate complementary information from distinct data modalities and (4) stratify mental disorders in a way that cuts across diagnostic classifications and accommodates different mechanisms converging on the same symptoms.
These innovations will have far-reaching impact; here, I will showcase them by predicting trajectories of resilience and risk in major depression and bipolar disorder which are a leading cause of worldwide disease burden. This will bring precision medicine within reach for psychiatry allowing early, personalized intervention, preventative treatments and a better understanding of disorder entities.
My vision is to provide a set of principled, next generation analysis tools to stratify mental disorders on the basis of biomarkers derived from population-scale neuroimaging and quantitative measures of behaviour from smartphone-based digital phenotyping.
I will build generative models to chart variation in brain organisation across massive neuroimaging samples from more than 40,000 individuals based on ‘brain growth charting’ methodology I have pioneered. This will provide a universal platform to understand shared and distinct mechanisms of mental disorders at the level of the individual, simulate clinical brain states and test putative interventions using synthetic data.
I will develop innovative machine learning tools to: (1) learn latent dynamics of digital phenotyping measures; (2) parse the dynamic interplay between brain systems and the behaviours they underpin; (3) integrate complementary information from distinct data modalities and (4) stratify mental disorders in a way that cuts across diagnostic classifications and accommodates different mechanisms converging on the same symptoms.
These innovations will have far-reaching impact; here, I will showcase them by predicting trajectories of resilience and risk in major depression and bipolar disorder which are a leading cause of worldwide disease burden. This will bring precision medicine within reach for psychiatry allowing early, personalized intervention, preventative treatments and a better understanding of disorder entities.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101001118 |
| Start date: | 01-06-2021 |
| End date: | 31-05-2026 |
| Total budget - Public funding: | 2 000 000,00 Euro - 2 000 000,00 Euro |
Cordis data
Original description
In many areas of medicine, biomarkers have revolutionised diagnosis and personalised treatment allocation (‘precision medicine’). Psychiatry lags behind: disorders are still diagnosed by symptoms and no biomarkers have been found. However, addressing this is a formidable task because of a lack of analysis tools to understand the complex disruptions of mental disorders at multiple levels –from neurobiology to behaviour– and to tackle their extreme heterogeneity at every level.My vision is to provide a set of principled, next generation analysis tools to stratify mental disorders on the basis of biomarkers derived from population-scale neuroimaging and quantitative measures of behaviour from smartphone-based digital phenotyping.
I will build generative models to chart variation in brain organisation across massive neuroimaging samples from more than 40,000 individuals based on ‘brain growth charting’ methodology I have pioneered. This will provide a universal platform to understand shared and distinct mechanisms of mental disorders at the level of the individual, simulate clinical brain states and test putative interventions using synthetic data.
I will develop innovative machine learning tools to: (1) learn latent dynamics of digital phenotyping measures; (2) parse the dynamic interplay between brain systems and the behaviours they underpin; (3) integrate complementary information from distinct data modalities and (4) stratify mental disorders in a way that cuts across diagnostic classifications and accommodates different mechanisms converging on the same symptoms.
These innovations will have far-reaching impact; here, I will showcase them by predicting trajectories of resilience and risk in major depression and bipolar disorder which are a leading cause of worldwide disease burden. This will bring precision medicine within reach for psychiatry allowing early, personalized intervention, preventative treatments and a better understanding of disorder entities.
Status
SIGNEDCall topic
ERC-2020-COGUpdate Date
27-04-2024
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