Description
Psychedelics have emerged as promising candidates for treating psychiatric disorders, particularly treatment-resistant depression. Recent clinical trials have shifted focus toward identifying biomarkers that elucidate the neurobiology of these substances and their sustained positive effects on mood. Given the rapid onset of psychedelic action, identifying baseline biomarkers is critical for predicting therapeutic success and personalizing patient care.
This project utilizes existing and prospective EEG and fMRI datasets from healthy volunteers and patients with depressive disorders. The objective is to evaluate established biomarkers while identifying novel candidates that reveal the mechanisms behind rapid, persisting antidepressant responses. By integrating individual biomarker analysis with modern deep learning, clustering, and embedding algorithms, the study aims to identify patient phenotypes most likely to respond to treatment.
Methodological Focus:
- Computational Modeling: Implementing neural mass and neural network modeling.
- Biophysical Analysis: Utilizing EEG/MEG source reconstruction and advanced pre-processing pipelines.
- Phenomenological Alignment: Mapping fMRI/EEG data to subjective experiences to isolate cognitive biomarkers.
The expected outcomes include the identification of biomarkers with high predictive validity and the establishment of standardized guidelines for data analysis and Python and Matlab toolboxes ultimately optimizing the clinical application of psychedelic-assisted therapies.
Location
National Institute of Mental Health (Klecany, Czech Republic)
Supervisors
Dr. Tomáš Páleníček, MD, PhD, Head of the Psychedelic Research Center at NIMH, is a psychiatrist and neuroscientist specializing in EEG and clinical trial leadership. Having served as Principal Investigator for multiple psychedelic studies, he focuses on the translational validation of preclinical and clinical data to advance neuropsychiatric treatments.
Dr. Vlastimil Koudelka, Ing., PhD, is a bioengineer and Head of the Data Analysis Team at NIMH. He specializes in advanced EEG methodology and co-supervises complex simultaneous EEG/fMRI analyses. Leading a multidisciplinary team, Dr. Koudelka bridges the gap between clinical and preclinical research through rigorous computational modelling and data integration.
Secondments
Institute: Methods and Development Group Brain Networks,Max Planck Institute for Human Cognitive and Brain Sciences (Leipzig, Germany)
Supervisors: Professor Dr. Habil. Thomas R. Knösche
Purpose: To implement methods for data analyses: Computational modelling of neural networks, neural mass modelling; Biophysical modelling of EEG/MEG, in particular source reconstruction.
Institute: Faculty of Psychology and Neuroscience, Maastricht University (Maastricht, The Netherlands)
Supervisors: Prof. Dr. Jan Ramaekers (J.G.)
Purpose: To analyse EEG/fMRI data from psychedelic studies and align with phenomenology; identification of cognitive biomarkers.
General eligibility criteria
- The position is open to candidates of any nationality (European and non-European) who fulfil the requirements set for the Doctoral Candidates (DCs) funded by Marie Skłodowska-Curie actions.
- Applicants must hold a Master’s degree in a relevant academic field, allowing enrolment in a PhD program at the hiring beneficiary.
- Applicants must not previously have been awarded a PhD degree.
- Applicants must not have resided or carried out their main activity (work, studies, etc.) in the country in which the DC project for which they are applying is based for more than 12 months in the 3 years prior to recruitment. This excludes short stays such as holidays, compulsory national service or time spent as part of a procedure for obtaining refugee status under the Geneva Convention.
- Applicants must be willing to undertake secondments at another institute of the network during the DC project, including at institutes in other countries.
- Applicants must be able to demonstrate their ability to understand and express themselves in both written and spoken English at a level that is sufficiently high to fully benefit from the network training (C1/C2 level).
- Applicants are expected to be motivated to work in the field of psychedelic therapy.
- Applicants are expected to work independently, well-structured and collaboratively in a multidisciplinary consortium.
Additional eligibility criteria
- A Master´s degree in neuroinformatics, bioengineering, computational neuroscience or a comparable education.
- Experience with MATLAB, Python programming.
- Experience with EEG and/or fMRI data collection and analysis.
- Interest in psychedelic and neuroimaging research.
- Basic understanding and experience in using statistical analyses in neuroimaging methods.
- Interdisciplinary collaboration: Good communication skills with an openness to learn to work together within large, diverse research teams.
- Adaptive thinking: An innovative, open-minded approach combined with the high flexibility required for dynamic research environments.