ArticleNeuroImage2018
Extracting orthogonal subject- and condition-specific signatures from fMRI data using whole-brain effective connectivity.
Article in NeuroImage, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06387719 (Precursors of Binge Eating Disorder in a Clinical Sample of Adolescents With Obesity), which is not on this 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.
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.
Precursors of Binge Eating Disorder in a Clinical Sample of Adolescents With Obesity: Early MRI Markers of Brain Reward and Inhibition Processing Dysfunction
Who cites it
26 citing papers in PubMed.
- Investigating effects of day-to-day variations in environmental exposure on the human brain: study protocol for the Day2Day Environment project.BMC neuroscience · 2026Article
- Article
- Longitudinal investigation of structural and resting-state effective connectivity alterations in a non-human primate model of Huntington's disease.Neurobiology of disease · 2025Article
- Network Specificity in Predicting Childhood Trauma Characteristics Using Effective Connectivity.Alpha psychiatry · 2025Article
- Structurally informed models of directed brain connectivity.Nature reviews. Neuroscience · 2025Review
- Evaluating machine learning pipelines for multimodal neuroimaging in small cohorts: an ALS case study.Frontiers in neuroinformatics · 2025Article
- Discovering individual fingerprints in resting-state functional connectivity using deep neural networks.Human brain mapping · 2024Article
- Estimating dynamic individual coactivation patterns based on densely sampled resting-state fMRI data and utilizing it for better subject identification.Brain structure & function · 2023Article
- Enhancing the network specific individual characteristics in rs-fMRI functional connectivity by dictionary learning.Human brain mapping · 2023Article
- Static and dynamic functional connectomes represent largely similar information.bioRxiv : the preprint server for biology · 2023Article
- Static and dynamic fMRI-derived functional connectomes represent largely similar information.Network neuroscience (Cambridge, Mass.) · 2023Article
- Meditation-induced effects on whole-brain structural and effective connectivity.Brain structure & function · 2022Article
- Improving Functional Connectome Fingerprinting with Degree-Normalization.Brain connectivity · 2022Article
- One session of fMRI-Neurofeedback training on motor imagery modulates whole-brain effective connectivity and dynamical complexity.Cerebral cortex communications · 2022Article
- Advancing brain network models to reconcile functional neuroimaging and clinical research.NeuroImage. Clinical · 2022Review
- Computational Models in Electroencephalography.Brain topography · 2022Review
- Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease.Human brain mapping · 2021Article
- Geodesic Distance on Optimally Regularized Functional Connectomes Uncovers Individual Fingerprints.Brain connectivity · 2021Article
- Effective connectivity extracts clinically relevant prognostic information from resting state activity in stroke.Brain communications · 2021Article
- Accurate prediction of individual subject identity and task, but not autism diagnosis, from functional connectomes.Human brain mapping · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The study of brain communication based on fMRI data is often limited because such measurements are a mixture of session-to-session variability with subject- and condition-related information. Disentangling these contributions is crucial for real-life applications, in particular when only a few recording sessions are available. The present study aims to define a reliable standard for the extraction of multiple signatures from fMRI data, while verifying that they do not mix information about the different modalities (e.g., subjects and conditions such as tasks performed by them). In particular, condition-specific signatures should not be contaminated by subject-related information, since they aim to generalize over subjects. Practically, signatures correspond to subnetworks of directed interactions between brain regions (typically 100 covering the whole brain) supporting the subject and condition identification for single fMRI sessions. The key for robust prediction is using effective connectivity instead of functional connectivity. Our method demonstrates excellent generalization capabilities for subject identification in two datasets, using only a few sessions per subject as reference. Using another dataset with resting state and movie viewing, we show that the two signatures related to subjects and tasks correspond to distinct subnetworks, which are thus topologically orthogonal. Our results set solid foundations for applications tailored to individual subjects, such as clinical diagnostic.
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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.