In new research from the University of Chicago Pritzker School of Molecular Engineering, Argonne National Laboratory and Purdue University, the AI-driven robotic lab Polybot designed and built new polymers that hit precise, targeted shades of orange and green – a task that takes researchers decades of experience to learn – in just 72 hours.

Neuroimaging analysis in brain disorders faces a persistent challenge: brain signals are complex and high-dimensional, while high-quality labeled datasets remain limited. This review article systematically examines how self-supervised learning can help address that gap by learning meaningful representations directly from unlabeled neuroimaging data. It covers major methodological families, including contrastive, generative, and hybrid generative-contrastive approaches, and discusses their applications in functional MRI, EEG, and multimodal brain network analysis.

The review argues that self-supervised learning offers more than annotation efficiency. It may enable more transferable and clinically useful representations for disease screening, diagnosis, and prognosis across heterogeneous datasets and disorders. At the same time, interpretability, data heterogeneity, missing modalities, and clinical validation remain major barriers. Future work will likely focus on stronger multimodal fusion, better cross-site generalization, and more clinically adaptable model design.

Analysing longitudinal data from 2,121 adolescents aged 13–15, researchers found that problematic social media use predicted increases in depressive symptoms one year later, particularly among younger teenagers and girls. Published in Scientific Reports, the study highlights how loss of control over social media use—rather than time spent online—may be a key mental health risk factor during early adolescence.