Anatomical Connectivity-Based Strategy for Targeting Transcranial Magnetic Stimulation as Antidepressant Therapy
Bibliographic record
Abstract
OBJECTIVES: Abnormal activity of the subgenual anterior cingulate cortex (sACC) is implicated in depression, suggesting the sACC as a potentially effective target for therapeutic modulation in cases resistant to conventional treatments (treatment-resistant depression, TRD). We hypothesized that areas in the prefrontal cortex (PFC) with direct fiber connections to the sACC may be particularly effective sites for treatment using transcranial magnetic stimulation (TMS). The aim of this study was to identify PFC sites most strongly connected to the sACC. METHODS: Two neuroimaging data sets were used to construct anatomic and functional connectivity maps using sACC as the seed region. Data set 1 included magnetic resonance (MR) images from 20 healthy controls and Data set 2 included MR images from 15 TRD patients and 15 additional healthy controls. PFC voxels with maximum values in the mean anatomic connection probability maps were identified as optimal sites for TMS. RESULTS: Both right and left PFC contained sites strongly connected to the sACC, but the coordinates (in Montreal Neurological Institute space) of peak anatomic connectivity differed slightly between hemispheres. The left PFC site connected directly to the sACC both anatomically and functionally, while the right PFC site was functionally connected to the posterior cingulate cortex (PCC). CONCLUSIONS: Both left and right PFC are functionally connected to regions implicated in depression, the sACC and PCC, respectively. These bilateral PFC sites may be effective TMS targets to treat TRD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".