Journal ArticleDOI
Sparse representation of whole-brain fMRI signals for identification of functional networks.
Jinglei Lv,Xi Jiang,Xiang Li,Dajiang Zhu,Hanbo Chen,Tuo Zhang,Tuo Zhang,Shu Zhang,Xintao Hu,Junwei Han,Heng Huang,Jing Zhang,Lei Guo,Tianming Liu +13 more
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TLDR
Experimental results have shown that this novel methodology can uncover multiple functional networks that can be well characterized and interpreted in spatial, temporal and frequency domains based on current brain science knowledge.About:
This article is published in Medical Image Analysis.The article was published on 2015-02-01. It has received 175 citations till now. The article focuses on the topics: Sparse approximation & Neural coding.read more
Citations
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Journal ArticleDOI
Hybrid High-order Functional Connectivity Networks Using Resting-state Functional MRI for Mild Cognitive Impairment Diagnosis.
TL;DR: A novel framework, namely “hybrid high-order FC networks” is proposed by exploiting the higher-level dynamic interaction among brain regions for early mild cognitive impairment (eMCI) diagnosis by achieving superior diagnosis accuracy and could be promising for understanding pathological changes of brain connectome.
Journal ArticleDOI
Automatic Recognition of fMRI-Derived Functional Networks Using 3-D Convolutional Neural Networks
Yu Zhao,Qinglin Dong,Shu Zhang,Wei Zhang,Hanbo Chen,Xi Jiang,Lei Guo,Xintao Hu,Junwei Han,Tianming Liu +9 more
TL;DR: This study designs, applies, and evaluates a deep 3-D CNN framework for automatic, effective, and accurate classification and recognition of large number of functional brain networks reconstructed by sparse representation of whole-brain fMRI signals and provides a new deep learning approach for modeling functional connectomes based on fMRI data.
Journal ArticleDOI
Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations
TL;DR: This paper proposes a novel two-stage sparse representation framework and results revealed that there are distinctive and descriptive atoms in the cross-subjects common dictionary that can effectively characterize and differentiate tfMRI and rsfMRI signals, achieving 100 % classification accuracy.
Journal ArticleDOI
Supervised Dictionary Learning for Inferring Concurrent Brain Networks
TL;DR: A novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data- driven method.
Journal ArticleDOI
The connectivity domain: Analyzing resting state fMRI data using feature-based data-driven and model-based methods
Armin Iraji,Vince D. Calhoun,Natalie Wiseman,Esmaeil Davoodi-Bojd,Mohammad R. N. Avanaki,E. Mark Haacke,Zhifeng Kou +6 more
TL;DR: The connectivity domain is proposed, which can be constructed from any technique that identifies sets of features that are similar across subjects and can greatly help researchers in the study of macro-connectome brain function by enabling a wide range of model-based and data-driven approaches on rsfMRI data.
References
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Compressed sensing
TL;DR: It is possible to design n=O(Nlog(m)) nonadaptive measurements allowing reconstruction with accuracy comparable to that attainable with direct knowledge of the N most important coefficients, and a good approximation to those N important coefficients is extracted from the n measurements by solving a linear program-Basis Pursuit in signal processing.
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Robust Face Recognition via Sparse Representation
TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.
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Statistical parametric maps in functional imaging: A general linear approach
Karl J. Friston,Andrew P. Holmes,Keith J. Worsley,J-B. Poline,Chris D. Frith,Richard S. J. Frackowiak +5 more
TL;DR: In this paper, the authors present a general approach that accommodates most forms of experimental layout and ensuing analysis (designed experiments with fixed effects for factors, covariates and interaction of factors).
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Matching pursuits with time-frequency dictionaries
Stéphane Mallat,Zhifeng Zhang +1 more
TL;DR: The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions, chosen in order to best match the signal structures.
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The human brain is intrinsically organized into dynamic, anticorrelated functional networks
Michael D. Fox,Abraham Z. Snyder,Justin L. Vincent,Maurizio Corbetta,David C. Van Essen,Marcus E. Raichle +5 more
TL;DR: It is suggested that both task-driven neuronal responses and behavior are reflections of this dynamic, ongoing, functional organization of the brain, featuring the presence of anticorrelated networks in the absence of overt task performance.