ArticleNature communications2025
Inferring intrinsic neural timescales using optimal control theory.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
The temporal evolution of whole-brain activity is contingent upon complex interactions within and between brain regions that are mediated by neurobiology and connectivity, respectively. Here, we provide a framework for studying these relationships that uses network control theory (NCT) to estimate regions' intrinsic neural timescales (INTs). Our approach broadens the range of dynamics supported by the connectome and improves the alignment between the brain's connectivity and its traversal through state-space. We find that our model-based INTs correlate with INTs measured empirically from functional neuroimaging data, neurobiological measures of gene expression and cell-type densities, as well as measures of cognition. We demonstrate consistent results across multiple datasets and species. Finally, we show that our model-based INTs enable the efficient control of brain states from fewer brain regions. Our results provide a flexible quantitative framework that more accurately captures the interplay between brain structure, function, and intrinsic dynamics with greater biophysical realism.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.