This Special Issue of Data Science in Science invites both original and survey manuscripts that bring together novel statistical, data science, machine learning, and AI models, methods, and applications in the brain sciences. We evaluate submissions on the basis of scientific rigor, technical depth, and ethical standards, regardless of perceived novelty. We also value high-quality papers with interesting and relevant research hypotheses and/or secondary analysis of biobank data (e.g., Adolescent Brain Cognitive Development Study, Human Connectome Project, UK Biobank). We welcome original, unpublished, and innovative submissions including, but not limited to the following areas of research:
- Advances in spatio-temporal methods, including their applications to local field potentials, electroencephalogram, functional near-infrared spectroscopy, imaging genetics, and structural and functional magnetic resonance imaging.
- Applications of deep learning, AI, and generative models in the brain sciences.
- Uncertainty quantification and procedures for large-scale statistical inference for spatio-temporal models applied to brain data.
- Graphs, networks, and connectivity for multivariate spatio-temporal brain data.
- Extracting novel biomarkers for behavioral, neurological, and psychiatric outcomes.
- Mathematical foundations, including advances in optimization and Bayesian methodology, with applications to data from the brain sciences.
- Causal inference using population-scale neuroimaging studies.
- Novel adaptations and applications of theories and methods stemming from the broader statistics literature to the brain sciences.
