Published 22 June 2023 by Ravichandran Rajkumar

Young Scientists at #LINO23: Ravichandran Rajkumar – Precision Psychiatry

Ravichandran is working on multimodal neuroimaging at University Hospital Aachen and Forschungszentrum Jülich. Photo/Credit: metamorworks/iStockphoto.

This Sunday, 25 June 2023, Ravichandran Rajkumar will finally attend the Opening Day of a Lindau Meeting. After being selected in 2020, he had to wait for three years to participate on site in Lindau in the 72nd Lindau Nobel Laureate Meeting dedicated to Physiology/Medicine as Lindau Alumnus of the 70th Meeting conducted online. He is looking forward to scientific exchange about his research topic Precision Psychiatry, especially with the other participants with whom he worked on a respective Sciathon project. His presentation in occasion of the Next Gen Science Sessions on Wednesday, 28 June, will be a good chance to get an insight into his work. Learn more about his career in this update of his first blog post from 2021.

Precision psychiatry is an emerging field within mental health that aims to tailor diagnosis, treatment, and care to the individual characteristics of each patient. It represents a shift from a one-size-fits-all approach to a more targeted and precise understanding of psychiatric disorders at individual level. By integrating multi-omics, neuroscience, and other cutting-edge technologies, precision psychiatry seeks to identify the specific biological, environmental, and psychosocial factors that contribute to mental illnesses and to develop evidence based personalised interventions.

Need for Precision Psychiatry

3D image of neural cells. Photo/Credit: Evgenii Kovalev/iStockphoto

The interaction between different levels, from genetics to structural changes, plays a crucial role in the development and manifestation of mental health disorders. It is important to note that the interactions between these levels are highly complex and interconnected. Genetic variations can influence molecular and cellular processes, which, in turn, affect neural circuitry and brain structure. Conversely, alterations in brain structure can impact molecular and cellular functioning. This bidirectional relationship highlights the intricate nature of mental disorders and the need for a multidimensional approach, such as precision psychiatry, to fully understand and address these conditions.

Challenges in Precision Psychiatry

Precision psychiatry relies on diverse and complex data from multiple sources. Photo/Credit: ArtemisDiana/iStockphoto

Precision psychiatry faces several challenges that need to be addressed for its successful implementation. In my opinion there are two key challenges. First, sample size and generalisability: Obtaining large and representative datasets for every mental health disorder can be challenging. Many psychiatric disorders are relatively rare, and collecting data from diverse populations with sufficient sample sizes is crucial for building accurate models. Limited sample sizes and selection biases may affect the generalisability of findings and hinder the development of universally applicable precision approaches. Secondly, data complexity and integration have to be faced: Precision psychiatry relies on diverse and complex data from multiple sources, including multi-omics, electrophysiology, neuroimaging, clinical records, and cognitive and behavioural data. Integrating and analysing these heterogeneous datasets pose technical and computational challenges, requiring robust methodologies for data management, quality control, and interoperability.

Besides the above-mentioned key challenges, other challenges related to ethical and privacy concerns in accessing sensitive and personal data, translational gap between research and clinical practice, cost and accessibility of advanced cutting edge medical technologies and computational tools also need to be addressed.

Role of Open-Source Datasets in Addressing Precision Psychiatry

Open-source data can play a significant role in implementing precision psychiatry by facilitating research, collaboration, and the development of robust models and tools. Precision psychiatry requires large and diverse datasets to capture the heterogeneity of mental disorders. Available Open-source data platforms, provide access to extensive datasets encompassing genetic, clinical, imaging, and phenotypic information. These datasets can aid in identifying patterns, detect rare variants, and validate hypotheses on a much larger scale than individual research projects with low sample sizes.

My Contribution to Precision Psychiatry

My PhD work focused on projects that combined multiple imaging modalities (positron emission tomography (PET), magnetic resonance imaging (MRI) and electroencephalography (EEG)) to further our understanding of the human brain, both in terms of psychiatry and neuroscience in general. It is anticipated that such studies will lay the foundation for the development of ‘multimodal fingerprints’, which will be applied as biomarkers for diagnosis, disease staging, treatment response and monitoring of neuropsychiatric disorders.

Currently, the principal focus of my research includes utilising ultra-high field MR imaging technologies (layer fMRI, MRS, structural MRI) to aid in diagnosis and monitor/predict the treatment responses in the psychiatric patients at the individual level / precision psychiatry using data-driven trans-diagnostic approach. In addition, via recently funded START grant from University Hospital RWTH Aachen, I am investigating the translational possibilities of research findings from multimodal neuroimaging studies to the psychiatry clinic via simultaneous functional near infrared spectroscopy (fNIRS)/EEG measurements at patient bed side. Also, I am investigating on a project related to Gut-Brain axis challenge using neuroimaging and metabolomics.

Video Content #LINO23

Available Open-Source Data Platforms

Ravichandran Rajkumar

Ravichandran Rajkumar is pursuing his lifelong interest in Physics, Mathematics and Biology, he chose Biomedical Engineering for his bachelor and specialised in medical imaging technologies throughout his master’s studies and research projects. In 2020, he completed his PhD in Aachen where he is working on multimodal neuroimaging at University Hospital Aachen and Forschungszentrum Jülich.