Intra- and interhemispheric connectivity between face-selective regions in the human brain
Bibliographic record
Abstract
Neuroimaging studies have revealed a number of regions in the human brain that respond to faces. However, the way these regions interact is a matter of current debate. The aim of this study was to use functional MRI to define face-selective regions in the human brain and then determine how these regions interact in a large population of subjects (n = 72). We found consistent face selectivity in the core face regions of the occipital and temporal lobes: the fusiform face area (FFA), occipital face area (OFA), and superior temporal sulcus (STS). Face selectivity extended into the intraparietal sulcus (IPS), precuneus (PCu), superior colliculus (SC), amygdala (AMG), and inferior frontal gyrus (IFG). We found evidence for significant functional connectivity between the core face-selective regions, particularly between the OFA and FFA. However, we found that the covariation in activity between corresponding face regions in different hemispheres (e.g., right and left FFA) was higher than between different face regions in the same hemisphere (e.g., right OFA and right FFA). Although functional connectivity was evident between regions in the core and extended network, there were significant differences in the magnitude of the connectivity between regions. Activity in the OFA and FFA were most correlated with the IPS, PCu, and SC. In contrast, activity in the STS was most correlated with the AMG and IFG. Correlations between the extended regions suggest strong functional connectivity between the IPS, PCu, and SC. In contrast, the IFG was only correlated with the AMG. This study reveals that interhemispheric as well as intrahemispheric connections play an important role in face perception.
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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 | 0.001 | 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".