ArticlePhysical review. E2020
Control of brain network dynamics across diverse scales of space and time.
Article in Physical review. E, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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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.
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Who cites it
11 citing papers in PubMed.
- Double-blind, randomized, placebo-controlled pilot clinical trial with gamma-band transcranial alternating current stimulation for the treatment of schizophrenia refractory auditory hallucinations.Translational psychiatry · 2025Trial
- Mindful attention promotes control of brain network dynamics for self-regulation and discontinues the past from the present.Proceedings of the National Academy of Sciences of the United States of America · 2023Trial
- A network control theory pipeline for studying the dynamics of the structural connectome.Nature protocols · 2024Article
- Using network control theory to study the dynamics of the structural connectome.bioRxiv : the preprint server for biology · 2023Article
- Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness.NeuroImage · 2023Article
- Reduced emergent character of neural dynamics in patients with a disrupted connectome.NeuroImage · 2023Article
- The expanding horizons of network neuroscience: From description to prediction and control.NeuroImage · 2022Review
- The network collapse in multiple sclerosis: An overview of novel concepts to address disease dynamics.NeuroImage. Clinical · 2022Review
- Time-evolving controllability of effective connectivity networks during seizure progression.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- A practical guide to methodological considerations in the controllability of structural brain networks.Journal of neural engineering · 2020Article
- Models of communication and control for brain networks: distinctions, convergence, and future outlook.Network neuroscience (Cambridge, Mass.) · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The human brain is composed of distinct regions that are each associated with particular functions and distinct propensities for the control of neural dynamics. However, the relation between these functions and control profiles is poorly understood, as is the variation in this relation across diverse scales of space and time. Here we probe the relation between control and dynamics in brain networks constructed from diffusion tensor imaging data in a large community sample of young adults. Specifically, we probe the control properties of each brain region and investigate their relationship with dynamics across various spatial scales using the Laplacian eigenspectrum. In addition, through analysis of regional modal controllability and partitioning of modes, we determine whether the associated dynamics are fast or slow, as well as whether they are alternating or monotone. We find that brain regions that facilitate the control of energetically easy transitions are associated with activity on short length scales and slow timescales. Conversely, brain regions that facilitate control of difficult transitions are associated with activity on long length scales and fast timescales. Built on linear dynamical models, our results offer parsimonious explanations for the activity propagation and network control profiles supported by regions of differing neuroanatomical structure.
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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.