Differing Time of Onset of Concurrent TMS-fMRI during Associative Memory Encoding: A Measure of Dynamic Connectivity
Bibliographic record
Abstract
There has been a distinct shift in neuroimaging from localization of function into a more network based approach focused on connectivity. While fMRI has proven very fruitful for this, the hemodynamic signal is inherently slow which limits the temporal resolution of fMRI-only connectivity measures. The brain, however, works on a time scale of milliseconds. This study utilized concurrent transcranial magnetic stimulation (TMS)-fMRI in a novel way to obtain measures of dynamic connectivity by measuring changes in fMRI signal amplitude in regions distal to the site of stimulation following differing TMS onset times. Seventeen healthy subjects completed an associative memory encoding task known to involve the DLPFC, viewing pairs of objects which could be semantically related or unrelated. Three pulses of 10 Hz repetitive TMS were applied over the left DLPFC starting either at 200, 600, or 1000 ms after stimulus onset. Associations for related pairs were better remembered than unrelated pairs in a post-scan cued recall test. Differences in neural activity were assessed across different TMS onsets, separately for related and unrelated pairs. Time specific TMS effects were observed in several regions, including those associated with higher-level processing (lateral frontal, anterior cingulate), visual areas (occipital), and regions involved in semantic processing (e.g., left mid-temporal and medial frontal). Activity in the frontal cortex was decreased at 200 ms post-stimulus for unrelated pairs, and 1000 ms post-stimulus for related pairs. This suggests differences in the timing across conditions in which the DLFPC interacts with other PFC regions, consistent with the notion that the DLPFC is facilitating extended semantic processing for related items. This study demonstrates that time-varying TMS onset inside the MRI can be used to reliably measure fast dynamic connectivity with a temporal resolution in the hundreds of milliseconds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".