Richard Dinga

Richard Dinga, PhD is a Researcher at Tilburg University's Tilburg School of Humanities and Digital Sciences, specializing in the Computational Cognitive Science department. His work focuses on applying advanced computational methods to neuroimaging data to understand brain structure and function in both healthy and clinical populations.

Dr. Dinga's primary research interests lie at the intersection of computational neuroscience and clinical psychiatry. His work prominently features normative modeling approaches to analyze neuroimaging data, with particular emphasis on cortical thickness, brain development, and aging patterns. He has made significant contributions to understanding first-episode psychosis through advanced neuroimaging techniques and has developed methodologies to address site variation in multi-center neuroimaging studies.

Analysis of Dr. Dinga's publication record reveals a strong focus on normative modeling approaches applied to neuroimaging data, with particular emphasis on psychosis research and brain development/aging trajectories. His work consistently integrates hierarchical Bayesian methods with neuroimaging data to address clinical questions, particularly in the domain of psychosis. The publications demonstrate an evolving trajectory from methodological development (addressing site variation in neuroimaging) to clinical applications (particularly in first-episode psychosis).

Dr. Dinga's research has garnered significant attention in the scientific community, with several publications accumulating notable citation counts. His 2022 paper in Elife on charting brain growth and aging has received over 100 citations, indicating substantial impact in the field. His work has been picked up by multiple news outlets and has generated discussion across various academic social media platforms.

As a researcher in Computational Cognitive Science, Dr. Dinga collaborates extensively with international research teams across multiple institutions. His work involves large-scale neuroimaging datasets and requires sophisticated computational approaches to extract meaningful patterns related to brain structure and function. While specific advising relationships aren't detailed in the available information, his position suggests involvement in guiding junior researchers within his field of expertise.