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A group model for stable multi-subject ICA on fMRI datasets

G. Varoquaux, S. Sadaghiani, P. Pinel, A. Kleinschmidt, J.B. Poline, B. Thirion
2010 NeuroImage  
In this paper, we propose a hierarchical model for patterns in multi-subject fMRI datasets, akin to mixed-effect group models used in linear-model-based analysis.  ...  Such group studies raise the question of modeling subject variability within ICA: how can the patterns representative of a group be modeled and estimated via ICA for reliable inter-group comparisons?  ...  Existing group models for ICA on multi-subject fMRI data ICA is a multivariate analysis technique: voxel-based time courses are not characterized as such, but as part of signal fluctuations in the entire  ... 
doi:10.1016/j.neuroimage.2010.02.010 pmid:20153834 fatcat:g3itqr67nnf57h7pozhzavpc3i

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  
Citation: Majeed W, Avison MJ (2014) Robust Data Driven Model Order Estimation for Independent Component Analysis of fMRI Data with Low Contrast to Noise. PLoS ONE 9(4): e94943.  ...  Independent component analysis (ICA) has been successfully utilized for analysis of functional MRI (fMRI) data for task related as well as resting state studies.  ...  Acknowledgments We are grateful to Feng Wang PhD, Robert Friedman PhD and Chaohui Tang MD for helpful discussions, and assistance with the collection of in vivo data.  ... 
doi:10.1371/journal.pone.0094943 pmid:24788636 pmcid:PMC4005775 fatcat:6j7bt5d6ungdpp45zfpq47kkci

Multi-echo fMRI: A review of applications in fMRI denoising and analysis of BOLD signals

Prantik Kundu, Valerie Voon, Priti Balchandani, Michael V. Lombardo, Benedikt A. Poser, Peter A. Bandettini
2017 NeuroImage  
A focus is on the use of ME-fMRI data to extract and classify components from spatial ICA, called multi-echo ICA (ME-ICA).  ...  After describing how ME-fMRI and ME-ICA lead to a general model for analysis of fMRI signals, applications in animal and human imaging will be discussed.  ...  Ed Bullmore and Souheil Inati for many important discussions on statistical and biophysical modelling of multi-echo fMRI signals.  ... 
doi:10.1016/j.neuroimage.2017.03.033 pmid:28363836 fatcat:n6mkrdv535gqfnf6nuu2nfzaxy

Snowball ICA: A Model Order Free Independent Component Analysis Strategy for Functional Magnetic Resonance Imaging Data

Guoqiang Hu, Abigail B Waters, Serdar Aslan, Blaise Frederick, Fengyu Cong, Lisa D Nickerson
2020 Frontiers in Neuroscience  
We present a new strategy for model order free ICA, called Snowball ICA, that obviates these issues.  ...  In independent component analysis (ICA), the selection of model order (i.e., number of components to be extracted) has crucial effects on functional magnetic resonance imaging (fMRI) brain network analysis  ...  Moreover, for fMRI data, ICA can be performed on single-subject fMRI data, or on multi-subject data (by either stacking the fMRI data across subjects or by temporally concatenating data across subjects  ... 
doi:10.3389/fnins.2020.569657 pmid:33071741 pmcid:PMC7530342 fatcat:jtulxje4p5bvlgvpkiojfftyca

CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series [article]

Gaël Varoquaux, Sepideh Sadaghiani, Bertrand Thirion (INRIA Saclay - Ile de France, LNAO)
2009 arXiv   pre-print
We start from a generative model of the fMRI group data to introduce a probabilistic ICA pattern-extraction algorithm, called CanICA (Canonical ICA).  ...  We compare our method to state-of-the-art multi-subject fMRI ICA methods and show that the features extracted are more reproducible.  ...  Model validation for inter-subject generalization The validation criteria for an ICA decomposition are unclear, as this algorithm is not based on a testable hypothesis.  ... 
arXiv:0911.4650v1 fatcat:qjbkuzumvffadlde5hl3uy477y

Joint, Partially-joint, and Individual Independent Component Analysis in Multi-Subject fMRI Data [article]

Mansooreh Pakravan, Mohammad Bagher Shamsollahi
2019 arXiv   pre-print
This source model improves the accuracy of source extraction methods developed for multi-subject datasets.  ...  In these datasets, some sources belong to all subjects (joint), a subset of subjects (partially-joint), or a single subject (individual).  ...  Results on Simulated fMRI Data We synthesize multi-subject fMRI datasets by using the SimTB toolbox 1 [23] to evaluate the JpJI-ICA algorithm.  ... 
arXiv:1909.03676v2 fatcat:rgbgfgmdfje2xi6fafdqwvqxoq

LEICA: Laplacian eigenmaps for group ICA decomposition of fMRI data

Chihuang Liu, Joseph JaJa, Luiz Pessoa
2018 NeuroImage  
In this paper, we propose an effective non-linear ICA-based model for extracting group-level spatial maps from multi-subject fMRI datasets.  ...  We introduce a number of methods to evaluate LEICA using 100-subject resting state and 100-subject working memory task fMRI datasets from the Human Connectome Project (HCP).  ...  Reproducibility results-Along Discussion In the present paper, we propose a nonlinear ICA-based model for extracting group-level spatial maps from multi-subject fMRI datasets.  ... 
doi:10.1016/j.neuroimage.2017.12.018 pmid:29246846 pmcid:PMC6293470 fatcat:emaqontiibe67cgkxxxvx4wwtm

Comparing functional connectivity based predictive models across datasets

Kamalaker Dadi, Alexandre Abraham, Mehdi Rahim, Bertrand Thirion, Gael Varoquaux
2016 2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI)  
Comparing functional connectivity based predictive models across datasets.  ...  Analysis pipelines consist of four steps: Delineation of brain regions of interest (ROIs), ROI-level rs-fMRI time series signal extraction, FC estimation and linear model classification analysis of FC  ...  Bootstrap Analysis of Stable Clusters (BASC) [10] is a multi scale functional atlas estimated using K-Means clustering on rs-fMRI dataset that consists of 43 healthy individuals.  ... 
doi:10.1109/prni.2016.7552359 dblp:conf/prni/DadiARTV16 fatcat:bem7ainwxbdppilk4aubrqrbuq

Combining PCA and multiset CCA for dimension reduction when group ICA is applied to decompose naturalistic fMRI data

Valeri Tsatsishvili, Fengyu Cong, Petri Toiviainen, Tapani Ristaniemi
2015 2015 International Joint Conference on Neural Networks (IJCNN)  
Group ICA (GICA) is commonly applied ICA variant on fMRI due to its advantage in drawing group inferences from multi-subject datasets.  ...  We selected group ICA with temporal concatenation strategy, whereas in [12] ICA was applied on each dataset separately.  ... 
doi:10.1109/ijcnn.2015.7280722 dblp:conf/ijcnn/TsatsishviliCTR15 fatcat:qhzidshc2zg3zh4h6wnkup25ca

Multi-level bootstrap analysis of stable clusters in resting-state fMRI

Pierre Bellec, Pedro Rosa-Neto, Oliver C. Lyttelton, Habib Benali, Alan C. Evans
2010 NeuroImage  
A simulation study validated the good performance of the multi-level BASC on purely synthetic data. Stable networks were also derived from a real resting-state study for 43 subjects.  ...  This bootstrap analysis of stable clusters (BASC) has several benefits: (1) it can be implemented in a multi-level fashion to investigate stable RSNs at the level of individual subjects and at the level  ...  The authors would like to thank Samir Das for editing an earlier version of this manuscript and to acknowledge the work of the International Consortium for Brain Mapping 16 (ICBM) fMRI community in creating  ... 
doi:10.1016/j.neuroimage.2010.02.082 pmid:20226257 fatcat:qjzx3mcrtney3otdbnmnhw32zm

Unsupervised clustering of track-weighted dynamic functional connectivity reveals white matter substrates of functional connectivity dynamics [article]

Gianpaolo Antonio Basile, Salvatore Bertino, Victor Nozais, Alessia Bramanti, Rosella Ciurleo, Giuseppe Anastasi, Demetrio Milardi, Alberto Cacciola
2021 bioRxiv   pre-print
Herein, we applied track-weighted dynamic functional connectivity (tw-dFC), a model integrating structural, functional, and dynamic connectivity, on high quality diffusion weighted imaging and resting-state  ...  fMRI data from two independent repositories.  ...  Acknowledgments The primary dataset (HCP) was provided by the Human Connectome Project, WU-Minn Consortium  ... 
doi:10.1101/2021.12.04.471233 fatcat:r5bzfn7je5fnjosuh7a47f5iju

Improving effect size estimation and statistical power with multi-echo fMRI and its impact on understanding the neural systems supporting mentalizing

Michael V. Lombardo, Bonnie Auyeung, Rosemary J. Holt, Jack Waldman, Amber N.V. Ruigrok, Natasha Mooney, Edward T. Bullmore, Simon Baron-Cohen, Prantik Kundu
2016 NeuroImage  
Without smoothing, group-level effect size estimates on two different mentalizing tasks were enhanced by ME-ICA at a median rate of 24% in regions canonically associated with mentalizing, while much more  ...  Non-BOLD variability identification and removal is achieved in a biophysical and statistically principled manner by combining multi-echo fMRI acquisition and independent components analysis (ME-ICA).  ...  Acknowledgments This work was supported by a Wellcome Trust (091774/Z/10/Z) project grant to SB-C and ETB. MVL was supported by the Wellcome Trust  ... 
doi:10.1016/j.neuroimage.2016.07.022 pmid:27417345 pmcid:PMC5102698 fatcat:r6ttgaldxzhs7flhjo7zr2lneu

Multimodal Data Fusion Using Source Separation: Two Effective Models Based on ICA and IVA and Their Properties

Tulay AdalI, Yuri Levin-Schwartz, Vince D. Calhoun
2015 Proceedings of the IEEE  
One solution is the Joint ICA model that has found wide application in medical imaging, and the second one is the the Transposed IVA model introduced here as a generalization of an approach based on multi-set  ...  on the selection of a model along with its associated parameters.  ...  Acknowledgments The authors would like to thank Geng-Shen Fu and Zois Boukouvalas for generating the ICA and IVA diversity figures as well as for their useful comments and suggestions.  ... 
doi:10.1109/jproc.2015.2461624 pmid:26525830 pmcid:PMC4624202 fatcat:eagkta35rnbkpebqwuyarbytru

Validation of Shared and Specific Independent Component Analysis (SSICA) for Between-Group Comparisons in fMRI

Mona Maneshi, Shahabeddin Vahdat, Jean Gotman, Christophe Grova
2016 Frontiers in Neuroscience  
However, the application of ICA in multi-group designs is not straightforward.  ...  Here, we investigated the performance of SSICA on realistic simulations, and task fMRI data and compared the results with one of the state-of-the-art group ICA approaches to infer between-group differences  ...  AUTHOR CONTRIBUTIONS SUPPLEMENTARY MATERIAL The Supplementary Material for this article can be found online at: 2016.00417  ... 
doi:10.3389/fnins.2016.00417 pmid:27729843 pmcid:PMC5037228 fatcat:p54p4rcgkrbmnkpqn7nbuyovxu

Denoising scanner effects from multimodal MRI data using linked independent component analysis

Huanjie Li, Stephen M. Smith, Staci Gruber, Scott E. Lukas, Marisa M. Silveri, Kevin P. Hill, William D.S. Killgore, Lisa D. Nickerson
2019 NeuroImage  
We utilized multi-study data to test our proposed method that were collected on a single 3T scanner, pre- and post-software and major hardware upgrades and using different acquisition parameters.  ...  Our method has great promise for denoising scanner effects in multi-study and in large-scale multi-site studies that may be confounded by scanner differences.  ...  Thus, if using single-modality ICA to denoise scanner effects from fMRI data, one should apply this method to the subject-series (activation maps for all subjects together), not the single-subject fMRI  ... 
doi:10.1016/j.neuroimage.2019.116388 pmid:31765802 fatcat:vi2trksk3vaq3mbwy3bxiyp2hq
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