A Multimodal Characterization of Low-Dimensional Thalamocortical Structural Connectivity Patterns
Bibliographic record
Abstract
Abstract The human thalamus is a bilateral and heterogeneous grey matter structure that plays a crucial role in coordinating whole-brain activity. Investigations of its complex structural and functional internal organization revealed to a certain degree overlapping parcellations, however, a consensus on thalamic subnuclei boundaries remains absent. Recent work suggests that thalamic organization might additionally reflect continuous axes transcending nuclear boundaries. In this study, we used a multimodal approach to uncover how low-dimensional axes that describe thalamic connectivity patterns to the cortex are related to internal thalamic microstructural features, functional connectivity, and structural covariance. We computed a thalamocortical structural connectome via probabilistic tractography on diffusion MRI and derived two main axes of thalamic organization. The principal thalamic gradient, extending from medial to lateral and differentiating between transmodal and unimodal nuclei, was related to intrathalamic myelin profiles, and patterns of functional connectivity, while the secondary axis showed correspondence to core-matrix cell type distributions. Lastly, exploring multimodal thalamocortical associations on a global scale, we observed that the medial-to- lateral gradient consistently differentiated limbic, frontoparietal, and default mode network nodes from dorsal and ventral attention networks across modalities. However, the link with sensory modalities varied. In sum, we show the coherence between lower dimensional patterns of thalamocortical structural connectivity and various modalities, shedding light on multiscale thalamic organization.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".