ReviewNeuroImage2018
Modeling and interpreting mesoscale network dynamics.
Review in NeuroImage, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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
54 citing papers in PubMed.
- Brain network dynamics reflect psychiatric illness status and transdiagnostic symptom profiles across health and disease.Nature communications · 2026Article
- Nursing Interns' Disgust Sensitivity, Ageism and Gerontological Nursing Career Motivation: A Network Analysis.Journal of nursing management · 2026Article
- Static and dynamic intracerebral signal analysis reveals protective networks against seizures in drug-resistant focal epilepsy.Brain communications · 2026Article
- The Possibility Space Concept in Neuroscience: Possibilities, Constraints, and Explanations.The European journal of neuroscience · 2025Review
- Revealing brain network dynamics during the emotional state of suspense using TDA.Network neuroscience (Cambridge, Mass.) · 2025Article
- Machine Learning and Graph Signal Processing Applied to Healthcare: A Review.Bioengineering (Basel, Switzerland) · 2024Review
- NBS-SNI, an extension of the network-based statistic: Abnormal functional connections between important structural actors.Network neuroscience (Cambridge, Mass.) · 2024Article
- Modelling and Controlling System Dynamics of the Brain: An Intersection of Machine Learning and Control Theory.Advances in neurobiology · 2024Review
- Towards a biologically annotated brain connectome.Nature reviews. Neuroscience · 2023Review
- The fractal brain: scale-invariance in structure and dynamics.Cerebral cortex (New York, N.Y. : 1991) · 2023Review
- Increased flexibility of brain dynamics in patients with multiple sclerosis.Brain communications · 2023Article
- NeuMapper: A scalable computational framework for multiscale exploration of the brain's dynamical organization.Network neuroscience (Cambridge, Mass.) · 2022Article
- Network controllability mediates the relationship between rigid structure and flexible dynamics.Network neuroscience (Cambridge, Mass.) · 2022Article
- Supervising the Decoder of Variational Autoencoders to Improve Scientific Utility.IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society · 2022Article
- Theoretical foundations of studying criticality in the brain.Network neuroscience (Cambridge, Mass.) · 2022Review
- Multiframe Evolving Dynamic Functional ConnectivityFrontiers in neuroscience · 2022Article
- Article
- Imaging the temporal dynamics of brain states with highly sampled fMRI.Current opinion in behavioral sciences · 2021Article
- Time-Varying Functional Connectivity Decreases as a Function of Acute Nicotine Abstinence.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2021Article
- Altered Brain Functional Network in Subtypes of Parkinson's Disease: A Dynamic Perspective.Frontiers in aging neuroscience · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Recent advances in brain imaging techniques, measurement approaches, and storage capacities have provided an unprecedented supply of high temporal resolution neural data. These data present a remarkable opportunity to gain a mechanistic understanding not just of circuit structure, but also of circuit dynamics, and its role in cognition and disease. Such understanding necessitates a description of the raw observations, and a delineation of computational models and mathematical theories that accurately capture fundamental principles behind the observations. Here we review recent advances in a range of modeling approaches that embrace the temporally-evolving interconnected structure of the brain and summarize that structure in a dynamic graph. We describe recent efforts to model dynamic patterns of connectivity, dynamic patterns of activity, and patterns of activity atop connectivity. In the context of these models, we review important considerations in statistical testing, including parametric and non-parametric approaches. Finally, we offer thoughts on careful and accurate interpretation of dynamic graph architecture, and outline important future directions for method development.
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.