Artificial intelligence (AI) is increasingly employed in health care fields such as oncology,
radiology, and dermatology. However, the use of AI in mental health care and …

Automation, including machine learning technologies, are becoming increasingly crucial in
agriculture to increase productivity. Machine vision is one of the most popular parts of …

It is typically assumed that large networks of neurons exhibit a large repertoire of nonlinear
behaviours. Here we challenge this assumption by leveraging mathematical models derived …

How individual differences in brain network organization track behavioral variability is a
fundamental question in systems neuroscience. Recent work suggests that resting-state and …

Deep learning has huge potential for accurate disease prediction with neuroimaging data,
but the prediction performance is often limited by training-dataset size and computing …

Algorithmic biases that favor majority populations pose a key challenge to the application of
machine learning for precision medicine. Here, we assessed such bias in prediction models …

Recently, deep learning has unlocked unprecedented success in various domains,
especially using images, text, and speech. However, deep learning is only beneficial if the …

Resting-state functional magnetic resonance imaging (rs-fMRI) allows estimation of
individual-specific cortical parcellations. We have previously developed a multi-session …

Recent critical commentaries unfavorably compare deep learning (DL) with standard
machine learning (SML) approaches for brain imaging data analysis. However, their …

A fundamental goal across the neurosciences is the characterization of relationships linking
brain anatomy, functioning, and behavior. Although various MRI modalities have been …