ArticleNeuroImage2009
Tractography-based priors for dynamic causal models.
Article in NeuroImage, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 1 of them a synthesis that pooled it.
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
78 citing papers in PubMed, 1 synthesis or guideline pooled it, 156 citations in OpenAlex.
- Meta-analysis in human neuroimaging: computational modeling of large-scale databases.Annual review of neuroscience · 2014Pooled it
- Atomoxetine restores the response inhibition network in Parkinson's disease.Brain : a journal of neurology · 2016Trial
- Inter-network Effective Connectivity During an Emotional Working Memory Task in Two Independent Samples of Young Adults.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Article
- Structurally informed resting-state effective connectivity recapitulates cortical hierarchy.bioRxiv : the preprint server for biology · 2025Article
- Habenula contributions to negative self-cognitions.Nature communications · 2025Article
- Structurally informed models of directed brain connectivity.Nature reviews. Neuroscience · 2025Review
- Reliability of dynamic causal modelling of resting-state magnetoencephalography.Human brain mapping · 2024Article
- Macroscale coupling between structural and effective connectivity in the mouse brain.Scientific reports · 2024Article
- A new causal centrality measure reveals the prominent role of subcortical structures in the causal architecture of the extended default mode network.Brain structure & function · 2023Article
- Increased meso-striatal connectivity mediates trait impulsivity inFrontiers in endocrinology · 2023Article
- Brain effective connectome based on fMRI and DTI data: Bayesian causal learning and assessment.PloS one · 2023Article
- Explainable Contrastive Multiview Graph Representation of Brain, Mind, and Behavior.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
- Structure supports function: Informing directed and dynamic functional connectivity with anatomical priors.Network neuroscience (Cambridge, Mass.) · 2022Article
- Article
- Whole-Brain Modelling: Past, Present, and Future.Advances in experimental medicine and biology · 2022Article
- fMRI-Based Effective Connectivity in Surgical Remediable Epilepsies: A Pilot Study.Brain topography · 2021Article
- Ageing and the Ipsilateral M1 BOLD Response: A Connectivity Study.Brain sciences · 2021Article
- Blue-Light Therapy Strengthens Resting-State Effective Connectivity within Default-Mode Network after Mild TBI.Journal of central nervous system disease · 2021Article
- Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model?Frontiers in neurology · 2021Article
- Top-down versus bottom-up attention differentially modulate frontal-parietal connectivity.Human brain mapping · 2020Article
18 more citing papers are in PubMed but not listed here.
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 3 institutions in 3 countries.
Funding
Abstract
Functional integration in the brain rests on anatomical connectivity (the presence of axonal connections) and effective connectivity (the causal influences mediated by these connections). The deployment of anatomical connections provides important constraints on effective connectivity, but does not fully determine it, because synaptic connections can be expressed functionally in a dynamic and context-dependent fashion. Although it is generally assumed that anatomical connectivity data is important to guide the construction of neurobiologically realistic models of effective connectivity; the degree to which these models actually profit from anatomical constraints has not yet been formally investigated. Here, we use diffusion weighted imaging and probabilistic tractography to specify anatomically informed priors for dynamic causal models (DCMs) of fMRI data. We constructed 64 alternative DCMs, which embodied different mappings between the probability of an anatomical connection and the prior variance of the corresponding of effective connectivity, and fitted them to empirical fMRI data from 12 healthy subjects. Using Bayesian model selection, we show that the best model is one in which anatomical probability increases the prior variance of effective connectivity parameters in a nonlinear and monotonic (sigmoidal) fashion. This means that the higher the likelihood that a given connection exists anatomically, the larger one should set the prior variance of the corresponding coupling parameter; hence making it easier for the parameter to deviate from zero and represent a strong effective connection. To our knowledge, this study provides the first formal evidence that probabilistic knowledge of anatomical connectivity can improve models of functional integration.
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.