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Record W4413341073 · doi:10.1101/2025.08.13.25333640

Neurophysiological signatures of Stanford Neuromodulation Therapy in treatment resistant depression

2025· preprint· en· W4413341073 on OpenAlexaff
Davide Momi, Derrick Matthew Buchanan, Masataka Wada, Andrew Geoly, Eleanor Cole, Adi Maron‐Katz, Claudia Tischler, Christopher C. Cline, Manish Saggar, Daphne Voineskos, John D. Griffiths, Camarin E. Rolle, Corey J. Keller, Nolan Williams

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldNeuroscience
TopicTranscranial Magnetic Stimulation Studies
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsNeuromodulationTreatment-resistant depressionNeurophysiologyDepression (economics)NeurosciencePsychologyPhysical medicine and rehabilitationMedicineMajor depressive disorderCentral nervous systemEconomicsCognitionKeynesian economics

Abstract

fetched live from OpenAlex

Treatment-resistant depression (TRD) affects approximately 30% of patients with major depressive disorder. Stanford Neuromodulation Therapy (SNT), a high-dose intermittent theta-burst transcranial magnetic stimulation protocol, produces rapid antidepressant effects, but its neurophysiological mechanisms remain unclear. Here, we used longitudinal TMS-EEG to characterize the progressive neurophysiological changes induced by SNT, assess their site-specificity, and explore whether baseline neural markers are associated with clinical response. We conducted a double-blind, randomized, sham-controlled trial at Stanford University (2017-2018; analysis August 2024-October 2025) in 24 TMS-naïve participants with TRD (Montgomery-Åsberg Depression Rating Scale ≥20; ≥1 failed antidepressant trial). Participants were randomized to active (n = 12) or sham (n = 12) SNT, consisting of 10 sessions per day over 5 consecutive days targeting the left dorsolateral prefrontal cortex (90,000 pulses). TMS-EEG was acquired at two baseline sessions, before and after each treatment session, and at 1-month follow-up (14 TMS-EEG sessions in total). Active SNT progressively reduced cortical excitability at the treatment site, with significant decreases by day 3 in the early window component (-27.9%; P < 0.01), while no changes were observed at the vertex control site. Site-specific comparisons confirmed early window reductions only at the left dorsolateral prefrontal cortex (t₂₂ = -3.82; P < 0.001). SNT also selectively decreased estimated medial prefrontal source activity consistent with the subgenual anterior cingulate cortex (sgACC) across sessions (F₁₃,₂₂₂ = 4.93; P < 0.001), with effects persisting at 1-month follow-up. In an exploratory analysis in the active group (n = 12), higher baseline estimated sgACC source activity was associated with greater clinical improvement (r = -0.67; P = 0.023); although promising, the latter preliminary finding requires replication in larger, adequately powered samples before predictive utility can be established. These findings indicate that SNT induces progressive, site-specific cortical modulation and selective downstream effects on estimated sgACC source activity. Early cortical excitability changes represent candidate neurophysiological markers of SNT response, while the observed association between baseline sgACC activity and clinical outcome, while preliminary, motivates prospective investigation of subcortical source activity as a potential predictor of treatment response in larger trials. ClinicalTrials.gov Identifier: NCT03068715.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.072
GPT teacher head0.319
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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