ArticleGigaScience2021
Population modeling with machine learning can enhance measures of mental health.
Article in GigaScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The application of artificial intelligence in the field of mental health: a systematic review.BMC psychiatry · 2025Pooled it
- Prediction of cognitive test scores: a comparison of brain structure, health, demographic, and cognitive data across adulthood.GeroScience · 2026Article
- Machine learning used to study risk factors for chronic diseases: A scoping review.Canadian journal of public health = Revue canadienne de sante publique · 2026Article
- Article
- Transcriptomic-Driven Drug Repurposing Reveals SP600125 as a Promising Drug Candidate for the Treatment of Glial-Mesenchymal Transition in Glioblastoma.International journal of molecular sciences · 2025Article
- Prediction of Verbal Abilities From Brain Connectivity Data Across the Lifespan Using a Machine Learning Approach.Human brain mapping · 2025Article
- Opaque ontology: neuroimaging classification of ICD-10 diagnostic groups in the UK Biobank.GigaScience · 2025Article
- Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes.Communications biology · 2024Article
- Lower motor performance is linked with poor sleep quality, depressive symptoms, and grey matter volume alterations.bioRxiv : the preprint server for biology · 2024Article
- Opaque Ontology: Neuroimaging Classification of ICD-10 Diagnostic Groups in the UK Biobank.bioRxiv : the preprint server for biology · 2024Article
- ANTsX neuroimaging-derived structural phenotypes of UK Biobank.Scientific reports · 2024Article
- Differential predictability of cognitive profiles from brain structure in older males and females.GeroScience · 2024Article
- Prediction of cognitive performance differences in older age from multimodal neuroimaging data.GeroScience · 2024Article
- Overcoming treatment-resistant depression with machine-learning based tools: a study protocol combining EEG and clinical data to personalize glutamatergic and brain stimulation interventions (SelecTool Project).Frontiers in psychiatry · 2024Article
- Research hotspots and trends of neuroimaging in social anxiety: a CiteSpace bibliometric analysis based on Web of Science and Scopus database.Frontiers in behavioral neuroscience · 2024Review
- Do try this at home: Age prediction from sleep and meditation with large-scale low-cost mobile EEG.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Personalized State Anxiety Detection: An Empirical Study with Linguistic Biomarkers and A Machine Learning Pipeline.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2023Article
- Population modeling with machine learning can enhance measures of mental health - Open-data replication.Neuroimage. Reports · 2023Article
- Classifying Conduct Disorder Using a Biopsychosocial Model and Machine Learning Method.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2023Article
- A systematic review of multimodal brain age studies: Uncovering a divergence between model accuracy and utility.Patterns (New York, N.Y.) · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundBiological aging is revealed by physical measures, e.g., DNA probes or brain scans. In contrast, individual differences in mental function are explained by psychological constructs, e.g., intelligence or neuroticism. These constructs are typically assessed by tailored neuropsychological tests that build on expert judgement and require careful interpretation. Could machine learning on large samples from the general population be used to build proxy measures of these constructs that do not require human intervention?
resultsHere, we built proxy measures by applying machine learning on multimodal MR images and rich sociodemographic information from the largest biomedical cohort to date: the UK Biobank. Objective model comparisons revealed that all proxies captured the target constructs and were as useful, and sometimes more useful, than the original measures for characterizing real-world health behavior (sleep, exercise, tobacco, alcohol consumption). We observed this complementarity of proxy measures and original measures at capturing multiple health-related constructs when modeling from, both, brain signals and sociodemographic data.
conclusionPopulation modeling with machine learning can derive measures of mental health from heterogeneous inputs including brain signals and questionnaire data. This may complement or even substitute for psychometric assessments in clinical populations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.