SynthesisMedical image analysis2019
Discovering hierarchical common brain networks via multimodal deep belief network.
Synthesis in Medical image analysis, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
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Who cites it
8 citing papers in PubMed.
- Advances in functional magnetic resonance imaging-based brain function mapping: a deep learning perspective.Psychoradiology · 2025Review
- Computing personalized brain functional networks from fMRI using self-supervised deep learning.Medical image analysis · 2023Article
- Control energy assessment of spatial interactions among macro-scale brain networks.Human brain mapping · 2022Article
- Spatiotemporal trajectories in resting-state FMRI revealed by convolutional variational autoencoder.NeuroImage · 2021Article
- Directed Spectral Measures Improve Latent Network Models Of Neural Populations.Advances in neural information processing systems · 2021Article
- Brain functional connectivity analysis based on multi-graph fusion.Medical image analysis · 2021Article
- Fundamental functional differences between gyri and sulci: implications for brain function, cognition, and behavior.Psychoradiology · 2021Review
- Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations.JMIR mHealth and uHealth · 2019Article
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.
Funding
Abstract
Studying a common architecture reflecting both brain's structural and functional organizations across individuals and populations in a hierarchical way has been of significant interest in the brain mapping field. Recently, deep learning models exhibited ability in extracting meaningful hierarchical structures from brain imaging data, e.g., fMRI and DTI. However, deep learning models have been rarely used to explore the relation between brain structure and function yet. In this paper, we proposed a novel multimodal deep believe network (DBN) model to discover and quantitatively represent the hierarchical organizations of common and consistent brain networks from both fMRI and DTI data. A prominent characteristic of DBN is that it is capable of extracting meaningful features from complex neuroimaging data with a hierarchical manner. With our proposed DBN model, three hierarchical layers with hundreds of common and consistent brain networks across individual brains are successfully constructed through learning a large dimension of representative features from fMRI/DTI data.
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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.