ArticleFrontiers in neurology2022
Resting state network mapping in individuals using deep learning.
Article in Frontiers in neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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.
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
11 citing papers in PubMed, 12 citations in OpenAlex.
- Muscle loss must be interpreted in the context of fluid imbalance: a dual-parameter model predicts functional outcomes after acute stroke.European geriatric medicine · 2026Article
- Predicting progression-free survival in glioblastoma with neuroimaging and machine learning.Journal of neuro-oncology · 2026Article
- Mapping Functional Brain Organization Using Artificial Intelligence.Chemical & biomedical imaging · 2026Review
- Commonality and Variability in Functional Networks in Children Under 5 Years Old.bioRxiv : the preprint server for biology · 2026Article
- Aging and the Spectral Properties of Brain Hemodynamics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Anchoring Functional Connectivity to Individual Sulcal Morphology Yields Insights in a Pediatric Study of Reasoning.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Evolution Trend of Brain Science Research: An Integrated Bibliometric and Mapping Approach.Brain and behavior · 2025Review
- Anchoring functional connectivity to individual sulcal morphology yields insights in a pediatric study of reasoning.bioRxiv : the preprint server for biology · 2025Article
- Data-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning.Journal of neurosurgery · 2023Article
- A state-of-the-art review on deep learning for estimating eloquent cortex from resting-state fMRI.Neurosurgical review · 2023Review
- Predicting survival in glioblastoma with multimodal neuroimaging and machine learning.Journal of neuro-oncology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 4 institutions in 1 country.
Funding
Abstract
Introduction: Resting state functional MRI (RS-fMRI) is currently used in numerous clinical and research settings. The localization of resting state networks (RSNs) has been utilized in applications ranging from group analysis of neurodegenerative diseases to individual network mapping for pre-surgical planning of tumor resections. Reproducibility of these results has been shown to require a substantial amount of high-quality data, which is not often available in clinical or research settings. Methods: In this work, we report voxelwise mapping of a standard set of RSNs using a novel deep 3D convolutional neural network (3DCNN). The 3DCNN was trained on publicly available functional MRI data acquired in Results: Our results indicate this method can be applied in individual subjects and is highly resistant to both noisy data and fewer RS-fMRI time points than are typically acquired. Further, our results show core regions within each network that exhibit high average probability and low STD. Discussion: The 3DCNN algorithm can generate individual RSN localization maps, which are necessary for clinical applications. The similarity between 3DCNN mapping results and task-based fMRI responses supports the association of specific functional tasks with RSNs.
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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.