Population Neuroscience 3.0: Bridging large-N and small-N paradigms in brain-wide association studies

Brain-wide association studies (BWAS) leveraging large datasets, have led to divergent and sometimes opposing views on research practices and priorities. The debates span multiple dimensions, including large-N vs. small-N sampling, cross-sectional vs. longitudinal, observational vs. interventional/causal, population generalizability vs. individual specificity, data-driven vs. hypothesis-driven, prediction-oriented vs. explanation-oriented, and molecular/cellular vs. systems-level. Taken together, these tensions are not only about methodology, but also about how large-N population studies and small-N, mechanism-focused studies should relate to one another. Shifts along these axes have sharpened disagreements and added to fragmentation in an already heterogeneous field of cognitive neuroscience. Here, we sketch an integrative response grounded in population neuroscience, organized around a closed-loop ``design--analysis--interpretation'' research cycle that aims to build consensus while bridging these divides. Our central claim is that population neuroscience offers a distinctive population-level vantage point for identifying general patterns, characterizing inter-individual variabilities, and benchmarking intra-individual changes, thereby providing reference frameworks through which small-N, mechanism-focused studies can co-evolve with large-N population studies. Population neuroscience is not simply about providing larger N for BWAS; more fundamentally, it is about accumulating cross-scale priors and shared infrastructures for design, analysis, and interpretation across human neuroscience studies. In this sense, we outline a ``third-generation'' agenda for population neuroscience that shifts emphasis from amassing isolated associations to building reference frameworks for cumulative mechanistic and translational research.