Disentangling metabolic and neurovascular timescales supporting cognitive processes
Bibliographic record
Abstract
The balance between neural excitation and inhibition (EIB), governed by glutamatergic and GABAergic neurotransmission, is an essential mechanism supporting cognitive processes. Yet, little is understood about how EIB shifts with engaging cognitive load and its impact on functional connectivity dynamics. Here, we combined time-resolved functional magnetic resonance spectroscopy and magnetic resonance imaging to investigate temporal profiles of the reciprocal modulation between EIB and functional network dynamics during working memory tasks in healthy subjects. We quantified EIB kinetics by measuring excitatory (Glx) and inhibitory (GABA+) levels and assessed their temporal coupling with dynamic functional connectivity states, revealing a dependence that scales with cognitive load. Notably, we found that as cognitive challenges intensify, brain networks transition toward more specialized and enduring connectivity configurations, accompanied by imbalances that favor excitation, changes that may impact cognitive adaptability. Fluctuations in EIB and functional connectivity occurred on comparable timescales and were both associated with individual differences in cognitive performance. Importantly, this experimental approach demonstrates a close synergy between EIB kinetics, brain network dynamics, and cognitive performance, defining the groundwork for exploring healthy and aberrant cognitive states.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".