Magnetic Resonance Imaging‐Guided Neuronavigation for Transcranial Magnetic Stimulation in Mood Disorders: Technical Foundation, Advances, and Emerging Tools
Bibliographic record
Abstract
Transcranial magnetic stimulation (TMS) guided by magnetic resonance imaging (MRI) has significantly advanced the treatment of mood disorders by enabling precise targeting of brain circuits implicated in their pathophysiology. The integration of neuronavigation systems, which utilize real-time MRI-based coil positioning, has improved spatial targeting accuracy, individualization, and therapeutic outcomes. Despite these advancements, achieving optimal stimulation efficacy requires careful consideration of MRI techniques, including anatomical imaging, functional MRI (fMRI), and connectivity-based methods. Anatomical MRI provides a reliable structural foundation for neuronavigation but lacks specificity regarding functional neural networks implicated in mood disorders. In contrast, fMRI, through task-based and resting-state paradigms, enhances target selection precision by identifying patient-specific neural activity and functional connectivity patterns, although this approach is vulnerable to variability and imaging artifacts. Connectivity-based MRI neuronavigation represents a promising advancement by explicitly targeting disrupted neural networks. This review critically examines recent technological and methodological progress in MRI-guided neuronavigation for TMS, addressing current challenges such as image acquisition quality, co-registration accuracy, artifact mitigation, and practical constraints in clinical settings. Finally, it discusses emerging opportunities and innovations poised to enhance neuronavigation precision, foster wider clinical adoption, and ultimately improve therapeutic outcomes in interventional psychiatry for mood disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".