Spectral imprint of structural embedding in effective connectivity
Bibliographic record
Abstract
Neural fluctuations exhibit rich spectral profiles that reflects both local dynamics and structural (or anatomical) embedding. Yet, standard models of resting-state effective connectivity neglect structural embedding and assume uniformity in the timescales of regions' endogenous fluctuations. We introduce a chromatic dynamic causal model (DCM) in which structural valency (or degree) modulates the spectral 'color' of endogenous fluctuations. Specifically, we assume a linear mapping between regional structural valency and the spectral exponent of scale-free auto-spectra. Simulations show this mapping can emerge as a generic consequence of structural embedding under minimal coupling in a non-equilibrium regime. We show chromatic DCM reliably recovers ground-truth parameters across network sizes and noise conditions, outperforming standard spectral DCM. Applied to empirical data, chromatic DCM reveals that valency-exponent mappings vary across a cortical hierarchy, and that its parameters are conserved across a homologous network in humans, macaques, marmosets, and mice. These findings advance a generative account of structure-function coupling and expand the repertoire of biophysical mechanisms available for inference in effective connectivity modeling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".