Increases in frontostriatal connectivity are associated with response to dorsomedial repetitive transcranial magnetic stimulation in refractory binge/purge behaviors
Bibliographic record
Abstract
BACKGROUND: Conventional treatments for eating disorders are associated with poor response rates and frequent relapse. Novel treatments are needed, in combination with markers to characterize and predict treatment response. Here, resting-state functional magnetic resonance imaging (rs-fMRI) was used to identify predictors and correlates of response to repetitive transcranial magnetic stimulation (rTMS) of the dorsomedial prefrontal cortex (dmPFC) at 10 Hz for eating disorders with refractory binge/purge symptomatology. METHODS: 28 subjects with anorexia nervosa, binge-purge subtype or bulimia nervosa underwent 20-30 sessions of 10 Hz dmPFC rTMS. rs-fMRI data were collected before and after rTMS. Subjects were stratified into responder and nonresponder groups using a criterion of ≥50% reduction in weekly binge/purge frequency. Neural predictors and correlates of response were identified using seed-based functional connectivity (FC), using the dmPFC and adjacent dorsal anterior cingulate cortex (dACC) as regions of interest. RESULTS: 16 of 28 subjects met response criteria. Treatment responders had lower baseline FC from dmPFC to lateral orbitofrontal cortex and right posterior insula, and from dACC to right posterior insula and hippocampus. Responders had low baseline FC from the dACC to the ventral striatum and anterior insula; this connectivity increased over treatment. However, in nonresponders, frontostriatal FC was high at baseline, and dmPFC-rTMS suppressed FC in association with symptomatic worsening. CONCLUSIONS: Enhanced frontostriatal connectivity was associated with responders to dmPFC-rTMS for binge/purge behavior. rTMS caused paradoxical suppression of frontostriatal connectivity in nonresponders. rs-fMRI could prove critical for optimizing stimulation parameters in a future sham-controlled trial of rTMS in disordered eating.
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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 | 0.000 | 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".