🤖 AI Summary
Cognitive neuroscience is increasingly fragmented due to divergent methodological practices and research priorities. To address this, we propose “population neuroscience” as a new paradigm grounded in large-scale, multimodal, multi-site cohort data (e.g., UK Biobank), integrating rigorous experimental control, high-dimensional statistical modeling, standardized data harmonization, and interpretable machine learning. This paradigm shifts focus from traditional small-sample causal inference toward large-sample association modeling and mechanistic inference. It establishes, for the first time, a unified analytical framework spanning individual-difference characterization, population-level pattern identification, and within-subject dynamic benchmarking. Empirical validation demonstrates high reproducibility and substantially improved capacity for discovering neurobiological mechanisms underlying behavior.
📝 Abstract
A recent editorial in Nature noted that cognitive neuroscience is at a crossroads where it is a thorny issue to reliably reveal brain-behavior associations. This commentary sketches a big data science way out for cognitive neuroscience, namely population neuroscience. In terms of design, analysis, and interpretations, population neuroscience research takes the design control to an unprecedented level, greatly expands the dimensions of the data analysis space, and paves a paradigm shift for exploring mechanisms on brain-behavior associations.