Toward Robust Anxiety Biomarkers: A Machine Learning Approach in a Large-Scale Sample

Emily A. Boeke, Avram J. Holmes, Elizabeth A. Phelps

Research output: Contribution to journalArticlepeer-review


Background: The field of psychiatry has long sought biomarkers that can objectively diagnose patients, predict treatment response, or identify individuals at risk of illness onset. However, reliable psychiatric biomarkers have yet to emerge. The recent application of machine learning techniques to develop neuroimaging-based biomarkers has yielded promising preliminary results. However, much of the work in this domain has not met best practice standards from the field of machine learning. This is especially true for studies of anxiety, creating uncertainty about the potential for anxiety biomarker development. Methods: We applied machine learning tools to predict trait anxiety from neuroimaging measurements in humans. Using publicly available data from the Brain Genomics Superstruct Project, we compared a suite of neuroimaging-based machine learning models predicting anxiety within a discovery sample (n = 531, 307 women) via k-fold cross-validation, and we tested the final model (a stacked model incorporating region-to-region functional connectivity, amygdala seed-to-voxel connectivity, and volumetric and cortical thickness data) in a held-out, unseen test sample (n = 348, 209 women). Results: Though the best model was able to predict anxiety within the discovery sample (cross-validated R2 of .06, permutation test p < .001), the generalization test within the holdout sample failed (R2 of −.04, permutation test p > .05). Conclusions: In this study, we did not find evidence of a generalizable anxiety biomarker. However, we encourage other researchers to investigate this topic, utilizing large samples and proper methodology, to clarify the potential of neuroimaging-based anxiety biomarkers.

Original languageEnglish (US)
Pages (from-to)799-807
Number of pages9
JournalBiological Psychiatry: Cognitive Neuroscience and Neuroimaging
Issue number8
StatePublished - Aug 2020


  • Anxiety
  • Biomarker
  • Functional connectivity
  • Machine learning
  • Predictive modeling
  • fMRI
  • Neuroimaging
  • Brain/diagnostic imaging
  • Humans
  • Biomarkers
  • Female
  • Machine Learning

ASJC Scopus subject areas

  • Clinical Neurology
  • Biological Psychiatry
  • Cognitive Neuroscience
  • Radiology Nuclear Medicine and imaging


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