A novel interleaved TMS-MRS approach with standard MRI hardware
Bibliographic record
Abstract
OBJECTIVE: Interleaved TMS-fMRI has advanced understanding of network modulation but is limited to hemodynamic measures. We introduce a novel interleaved TMS-MRS platform, using standard MRI hardware, to assess real-time neurochemical changes and demonstrate feasibility in a clinical sample of patients with treatment-resistant depression (TRD). METHODS: H-MRS spectra were acquired at baseline and during 10 Hz burst stimulation. Spectral quality and metabolite concentrations were compared across conditions. RESULTS: Spectral quality was preserved across conditions (FWHM baseline: 0.041 ± 0.005; active: 0.040 ± 0.005; p = 0.699) and signal-to-noise remained stable (baseline: 38.29 ± 5.38; active: 34.67 ± 9.12; p = 0.790). Two metabolites differed significantly: alanine increased during stimulation (0.58 ± 0.16 vs. 0.46 ± 0.14; p = 0.031), while NAA + NAAG decreased (9.07 ± 2.02 vs. 8.83 ± 0.99; p = 0.031). Exploratory analyses suggested associations between baseline and stimulation-induced metabolites (e.g., GSH, GABA, lactate) and clinical improvement following accelerated iTBS. CONCLUSION: Interleaved TMS-MRS is feasible with standard MRI hardware in TRD patients, enabling in-vivo detection of acute neurochemical changes during stimulation. This method offers a new avenue for probing excitatory/inhibitory balance, neuronal metabolism, and treatment mechanisms.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".