A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series
[article]
2009
arXiv
pre-print
Preserved Fulltext
We start from a generative model of the fMRI group data to introduce a probabilistic ICA pattern-extraction algorithm, called CanICA (Canonical ICA). ...
The validity of the extracted patterns is hard to establish, as well as the significance of differences between patterns extracted from different groups of subjects. ...
In this paper, we present a novel model and a method, that we dub CanICA, to extract only the reproducible ICA maps from group data. ...
arXiv:0911.4650v1
fatcat:qjbkuzumvffadlde5hl3uy477y
A group model for stable multi-subject ICA on fMRI datasets
2010
NeuroImage
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Other Versions
to the group iii) ICA-based pattern extraction. ...
We compare our method with state-of-the-art multi-subject fMRI ICA methods and show that the features extracted using our procedure are more reproducible at the group level on two datasets of 12 healthy ...
for long fMRI time series and many non-meaningful ICA components are extracted. ...
doi:10.1016/j.neuroimage.2010.02.010
pmid:20153834
fatcat:g3itqr67nnf57h7pozhzavpc3i
Deriving Autism Spectrum Disorder Functional Networks from RS-FMRI Data using Group ICA and Dictionary Learning
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 Computer Science & Information Technology (CS & IT) unpublished doi:10.5121/csit.2021.110714 fatcat:enmkxkxmtvdlneei33wgv6xz4a
First, we utilize a group ICA model to extract functional networks from the ASD group and rank the top 20 regions of interest (ROIs). ...
Fourth, we generate three corresponding masks based on the 20 selected ROIs from group ICA, the 20 ROIs selected from dictionary learning, and the 40 combined ROIs selected from both. ...
For the group-level extraction of ICA patterns, researchers have adopted different strategies. ...
arXiv:2106.09000v1
fatcat:3htmohdmn5g2fexbogfplcfnhm
Robust brain network identification from multi-subject asynchronous fMRI data
2020
NeuroImage
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Bootstrap analysis demonstrates increased robustness of networks found using the CP tensor approach relative to ICA-based methods. ...
We compare results to those found using group independent component analysis (ICA) and canonical ICA. ...
Acknowledgment This work was supported by National Institutes of Health [grant number R01-EB009048, R01-EB026299, R01-NS089212, and K23-HD099309]. ...
doi:10.1016/j.neuroimage.2020.117615
pmid:33301936
pmcid:PMC7983296
fatcat:7f47urxun5hvdipqwxyfzpeugq
Multifractal analysis of Resting State Networks in functional MRI
2011
2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf.
In this paper, we combine two recent methodologies: group-level canonical ICA for multi-subject segmentation of brain network, and wavelet leader-based multifractal formalism for the analysis of RSN scaling ...
Concomitantly, multivariate modelfree analysis of spatial patterns , such as spatial Independent Component Analysis (sICA) [2], has been successfully used to segment from spontaneous activity Resting-State ...
At the group-level however, classical sICA schemes (group ICA, tensor ICA,...) lack of reproducibility due to between-subject variability. ...
doi:10.1109/isbi.2011.5872448
dblp:conf/isbi/CiuciuVAA11
fatcat:ojhyjzsqejau7bxyxrgtj43pla
Validation of Shared and Specific Independent Component Analysis (SSICA) for Between-Group Comparisons in fMRI
2016
Frontiers in Neuroscience
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Furthermore, we proposed a modified formulation of the back-reconstruction method to generate group-level t-statistics maps based on SSICA results. ...
We demonstrated that SSICA is a powerful data-driven approach to detect patterns of differences in functional connectivity across groups/conditions, particularly in model-free designs such as resting-state ...
METHODS
Standard ICA and Group-Level ICA In linear ICA, an observed T×M matrix of random variables Y is decomposed based on the following generative model: Y = AS (1) Where S is an N×M dimensional matrix ...
doi:10.3389/fnins.2016.00417
pmid:27729843
pmcid:PMC5037228
fatcat:p54p4rcgkrbmnkpqn7nbuyovxu
Benchmarking functional connectome-based predictive models for resting-state fMRI
2019
NeuroImage
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
time-series, and 10 classification models to compare functional interactions across subjects. ...
For each step we benchmark typical choices: 8 different ways of defining regions -either pre-defined or generated from the rest-fMRI data- 3 measures to build functional connectomes from the extracted ...
Time-series signals extraction In this appendix, we give more details on time-series extraction, to complement subsection 2.3. ...
doi:10.1016/j.neuroimage.2019.02.062
pmid:30836146
fatcat:gyc6jxopp5gihn3alwgrf7zcge
Machine Learning for Neuroimaging with Scikit-Learn
[article]
2014
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. ...
Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain. ...
To extract functional networks or regions, we use methods that group together similar voxels by comparing their time series. ...
arXiv:1412.3919v1
fatcat:fiepevd7gzbl3ecit47e7onl6m
Machine learning for neuroimaging with scikit-learn
2014
Frontiers in Neuroinformatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
To extract functional networks or regions, we use methods that group together similar voxels by comparing their time series. ...
For instance, sparse inverse covariance can extract the functional interaction structure from fMRI time-series (Varoquaux and Craddock, 2013) using the graph-lasso estimator. ...
Funding: We acknowledge funding from the NiConnect project and NIDA R21 DA034954, SUBSample project from the DIGITEO Institute, France. ...
doi:10.3389/fninf.2014.00014
pmid:24600388
pmcid:PMC3930868
fatcat:q4lmq2jto5b7bmslzs3shfbhza
Modeling Shared Responses in Neuroimaging Studies through MultiView ICA
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2020 pre-print arXiv:2006.06635v2 fatcat:46vprfxvyrd7vl4g3z7njmfhkiOther Versions
Contrary to most group-ICA procedures, the likelihood of the model is available in closed form. ...
We propose a novel MultiView Independent Component Analysis (ICA) model for group studies, where data from each subject are modeled as a linear combination of shared independent sources plus noise. ...
The source type can be either temporal if extracted sources are time courses or spatial if they are spatial patterns. ...
arXiv:2006.06635v4
fatcat:nx4oktnnargetj3i2vdakbcgki
Beyond consensus: Embracing heterogeneity in curated neuroimaging meta-analysis
2019
NeuroImage
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Here, we demonstrate the use of the author-topic model to automatically determine if heterogeneities within ALE-type meta-analyses can be robustly explained by a small number of latent patterns. ...
Coordinate-based meta-analysis can provide important insights into mind-brain relationships. ...
CanICA extracts representative patterns of multisubject fMRI data by performing ICA on a data subspace common to the group (Varoquaux et al., 2010) . ...
doi:10.1016/j.neuroimage.2019.06.037
pmid:31229658
pmcid:PMC6703957
fatcat:xj5npuaty5d2vkes7g3x6myc5y
fMRIflows: a consortium of fully automatic univariate and multivariate fMRI processing pipelines
[article]
2021
bioRxiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Here, we introduce fMRIflows: a consortium of fully automatic neuroimaging pipelines for fMRI analysis, which performs standard preprocessing, as well as 1st- and 2nd-level univariate and multivariate ...
The outcome of the validation analysis shows that fMRIflows preprocessing pipeline performs comparably to the ones obtained from other toolboxes. ...
The unthresholded group-level T-statistic maps of each analysis was . ...
doi:10.1101/2021.03.23.436650
fatcat:5acg3vu6vnfgjof32bgpgqaa2q
A Study of Resting State FMRI Dynamic Functional Network Analysis of MTBI
2015
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Our overall analysis pipeline is data-driven using a dataset of 47 MTBI subjects and a demographically matching healthy control group size of 30. ...
Static functional network analysis using resting state brain fMRI images has shown some promising results in identifying characteristics of MTBI. ...
maps are extracted from the group level fMRI, and those patterns that are likely produced by noise are eliminated.The reproducibility of CANICA can be proven by a cross validation to generate stable group-level ...
doi:10.13016/m28w4m
fatcat:ba2xrjbklzbyrlej2x45ksbphq