ArticleNetwork neuroscience (Cambridge, Mass.)2019
Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods.
Article 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 26 papers.
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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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Who cites it
26 citing papers in PubMed, 83 citations in OpenAlex.
- State-dependent signal flow hierarchy in the human cerebral cortex.Nature neuroscience · 2026Article
- The Human Cerebellum Encodes Temporally Sensitive Reinforcement Learning Signals.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- NeuroTree: Hierarchical Functional Brain Pathway Decoding for Mental Health Disorders.Proceedings of machine learning research · 2025Article
- Instantaneous Frequency: A New Functional Biomarker for Dynamic Brain Causal Networks.AI in neuroscience · 2025Article
- Mapping effective connectivity by virtually perturbing a surrogate brain.Nature methods · 2025Article
- Whole-brain causal discovery using fMRI.Network neuroscience (Cambridge, Mass.) · 2025Article
- Regularized partial correlation provides reliable functional connectivity estimates while correcting for widespread confounding.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- A Deep Dynamic Causal Learning Model to Study Changes in Dynamic Effective Connectivity During Brain Development.IEEE transactions on bio-medical engineering · 2024Article
- Interpretable Spatio-Temporal Embedding for Brain Structural-Effective Network with Ordinary Differential Equation.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Article
- Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees.Advances in neural information processing systems · 2023Article
- Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations.NeuroImage · 2023Article
- Amortization Transformer for Brain Effective Connectivity Estimation from fMRI Data.Brain sciences · 2023Article
- Application of quantum computing to a linear non-Gaussian acyclic model for novel medical knowledge discovery.PloS one · 2023Article
- GRACE-C: Generalized Rate Agnostic Causal Estimation via Constraints.... International Conference on Learning Representations · 2023Article
- Review
- Causal machine learning for healthcare and precision medicine.Royal Society open science · 2022Review
- The resting-state causal human connectome is characterized by hub connectivity of executive and attentional networks.NeuroImage · 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
- Statistical Perspective on Functional and Causal Neural Connectomics: A Comparative Study.Frontiers in systems neuroscience · 2022Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
We test the adequacies of several proposed and two new statistical methods for recovering the causal structure of systems with feedback from synthetic BOLD time series. We compare an adaptation of the first correct method for recovering cyclic linear systems; Granger causal regression; a multivariate autoregressive model with a permutation test; the Group Iterative Multiple Model Estimation (GIMME) algorithm; the Ramsey et al. non-Gaussian methods; two non-Gaussian methods by Hyvärinen and Smith; a method due to Patel et al.; and the GlobalMIT algorithm. We introduce and also compare two new methods, Fast Adjacency Skewness (FASK) and Two-Step, both of which exploit non-Gaussian features of the BOLD signal. We give theoretical justifications for the latter two algorithms. Our test models include feedback structures with and without direct feedback (2-cycles), excitatory and inhibitory feedback, models using experimentally determined structural connectivities of macaques, and empirical human resting-state and task data. We find that averaged over all of our simulations, including those with 2-cycles, several of these methods have a better than 80% orientation precision (i.e., the probability of a directed edge is in the true structure given that a procedure estimates it to be so) and the two new methods also have better than 80% recall (probability of recovering an orientation in the true 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.