ArticleBrain sciences2024
From Connectivity to Controllability: Unraveling the Brain Biomarkers of Major Depressive Disorder.
Article in Brain sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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.
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Who cites it
6 citing papers in PubMed.
- Brain energetic landscapes shape state dysregulation in major depressive disorder: a morphological network controllability perspective.Translational psychiatry · 2026Article
- Controllability of morphometric network colocalize with underlying neurobiology in major depression.Psychological medicine · 2026Article
- Brain Network Controllability and Its Regional Gene Expression Profile Associated With Suicidal Tendencies in Major Depressive Disorder Patients.Depression and anxiety · 2025Article
- Task-Related Controllability of Functional Connectome During a Working Memory Task in Schizophrenia, Bipolar Disorder, and Major Depressive Disorder.Research (Washington, D.C.) · 2025Article
- Psychiatry in the age of AI: transforming theory, practice, and medical education.Frontiers in public health · 2025Review
- Module control of network analysis in psychopathology.iScience · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain's dynamic and complex nature, exploring its mechanisms from a network control standpoint provides a fresh and insightful framework. This research investigates the integration of network controllability and machine learning to pinpoint essential biomarkers for MDD using functional magnetic resonance imaging (fMRI) data. By employing network controllability methods, we identify crucial brain regions that are instrumental in facilitating transitions between brain states. These regions demonstrate the brain's ability to navigate various functional states, emphasizing the utility of network controllability metrics as potential biomarkers. Furthermore, these metrics elucidate the complex dynamics of MDD and support the development of precision medicine strategies that incorporate machine learning to improve the precision of diagnostics and the efficacy of treatments. This study underscores the value of merging machine learning with network neuroscience to craft personalized interventions that align with the unique pathological profiles of individuals, ultimately enhancing the management and treatment of MDD.
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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.