The Hurst exponent as a marker of inhibition in the developing brain
Bibliographic record
Abstract
Abstract The maturation of inhibitory neurons is crucial for regulating plasticity in developing brains. Previous work using computational models has suggested that the Hurst exponent, the decay in power over frequency, reflects inhibition, but empirical data supporting this link is sparse. Here, we took a cross-species approach to validating the Hurst exponent of fMRI as a marker of inhibition, then characterized the development of the Hurst exponent in childhood. We found significant spatial correlations between the Hurst exponent and ex vivo parvalbumin mRNA expression in human children and adults, and between the Hurst exponent and parvalbumin-positive cell counts in mice. We identified a plateau in the mRNA expression by late childhood, aligning with the Hurst exponent plateau in both humans and rats. In sum, this work suggests that the Hurst exponent can be used to study the development of inhibition in vivo , and in the future, to understand individual differences in plasticity.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".