Our Research

Sex matters: Identifying the neurodevelopmental origins of sex differences in depression

In this SFI-funded Frontiers for the Future project, PI Clare Kelly is working with Prof Andrew Harkin to examine when, where, and how chronic stress derails typical brain development.

Depression is the greatest public health concern of our time; it is the leading cause of illness and disability, worldwide. Depression is rooted in stress-linked disruption of brain function, and affects twice as many women as men. This disparity begins in adolescence and continues throughout the reproductive life cycle, telling us that both sex and development matter for our understanding of depression. Yet, most preclinical research on stress-linked psychopathology is conducted in adult male rodents. This reduces the translational validity of preclinical work, impedes progress in diagnostics and treatments, and disadvantages women. This project will transform our understanding of why women are more vulnerable to stress-linked illness by examining sex differences in how stress disrupts brain development in a rodent model. In a series of longitudinal studies spanning pre-puberty to adulthood, we will delineate the impact of sex on when and where chronic psychosocial stress alters typical brain development. To elucidate how this pathological process is altered by treatment, we will examine the effects of (1) acute ketamine administration and (2) restoration of a normal psychosocial environment (i.e., stress cessation) on brain development and behaviour. By using non-invasive fMRI methods, the study will provide a direct translational bridge to humans.

We are currently recruiting a PhD student and Postdoctoral Fellow to work on this exciting project – see our Join the Lab page, and come join us in Dublin!

Improving neuroimaging-based prediction of neurodevelopmental disorders (IMP-ND)

By providing a safe, non-invasive, accurate, and reliable window on brain structure and function across the entire lifespan, and Magnetic Resonance Imaging (MRI) has transformed our understanding of the biological bases of human behaviour. The future of neuroimaging is the identification of brain features, biomarkers, which can predict health-related outcomes, particularly for neuropsychiatric and neurological disease. To date, however, no imaging-based biomarker has attained clinical viability. Key limiting factors include small and heterogeneous samples, diagnostic variations and comorbidity, and the influence of an array of confounds, including variation across scanners/sites, age, sex/gender, IQ, and participant motion.

To overcome these barriers to progress, methodological advances are required. The aim of this IRC-funded project, which forms the focus of Mélanie Garcia’s PhD, is to improve the neuroimaging-based prediction of neurodevelopmental disorders by examining, isolating, and harmonising the influence of these confounding factors on deep learning-based prediction of neurodevelopmental disorders. This will be achieved through a series of studies that will use deep learning models to predict a neurodevelopmental diagnosis using structural and functional MRI data collected from typically developing and neurodevelopmental populations (particularly Autism Spectrum Disorder – ASD), while systematically varying, quantifying, and adjusting the effect of confounds on the predictions obtained. We will leverage pre-existing, publicly available neuroimaging and phenotypic data, such as the Autism Brain Imaging Data Exchange (ABIDE), the Human Connectome Project (HCP), the Child Mind Institute Health Brain Network and the Adolescent Brain and Cognitive Development (ABCD) study.

Improving the sensitivity of functional connectomics analyses for neurodevelopmental and mental health conditions

Functional connectomics analyses of fMRI data offer a highly robust, valid, and translational approach to mapping brain functional circuitry and to understanding typical and atypical brain development. Recent work has demonstrated that passive or naturalistic viewing paradigms (wherein participants watch movies while being scanned) offer some advantages over “resting state” fMRI by increasing the robustness and reliability of functional connectomics measures. Promisingly, naturalistic paradigms also boost our ability to detect individual differences in brain functional organisation, a key requisite for neuroimaging-based biomarkers that have diagnostic and prognostic validity. This project, which is funded by the TCD Provost’s PhD Awards and which the focus of Jivesh Ramduny’s PhD, will build on these advances to investigate new methods for analysing both resting state and naturalistic fMRI data, with the goal of enhancing their sensitivity to phenotypic differences of clinical relevance (e.g., age, diagnosis, symptom severity). The project will utilise data collected on-site in Trinity College Institute of Neuroscience from two neurodevelopmental populations (Autism Spectrum Disorder, adolescent depression) as well as publicly available data (e.g., ABCD project; Health Brain Network).