ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2016
Reveal Consistent Spatial-Temporal Patterns from Dynamic Functional Connectivity for Autism Spectrum Disorder Identification.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis.Scientific reports · 2022Pooled it
- Control energy assessment of spatial interactions among macro-scale brain networks.Human brain mapping · 2022Article
- Identifying Autism Spectrum Disorder With Multi-Site fMRI via Low-Rank Domain Adaptation.IEEE transactions on medical imaging · 2020Article
- Fusion of ULS Group Constrained High- and Low-Order Sparse Functional Connectivity Networks for MCI Classification.Neuroinformatics · 2020Article
- A Deep Network Model on Dynamic Functional Connectivity With Applications to Gender Classification and Intelligence Prediction.Frontiers in neuroscience · 2020Article
- Transient states of network connectivity are atypical in autism: A dynamic functional connectivity study.Human brain mapping · 2019Article
- Resting-state functional MRI studies on infant brains: A decade of gap-filling efforts.NeuroImage · 2019Review
- Spatio-temporal modeling of connectome-scale brain network interactions via time-evolving graphs.NeuroImage · 2018Review
- Multimodal approaches to functional connectivity in autism spectrum disorders: An integrative perspective.Developmental neurobiology · 2018Review
- Unsupervised Manifold Learning Using High-Order Morphological Brain Networks Derived From T1-w MRI for Autism Diagnosis.Frontiers in neuroinformatics · 2018Article
- Maximum Mean Discrepancy Based Multiple Kernel Learning for Incomplete Multimodality Neuroimaging Data.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2017Article
- A Tensor Statistical Model for Quantifying Dynamic Functional Connectivity.Information processing in medical imaging : proceedings of the ... conference · 2017Article
- Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset.Frontiers in neuroscience · 2017Article
- Low-Rank Graph-Regularized Structured Sparse Regression for Identifying Genetic Biomarkers.IEEE transactions on big dataArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Functional magnetic resonance imaging (fMRI) provides a non-invasive way to investigate brain activity. Recently, convergent evidence shows that the correlations of spontaneous fluctuations between two distinct brain regions dynamically change even in resting state, due to the condition-dependent nature of brain activity. Thus, quantifying the patterns of functional connectivity (FC) in a short time period and changes of FC over time can potentially provide valuable insight into both individual-based diagnosis and group comparison. In light of this, we propose a novel computational method to robustly estimate both static and dynamic spatial-temporal connectivity patterns from the observed noisy signals of individual subject. We achieve this goal in two folds: (1)
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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.