We are pleased to announce an opportunity for a tax-free fully funded PhD studentship - Multimodal AI-based Diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) - at Plymouth University, UK. This exciting project aims to transform ADHD diagnosis by developing a multimodal Artificial Intelligence (AI) framework that addresses the significant limitations of current, subjective diagnostic practices. Although AI is emerging in ADHD research, its integration into standard clinical practices remains minimal. This project seeks to enhance diagnostic accuracy through a sophisticated integration of AI-driven insights that complement existing approaches. Some basic questions (among others) that this project will try to explore are: How can machine learning and deep learning models be tailored to various data types like neuroimaging to uncover distinct ADHD diagnostic patterns? What methods can be used to analyse fMRI data to delineate active brain regions and their connections, and how can these findings be linked to ADHD behaviours and cognitive functions? How can we refine AI models to handle high data dimensionality and heterogeneity and enhance decision-making transparency in clinical settings using Explainable AI (XAI) methods? What are the best practices to assess the robustness of AI models against the variability in ADHD diagnostic data? This ambitious project will allow the student to engage in a groundbreaking study at the intersection of AI, neuropsychiatry, and healthcare and gain experience in a highly collaborative environment supported by a strong supervisory team and international experts. The research leverages our team's extensive background in neuro-developmental disorders like Autism Spectrum Disorder (ASD), where we recently discussed important brain regions related to ASD diagnosis. This PhD opportunity offers a deep dive not only into the diagnosis of ADHD using explainable AI but also into other related co-occurring disorders like ASD, providing a holistic perspective on patient care and intervention strategies across the spectrum of these interrelated conditions.

Vulnerability to distraction varies across the general population and significantly affects one’s capacity to stay focused on and successfully complete the task at hand, whether at school, on the road, or at work. In this talk, I will begin by discussing how distractibility is typically assessed in the literature and introduce our innovative ERP approach to measuring it. Since distractibility is a cardinal symptom of ADHD, I will introduce its most widely used paper-and-pencil screening tool for the general population as external validation. Following that, I will present the Load Theory of Attention and explain how we used perceptual load to test the reliability of our neural marker of distractibility. Finally, I will highlight potential future applications of this marker in clinical and educational settings.

Genome-wide association studies (GWAS) have identified multiple disease-associated genetic variations across different psychiatric disorders raising the question of how these genetic variants relate to the corresponding pharmacological treatments. In this talk, I will outline our work investigating whether functional information from a range of open bioinformatics datasets such as protein interaction network (PPI), brain eQTL, and gene expression pattern across the brain can uncover the relationship between GWAS-identified genetic variation and the genes targeted by current drugs for psychiatric disorders. Focusing on four psychiatric disorders---ADHD, bipolar disorder, schizophrenia, and major depressive disorder---we assess relationships between the gene targets of drug treatments and GWAS hits and show that while incorporating information derived from functional bioinformatics data, such as the PPI network and spatial gene expression, can reveal links for bipolar disorder, the overall correspondence between treatment targets and GWAS-implicated genes in psychiatric disorders rarely exceeds null expectations. This relatively low degree of correspondence across modalities suggests that the genetic mechanisms driving the risk for psychiatric disorders may be distinct from the pathophysiological mechanisms used for targeting symptom manifestations through pharmacological treatments and that novel approaches for understanding and treating psychiatric disorders may be required.

Mental Health problems among youth constitutes an area of significant social, educational, clinical, policy and public health concern. Understanding processes and mechanisms that underlie the development of mental health problems during childhood and adolescence requires theoretical and methodological integration across multiple scientific domains, including developmental science, neuroscience, genetics, education and prevention science. The primary focus of this presentation is to examine the relative role of genetic and family environmental influences on children’s emotional and behavioural development. Specifically, a complementary array of genetically sensitive and longitudinal research designs will be employed to examine the role of early environmental adversity (e.g. inter-parental conflict, negative parenting practices) relative to inherited factors in accounting for individual differences in children’s symptoms of psychopathology (e.g. depression, aggression, ADHD ). Examples of recent applications of this research to the development of evidence-based intervention programmes aimed at reducing psychopathology in the context of high-risk family settings will also be presented.

The human brain is a heavily interconnected structure giving rise to complex functions. While brain functionality is mostly revealed during wakefulness, the sleeping brain might offer another view into physiological and pathological brain connectivity. Furthermore, there is a large body of evidence supporting that sleep mediates plastic changes in brain connectivity. Although brain plasticity depends on environmental input which is provided in the waking state, disconnection during sleep might be necessary for integrating new into existing information and at the same time restoring brain efficiency. In this talk, I will present structural, molecular, and electrophysiological markers of brain connectivity and sleep-dependent restoration that we have evaluated using Magnetic Resonance Imaging and electroencephalography in a healthy population. In a second step, I will show how we translated the gained findings into two clinical populations in which alterations in brain connectivity have been described, the neuropsychiatric disorder attention-deficit/hyperactivity disorder (ADHD) and the neurologic disorder thalamic ischemic stroke.