ArticleInformation processing in medical imaging : proceedings of the ... conference2019
Variational Autoencoder with Truncated Mixture of Gaussians for Functional Connectivity Analysis.
Article in Information processing in medical imaging : proceedings of the ... conference, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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.
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Who cites it
7 citing papers in PubMed.
- SpaRG: Sparsely Reconstructed Graphs for Generalizable fMRI Analysis.Machine learning in clinical neuroimaging : 7th international workshop, MLCN 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. MLCN (Workshop) (7th : 2024 : Marrakesh, Morocco) · 2025Article
- Deep learning models reveal the link between dynamic brain connectivity patterns and states of consciousness.Scientific reports · 2024Article
- Supervised Multi-Layer Conditional Variational Auto-Encoder for Process Modeling and Soft Sensor.Sensors (Basel, Switzerland) · 2023Article
- Graph auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets.NeuroImage · 2021Article
- Article
- Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020Article
- Variational AutoEncoder For Regression: Application to Brain Aging Analysis.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Resting-state functional connectivity states are often identified as clusters of dynamic connectivity patterns. However, existing clustering approaches do not distinguish major states from rarely occurring minor states and hence are sensitive to noise. To address this issue, we propose to model major states using a non-linear generative process guided by a Gaussian-mixture distribution in a low-dimensional latent space, while separately modeling the connectivity patterns of minor states by a non-informative uniform distribution. We embed this truncated Gaussian-Mixture model in a Variational Autoencoder framework to obtain a general joint clustering and outlier detection approach, tGM-VAE. When applied to synthetic data with known ground-truth, tGM-VAE is more accurate in clustering connectivity patterns than existing approaches. On the rs-fMRI of 593 healthy adolescents, tGM-VAE identifies meaningful major connectivity states. The dwell time of these states significantly correlates with age.
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.