Summary
The current nosology of neuropsychiatric disorders allows for a pragmatic approach to treatment choice, regulation and clinical research. However, without a biological rationale for these disorders, drug development has dramatically stagnated in the past decades. In a coordinated effort encompassing academic experts, SMEs, patient and family organizations, regulators, ECNP and EFPIA partners, this project aims to develop a quantitative biological approach to the understanding and classification of neuropsychiatric diseases to accelerate the discovery and development of better treatments for patients. This project will concentrate on Schizophrenia (SZ), Alzheimer’s disease (AD), and Major Depression (MD), as these disorders share part of their symptomatology, in particular social withdrawal and certain cognitive deficits, such as deficits in attention, working memory and sensory processing. By applying innovative technologies (e.g. EEG, cognitive tasks, (f)MRI,
smartphone monitoring, and (epi-)genetics) to deep phenotype a clinical cohort of SZ and AD patients combined with a wider analysis of existing clinical data sets from major European and global disease cohorts that also include MD, we will define a set of quantifiable biological parameters best able to cluster and differentiate SZ, AD, and MD patients that do, or do not, exhibit social withdrawal. First, by mining large European SZ, AD and MD cohort datasets with already available social and cognitive proxy measures, and, second, by obtaining objective measures of social exploration levels (using a novel smartphone application), phenotypic relationships with social and cognitive measures will be further tested. For instance we might predict that socially withdrawn individuals may have lower cognitive functioning and poorer clinical course compared to those who are more socially engaged.
smartphone monitoring, and (epi-)genetics) to deep phenotype a clinical cohort of SZ and AD patients combined with a wider analysis of existing clinical data sets from major European and global disease cohorts that also include MD, we will define a set of quantifiable biological parameters best able to cluster and differentiate SZ, AD, and MD patients that do, or do not, exhibit social withdrawal. First, by mining large European SZ, AD and MD cohort datasets with already available social and cognitive proxy measures, and, second, by obtaining objective measures of social exploration levels (using a novel smartphone application), phenotypic relationships with social and cognitive measures will be further tested. For instance we might predict that socially withdrawn individuals may have lower cognitive functioning and poorer clinical course compared to those who are more socially engaged.
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| Web resources: | https://cordis.europa.eu/project/id/115916 |
| Start date: | 01-04-2016 |
| End date: | 30-09-2019 |
| Total budget - Public funding: | 16 195 875,00 Euro - 8 080 000,00 Euro |
Cordis data
Original description
The current nosology of neuropsychiatric disorders allows for a pragmatic approach to treatment choice, regulation and clinical research. However, without a biological rationale for these disorders, drug development has dramatically stagnated in the past decades. In a coordinated effort encompassing academic experts, SMEs, patient and family organizations, regulators, ECNP and EFPIA partners, this project aims to develop a quantitative biological approach to the understanding and classification of neuropsychiatric diseases to accelerate the discovery and development of better treatments for patients. This project will concentrate on Schizophrenia (SZ), Alzheimer’s disease (AD), and Major Depression (MD), as these disorders share part of their symptomatology, in particular social withdrawal and certain cognitive deficits, such as deficits in attention, working memory and sensory processing. By applying innovative technologies (e.g. EEG, cognitive tasks, (f)MRI,smartphone monitoring, and (epi-)genetics) to deep phenotype a clinical cohort of SZ and AD patients combined with a wider analysis of existing clinical data sets from major European and global disease cohorts that also include MD, we will define a set of quantifiable biological parameters best able to cluster and differentiate SZ, AD, and MD patients that do, or do not, exhibit social withdrawal. First, by mining large European SZ, AD and MD cohort datasets with already available social and cognitive proxy measures, and, second, by obtaining objective measures of social exploration levels (using a novel smartphone application), phenotypic relationships with social and cognitive measures will be further tested. For instance we might predict that socially withdrawn individuals may have lower cognitive functioning and poorer clinical course compared to those who are more socially engaged.
Status
CLOSEDCall topic
IMI2-2015-03-03Update Date
26-10-2022
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Organisations
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| RIJKSUNIVERSITEIT GRONINGEN (RUG) (Coördinator)
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| ACADEMISCH ZIEKENHUIS LEIDEN (LUMC)
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| ALMA MATER STUDIORUM-UNIVERSITA DI BOLOGNA
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| BIOTRIAL
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| BOEHRINGER INGELHEIM INTERNATIONALGMBH
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| CONCENTRIS RESEARCH MANAGEMENT GMBH
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| CONSORCIO CENTRO DE INVESTIGACION BIOMEDICA EN RED M.P. (CIBER)
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| DRUG TARGET ID BV
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| ELI LILLY AND COMPANY LTD
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| ERASMUS UNIVERSITAIR MEDISCH CENTRUM ROTTERDAM
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| EUROPESE FEDERATIE VAN FAMILIEVERENIGINGEN VAN PSYCHIATRISCH ZIEKE PERSONEN IVZW
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| F. HOFFMANN-LA ROCHE AG
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| JANSSEN PHARMACEUTICA NV
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| NOVARTIS PHARMA AG
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| P1VITAL LIMITED