Spectral normative modeling of brain structure
Bibliographic record
Abstract
Abstract Normative modeling in neuroscience aims to characterize interindividual variation in brain phenotypes and establish reference ranges, or brain charts, against which individuals can be compared. Normative models are typically limited to coarse spatial scales due to computational constraints, limiting their spatial specificity. Furthermore, dependence on fixed parcellation atlases limits their adaptability to alternative parcellation schemes. To overcome these key limitations, we propose spectral normative modeling (SNM), which leverages brain eigenmodes to efficiently generate normative ranges for arbitrarily defined regions of interest. Training SNM on over 78,000 healthy brain scans, we generate accurate lifespan thickness growth charts across different spatial scales, from millimeters to the whole brain. These charts reveal three principal thickness growth gradients, aligning neurotypical cortical change with established anatomical, genetic, and functional hierarchies. We further demonstrate SNM’s utility by elucidating high-resolution individual cortical atrophy patterns that characterize the heterogeneous expression of neurodegeneration in Alzheimer’s disease. SNM lays the groundwork for a new generation of spatially precise brain charts, offering substantial potential to drive advances in individualized precision medicine.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".