Mental health and neuroimaging researcher
Principal Investigator of the
IMARD group at IDIBAPS Barcelona 
Catedràtic d'Universitat (Full Professor) at the
University of Barcelona
Honorary Research Associate at the
IoPPN, King's College London
Dr. Joaquim Radua
Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)
Barcelona
Department of Medicine
University of Barcelona (UB)
Can brain imaging help improve the treatment of mood and anxiety disorders?
My research focuses on this question. It sits at the intersection of neuroscience, psychiatry, and data science, aiming to personalize mental health care to improve prognosis, reduce relapse risk, and enhance quality of life. To advance this aim, I conduct original neuroimaging studies, lead or participate in international collaborations (especially within the ENIGMA consortium ), and carry out meta-analyses. Over the years, I’ve synthesized evidence for neural mechanisms in mental health conditions and core processes such as fear learning; developed widely adopted methods like SDM ; adapted machine learning approaches for neuroimaging; and built predictive models with practical applications in psychosis and mania.
With a background in both psychiatry and statistics, I conducted a PhD in neuroimaging biostatistics at the IoPPN-King's College London , followed by a postdoctoral research stay at Yale. In 2015, I established my research group at the FIDMAG Research Foundation . And since 2018, I’ve been based at IDIBAPS —the research center of Hospital Clínic Barcelona and the University of Barcelona—where I lead the Imaging of Mood, Anxiety and Related Disorders (IMARD) and serve as a full professor at the University of Barcelona .
With a strong translational focus, my work bridges research and clinical practice to support more personalized approaches in psychiatry. It has been featured in media outlets including NBC News, and I’ve been included in the Highly Cited Researchers List every year since 2017.
Contact Dr. Radua at radua at recerca dot clinic dot cat.
Please feel free to download and use the following software. References can be found below in "Publications".
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a new meta-analytic method that allows an unbiased inclusion of studies with non-statistically significant unreported effects (NSUEs). Note that some published studies may not report any statistics (e.g., t- or p-values) from those findings that did not reach statistical significance. It can be shown that excluding these studies leads to strong positive biases, whereas considering these effects as null leads to negative biases. MetaNSUE uses maximum likelihood estimation and multiple imputation techniques to unbiasedly include these studies.:
a tool designed to facilitate the realization of umbrella reviews with evidence stratification. It automatically performs all calculations required to conduct umbrella reviews (including but not limited to meta-analyses), stratifies evidence according to various criteria, and generates visual representations of the results.:
The effects of the site may severely bias the accuracy of a multisite machine-learning model, even if the analysts removed them when fitting the model in the 'training set' and applying the model in the 'test set'. This simple R package estimates the accuracy of a multisite machine-learning model unbiasedly.:
a new meta-analytic method for voxel-based neuroimaging modalities such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), voxel-based morphometry (VBM) or diffusion tensor imaging (DTI), as well as its application for investigating a range of neuropsychiatric disorders. It also includes some utilities to perform simple standard meta-analyses (e.g., for global grey matter volume) and to conduct simple arithmetic or statistical operations with 3D images.:
function to estimate positron emission tomography (PET) neuroreceptor occupancies with different fitting methods.......................................................................................................................................................................................................................................................................................................................................................................................................