rTMS to the OFA shows increased correlation to right and left FFA
Bibliographic record
Abstract
Face processing is one of the most developed visual skills in humans, giving us the ability to quickly and accurately perceive the unique identity of an individual and guide our social interactions. Functional magnetic resonance imaging (fMRI) shows that brain areas such as the fusiform face area (FFA), the superior temporal sulcus (STS), and the occipital face area (OFA) form a network of key face processing regions. We sought to measure the level of functional synchrony within and across hemispheres in the face network. We measured the effect of repetitive transcranial magnetic stimulation (rTMS) to the right OFA on BOLD signal within the face network using a consecutive TMS-fMRI paradigm. Participants underwent 20 min of 1Hz rTMS followed by an fMR-adaptation paradigm. In separate sessions in counterbalanced order, rTMS was delivered in three different conditions: 1) rTMS to the right OFA, 2) sham rTMS, and 3) the control region, right lateral occipital area (LO). rTMS was immediately followed by a face-adaptation fMRI task to measure its effects on BOLD signal. Prior to the rTMS sessions participants underwent two functional localizers in order to extract individual face-processing regions-of-interest (ROIs). Individual Pearson's Correlation Coefficient (PCC) matrices were constructed across ROIs in the different TMS conditions. There was a general increase in the correlation between FFA and OFA BOLD signal in the right and left hemispheres after rTMS to the right OFA compared to sham and TMS to LO conditions. TMS to the OFA reduced BOLD signal, which correlated with a reduction in BOLD signal in the left OFA and bilateral FFA. These results are consistent with previous findings showing that TMS to the OFA has remote effects in the FFA both within and across hemispheres. Meeting abstract presented at VSS 2016
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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) | 0.003 | 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".