ArticlePsychology research and behavior management2025
Dynamic Brain Network Biomarkers for Depression Prediction: A Multi-Cohort Analysis of Global Neuroimaging Databases.
Article in Psychology research and behavior management, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Major depressive disorder affects over 300 million people worldwide, yet clinicians lack reliable biomarkers for early identification of at-risk individuals. Recent advances in computational neuroscience suggest that dynamic brain network reorganization during emotional challenges may provide objective indicators of depression vulnerability that could enhance clinical practice. Objective: To determine whether individual differences in dynamic brain network flexibility can predict depression onset and inform personalized clinical interventions through comprehensive analysis of large-scale neuroimaging databases. Methods: We conducted a comprehensive analysis of 14,376 adults aged 18-72 years from seven major international neuroimaging databases (UK Biobank, Human Connectome Project, ADNI, ABIDE, OpenfMRI, NITRC, and COINS) spanning 2018-2024. Participants underwent standardized emotion regulation tasks during functional MRI with concurrent EEG. We quantified brain network flexibility using advanced graph-theoretical approaches and employed machine learning to identify distinct phenotypic patterns. Depression outcomes were assessed using validated clinical instruments over 30-month follow-up periods available in longitudinal sub-cohorts. Results: Unsupervised machine learning revealed four distinct brain network flexibility phenotypes with remarkable cross-database consistency. The Rigid-Inflexible phenotype (18.7% prevalence) was associated with 4.3-fold higher depression incidence compared to Adaptive-Flexible individuals (38.7% vs 8.9%, P<0.001). Network flexibility metrics predicted depression onset with 83.2% accuracy (AUC=0.89), significantly outperforming traditional risk models (AUC=0.69, P<0.001). Network flexibility moderated stress-depression relationships (β=-0.61, P<0.001), with flexible individuals maintaining psychological resilience under high stress conditions while rigid individuals showed steep symptom escalation. Conclusion: Dynamic brain network biomarkers represent a promising advance toward predictive, personalized psychiatry, pending external validation. These findings provide a neurobiological foundation for early intervention strategies and suggest novel therapeutic targets for depression prevention. The consistency across diverse global populations indicates potential universality of these mechanisms, supporting further clinical translation efforts.
Indexed as
Identifiers
What OpenQuestion holds
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.