Independent component analysis and beyond in brain imaging: EEG, MEG, fMRI, and PET

2004 ◽  
Author(s):  
J.C. Rajapakse ◽  
A. Cichocki ◽  
V.D. Sanchez A
Keyword(s):  
Independent Component Analysis ◽  
Brain Imaging ◽  
Component Analysis ◽  
Independent Component

scholarly journals Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis

2019 ◽  
Vol 41 (1) ◽  
pp. 241-255 ◽  
Author(s):  
Luigi A. Maglanoc ◽  
Tobias Kaufmann ◽  
Rune Jonassen ◽  
Eva Hilland ◽  
Dani Beck ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Brain Imaging ◽  
Component Analysis ◽  
Functional Brain Imaging ◽  
Independent Component ◽  
Multimodal Fusion ◽  
Functional Brain

scholarly journals Multimodal fusion of structural and functional brain imaging in depression using linked independent component analysis

2019 ◽  
Author(s):  
Luigi A. Maglanoc ◽  
Tobias Kaufmann ◽  
Rune Jonassen ◽  
Eva Hilland ◽  
Dani Beck ◽  
...  
Keyword(s):  
Machine Learning ◽  
Independent Component Analysis ◽  
Brain Imaging ◽  
Component Analysis ◽  
Case Control ◽  
Independent Component ◽  
Multimodal Fusion ◽  
Imaging Biomarkers ◽  
Depression And Anxiety ◽  
Age And Sex

AbstractBackgroundPrevious structural and functional neuroimaging studies have implicated distributed brain regions and networks in depression. However, there are no robust imaging biomarkers that are specific to depression, which may be due to clinical heterogeneity and neurobiological complexity. A dimensional approach and fusion of imaging modalities may yield a more coherent view of the neuronal correlates of depression.MethodsWe used linked independent component analysis to fuse cortical macrostructure (thickness, area, gray matter density), white matter diffusion properties and resting-state fMRI default mode network amplitude in patients with a history of depression (n = 170) and controls (n = 71). We used univariate and machine learning approaches to assess the relationship between age, sex, case-control status, and symptom loads for depression and anxiety with the resulting brain components.ResultsUnivariate analyses revealed strong associations between age and sex with mainly global but also regional specific brain components, with varying degrees of multimodal involvement. In contrast, there were no significant associations with case-control status, nor symptom loads for depression and anxiety with the brain components, nor any interaction effects with age and sex. Machine learning revealed low model performance for classifying patients from controls and predicting symptom loads for depression and anxiety, but high age prediction accuracy.ConclusionMultimodal fusion of brain imaging data alone may not be sufficient for dissecting the clinical and neurobiological heterogeneity of depression. Precise clinical stratification and methods for brain phenotyping at the individual level based on large training samples may be needed to parse the neuroanatomy of depression.

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.

Exploring Independent Component Analysis for GPR Signal Processing

PIERS Online ◽  
2005 ◽  
Vol 1 (6) ◽  
pp. 750-753 ◽  
Author(s):  
Anxing Zhao ◽  
Yansheng Jiang ◽  
Wenbing Wang
Keyword(s):  
Signal Processing ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component
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