SPHARM-Based Spatial fMRI Characterization With Intersubject Anatomical Variability Reduction
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Bibliographic record
Abstract
It has been recently shown that spatial patterns of activation within regions of interest (ROIs) in functional magnetic resonance imaging (fMRI) data can be used as sensitive markers of brain activation differences. In this paper, we propose novel invariant features for characterizing such spatial activation patterns based on spherical harmonic (SPHARM) data representations. The proposed three dimensional (3-D) spatial features are novel in that; first, they provide a unique representation of any ROI's functional data; second, they simultaneously account for inherent inter-subject anatomical variability that may influence any spatial characterization; third, they are invariant to similarity transformations and hence allow for direct comparisons between ROIs without any requirement for normalization to an atlas. We present quantitative validation demonstrating our method's improved sensitivity in performing group analysis when compared to traditional spatial normalization using synthetic data at the ROI level. We also use the proposed technique along with traditional normalization approach on real fMRI data collected from PD patients and normal subjects. The proposed features provide a powerful means to sensitively detect group-wise changes in ROI-based fMRI activation patterns even in the presence of anatomical variability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it