ReviewNetwork neuroscience (Cambridge, Mass.)2019
Disentangling causal webs in the brain using functional magnetic resonance imaging: A review of current approaches.
Review in Network neuroscience (Cambridge, Mass.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 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
35 citing papers in PubMed, 77 citations in OpenAlex.
- Altered Driving Structures and Controllability Patterns of Brain Effective Networks in Mild Cognitive Impairment and Alzheimer's Disease.Brain sciences · 2026Article
- A Deep Learning Framework for Spatiotemporal Modeling of Visual Task fMRI.bioRxiv : the preprint server for biology · 2026Article
- Causal Brain Network Alterations Across Disease Stages in Herpes Zoster and Postherpetic Neuralgia.Journal of pain research · 2026Article
- Communication of perceptual predictions from the hippocampus to the deep layers of the parahippocampal cortex.Science advances · 2025Article
- CAUSAL MODELING OF FMRI TIME-SERIES FOR INTERPRETABLE AUTISM SPECTRUM DISORDER CLASSIFICATION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Exploring the neurobiological correlates of psilocybin-assisted psychotherapy in eating disorders: a review of potential methodologies and implications for the psychedelic study design.Journal of eating disorders · 2024Review
- Identification of abnormal closed-loop pathways in patients with MRI-negative pharmacoresistant epilepsy.Brain imaging and behavior · 2024Article
- Developing a novel causal inference algorithm for personalized biomedical causal graph learning using meta machine learning.BMC medical informatics and decision making · 2024Article
- Causation in neuroscience: keeping mechanism meaningful.Nature reviews. Neuroscience · 2024Review
- Chemogenetic inhibition of dopaminergic neurons reduces stimulus-induced dopamine release, thereby altering the hemodynamic response function in the prefrontal cortex.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- A new transfer entropy method for measuring directed connectivity from complex-valued fMRI data.Frontiers in neuroscience · 2024Article
- The subcortical brain regions influence the cortical areas during resting-state: an fMRI study.Frontiers in human neuroscience · 2024Article
- Novel machine learning approaches for improving the reproducibility and reliability of functional and effective connectivity from functional MRI.Journal of neural engineering · 2023Article
- Locating causal hubs of memory consolidation in spontaneous brain network in male mice.Nature communications · 2023Article
- Latent Similarity Identifies Important Functional Connections for Phenotype Prediction.IEEE transactions on bio-medical engineering · 2023Article
- Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine.JMIR medical informatics · 2023Article
- Restoring the missing person to personalized medicine and precision psychiatry.Frontiers in neuroscience · 2023Article
- Functional Magnetic Resonance Imaging Studies in Sexual Medicine: A Primer.The journal of sexual medicine · 2022Article
- Forecasting brain activity based on models of spatiotemporal brain dynamics: A comparison of graph neural network architectures.Network neuroscience (Cambridge, Mass.) · 2022Article
- Dynamical differential covariance recovers directional network structure in multiscale neural systems.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the past two decades, functional Magnetic Resonance Imaging (fMRI) has been used to relate neuronal network activity to cognitive processing and behavior. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this paper, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, Linear Non-Gaussian Acyclic Models, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area.
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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.