Researchers shouldn’t fear papers that test and find flaws in methods. Such work contributes to better experimental designs and better science.
by Scott Marek at Washington University School of Medicine in St. Louis, Missouri, and his colleagues that investigated the reproducibility of brain-wide association studies. Such studies use neuroimaging techniques to explore how variations in brain structure or function affect behaviour, cognition or mental health.
Marekfound that sample sizes in the thousands are needed to reliably characterize such relationships, although the authors note that they did not investigate all possible techniques or populations. The paperThis week, Abigail Greene at Yale University School of Medicine in New Haven, Connecticut, and her colleagues tackle the reliability of predictive modelling in cognitive neuroscience. The method, which is used widely in the biological sciences, uses existing data sets to forecast future outcomes. It has been applied to cognitive neuroscience in an effort to determine the relationship between patterns of brain activity and various cognitive and behavioural traits. Unlike brain-wide association studies, predictive-modelling studies can be reliable with smaller sample sizes.Greene and her co-workers systematically characterized the cases for which predictive models fail to generate accurate predictions in cognitive neuroscience, and show that this failure is not random. Rather, it tends to occur for certain groups of people regardless of the data set — groups that aren’t average. This might be interpreted as showing that, in cognitive neuroscience, predictive models lack methodological robustness, fuelling wider concerns about the field. Some researchers have toldthat, since the publication of Marek and colleagues’ work, reviewers of papers and grants have had a more negative view of neuroimaging studies with small sample sizes — even if they are not brain-wide association studies. The implication is that grants need to get larger, involving consortia that can collect data from thousands, which could crowd out small research groups and researchers in low-resource settings. Others fear that the findings will contribute to a perception among scientists outside the field that cognitive neuroscience is statistically underpowered and based on models that systematically fail. However, these studies provide the opportunity for significant growth in the field, as they have done in others.Around 20 years ago, the genetics community needed to confront the reality that studies looking to determine the genetic basis of traits using candidate-gene approaches were not producing results that said meaningful things about genes and diseases. Genetics was much more complex than they had originally realized and, among other things, needed greater statistical firepower. Researchers turned to genome-wide association studies, which scan the genomes of many people in an effort to determine whether and how variations are associated with particular diseases, such as heart disease or cancer. One of the earliest such studies, of 96 people with age-related macular degeneration — a major cause of blindness in older people — and 50 control participants, revealed more about the hereditary nature of the condition
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