Integrative Omics and Neuroimaging of Treatment Resistance in Psychiatric Disorders: An AI Approach

Research project description

PhD will investigate treatment response in MDD, schizophrenia, and other mental disorders which do not respond to available strategies, focusing on novel therapies like neuromodulation, remediation, and psychotherapy. 

Treatment-resistant neuropsychiatric disorders demand innovative treatment strategies. Our 3-year PhD project aims to decode treatment response in MDD, schizophrenia, and other mental disorders which do not respond to available strategies, focusing on novel therapies like neuromodulation, remediation, and psychotherapy. Using advanced neuroimaging and biochemical assessments, we’ll delve into brain and peripheral system changes linked to newly developed treatment outcomes. 

• Objective 1: Employ MRI to capture neural circuitry changes in MDD patients undergoing various innovative and promising treatments. Identify distinct neural signatures associated with positive treatment responses. 
• Objective 2: Combine profiles of biochemical markers indicative of treatment efficacy, including neurotrophic factors, inflammatory markers, and metabolic indicators, to holistically understand treatment response. From the achievement of objectives 1 and 2 above, we will have available a database putting together biological information about a patient (omics and neuroimaging), as well as treatment response and other clinical variables. Such database will serve as basis for the next objective. 
• Objective 3: To use AI techniques to identify neuroimaging patterns and predictive omics, enhancing our understanding of treatment mechanisms and facilitating personalized interventions. Particularly, Machine Learning techniques support the extraction of patterns that can predict the efficacy of a treatment according to the characteristics of a particular patient. 

Real-world data, including electronic health records and patient-reported outcomes, will also be incorporated to achieve a comprehensive model predicting treatment outcomes based on individual patient characteristics. 

In conclusion, this transformative PhD program integrates neuroimaging, omics profiling, and AI to decode treatment response in psychiatric disorders. Our transdisciplinary approach holds immense promise for revolutionizing mental health care, advancing precision psychiatry, and improving patient outcomes.

Academic background / Skills

Candidates must hold a degree that allows admission to the official doctoral programme at UAB.  

Additional requirements for a stronger application are: 

• The ideal candidate for this PhD program should possess some foundation in neuroscience, psychology, or a related field.  
• They must comply with the academic requirements to access the PhD in Psychiatry, which, among others, requires a Master degree and a good level of speaking and writing in English.  
• Interest in neuroimaging techniques (MRI) and biochemical assays is advisable, while knowledge and training will be offered.  
• Familiarity with statistical methods and/or programming languages (e.g., Python, R, STATA, and/or SPSS) is preferable.  
• The candidate should demonstrate a keen interest in psychiatric disorders, treatment mechanisms, and innovative therapeutic modalities. 
• Strong communication and collaboration skills are essential, as the project involves transdisciplinary teamwork.  
• Prior experience with AI analytics is advantageous but not mandatory, as comprehensive training will be provided.  
• The candidate should exhibit a commitment to advancing mental health research and a capacity for independent, creative thinking within a collaborative research environment. 

Research group/s description

The Sant Pau Mental Health Group, part of the Institut de Recerca Sant Pau’s Neurological Diseases, Neuroscience & Mental Health division, is resolutely dedicated to the pursuit of innovative therapeutic modalities, underpinned by a comprehensive understanding of etiopathogenesis, derived from their accrued knowledge. Main scientific challenges: research that reduces the healthcare, social and personal costs of mental illness by exploring areas such as epidemiology, etiopathogenesis, physiopathology, prevention and treatment; that delve into innovative mental health therapies; and to improve quality of life of patients with mental disorders.  

The AI Research Institute (IIIA) research theme on AI and healthcare aims at applying some of the IIIA techniques to the field of healthcare. Specifically, it is focused on the design of novel algorithms to provide solutions able to incorporate advanced Descriptive, Diagnostic, Predictive, and Prescriptive capabilities to Clinical Decision Support Systems (CDSS).