Toward Robust Anxiety Biomarkers: A Machine Learning Approach in a Large-Scale Sample
Open accessAbstract
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 R 2 of .06, permutation test p < .001), the generalization test within the holdout sample failed (R 2 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.
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There is a lack of clinically validated biomarkers in mood, anxiety, psychotic, substance use and eating disorders to guide treatment.
“Evidence of this is the lack of clinically validated biomarkers in mood,28 anxiety,29 psychotic,30 substance use31 and eating disorders32 to guide treatment.”
From Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders (2021) · cites this paper as [29]
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Lueken, U., Hilbert, K., Wïttchen, H., Reif, A., & Hahn, T. (2014) · Journal of Neural Transmission · doi:10.1007/s00702-014-1272-5 · Cited by 24 · see citing works
Makovac, E., Mancini, M., Fagioli, S., Watson, D. R., Meeten, F., Rae, C. L., Critchley, H., & Ottaviani, C. (2018) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2018.09.006 · Cited by 24 · see citing works
Newman, M. G., Lafreniere, L. S., & Jacobson, N. C. (2018) · Psychotherapy Research · doi:10.1080/10503307.2016.1253891 · Cited by 21 · see citing works
Brain activity associated with illusory correlations in animal phobia
Wiemer, J., Schulz, S. M., Reicherts, P., Glotzbach-Schoon, E., Andreatta, M., & Pauli, P. (2014) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsu142 · Cited by 16 · see citing works
Kim, D.-H., & Lee, J.-H. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.00414 · Cited by 9 · see citing works
Gaze cuing of attention in snake phobic women: the influence of facial expression
Pletti, C., Dalmaso, M., Sarlo, M., & Galfano, G. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.00454 · Cited by 7 · see citing works
Effectiveness of cognitive-behavioral group therapy for panic disorder in a specialized unit.
Prats, E., Domínguez‐Clavé, E., Rosado, S., Pailhez, G., Bulbena, A., & Fullana, M. A. (2014) · PubMed · Cited by 7 · see citing works
From phenotypic chaos to neurobiological order
Holmes, A. J., & Yeo, B. T. T. (2015) · Nature Neuroscience · doi:10.1038/nn.4145 · Cited by 7 · see citing works
Self-citationMasdrakis, V. G., Legaki, E.-M., Vaidakis, N., Ploumpidis, D., Soldatos, C. R., Papageorgiou, C., Papadimitriou, G. N., & Oulis, P. (2013) · Behavioural and Cognitive Psychotherapy · doi:10.1017/s135246581300101x · Cited by 5 · see citing works
Flagged first, then by citations
1 of the 101 references couldn’t be matched to a known paper.
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Trait and state anxiety are mapped differently in the human brain
Saviola, F., Pappaianni, E., Monti, A., Grecucci, A., Jovicich, J., & Pisapia, N. D. (2020) · Scientific Reports · doi:10.1038/s41598-020-68008-z · Cited by 319 · see citing works
A naturalistic neuroimaging database for understanding the brain using ecological stimuli
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Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia
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Functional Connectome Prediction of Anxiety Related to the COVID-19 Pandemic
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Machine learning applied to functional magnetic resonance imaging in anxiety disorders
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Self-citationMachine learning to advance the prediction, prevention and treatment of eating disorders
Wang, S. B. (2021) · European Eating Disorders Review · doi:10.1002/erv.2850 · Cited by 29 · see citing works
Distributed functional connectivity predicts neuropsychological test performance among older adults
Kwak, S., Kim, H., Kim, H., Youm, Y., & Chey, J. (2021) · Human Brain Mapping · doi:10.1002/hbm.25436 · Cited by 19 · see citing works
Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods
Romaniszyn‐Kania, P., Pollak, A. M., Bugdol, M. D., Bugdol, M. N., Kania, D., Mańka, A., Danch–Wierzchowska, M., & Mitas, A. W. (2021) · Sensors · doi:10.3390/s21144853 · Cited by 18 · see citing works
A review of systems biology research of anxiety disorders
Mufford, M. S., van der Meer, D., Andreassen, O. A., Ramesar, R., Stein, D. J., & Dalvie, S. (2020) · Brazilian Journal of Psychiatry · doi:10.1590/1516-4446-2020-1090 · Cited by 17 · see citing works
Li, M., Dahmani, L., Hubbard, C. S., Hu, Y., Wang, M., Wang, D., & Liu, H. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-022-01500-4 · Cited by 18 · see citing works
Detecting Anxiety via Machine Learning Algorithms: A Literature Review
Najaran, M. H. T., & Shahid, S. I. (2025) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2025.3543307 · Cited by 16 · see citing works
Depression Detection Using Feature Extraction and Deep Learning from sMRI Images
Mousavian, M., Chen, J., & Greening, S. G. (2019) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2019.00281 · Cited by 11 · see citing works
Fear-induced brain activations distinguish anxious and trauma-exposed brains
Wen, Z., Marin, M., Blackford, J. U., Chen, Z. S., & Milad, M. R. (2021) · Translational Psychiatry · doi:10.1038/s41398-020-01193-7 · Cited by 9 · see citing works
Metagenomics reveals unique gut mycobiome biomarkers in psoriasis
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Developing personalized algorithms for sensing mental health symptoms in daily life
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Machine Learning Techniques for Anxiety Disorder
ALTINTAŞ, E., Aksu, Z. U., & DEMİR, Z. G. (2021) · European Journal of Science and Technology · doi:10.31590/ejosat.999914 · Cited by 5 · see citing works
Differential predictability of cognitive profiles from brain structure in older males and females
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Predictors of Student Apathy amongst Generation Z
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Enhancing smart healthcare with female students’ stress and anxiety detection using machine learning
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- Authors
- Boeke, E. A., Holmes, A. J., & Phelps, E. A.
- Affiliations
- New York University (US)
- Yale University (US)
- Harvard University (US)
- Corresponding author
- Phelps, Elizabeth A.
- Venue
- Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 5
- Issue
- 8
- Pages
- 799-807
- ISSN
- 2451-9022 (ISSN-L)
- PMC
- PMC6925354
- OpenAlex
- W2951971452
- Cited by
- 50
- References
- 101
- Citations by year
- 2026: 1 · 2025: 10 · 2024: 6 · 2023: 5 · 2022: 12 · 2021: 9 · 2020: 6 · 2019: 1
- FWCI
- 4.07
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Biological Psychiatry Cognitive Neuroscience and Neuroimaging (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- anxiety biomarkers, trait anxiety, functional connectivity, cortical thickness, brain volumetry, model generalization, women
- MeSH terms
- Machine Learning, Anxiety, Brain, Female, Humans, Biomarkers, Neuroimaging
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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