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Identifying Sparse Connectivity Patterns in the brain using resting-state fMRI

Harini Eavani, Theodore D. Satterthwaite, Roman Filipovych, Raquel E. Gur, Ruben C. Gur, Christos Davatzikos
2015 NeuroImage  
Hence we exploit recent advances in the mathematics of sparse modeling to develop a methodological framework aiming to understand complex resting-state fMRI connectivity data.  ...  By favoring networks that explain the data via a relatively small number of participating brain regions, we obtain a parsimonious representation of brain function in terms of multiple "Sparse Connectivity  ...  Satterthwaite was supported by K23MH098130 and the Marc Rapport Family Investigator grant through the Brain and Behavior Foundation.  ... 
doi:10.1016/j.neuroimage.2014.09.058 pmid:25284301 pmcid:PMC4262564 fatcat:h3u2syq4wvabvkjkkfonpxpovm

Convolutional Sparse Coded Dynamic Brain Functional Connectivity [article]

Jin Yan, Yingying Zhu
2018 bioRxiv   pre-print
However, most previous work focus on static dynamic brain network research. Lots of recent work reveals that the brain shows dynamic activity even in resting state.  ...  Such dynamic brain functional connectivity reveals discriminative patterns for identifying many brain disorders.  ...  We conducted various experiments on resting-state fMRI images using two Autism data sets in order to demonstrate the generality of our method.  ... 
doi:10.1101/476663 fatcat:v5w25f32ejc5bbw23d67bbza3y

Deriving Autism Spectrum Disorder Functional Networks from RS-FMRI Data using Group ICA and Dictionary Learning [article]

Xin Yang, Ning Zhang, Donglin Wang
2021 arXiv   pre-print
In our experiments, the ASD functional networks were derived from resting-state functional magnetic resonance imaging (rs-fMRI) data.  ...  developing (TD) participants using the functional connectivity calculated from the derived functional networks.  ...  Scientists have come to recognize the usefulness of studying whether patterns identified in resting-state fMRI data exhibit the same characteristics under different conditions.  ... 
arXiv:2106.09000v1 fatcat:3htmohdmn5g2fexbogfplcfnhm

Signal sampling for efficient sparse representation of resting state FMRI data

Bao Ge, Milad Makkie, Jin Wang, Shijie Zhao, Xi Jiang, Xiang Li, Jinglei Lv, Shu Zhang, Wei Zhang, Junwei Han, Lei Guo, Tianming Liu
2015 Brain Imaging and Behavior  
represent the whole brain's signals and identify the resting state networks.  ...  To effectively address this problem, this work proposes to represent resting state fMRI (rs-fMRI) signals of a whole brain via a statistical sampling based sparse representation.  ...  The authors would like to thank the anonymous reviewers for their constructive comments.  ... 
doi:10.1007/s11682-015-9487-0 pmid:26646924 pmcid:PMC4899318 fatcat:yiinkc7ml5b2riujqdos3mgx2e

Characterization of task-free and task-performance brain states via functional connectome patterns

Xin Zhang, Lei Guo, Xiang Li, Tuo Zhang, Dajiang Zhu, Kaiming Li, Hanbo Chen, Jinglei Lv, Changfeng Jin, Qun Zhao, Lingjiang Li, Tianming Liu
2013 Medical Image Analysis  
Both resting state fMRI (R-fMRI) and task-based fMRI (T-fMRI) have been widely used to study the functional activities of the human brain during task-free and task-performance periods, respectively.  ...  then an effective sparse representation method was applied to learn the atomic connectome patterns (ACP) of both task-free and task-performance states.  ...  The authors would like to thank the anonymous reviewers for their constructive comments.  ... 
doi:10.1016/ pmid:23938590 pmcid:PMC3956081 fatcat:dysextzp5nehxip4vgnhh7lau4

Reveal Consistent Spatial-Temporal Patterns from Dynamic Functional Connectivity for Autism Spectrum Disorder Identification [chapter]

Yingying Zhu, Xiaofeng Zhu, Han Zhang, Wei Gao, Dinggang Shen, Guorong Wu
2016 Lecture Notes in Computer Science  
We applied the learned spatialtemporal patterns from fMRI images to identify autism subjects.  ...  It is hard to synchronize the estimated dynamic FC patterns and the real cognitive state changes, even using learning-based methods.  ...  For simplicity, many FC characterization methods assume that connectivity patterns in the brain do not change over the course of a resting-state fMRI scan.  ... 
doi:10.1007/978-3-319-46720-7_13 pmid:28149963 pmcid:PMC5278798 fatcat:5djcfqp5vvgj5drpilamklrh5q

Diagnosis of Autism Spectrum Disorders Using Temporally Distinct Resting-State Functional Connectivity Networks

Chong-Yaw Wee, Pew-Thian Yap, Dinggang Shen
2016 CNS Neuroscience & Therapeutics  
Resting-state functional magnetic resonance imaging (R-fMRI) is dynamic in nature as neural activities constantly change over the time and are dominated by repeating brief activations and deactivations  ...  Aims: We develop a novel framework that uses short-time activation patterns of brain connectivity to better detect subtle disease-induced disruptions of brain connectivity.  ...  Acknowledgement This work was supported in part by NIH grants (EB006733, EB008374, EB009634, MH100217, AG041721, AG049371, AG042599).  ... 
doi:10.1111/cns.12499 pmid:26821773 pmcid:PMC4839002 fatcat:hranpsrv25cunic5v4xllx5mky

Test-retest reliability of regression dynamic causal modeling

Stefan Frässle, Klaas E. Stephan
2021 Network Neuroscience  
Third, whole-brain connectivity patterns by rDCM allowed for identifying individual participants with high (and in some cases perfect) accuracy.  ...  Generally, for all methods and metrics, task-based connectivity estimates showed greater reliability than those from the resting state.  ...  Resting-state and task-based fMRI data from the Human Connectome Project (HCP) are used for the analysis.  ... 
doi:10.1162/netn_a_00215 pmid:35356192 pmcid:PMC8959103 fatcat:tgkavkq3lfgl7obtsmgy47way4

Identification of functional networks in resting state fMRI data using adaptive sparse representation and affinity propagation clustering

Xuan Li, Haixian Wang
2015 Frontiers in Neuroscience  
2 -norm for linear representation and then identify connectivity patterns with Affinity Propagation (AP) clustering algorithm.  ...  Specifically, we first establish a global functional connection network by using the Adaptive Sparse Representation (ASR), adaptively integrating the sparsity of ℓ 1 -norm and the grouping effect of ℓ  ...  In short, we propose a novel scheme to analyze resting state fMRI by constructing the global functional connection network via ASR and identifying sub-networks via AP.  ... 
doi:10.3389/fnins.2015.00383 pmid:26528123 pmcid:PMC4607787 fatcat:mxywwpxtuvdxrfb73l2hwineza

Dynamics of large-scale fMRI networks: Deconstruct brain activity to build better models of brain function

Fikret Işık Karahanoğlu, Dimitri Van De Ville
2017 Current Opinion in Biomedical Engineering  
In particular, the HMM framework has been applied for covariance states in terms of a sparse basis of co-activation patterns [53] .  ...  The brain states can be defined either at the activity or at the connectivity level. (B) Sparsely coupled HMMs exploit the transient activity to model the coupling between the states.  ... 
doi:10.1016/j.cobme.2017.09.008 fatcat:oo4evu6w2jgtvbithzy3emlyny

Statistical Learning for Resting-State fMRI: Successes and Challenges [chapter]

Gaël Varoquaux, Bertrand Thirion
2012 Lecture Notes in Computer Science  
Exploring two specific descriptions of resting-state fMRI, namely spatial analysis and connectivity graphs, we discuss the progress brought by statistical learning techniques, but also the neuroscientific  ...  In the absence of external stimuli, fluctuations in cerebral activity can be used to reveal intrinsic structures.  ...  The brain networks identified by spatial analysis suggest that brain connectivity contains large structures.  ... 
doi:10.1007/978-3-642-34713-9_22 fatcat:fan3pmyomfcxrjozf7cnsa7vay

Robust brain parcellation using sparse representation on resting-state fMRI

Yu Zhang, Svenja Caspers, Lingzhong Fan, Yong Fan, Ming Song, Cirong Liu, Yin Mo, Christian Roski, Simon Eickhoff, Katrin Amunts, Tianzi Jiang
2014 Brain Structure and Function  
Resting-state fMRI (rs-fMRI) has been widely used to segregate the brain into individual modules based on the presence of distinct connectivity patterns.  ...  Our results demonstrate a new promising approach to robust brain parcellation using resting-state fMRI by sparse representation.  ...  Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s)  ... 
doi:10.1007/s00429-014-0874-x pmid:25156576 pmcid:PMC4575697 fatcat:dac2236pdrdylp6wzpd3nqp2eq

Machine learning in resting-state fMRI analysis [article]

Meenakshi Khosla, Keith Jamison, Gia H. Ngo, Amy Kuceyeski, Mert R. Sabuncu
2018 arXiv   pre-print
Here, we present an overview of various unsupervised and supervised machine learning applications to rs-fMRI. We present a methodical taxonomy of machine learning methods in resting-state fMRI.  ...  Machine learning techniques have gained prominence for the analysis of resting-state functional Magnetic Resonance Imaging (rs-fMRI) data.  ...  Acknowledgements This work was supported by NIH R01 grants (R01LM012719 and R01AG053949), the NSF NeuroNex grant 1707312, and NSF CAREER grant (1748377).  ... 
arXiv:1812.11477v1 fatcat:nd6j5jbspzh2rmxyyufdyesxom

Sparse Dictionary Learning of Resting State fMRI Networks

Harini Eavani, Roman Filipovych, Christos Davatzikos, Theodore D. Satterthwaite, Raquel E. Gur, Ruben C. Gur
2012 2012 Second International Workshop on Pattern Recognition in NeuroImaging  
Research in resting state fMRI (rsfMRI) has revealed the presence of stable, anti-correlated functional subnetworks in the brain.  ...  In this paper, based on the assumption that the structure of the resting state functional brain connectivity is sparse, we utilize sparse dictionary modeling to identify distinct functional sub-networks  ...  Seed-based analyses in resting state fMRI have shown that the brain is organized into functionally modular subnetworks, which are replicable across subjects.  ... 
doi:10.1109/prni.2012.25 pmid:25178438 pmcid:PMC4145006 fatcat:5z2bjlnujzfqbgmpemhh26rza4

Connectome-scale functional intrinsic connectivity networks in macaques

Wei Zhang, Xi Jiang, Shu Zhang, Brittany R Howell, Yu Zhao, Tuo Zhang, Lei Guo, Mar M. Sanchez, Xiaoping Hu, Tianming Liu
2017 Neuroscience  
There have been extensive studies of intrinsic connectivity networks (ICNs) in the human brains using resting state fMRI in the literature.  ...  In this work, we propose a computational framework to identify connectome-scale group-wise consistent ICNs in macaques via sparse representation of whole-brain resting state fMRI data.  ...  The YNPRC is fully accredited by the Association for the Assessment and Accreditation of Laboratory Animal Care AAALAC), International.  ... 
doi:10.1016/j.neuroscience.2017.08.022 pmid:28842187 pmcid:PMC5653451 fatcat:dlg6v2gc7ndwfdf36ivk56cngy
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