Balancing scale and depth: Publishing models for large-scale and small-N research in systems neuroscience

The future of scientific publishing must account for methodological pluralism. In systems neuroscience, large-scale population studies offer unprecedented statistical power, generalizability, and opportunities for data-driven discovery. At the same time, small-N, investigator-led approaches such as dense sampling and precision brain mapping generate mechanistic insight and methodological innovation. These approaches are not competitive but complementary and represent two ends of a continuum. On one end, population datasets reveal broad patterns and heterogeneity, while on the other, small-N studies probe the causal and contextual mechanisms that give those patterns meaning. At the midpoint, recent advances in computational approaches highlight the value of hybrid approaches that leverage synthetic and augmented data to drive scientific discovery. Yet current publishing models often privilege scale over depth, reinforcing biases that undervalue investigator-led research and limit recognition of non-traditional outputs such as code, workflows, or reproducible tutorials. This commentary argues for reforms in peer review criteria, attribution, and impact metrics that embrace diverse forms of rigor and transparency. To accelerate cumulative, mechanistic, innovative, and reproducible neuroscience, the publishing ecosystem must reward the spectrum of methodological contributions to scientific inquiry.