Big data holds great promise for advancing neuroimaging by addressing long-standing issues associated with small sample studies-namely, low statistical power, inflated effect sizes, and limited reproducibility. Many earlier findings in the field of brain mapping were based on modest sample sizes, leading to biased estimates and fragile inferences about brain structure, function, and connectivity. As MRI and larger datasets become more accessible, the potential to generate more reliable and generalizable insights increased. However, the collection of large data sets is not beyond question wherein the pursuit of ever-smaller p-values can obscure the larger goals of scientific discovery. Indeed, statistical significance alone is not a guarantee of meaningful results. Instead of chasing significance thresholds, the field must prioritize more rigorously defined and testable models and, importantly, more thoughtful questions-ones that align with the complexity of the brain and aim to deepen our understanding of its form, function, and connectivity. In this lecture, I will review the role of data set size considerations in brain imaging studies, the role of large-scale databases, our general lack of governing models, and the need for more useful but testable hypotheses.
Join at: imt.lu/aula1