Reductions in Cortico-Striatal Hyperconnectivity Accompany Successful Treatment of Obsessive-Compulsive Disorder with Dorsomedial Prefrontal rTMS
Bibliographic record
Abstract
Obsessive-compulsive disorder (OCD) is a disabling illness with high rates of nonresponse to conventional treatments. OCD pathophysiology is believed to involve abnormalities in cortico-striatal-thalamic-cortical circuits through regions such as dorsomedial prefrontal cortex (dmPFC) and ventral striatum. These regions may constitute therapeutic targets for neuromodulation treatments, such as repetitive transcranial magnetic stimulation (rTMS). However, the neurobiological predictors and correlates of successful rTMS treatment for OCD are unclear. Here, we used resting-state functional magnetic resonance imaging (fMRI) to identify neural predictors and correlates of response to 20-30 sessions of bilateral 10 Hz dmPFC-rTMS in 20 treatment-resistant OCD patients, with 40 healthy controls as baseline comparators. A region of interest in the dmPFC was used to generate whole-brain functional connectivity maps pre-treatment and post treatment. Ten of 20 patients met the response criteria (⩾50% improvement on Yale-Brown Obsessive-Compulsive Scale, YBOCS); response to dmPFC-rTMS was sharply bimodal. dmPFC-rTMS responders had higher dmPFC-ventral striatal connectivity at baseline. The degree of reduction in this connectivity, from pre- to post-treatment, correlated to the degree of YBOCS symptomatic improvement. Baseline clinical and psychometric data did not predict treatment response. In summary, reductions in fronto-striatal hyperconnectivity were associated with treatment response to dmPFC-rTMS in OCD. This finding is consistent with previous fMRI studies of deep brain stimulation in OCD, but opposite to previous reports on mechanisms of dmPFC-rTMS in major depression. fMRI could prove useful in predicting the response to dmPFC-rTMS in OCD.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".