Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging
Clinical Snapshot
PICO Framework
| P — Population | Not applicable - this is a narrative review of neuroimaging methods in psychiatry |
| I — Intervention | Not applicable - review of analytical methods |
| C — Comparator | Not applicable - review article |
| O — Outcomes | Not applicable - review article |
Bottom Line
This narrative review examines emerging computational approaches in psychiatric neuroimaging, focusing on multimodal representation learning methods. While the authors highlight promising developments in machine learning and deep learning approaches for understanding psychiatric disorders, this is primarily a methodological review rather than a systematic evaluation of clinical evidence. The review suggests that traditional univariate neuroimaging approaches have yielded limited clinical biomarkers, and proposes that multimodal, multivariate methods may be more promising. However, the lack of systematic methodology, formal quality assessment, and quantitative synthesis limits the strength of conclusions. The review is most valuable for researchers developing neuroimaging analysis methods rather than clinicians seeking evidence-based practice guidance. Current clinical practice should continue to rely on established diagnostic and treatment approaches while these research methods undergo further validation.
Key Findings
Effect Size: Not applicable - no meta-analysis performed
Primary Outcome: Not applicable - narrative review of methodological approaches
Nnt Or Sensitivity: Not applicable - methodological review
Clinical Application
Limited immediate clinical applicability - primarily research-focused methodological guidance Relevant to Australian psychiatric research centres but limited direct clinical practice implications. No specific consideration of Australian healthcare context, PBS, or TGA regulations Researchers and clinicians involved in psychiatric neuroimaging research
Abstract
For decades, psychiatric neuroimaging has searched for biomarkers of depression and other disorders, but they remain elusive in clinical practice. While the last 5 years have seen rapid progress, other large-scale correlative studies have found only small, unreliable links between brain measures and clinical symptoms. Growing evidence suggests that such limitations are not just about sample size but depend critically on how models represent data. This review traces a recent shift away from univariate methods to multivariate/multiview approaches that learn more effective representations of biological and symptom measures by flexibly learning multimodal latent representations. First, we review how linear multiview embedding methods have revealed reproducible biological depression subtypes but do not perform well in small samples or samples enriched for mild symptoms. Then, we consider newer work exploring more sophisticated representations for neuroimaging data, including deep-learning and graph-based representations, and multimodal extensions that uncover complex latent patterns that single-modality studies miss. Then, we review recent developments in foundation models, which, once trained on large corpora, can "transfer learn" readily to small clinical cohorts, potentially bringing the advantages of large-scale learning to small, privacy-limited data. Finally, we highlight emerging representation tools that treat the brain as a dynamic, stateful multivariate process. Taken together, these advances point to a future in which the value of neuroimaging will be determined not only by ever-larger sample sizes but also by data quality and by how well our algorithms capture the distributed, multimodal, and evolving nature of psychiatric disorders.
References
- 1.Grosenick, L., & Liston, C. (2026). Multimodal representation learning for parsing biological heterogeneity in psychiatric neuroimaging. Biological Psychiatry. https://doi.org/10.1016/j.biopsych.2026.01.023
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