Results 41 to 50 of about 265,367 (299)
Independent Component Analysis of Spatiotemporal Chaos [PDF]
, 2005 Two types of spatiotemporal chaos exhibited by ensembles of coupled nonlinear oscillators are analyzed using independent component analysis (ICA). For diffusively coupled complex Ginzburg-Landau oscillators that exhibit smooth amplitude patterns, ICA ...
Amari S. +27 more
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A Tutorial on Independent Component Analysis [PDF]
, 2014 Independent component analysis (ICA) has become a standard data analysis technique applied to an array of problems in signal processing and machine learning.
Shlens, Jonathon
core
Binary Independent Component Analysis with OR Mixtures
, 2010 Independent component analysis (ICA) is a computational method for separating a multivariate signal into subcomponents assuming the mutual statistical independence of the non-Gaussian source signals.
Nguyen, Huy, Zheng, Rong
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Impact of Asymptomatic Intracranial Hemorrhage on Outcome After Endovascular Stroke Treatment
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background Endovascular treatment (EVT) achieves high rates of recanalization in acute large‐vessel occlusion (LVO) stroke, but functional recovery remains heterogeneous. While symptomatic intracranial hemorrhage (sICH) has been well studied, the prognostic impact of asymptomatic intracranial hemorrhage (aICH) after EVT is less certain ...
Shihai Yang +22 more
wiley +1 more source
A Feature-Selective Independent Component Analysis Method for Functional MRI
International Journal of Biomedical Imaging, 2007 In this work, we propose a simple and effective scheme to incorporate prior knowledge about the sources of interest (SOIs) in independent component analysis (ICA) and apply the method to estimate brain activations from functional magnetic resonance ...
Yi-Ou Li, Tülay Adali, Vince D. Calhoun
doaj +1 more source
The principal independent components of images [PDF]
, 2010 This paper proposes a new approach for the encoding of images by only a few important components. Classically, this is done by the Principal Component Analysis (PCA).
Arlt, Björn, Brause, Rüdiger W.
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Manifold Learning in MR spectroscopy using nonlinear dimensionality reduction and unsupervised clustering [PDF]
, 2014 Purpose To investigate whether nonlinear dimensionality reduction improves unsupervised classification of 1H MRS brain tumor data compared with a linear method. Methods In vivo single-voxel 1H magnetic resonance spectroscopy (55 patients) and 1H magnetic
Barrick, TR +3 more
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Journal of Neuroscience Methods, 2022 FMRI resting state networks (RSNs) are used to characterize brain disorders. They also show extensive heterogeneity across patients. Identifying systematic differences between RSNs in patients, i.e. discovering neurofunctional subtypes, may further increase our understanding of disease heterogeneity.
Durieux, J. +4 more
openaire +5 more sources
Functional and Structural Evidence of Neurofluid Circuit Aberrations in Huntington Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Disrupted neurofluid regulation may contribute to neurodegeneration in Huntington disease (HD). Because neurofluid pathways influence waste clearance, inflammation, and the distribution of central nervous system (CNS)–delivered therapeutics, understanding their dysfunction is increasingly important as targeted treatments emerge.
Kilian Hett +8 more
wiley +1 more source
An Independent Component Analysis Based Tool for Exploring Functional Connections in the Brain [PDF]
, 2007 This thesis describes the use of independent component analysis (ICA) as a measure of voxel similarity, which allows the user to find and view statistically independent maps of correlated voxel activity. The tool developed in this work uses a specialized
Rolfe, Sara
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