ArticlePLoS computational biology2018
Simulations to benchmark time-varying connectivity methods for fMRI.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Increased sensitivity in identifying language-related functional connectivity using jackknife resampling analyses.Network neuroscience (Cambridge, Mass.) · 2026Article
- Identifying dynamic reproducible brain states using a predictive modelling approach.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- The Connectional Diaschisis and Normalization of Cortical Language Network Dynamics After Basal Ganglia and Thalamus Stroke.Neurobiology of language (Cambridge, Mass.) · 2025Article
- Provincial and connector qualities of somatosensory brain network hubs in bipolar disorder.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Time-varying functional connectivity as Wishart processes.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- Brain network segregation and integration during painful thermal stimulation.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Revisiting sex differences in the acquisition and extinction of threat conditioning in humans.Learning & memory (Cold Spring Harbor, N.Y.) · 2022Article
- Article
- Multiple spatial scale mapping of time-resolved brain network reconfiguration during evoked pain in patients with rheumatoid arthritis.Frontiers in neuroscience · 2022Article
- High-level cognition during story listening is reflected in high-order dynamic correlations in neural activity patterns.Nature communications · 2021Article
- Fear extinction learning modulates large-scale brain connectivity.NeuroImage · 2021Article
- Tools of the trade: estimating time-varying connectivity patterns from fMRI data.Social cognitive and affective neuroscience · 2021Review
- The ascending arousal system promotes optimal performance through mesoscale network integration in a visuospatial attentional task.Network neuroscience (Cambridge, Mass.) · 2021Article
- Brain Connectivity Studies on Structure-Function Relationships: A Short Survey with an Emphasis on Machine Learning.Computational intelligence and neuroscience · 2021Review
- A novel dynamic network imaging analysis method reveals aging-related fragmentation of cortical networks in mouse.Network neuroscience (Cambridge, Mass.) · 2021Article
- Investigating time-varying functional connectivity derived from the Jackknife Correlation method for distinguishing between emotions in fMRI data.Cognitive neurodynamics · 2020Article
- Time-varying nodal measures with temporal community structure: A cautionary note to avoid misinterpretation.Human brain mapping · 2020Article
- Statistical model for dynamically-changing correlation matrices with application to brain connectivity.Journal of neuroscience methods · 2020Review
- Questions and controversies in the study of time-varying functional connectivity in resting fMRI.Network neuroscience (Cambridge, Mass.) · 2020Review
- Human cognition involves the dynamic integration of neural activity and neuromodulatory systems.Nature neuroscience · 2019Article
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
No grant is acknowledged in the PubMed record.
Abstract
There is a current interest in quantifying time-varying connectivity (TVC) based on neuroimaging data such as fMRI. Many methods have been proposed, and are being applied, revealing new insight into the brain's dynamics. However, given that the ground truth for TVC in the brain is unknown, many concerns remain regarding the accuracy of proposed estimates. Since there exist many TVC methods it is difficult to assess differences in time-varying connectivity between studies. In this paper, we present tvc_benchmarker, which is a Python package containing four simulations to test TVC methods. Here, we evaluate five different methods that together represent a wide spectrum of current approaches to estimating TVC (sliding window, tapered sliding window, multiplication of temporal derivatives, spatial distance and jackknife correlation). These simulations were designed to test each method's ability to track changes in covariance over time, which is a key property in TVC analysis. We found that all tested methods correlated positively with each other, but there were large differences in the strength of the correlations between methods. To facilitate comparisons with future TVC methods, we propose that the described simulations can act as benchmark tests for evaluation of methods. Using tvc_benchmarker researchers can easily add, compare and submit their own TVC methods to evaluate its performance.
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.