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Five best practices for fMRI research: Towards a biologically grounded understanding of mental phenomena

Published onMar 24, 2021
Five best practices for fMRI research: Towards a biologically grounded understanding of mental phenomena


The replication crisis in science has not spared functional magnetic resonance imaging (fMRI) research. A range of issues including insufficient control of false positives, code bugs, concern regarding generalizability and replicability of findings, inadequate characterization of physiological confounds, over-mining of repository datasets, and the small sample sizes/low power of many early studies have led to hearty debate in both the field and the press about the usefulness and viability of fMRI. Others still see enormous potential for fMRI in diagnosing conditions that do not otherwise lend themselves to non-invasive biological measurement, from chronic pain to neurological and psychiatric illness. How do we reconcile the limitations of fMRI with the hype over its potential? Despite many papers hailed by the press as the nail in the coffin for fMRI, from the dead salmon incident of 2009 to cluster failure more recently, funders, researchers, and the general public do not seem to have reduced their appetite for pictures of brain maps, or gadgets with the word “neuro” in the name. Multiple blogs exist for the sole purpose of criticizing such enterprise. The replicability crisis should certainly give ‘neuroimagers’ pause, and reason to soul-search. It is more important than ever to clarify when fMRI is and when it is not useful. The method remains the best noninvasive imaging tool for many research questions, however imperfect and imprecise it may be. However, to address past limitations, I argue neuroimaging researchers planning future studies need to consider the following five factors: power/effect size, design optimization, replicability, physiological confounds, and data sharing.

Keywords: fMRI, open science


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