ArticleBiological psychiatry2020
Multiple Holdouts With Stability: Improving the Generalizability of Machine Learning Analyses of Brain-Behavior Relationships.
Article in Biological psychiatry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Multivariate brain-behaviour associations in psychiatric disorders.Translational psychiatry · 2024Pooled it
- Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data.JMIR mental health · 2025Trial
- Exposome factors predict the association between sleep health and depression.medRxiv : the preprint server for health sciences · 2026Article
- Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging.Biological psychiatry · 2026Review
- Whole-brain mapping of TMS-induced discomfort with large-scale concurrent TMS-fMRI.bioRxiv : the preprint server for biology · 2026Article
- Multivariate associations of motor performance, sleep quality, depressive symptoms, and grey matter volume in younger and mid-to-older adults.Scientific reports · 2026Article
- Functional Brain Connectivity Patterns of Headache-Mental Disorder Comorbidity in Patients With Migraine.CNS neuroscience & therapeutics · 2025Article
- Cardiometabolic health and physical robustness map onto distinct patterns of brain structure and neurotransmitter systems.PLoS biology · 2025Article
- Seeing Is Craving: Neural Dynamics of Appetitive Processing During Food-Cue Video Watching and Its Impact on Obesity.Nutrients · 2025Article
- Beyond out-of-sample: robust and generalizable multivariate neuroanatomical patterns of psychiatric problems in youth.Molecular psychiatry · 2025Article
- The functional overlap between respiration and global signal and its behavioral relevance.Communications biology · 2025Article
- Estimating correlations in low-reliability settings with constrained hierarchical models.Behavior research methods · 2025Article
- Interpretable and integrative deep learning for discovering brain-behaviour associations.Scientific reports · 2025Article
- Elevated Activity in Left Homologous Music Circuits Is Inhibitory for Music Perception but Mediated by Structure-Function Coupling.CNS neuroscience & therapeutics · 2024Article
- Brain Functional Connectome Defines a Transdiagnostic Dimension Shared by Cognitive Function and Psychopathology in Preadolescents.Biological psychiatry · 2024Article
- Lower motor performance is linked with poor sleep quality, depressive symptoms, and grey matter volume alterations.bioRxiv : the preprint server for biology · 2024Article
- Limited generalizability of multivariate brain-based dimensions of child psychiatric symptoms.Communications psychology · 2024Article
- On the stability of canonical correlation analysis and partial least squares with application to brain-behavior associations.Communications biology · 2024Article
- Covariance patterns between sleep health domains and distributed intrinsic functional connectivity.Nature communications · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
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Authors and funding
17 authors.
Funding
Abstract
backgroundIn 2009, the National Institute of Mental Health launched the Research Domain Criteria, an attempt to move beyond diagnostic categories and ground psychiatry within neurobiological constructs that combine different levels of measures (e.g., brain imaging and behavior). Statistical methods that can integrate such multimodal data, however, are often vulnerable to overfitting, poor generalization, and difficulties in interpreting the results.
methodsWe propose an innovative machine learning framework combining multiple holdouts and a stability criterion with regularized multivariate techniques, such as sparse partial least squares and kernel canonical correlation analysis, for identifying hidden dimensions of cross-modality relationships. To illustrate the approach, we investigated structural brain-behavior associations in an extensively phenotyped developmental sample of 345 participants (312 healthy and 33 with clinical depression). The brain data consisted of whole-brain voxel-based gray matter volumes, and the behavioral data included item-level self-report questionnaires and IQ and demographic measures.
resultsBoth sparse partial least squares and kernel canonical correlation analysis captured two hidden dimensions of brain-behavior relationships: one related to age and drinking and the other one related to depression. The applied machine learning framework indicates that these results are stable and generalize well to new data. Indeed, the identified brain-behavior associations are in agreement with previous findings in the literature concerning age, alcohol use, and depression-related changes in brain volume.
conclusionsMultivariate techniques (such as sparse partial least squares and kernel canonical correlation analysis) embedded in our novel framework are promising tools to link behavior and/or symptoms to neurobiology and thus have great potential to contribute to a biologically grounded definition of psychiatric disorders.
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