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Collective sparse symmetric non-negative matrix factorization for identifying overlapping communities in resting-state brain functional networks

Xuan Li, John Q. Gan, Haixian Wang
2018 NeuroImage  
To comprehensively validate cssNMF, a simulated fMRI dataset with ground-truth, a real rs-fMRI dataset with two repeated sessions and another different real rs-fMRI dataset have been used for performance  ...  Resting-state functional magnetic resonance imaging (rs-fMRI) provides a valuable tool to study spontaneous brain activity.  ...  Xinian Zuo for providing the CoRR dataset and his help in data preprocessing and analysis. This work was supported in part by the National Basic Research Program of China under  ... 
doi:10.1016/j.neuroimage.2017.11.003 pmid:29117581 fatcat:3he4fcrsbrfefdyfexte76gi74

A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia

Ünal Sakoğlu, Godfrey D. Pearlson, Kent A. Kiehl, Y. Michelle Wang, Andrew M. Michael, Vince D. Calhoun
2010 Magnetic Resonance Materials in Physics, Biology and Medicine  
Materials and methods-We study the theoretical basis of the approach, perform a simulation analysis and apply it to fMRI data from schizophrenia patients (SP) and healthy controls (HC).  ...  Analyses on the fMRI data include: (a) group sICA to determine regions of significant task-related activity, (b) static and dynamic FNC analysis among these networks by using maximal laggedcorrelation  ...  The results based on the resting-state data analysis are not included in this paper and they are the subject of a separate study.  ... 
doi:10.1007/s10334-010-0197-8 pmid:20162320 pmcid:PMC2891285 fatcat:uctd7odovrgjthvbm5urgxmpju

Characterization of neuromagnetic brain rhythms over time scales of minutes using spatial independent component analysis

Pavan Ramkumar, Lauri Parkkonen, Riitta Hari, Aapo Hyvärinen
2011 Human Brain Mapping  
In functional magnetic resonance imaging (fMRI), by contrast, most studies use spatial ICA: each time point constitutes a row of the data matrix, and independence of the spatial patterns is maximized.  ...  The resulting method, spatial Fourier-ICA, provides a concise summary of the spatiotemporal and spectral content of spontaneous neuromagnetic oscillations in cortical source space over time scales of minutes  ...  Sanna Malinen for the original stimuli and Maarit Aro for contributing the Presentation TM code.  ... 
doi:10.1002/hbm.21303 pmid:21915941 fatcat:pmpm6iwxmbcuba5x3o32ltrtci

Shift-Invariant Canonical Polyadic Decomposition of Complex-Valued Multi-Subject fMRI Data with a Phase Sparsity Constraint

Li-Dan Kuang, Qiu-Hua Lin, Xiao-Feng Gong, Fengyu Cong, Yu-Ping Wang, Vince D. Calhoun
2019 IEEE Transactions on Medical Imaging  
The results from both the simulated and experimental fMRI data demonstrate improvements of the proposed method over three complex-valued algorithms, namely, tensor-based spatial ICA, shift-invariant CPD  ...  When comparing with a real-valued algorithm combining shift-invariant CPD and ICA, the proposed method detects 178.7% more contiguous task-related activations.  ...  We use the simulated fMRI data to evaluate the effects of spatial and temporal changes and the noise effect on pcsCPD, csCPD, CPD, and T-sICA. B.  ... 
doi:10.1109/tmi.2019.2936046 pmid:31425066 pmcid:PMC7473454 fatcat:4v5s2ftpbncr3bwjf5wogsooou

Robust Data Driven Model Order Estimation for Independent Component Analysis of fMRI Data with Low Contrast to Noise

Waqas Majeed, Malcolm J. Avison, Daniele Marinazzo
2014 PLoS ONE  
Independent component analysis (ICA) has been successfully utilized for analysis of functional MRI (fMRI) data for task related as well as resting state studies.  ...  However, there has been no consensus on the optimal method for nIC selection, and many studies utilize arbitrarily chosen values for nIC.  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of these institutions.  ... 
doi:10.1371/journal.pone.0094943 pmid:24788636 pmcid:PMC4005775 fatcat:6j7bt5d6ungdpp45zfpq47kkci

Impact of autocorrelation on functional connectivity

Mohammad R. Arbabshirani, Eswar Damaraju, Ronald Phlypo, Sergey Plis, Elena Allen, Sai Ma, Daniel Mathalon, Adrian Preda, Jatin G. Vaidya, Tülay Adali, Vince D. Calhoun
2014 NeuroImage  
In this paper, first we assess the effect of autocorrelation on Pearson correlation coefficient through theoretical approximation and simulation. Then we present this effect on real fMRI data.  ...  Some recent studies based on results from economics have questioned the conventional estimation of functional connectivity and argue that not correcting for autocorrelation in fMRI time-series results  ...  Acknowledgments We would like to acknowledge the efforts of all parties responsible for designing and implementing the experiment, collecting the data, and facilitating the brain imaging that made this  ... 
doi:10.1016/j.neuroimage.2014.07.045 pmid:25072392 pmcid:PMC4253536 fatcat:oxskide3ifacvdige2w2dwppj4

A Data-Driven Sparse GLM for fMRI Analysis Using Sparse Dictionary Learning With MDL Criterion

Kangjoo Lee, Sungho Tak, Jong Chul Ye
2011 IEEE Transactions on Medical Imaging  
Using simulation and real fMRI experiments, we show that the proposed method can adapt individual variation better compared to the conventional ICA methods.  ...  The main contribution of this paper is, therefore, a new data driven fMRI analysis that is derived solely based upon the sparsity of the signals.  ...  ACKNOWLEDGMENT This research was supported by the Korea Science and Engineering Foundation, Grant No.:2010-N01100084. We thank Dr. Yong Jeong and Mr.  ... 
doi:10.1109/tmi.2010.2097275 pmid:21138799 fatcat:ewpwapekdfgv7lam44dxxpplge

Wavelet-based fMRI analysis: 3-D denoising, signal separation, and validation metrics

Siddharth Khullar, Andrew Michael, Nicolle Correa, Tulay Adali, Stefi A. Baum, Vince D. Calhoun
2011 NeuroImage  
We verified our method using simulated as well as real fMRI data and compared our results against the conventional scheme (Gaussian smoothing + spatial ICA: s-ICA).  ...  The results show significant improvements based on two important features: (1) preservation of shape of the activation region (shape metrics) and (2) receiver operating characteristic curves.  ...  Traditional smoothing filters such as mean, median and Gaussian filters normally employed in the spatial domain smooth the signals with blurring effects and consequently result in loss of edge details.  ... 
doi:10.1016/j.neuroimage.2010.10.063 pmid:21034833 pmcid:PMC3058245 fatcat:ilh7vjkizfd63h6xcupuqcc7uy

Group-level spatial independent component analysis of Fourier envelopes of resting-state MEG data

Pavan Ramkumar, Lauri Parkkonen, Aapo Hyvärinen
2014 NeuroImage  
Abstract We developed a data-driven method to spatiotemporally and spectrally characterize the dynamics of brain oscillations in resting-state magnetoencephalography (MEG) data.  ...  We compared this method using a simulated data set against 5 other variants of independent component analysis and found that eSFICA performed best in characterizing dynamics at time scales of the order  ...  Analysis of real MEG data Based on the results from the simulations (see Section 3.1), we selected eSFICA to be applied on resting-state MEG data and studied the dynamics of the estimated components during  ... 
doi:10.1016/j.neuroimage.2013.10.032 pmid:24185028 fatcat:rmwxjskq5rgnbj6rkqdrngs56m

Large-scale functional network overlap is a general property of brain functional organization: Reconciling inconsistent fMRI findings from general-linear-model-based analyses

Jiansong Xu, Marc N. Potenza, Vince D. Calhoun, Rubin Zhang, Sarah W. Yip, John T. Wall, Godfrey D. Pearlson, Patrick D. Worhunsky, Kathleen A. Garrison, Joseph M. Moran
2016 Neuroscience and Biobehavioral Reviews  
This paper selectively reviews findings of histological and electrophysiological studies and fMRI spatial independent component analysis (sICA) and reports new findings by applying sICA to two existing  ...  Functional magnetic resonance imaging (fMRI) studies regularly use univariate general-linear-model-based analyses (GLM).  ...  Several other studies further investigated the above-discussed finding of anti-correlations of task-positive and task-negative networks during rest condition by analyzing simulated and real fMRI data acquired  ... 
doi:10.1016/j.neubiorev.2016.08.035 pmid:27592153 pmcid:PMC5140707 fatcat:zycoe33nonfmjl35qh6hat5jcm

CORSICA: correction of structured noise in fMRI by automatic identification of ICA components

Vincent Perlbarg, Pierre Bellec, Jean-Luc Anton, Mélanie Pélégrini-Issac, Julien Doyon, Habib Benali
2007 Magnetic Resonance Imaging  
with a high sensitivity and a specificity of 1.  ...  On short-TR datasets, we validate that the proposed method of noise reduction allows a substantial improvement of the signal-to-noise ratio evaluated at the cardiac and respiratory frequencies, even in  ...  This kind of simulator would allow one to really compare the different methods of noise reduction in fMRI.  ... 
doi:10.1016/j.mri.2006.09.042 pmid:17222713 fatcat:l7wh6zvhmraprahflrw36662oy

Modeling Resting-State Brain Functional Integration

Xi-Nian Zuo
2010 Nature Precedings  
On the other hand, resting-state fMRI which was first studied in Biswal's paper [10] is a very good way to investigate the influence of disease and/or medication on the brain.  ...  Some rs-fMRI studies indicated a resting-state FSIN [6, 30, 87, 29, 68, 33, 38] , which consists of LPFC, dACC, insula, thalamus and striatum etc.  ...  Resting state fixation runs from healthy control subjects (four 7 minute runs per subject) who were part of a study on the behavioral effects of spontaneous BOLD fluctuations.  ... 
doi:10.1038/npre.2010.4380.1 fatcat:fuptk3khkjfv3bezdvhsk3d4di

The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery

Vince D. Calhoun, Robyn Miller, Godfrey Pearlson, Tulay Adalı
2014 Neuron  
We primarily focus on multivariate approaches developed in our group and review a number of approaches with an emphasis on matrix decompositions such as principle component analysis and independent component  ...  There are a number of methodological directions that need to be developed further, but chronnectome approaches already show great promise for the study of both the healthy and the diseased brain.  ...  ACKNOWLEDGMENTS We thank Elena Allen, Mohammad Arbabshirani, Sai Ma, Barnaly Rashid, Maziar Yaesoubi, and Qingbao Yu for their input and suggestions on data analysis.  ... 
doi:10.1016/j.neuron.2014.10.015 pmid:25374354 pmcid:PMC4372723 fatcat:l4vezjg7hnhv5pnbpuy4nn62qu

Large-scale Probabilistic Functional Modes from resting state fMRI

Samuel J. Harrison, Mark W. Woolrich, Emma C. Robinson, Matthew F. Glasser, Christian F. Beckmann, Mark Jenkinson, Stephen M. Smith
2015 NeuroImage  
In this paper, we introduce a method for identifying modes of coherent activity from resting state fMRI (rfMRI) data.  ...  Our model characterises a mode as the outer product of a spatial map and a time course, constrained by the nature of both the between-subject variation and the effect of the haemodynamic response function  ...  , visualise, and share these data sets.  ... 
doi:10.1016/j.neuroimage.2015.01.013 pmid:25598050 pmcid:PMC4349633 fatcat:2opa6duuizffnbdwv2gqzrf3le

Cerebellar Functional Parcellation Using Sparse Dictionary Learning Clustering

Changqing Wang, Judy Kipping, Chenglong Bao, Hui Ji, Anqi Qiu
2016 Frontiers in Neuroscience  
We aimed to identify the functional parcellation of the cerebellum using information from resting-state functional magnetic resonance imaging (rs-fMRI).  ...  SDLC integrates dictionary learning, sparse representation of rs-fMRI, and k-means clustering into one optimization problem.  ...  In the last decade, resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a useful imaging technique for mapping intrinsic functional organization of the brain.  ... 
doi:10.3389/fnins.2016.00188 pmid:27199650 pmcid:PMC4852537 fatcat:3gjfxrdrhrc2bludmbg5pnmur4
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