Functional Brain Mapping of Body Size Estimation Using a 3D Avatar
Bibliographic record
Abstract
Background: Body size estimation-the ability to judge the size and shape of one's own body-is a key perceptual component of body image. However, its neural basis, and the basis for inter-individual differences in accuracy, remain poorly understood, partly due to limitations in existing assessment tools. Methods: We developed Somatomap 3D, an interactive fMRI-compatible task allowing participants to manipulate a rotatable 3D avatar by adjusting the size and shape of 26 individual body parts to match their perceived body. Twenty-eight healthy male and female adults completed the task during fMRI. Brain activity in a priori regions of interest from previous studies of body processing was modeled using a general linear model incorporating event-specific parameters and parametric modulators related to task performance. Inter-individual differences in body size estimation accuracy were calculated using multidimensional scaling of body part estimation errors, and scores were correlated with BOLD signal eigenvariates from regions of interest. Results: Task engagement was associated with significant activation in hypothesized body-selective and multisensory regions, including bilateral extrastriate body area, right fusiform body area, right superior parietal lobule, and bilateral premotor cortex. Multidimensional scaling identified a primary subdimension reflecting distortions in body part girths, which was significantly associated with neural responses in the superior parietal lobule. No other brain regions showed significant associations with inter-individual differences in estimation accuracy. Conclusions: These results suggest that body size estimation engages a distributed network of visual, motor, and parietal regions. Among these, only the superior parietal lobule showed a significant association with inter-individual variation in body size estimation accuracy for body part girths, supporting its role as a candidate neural substrate for altered body representation in psychiatric conditions such as eating disorders and body dysmorphic disorder.
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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) | 0.001 | 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".