Beyond the Epileptic Focus: Functional Epileptic Networks in Focal Epilepsy

2019 ◽  
Vol 30 (4) ◽  
pp. 2338-2357 ◽  
Author(s):  
Francisco Gil ◽  
Nelly Padilla ◽  
Sara Soria-Pastor ◽  
Xavier Setoain ◽  
Teresa Boget ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Focal Epilepsy ◽  
Component Analysis ◽  
Epileptic Activity ◽  
Independent Component ◽  
Electrode Placement ◽  
Functional Architecture ◽  
Brain Areas ◽  
Time Courses ◽  
Epileptic Networks

Abstract Focal epilepsy can be conceptualized as a network disorder, and the functional epileptic network can be described as a complex system of multiple brain areas that interact dynamically to generate epileptic activity. However, we still do not fully understand the functional architecture of epileptic networks. We studied a cohort of 21 patients with extratemporal focal epilepsy. We used independent component analysis of functional magnetic resonance imaging (fMRI) data. In order to identify the epilepsy-related components, we examined the general linear model-derived electroencephalography-fMRI (EEG–fMRI) time courses associated with interictal epileptic activity as intrinsic hemodynamic epileptic biomarkers. Independent component analysis revealed components related to the epileptic time courses in all 21 patients. Each epilepsy-related component described a network of spatially distributed brain areas that corresponded to the specific epileptic network in each patient. We also provided evidence for the interaction between the epileptic activity generated at the epileptic network and the physiological resting state networks. Our findings suggest that independent component analysis, guided by EEG–fMRI epileptic time courses, have the potential to define the functional architecture of the epileptic network in a noninvasive way. These data could be useful in planning invasive EEG electrode placement, guiding surgical resections, and more effective therapeutic interventions.

BOLD correlates of continuously fluctuating epileptic activity isolated by independent component analysis

NeuroImage ◽  
2008 ◽  
Vol 42 (2) ◽  
pp. 635-648 ◽  
Author(s):  
Kay Jann ◽  
Roland Wiest ◽  
Martinus Hauf ◽  
Klaus Meyer ◽  
Chris Boesch ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Epileptic Activity ◽  
Independent Component

A Comparison of Independent Component Analysis (ICA) of fMRI and Electrical Source Imaging (ESI) in Focal Epilepsy Reveals Misclassification Using a Classifier

2015 ◽  
Vol 28 (6) ◽  
pp. 813-831 ◽  
Author(s):  
Danilo Maziero ◽  
Marcio Sturzbecher ◽  
Tonicarlo Rodrigues Velasco ◽  
Carlo Rondinoni ◽  
Agustin Lage Castellanos ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Focal Epilepsy ◽  
Component Analysis ◽  
Independent Component ◽  
Source Imaging ◽  
Electrical Source

scholarly journals A Feature-Selective Independent Component Analysis Method for Functional MRI

2007 ◽  
Vol 2007 ◽  
pp. 1-12 ◽  
Author(s):  
Yi-Ou Li ◽  
Tülay Adali ◽  
Vince D. Calhoun
Keyword(s):  
Independent Component Analysis ◽  
Resting State Fmri ◽  
Component Analysis ◽  
Independent Component ◽  
Sample Space ◽  
Fmri Data ◽  
Independent Component Analysis Method ◽  
Show Evidence ◽  
Time Courses ◽  
Ica Algorithm

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 imaging (fMRI) data. We name the proposed method as feature-selective ICA since it incorporates the features in the sample space of the independent components during ICA estimation. The feature-selective scheme is achieved through a filtering operation in the source sample space followed by a projection onto the demixing vector space by a least squares projection in an iterative ICA process. We perform ICA estimation of artificial activations superimposed into a resting state fMRI dataset to show that the feature-selective scheme improves the detection of injected activation from the independent component estimated by ICA. We also compare the task-related sources estimated from true fMRI data by a feature-selective ICA algorithm versus an ICA algorithm and show evidence that the feature-selective scheme helps improve the estimation of the sources in both spatial activation patterns and the time courses.

Skin Chromophore Estimation from Mobile Selfie Images using Constrained Independent Component Analysis

2020 ◽  
Vol 2020 (14) ◽  
pp. 357-1-357-6
Author(s):  
Luisa F. Polanía ◽  
Raja Bala ◽  
Ankur Purwar ◽  
Paul Matts ◽  
Martin Maltz
Keyword(s):  
Independent Component Analysis ◽  
Spectral Reflectance ◽  
Source Separation ◽  
Principal Component ◽  
Component Analysis ◽  
Previous Method ◽  
Independent Component ◽  
Light Transport ◽  
Camera Model ◽  
Constrained Independent Component Analysis

Human skin is made up of two primary chromophores: melanin, the pigment in the epidermis giving skin its color; and hemoglobin, the pigment in the red blood cells of the vascular network within the dermis. The relative concentrations of these chromophores provide a vital indicator for skin health and appearance. We present a technique to automatically estimate chromophore maps from RGB images of human faces captured with mobile devices such as smartphones. The ultimate goal is to provide a diagnostic aid for individuals to monitor and improve the quality of their facial skin. A previous method approaches the problem as one of blind source separation, and applies Independent Component Analysis (ICA) in camera RGB space to estimate the chromophores. We extend this technique in two important ways. First we observe that models for light transport in skin call for source separation to be performed in log spectral reflectance coordinates rather than in RGB. Thus we transform camera RGB to a spectral reflectance space prior to applying ICA. This process involves the use of a linear camera model and Principal Component Analysis to represent skin spectral reflectance as a lowdimensional manifold. The camera model requires knowledge of the incident illuminant, which we obtain via a novel technique that uses the human lip as a calibration object. Second, we address an inherent limitation with ICA that the ordering of the separated signals is random and ambiguous. We incorporate a domain-specific prior model for human chromophore spectra as a constraint in solving ICA. Results on a dataset of mobile camera images show high quality and unambiguous recovery of chromophores.

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