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A group model for stable multi-subject ICA on fMRI datasets
2010
NeuroImage
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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
2014
PLoS ONE
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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
2017
NeuroImage
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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
2020
Frontiers in Neuroscience
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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]
2009
arXiv
pre-print
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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]
2019
arXiv
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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
2018
NeuroImage
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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
2016
2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI)
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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
2015
2015 International Joint Conference on Neural Networks (IJCNN)
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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
2010
NeuroImage
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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]
2021
bioRxiv
pre-print
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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
2016
NeuroImage
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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
2015
Proceedings of the IEEE
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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
2016
Frontiers in Neuroscience
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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: http://journal.frontiersin.org/article/10.3389/fnins. 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
2019
NeuroImage
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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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