Healthcare traditionally relies on single-modality approaches, which limit the information
available for medical decisions. However, advancements in technology and the availability …

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

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 …

Biomedical image segmentation is a crucial part of both scientific research and clinical care.
With enough labelled data, deep learning models can be trained to accurately automate …

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early
adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior …

Collaborative networks and data sharing initiatives are broadening the opportunities for the
advancement of science. These initiatives offer greater transparency in science, with the …

It has been revealed that abnormal voxel-mirrored homotopic connectivity (VMHC) is
present in patients with schizophrenia, yet there are inconsistencies in the relevant findings …

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional,
and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of …

Cognitive impairment is a feature of many psychiatric diseases, including schizophrenia.
Here we aim to identify multimodal biomarkers for quantifying and predicting cognitive …

Neuroimaging studies have shown that schizophrenia is associated with disruption of
resting-state local functional connectivity. However, these findings vary considerably, which …