ArticleMagnetic resonance imaging2010
Functional MRI and multivariate autoregressive models.
Article in Magnetic resonance imaging, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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.
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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
29 citing papers in PubMed, 68 citations in OpenAlex.
- Altered Interictal Bed Nucleus of Stria Terminalis Connectivity in Patients With Temporal Lobe Epilepsy.Neurology · 2025Article
- Time-resolved instantaneous functional loci estimation (TRIFLE): Estimating time-varying allocation of spatially overlapping sources in functional magnetic resonance imaging.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- A benchmark of individual auto-regressive models in a massive fMRI dataset.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- The arrow-of-time in neuroimaging time series identifies causal triggers of brain function.Human brain mapping · 2023Article
- Article
- Exploring communication between the thalamus and cognitive control-related functional networks in the cerebral cortex.Cognitive, affective & behavioral neuroscience · 2021Article
- Use of random matrix theory in the discovery of resting state brain networks.Magnetic resonance imaging · 2021Article
- Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate dependencies between brain regions.Human brain mapping · 2020Article
- Questions and controversies in the study of time-varying functional connectivity in resting fMRI.Network neuroscience (Cambridge, Mass.) · 2020Review
- Resting brain dynamics at different timescales capture distinct aspects of human behavior.Nature communications · 2019Article
- Multi-Level Clustering of Dynamic Directional Brain Network Patterns and Their Behavioral Relevance.Frontiers in neuroscience · 2019Article
- Nonparametric test for connectivity detection in multivariate autoregressive networks and application to multiunit activity data.Network neuroscience (Cambridge, Mass.) · 2018Article
- Sparse Estimation of Resting-State Effective Connectivity From fMRI Cross-Spectra.Frontiers in neuroscience · 2018Article
- Dynamic brain connectivity is a better predictor of PTSD than static connectivity.Human brain mapping · 2017Article
- Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI.NeuroImage · 2017Article
- Time-domain analysis for extracting fast-paced pupil responses.Scientific reports · 2017Article
- A whole-brain computational modeling approach to explain the alterations in resting-state functional connectivity during progression of Alzheimer's disease.NeuroImage. Clinical · 2017Article
- Joint Estimation of Multiple Graphical Models from High Dimensional Time Series.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2016Article
- Evolution of functional connectivity of brain networks and their dynamic interaction in temporal lobe epilepsy.Brain connectivity · 2015Article
- The equivalence of linear Gaussian connectivity techniques.Human brain mapping · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Connectivity refers to the relationships that exist between different regions of the brain. In the context of functional magnetic resonance imaging (fMRI), it implies a quantifiable relationship between hemodynamic signals from different regions. One aspect of this relationship is the existence of small timing differences in the signals in different regions. Delays of 100 ms or less may be measured with fMRI, and these may reflect important aspects of the manner in which brain circuits respond as well as the overall functional organization of the brain. The multivariate autoregressive time series model has features to recommend it for measuring these delays and is straightforward to apply to hemodynamic data. In this review, we describe the current usage of the multivariate autoregressive model for fMRI, discuss the issues that arise when it is applied to hemodynamic time series and consider several extensions. Connectivity measures like Granger causality that are based on the autoregressive model do not always reflect true neuronal connectivity; however, we conclude that careful experimental design could make this methodology quite useful in extending the information obtainable using fMRI.
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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.