Ignite Creation Date:
2024-10-26 @ 3:35 PM
Last Modification Date:
2024-10-26 @ 3:35 PM
Study NCT ID:
NCT06515522
Status:
NOT_YET_RECRUITING
Last Update Posted:
None
First Post:
2024-07-17
Brief Title:
A Biological Signature for the Early Differential Diagnosis of Psychosis
Sponsor:
None
Organization:
None
Study Overview
Official Title:
A Biological Signature for the Early Differential Diagnosis of Psychosis Unveiling the Differences Between Mood Disorders and Schizophrenia With Multimodal Machine Learning Techniques
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Schizophrenia SZ and mood disorders BD MDD are among the most disabling disorders worldwide with a relevant social functional and economic burden Although they are identified as distinct disorders the potential overlapping symptomatology poses important challenges for the differential diagnosis A consistent literature affirms that brain structure and function reflect an intermediate phenotype of an underlying genetic vulnerability for the disorders shaped by interaction with environmental experiences Such experiences include early life stress and trauma which seem to characterize psychiatric patients and have been associated with brain abnormalities Further early life experiences have been associated with inflammation in a subpopulation of psychiatric patients However imaging inflammatory and genetic group-level differences albeit consistent do not impact clinical practice since they have not been translated into individual prediction To address these issues a rapidly growing body of scientific literature implemented computational techniques such as machine learning ML In this project we will develop cutting-edge ML algorithms to predict the differential diagnosis between mood disorders and SZ from genetic neuroimaging inflammatory and environmental data in a unique cohort of 1850 patients and 1000 healthy controls recruited in 4 different centers in Italy The project will address three different aims in aim 1 we will develop algorithms for the differential diagnosis between SZ and MD combining multimodal neuroimaging and genetic data in aim 2 we will predict the differential diagnosis between SZ and MD from immuno-inflammatory and environmental data finally with aim three we will exploit an animal model to identify the underlying mechanisms of brain alterations associated with exposure to early life stress Machine learning analyses will include algorithms for data harmonization and feature reduction as well as for generating normative models Finally different classifying models will be compared considering the specific features to achieve the best performanceThe definition of reliable and objective biomarkers combined with cutting-edge computational methodology could help clinicians in providing more precise diagnoses and early interventions also considering dimensional constructs factors influencing outcomes such as affective vs non-affective psychosis and breadth of exposure to traumatic events
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None