From gradients to cognition: linking cortical manifolds to brain flexibility and disorder
Bibliographic record
Abstract
Traditional neuroscience describes the cerebral cortex as a mosaic of discrete, functionally specialized regions. However, a complementary view has emerged, demonstrating that the brain is also organized along continuous gradients that capture large-scale transitions in connectivity, microstructure, and function. These gradients, derived using dimensionality reduction techniques on neuroimaging data, provide a low-dimensional manifold framework that unifies our understanding of how cortical architecture supports cognitive flexibility, learning, and clinical disorders. In this review, we integrate evidence from genetics, phylogeny, development, and multimodal neuroimaging to outline how macroscale gradients emerge from underlying biological constraints, become progressively decoupled from local microstructure in transmodal cortex, and dynamically reorganize during cognitive and clinical states. We further discuss how this framework provides new insights into individual differences, disease mechanisms, and recovery following brain injury. By bridging anatomy, function, and behavior, gradient-based approaches offer a powerful lens for mapping the architecture of human cognition and its disruption in disease.
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How this classification was reachedexpand
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 | 0.009 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".