Multimodal fusion in neuroimaging combines data from multiple imaging modalities to
overcome the fundamental limitations of individual modalities. Neuroimaging fusion can …

Importance Schizophrenia is a common, severe mental illness that most clinicians will
encounter regularly during their practice. This report provides an overview of the clinical …

Observational studies have reported the correlations between brain imaging-derived
phenotypes (IDPs) and psychiatric disorders; however, whether the relationships are causal …

In this work, we expand the normative model repository introduced in Rutherford et al.,
2022a to include normative models charting lifespan trajectories of structural surface area …

The neuroimaging community has witnessed a paradigm shift in biomarker discovery from
using traditional univariate brain mapping approaches to multivariate predictive models …

Neuroimaging-based single subject prediction of brain disorders has gained increasing
attention in recent years. Using a variety of neuroimaging modalities such as structural …

Collecting comprehensive data sets of the same subject has become a standard in
neuroscience research and uncovering multivariate relationships among collected data sets …

Aims Major depression disorder (MDD) is the single greatest cause of disability and
morbidity, and affects about 10% of the population worldwide. Currently, there are no …

It is becoming increasingly clear that combining multimodal brain imaging data provides
more information for individual subjects by exploiting the rich multimodal information that …

An interaction between external stressors and intrinsic vulnerability is one of the longest
standing pathoaetiological explanations for schizophrenia. However, novel lines of evidence …