Explanations and accurate predictions are the fundamental deliverables for a mechanistic or pragmatic approach that academic psychiatric research can provide to stakeholders. Starting with this issue, we are publishing a series of Viewpoints describing the research boundaries and challenges to progress in our field. In this issue, Simon1 raises the need for better explanatory model using data from electronic health records. This Viewpoint acknowledges an important issue: variables or constructs that are used to help explain the current state of individuals or to generate predictions need to account for a substantial proportion of the variance of the dependent variable or outcome measure to be clinically useful. However, similar to findings from genetics literature, systems neuroscience approaches using brain imaging are beginning to show that variability in structural and functional brain imaging only accounts for a small percentage of the explained variance when considering a variety of clinical phenotypes, especially in large population-representative samples.2 For example, in a 2016 analysis of UK Biobank data,3 the functional activation related to a face processing task, which activated the fusiform gyrus and amygdala, accounted for a maximum of 1.8% of the variance of 1100 nonimaging variables. These findings are in line with emerging results from the Adolescent Brain Cognitive Development study4 focused on the association between screen media behavior and structural MRI characteristics. Importantly, these large-scale studies have used robust and reliable estimators to reduce false-positive discoveries. Thus, similar to genetics literature, it appears that individual processing differences as measured by neuroimaging account for little symptomatic or behavioral variance.
Paulus MP, Thompson WK. The Challenges and Opportunities of Small Effects: The New Normal in Academic Psychiatry. JAMA Psychiatry. 2019;76(4):353–354. doi:10.1001/jamapsychiatry.2018.4540
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