ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2020
Toward Robust Anxiety Biomarkers: A Machine Learning Approach in a Large-Scale Sample.
Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Multimodal phenotypic classification of generalized anxiety and panic using structural MRI data and psychosocial factors: machine learning results from the German National Cohort (NAKO) study.Translational psychiatry · 2026Article
- Developing personalized algorithms for sensing mental health symptoms in daily life.Npj mental health research · 2025Article
- Article
- Neuroanatomical prediction of individual anxiety problems level using machine learning models: A population-based cohort study of young adults.Neurobiology of stress · 2025Article
- Generalizable and replicable brain-based predictions of cognitive functioning across common psychiatric illness.Science advances · 2024Article
- Quantitative Expression of Latent Disease Factors in Individuals Associated with Psychopathology Dimensions and Treatment Response.Neuroscience bulletin · 2024Article
- Metagenomics reveals unique gut mycobiome biomarkers in psoriasis.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Article
- Differential predictability of cognitive profiles from brain structure in older males and females.GeroScience · 2024Article
- A systematic review of structural neuroimaging markers of psychotherapeutic and pharmacological treatment for obsessive-compulsive disorder.Frontiers in psychiatry · 2024Review
- Individualized functional connectome identified generalizable biomarkers for psychiatric symptoms in transdiagnostic patients.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2023Article
- Modeling inter-individual differences in ambulatory-based multimodal signals via metric learning: a case study of personalized well-being estimation of healthcare workers.Frontiers in digital health · 2023Article
- Performance scaling for structural MRI surface parcellations: a machine learning analysis in the ABCD Study.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Artificial intelligence assisted tools for the detection of anxiety and depression leading to suicidal ideation in adolescents: a review.Cognitive neurodynamics · 2022Review
- Generalisable machine learning models trained on heart rate variability data to predict mental fatigue.Scientific reports · 2022Article
- Graph Convolutional Networks Reveal Network-Level Functional Dysconnectivity in Schizophrenia.Schizophrenia bulletin · 2022Article
- Linking interindividual variability in brain structure to behaviour.Nature reviews. Neuroscience · 2022Review
- Identifying Individuals Who Currently Report Feelings of Anxiety Using Walking Gait and Quiet Balance: An Exploratory Study Using Machine Learning.Sensors (Basel, Switzerland) · 2022Article
- Prediction of generalized anxiety levels during the Covid-19 pandemic: A machine learning-based modeling approach.Informatics in medicine unlocked · 2022Article
- Machine learning to advance the prediction, prevention and treatment of eating disorders.European eating disorders review : the journal of the Eating Disorders Association · 2021Article
- Affective State during Physiotherapy and Its Analysis Using Machine Learning Methods.Sensors (Basel, Switzerland) · 2021Article
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Abstract
backgroundThe 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.
methodsWe 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).
resultsThough the best model was able to predict anxiety within the discovery sample (cross-validated R
conclusionsIn 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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