Multimodal Brain Parcellation Based on Functional and Anatomical Connectivity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Brain parcellation is often a prerequisite for network analysis due to the statistical challenges, computational burdens, and interpretation difficulties arising from the high dimensionality of neuroimaging data. Predominant approaches are largely unimodal with functional magnetic resonance imaging (fMRI) being the primary modality used. These approaches thus neglect other brain attributes that relate to brain organization. In this paper, we propose an approach for integrating fMRI and diffusion MRI (dMRI) data. Our approach introduces a nonlinear mapping between the connectivity values of two modalities, and adaptively balances their weighting based on their voxel-wise test-retest reliability. An efficient region level extension that additionally incorporates structural information on gyri and sulci is further presented. To validate, we compare multimodal parcellations with unimodal parcellations and existing atlases on the Human Connectome Project data. We show that multimodal parcellations achieve higher reproducibility, comparable/higher functional homogeneity, and comparable/higher leftout data likelihood. The boundaries of multimodal parcels are observed to align to those based on cyto-architecture, and subnetworks extracted from multimodal parcels matched well with established brain systems. Our results thus show that multimodal information improves brain parcellation.
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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 | 0.001 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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