AUTHOR=Weber Kenneth A. , Teplin Zachary M. , Wager Tor D. , Law Christine S. W. , Prabhakar Nitin K. , Ashar Yoni K. , Gilam Gadi , Banerjee Suchandrima , Delp Scott L. , Glover Gary H. , Hastie Trevor J. , Mackey Sean TITLE=Confounds in neuroimaging: A clear case of sex as a confound in brain-based prediction JOURNAL=Frontiers in Neurology VOLUME=13 YEAR=2022 URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.960760 DOI=10.3389/fneur.2022.960760 ISSN=1664-2295 ABSTRACT=

Muscle weakness is common in many neurological, neuromuscular, and musculoskeletal conditions. Muscle size only partially explains muscle strength as adaptions within the nervous system also contribute to strength. Brain-based biomarkers of neuromuscular function could provide diagnostic, prognostic, and predictive value in treating these disorders. Therefore, we sought to characterize and quantify the brain's contribution to strength by developing multimodal MRI pipelines to predict grip strength. However, the prediction of strength was not straightforward, and we present a case of sex being a clear confound in brain decoding analyses. While each MRI modality—structural MRI (i.e., gray matter morphometry), diffusion MRI (i.e., white matter fractional anisotropy), resting state functional MRI (i.e., functional connectivity), and task-evoked functional MRI (i.e., left or right hand motor task activation)—and a multimodal prediction pipeline demonstrated significant predictive power for strength (R2 = 0.108–0.536, p ≤ 0.001), after correcting for sex, the predictive power was substantially reduced (R2 = −0.038–0.075). Next, we flipped the analysis and demonstrated that each MRI modality and a multimodal prediction pipeline could significantly predict sex (accuracy = 68.0%−93.3%, AUC = 0.780–0.982, p < 0.001). However, correcting the brain features for strength reduced the accuracy for predicting sex (accuracy = 57.3%−69.3%, AUC = 0.615–0.780). Here we demonstrate the effects of sex-correlated confounds in brain-based predictive models across multiple brain MRI modalities for both regression and classification models. We discuss implications of confounds in predictive modeling and the development of brain-based MRI biomarkers, as well as possible strategies to overcome these barriers.