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Independent component analysis of nondeterministic fMRI signal sources

Vesa Kiviniemi, Juha-Heikki Kantola, Jukka Jauhiainen, Aapo Hyvärinen, Osmo Tervonen
2003 NeuroImage  
ICA analysis of fMRI can be used for both assessing the statistical independence of brain signals and segmenting nondeterministic signal sources for further analysis.  ...  Neuronal activation can be separated from other signal sources of functional magnetic resonance imaging (fMRI) data by using independent component analysis (ICA).  ...  Independent component analysis (ICA) has recently been shown to be able to separate activation, physiological, and other signal sources in fMRI studies .  ... 
doi:10.1016/s1053-8119(03)00097-1 pmid:12814576 fatcat:haigxfkjcnfvfnsxd7usphahh4

Page 1491 of Journal of Cognitive Neuroscience Vol. 16, Issue 9 [page]

2004 Journal of Cognitive Neuroscience  
Principles of psychology. New York: Holt. Kiviniemi, V., Kantola, J. H., Jauhiainen, J., Hyvarinen, A., & Tervonen, O. (2003). Independent component analysis of nondeterministic fMRI signal sources.  ...  D. (2001). fMRI activation in a visual—perception task: Network of areas detected using the general linear model and independent components analysis. Neuroimage, 14, 1080-1088 ‘alhoun, V.  ... 

The effect of respiration variations on independent component analysis results of resting state functional connectivity

Rasmus M. Birn, Kevin Murphy, Peter A. Bandettini
2008 Human Brain Mapping  
O (2003): Independent component analysis of nondeterministic Stillman AE, Hu X, Jerosch-Herold M (1995): Functional MRI of fMRI signal sources.  ...  in the resting brain: A network analysis of the separation into independent spatial components.  ... 
doi:10.1002/hbm.20577 pmid:18438886 pmcid:PMC2715870 fatcat:ylflrgrrcjc75o725v6af5ubtm

Performance of blind source separation algorithms for fMRI analysis using a group ICA method

Nicolle Correa, Tülay Adalı, Vince D. Calhoun
2007 Magnetic Resonance Imaging  
Independent component analysis (ICA) is a popular blind source separation technique that has proven to be promising for the analysis of functional magnetic resonance imaging (fMRI) data.  ...  Our results greatly improve our confidence in the consistency of ICA for fMRI data analysis. D  ...  Acknowledgment This research was supported, in part, by National Institutes of Health grant 5R01EB005846-02.  ... 
doi:10.1016/j.mri.2006.10.017 pmid:17540281 pmcid:PMC2358930 fatcat:ajeu26qnibdwlbpovntst6r6ay

Resting state connectivity patterns with near-infrared spectroscopy data of the whole head

Sergio L. Novi, Renato B. M. L. Rodrigues, Rickson C. Mesquita
2016 Biomedical Optics Express  
We also identified the most frequent connections between brain regions and the main hubs that participate in the spontaneous activity of brain hemodynamics.  ...  Tervonen, "Independent component analysis of nondeterministic fMRI signal sources," Neuroimage 19(2), 253-260 (2003). 26. M. D. Fox and M. E.  ...  A variety of procedures were developed to assess rs-FC from fMRI data, such as seedbased correlation and Independent Component Analysis (ICA) [13, [22] [23] [24] [25] .  ... 
doi:10.1364/boe.7.002524 pmid:27446687 pmcid:PMC4948611 fatcat:r2msvmkui5ayxgwsqnbbgfkluu

Fine-grain atlases of functional modes for fMRI analysis [article]

Kamalaker Dadi, Antonia Machlouzarides-Shalit, Krzysztof J. Gorgolewski, Demian Wassermann
2020 arXiv   pre-print
We demonstrate the benefits of extracting reduced signals on our fine-grain atlases for many classic functional data analysis pipelines: stimuli decoding from 12,334 brain responses, standard GLM analysis  ...  of fMRI across sessions and individuals, extraction of resting-state functional-connectomes biomarkers for 2,500 individuals, data compression and meta-analysis over more than 15,000 statistical maps.  ...  Independent component analysis of nondeterministic fmri signal sources. Neuroimage 19, 253. Kiviniemi, V., Starck, T., Remes, J., Long, X., Nikkinen, J., Haapea, M., Veijola, J., et al., 2009.  ... 
arXiv:2003.05405v1 fatcat:3ed452xtnnfylbvcveixb3hdqa

Identification of Discriminative Subgraph Patterns in fMRI Brain Networks in Bipolar Affective Disorder [chapter]

Bokai Cao, Liang Zhan, Xiangnan Kong, Philip S. Yu, Nathalie Vizueta, Lori L. Altshuler, Alex D. Leow
2015 Lecture Notes in Computer Science  
However, the complexity of such linkage information raises major challenges in the era of big data in brain informatics.  ...  In a brain network, the nodes of the network correspond to a set of brain regions and the link or edges correspond to the functional or structural connectivity between these regions.  ...  of activations of tens of thousands of voxels over time, among which a complex interaction of signals and noise is embedded [7] .  ... 
doi:10.1007/978-3-319-23344-4_11 fatcat:wn3ldqlm6vgtvkdxcvg5y6zwhm

Brain responses in humans reveal ideal observer-like sensitivity to complex acoustic patterns

Nicolas Barascud, Marcus T. Pearce, Timothy D. Griffiths, Karl J. Friston, Maria Chait
2016 Proceedings of the National Academy of Sciences of the United States of America  
signal from within the brouhaha of a busy scene.  ...  Source analysis demonstrates an interaction between primary auditory cortex, hippocampus, and inferior frontal gyrus in the process of discovering the regularity within the ongoing sound sequence.  ...  the medial temporal lobe in the rapid detection of regularities within continuously presented sensory signals. fMRI analysis was focused on identifying BOLD response differences between REG and RAND.  ... 
doi:10.1073/pnas.1508523113 pmid:26787854 pmcid:PMC4747708 fatcat:nnk4hfk5jjgmbppctbnibgjb7i

Evaluation of confound regression strategies for the mitigation of micromovement artifact in studies of dynamic resting-state functional connectivity and multilayer network modularity

David M. Lydon-Staley, Rastko Ciric, Theodore D. Satterthwaite, Danielle S. Bassett
2019 Network Neuroscience  
Methods that included global signal regression were the most consistently effective de-noising strategies.  ...  This report provides a systematic evaluation of 12 commonly used participant-level confound regression strategies designed to mitigate the effects of micromovements in a sample of 393 youths (ages 8-22  ...  analysis (PCA), and signals isolated using independent components analysis (ICA).  ... 
doi:10.1162/netn_a_00071 pmid:30793090 pmcid:PMC6370491 fatcat:yuze35t3k5fujn32q5swflvrc4

Assessing and Compensating for Zero-Lag Correlation Effects in Time-Lagged Granger Causality Analysis of fMRI

Gopikrishna Deshpande, K Sathian, Xiaoping Hu
2010 IEEE Transactions on Biomedical Engineering  
The effectiveness of this method is demonstrated using fMRI data obtained from healthy humans performing a verbal working memory task.  ...  Due to the smoothing of the neuronal activity by the hemodynamic response inherent in the functional magnetic resonance imaging (fMRI) acquisition process, Granger causality, as normally computed from  ...  In addition, Geweke's fundamental assumption that the time series be perfectly nondeterministic [3] is often violated in the case of experimental fMRI data where a deterministic component is introduced  ... 
doi:10.1109/tbme.2009.2037808 pmid:20659822 pmcid:PMC3063610 fatcat:relafnth5bdmzib3fxq5zd7cym

Competitive and cooperative dynamics of large-scale brain functional networks supporting recollection

A. Fornito, B. J. Harrison, A. Zalesky, J. S. Simons
2012 Proceedings of the National Academy of Sciences of the United States of America  
The results were replicated when the analysis was repeated after partialing covariance with all other 19 components identified by the ICA as representing distinct sources of signal and noise in the data  ...  Task-related functional networks corresponding to the DMN and EAS were identified in an unbiased, data-driven manner using spatial independent component analysis (ICA) (34) (SI Text, section S.3).  ...  Component identification. We used spatial independent component analysis (ICA) to identify spatially independent, temporally coherent networks of voxels in the functional data.  ... 
doi:10.1073/pnas.1204185109 pmid:22807481 pmcid:PMC3412011 fatcat:tico3irsejfprasxghrkthzop4

EEG source localization using a sparsity prior based on Brodmann areas [article]

S. Saha, Ya.I. Nesterets, Rajib Rana, M. Tahtali, Frank de Hoog and T.E. Gureyev
2014 arXiv   pre-print
Localizing the sources of electrical activity in the brain from Electroencephalographic (EEG) data is an important tool for non-invasive study of brain dynamics.  ...  In the context of EEG source localization, we propose a novel approach that is based on dividing the cerebral cortex of the brain into a finite number of Functional Zones which correspond to unitary functional  ...  fMRI.  ... 
arXiv:1406.2434v1 fatcat:7msyonucbvfalhwggvpzp3q3q4

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part II: Brain Signals

Radek Martinek, Martina Ladrova, Michaela Sidikova, Rene Jaros, Khosrow Behbehani, Radana Kahankova, Aleksandra Kawala-Sterniuk
2021 Sensors  
In this paper, which is a Part II work—various innovative methods for the analysis of brain bioelectrical signals were presented and compared.  ...  source separation, and wavelet transform.  ...  Independent Component Analysis The algorithm of ICA is based on the assumption of the linear combination of the independent sources of signals which are represented with a vector with a mixing matrix which  ... 
doi:10.3390/s21196343 pmid:34640663 fatcat:mfe4taom5rhfpcp7m744msmgry

Brain Systems for Probabilistic and Dynamic Prediction: Computational Specificity and Integration

Jill X. O'Reilly, Saad Jbabdi, Matthew F. S. Rushworth, Timothy E. J. Behrens, John P. O'Doherty
2013 PLoS Biology  
Hence (using fMRI) we showed that activity in two brain systems (typically associated with reward learning and motor control) could be dissociated in terms of the forms of computations that were performed  ...  A computational approach to functional specialization suggests that brain systems can be characterized in terms of the types of computations they perform, rather than their sensory or behavioral domains  ...  In each case we defined a region of interest as the cluster of voxels with p,0.01 uncorrected, identified in the whole brain analysis for the prediction error signal above.  ... 
doi:10.1371/journal.pbio.1001662 pmid:24086106 pmcid:PMC3782423 fatcat:bnbmjglenzfpnl6r2qgnrhn7jq

Denoising and Averaging Techniques for Electrophysiological Data [chapter]

Matthias Ihrke, Hecke Schrobsdorff, J. Michael Herrmann
2009 Coordinated Activity in the Brain  
Machine learning algorithms generate a classification of data segments, algebraic methods like the independent component analysis (ICA) reduce the dimensionality of the data by identifying data prototypes  ...  of the original signal by an overlay of activity from other sources uninteresting to the observer on the one hand and intrinsic stochastic behavior of the neural signal source itself on the other hand  ... 
doi:10.1007/978-0-387-93797-7_9 fatcat:mqsdyexqyfb5hcvvuatsho7h5q
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