[PDF][PDF] Ten key observations on the analysis of resting-state functional MR imaging data using independent component analysis
VD Calhoun, N de Lacy - Neuroimaging Clinics of North America, 2017 - Elsevier
Independent component analysis (ICA) has grown to be a widely used and continually
develo** staple for analyzing fMRI functional connectivity data. In this paper we discuss …
develo** staple for analyzing fMRI functional connectivity data. In this paper we discuss …
[HTML][HTML] Improved physiological noise regression in fNIRS: a multimodal extension of the general linear model using temporally embedded canonical correlation …
For the robust estimation of evoked brain activity from functional Near-Infrared Spectroscopy
(fNIRS) signals, it is crucial to reduce nuisance signals from systemic physiology and motion …
(fNIRS) signals, it is crucial to reduce nuisance signals from systemic physiology and motion …
Diversity in independent component and vector analyses: Identifiability, algorithms, and applications in medical imaging
Starting with a simple generative model and the assumption of statistical independence of
the underlying components, independent component analysis (ICA) decomposes a given …
the underlying components, independent component analysis (ICA) decomposes a given …
Multimodal fusion of multiple rest fMRI networks and MRI gray matter via parallel multilink joint ICA reveals highly significant function/structure coupling in Alzheimer's …
In this article, we focus on estimating the joint relationship between structural magnetic
resonance imaging (sMRI) gray matter (GM), and multiple functional MRI (fMRI) intrinsic …
resonance imaging (sMRI) gray matter (GM), and multiple functional MRI (fMRI) intrinsic …
Joint blind source separation for neurophysiological data analysis: Multiset and multimodal methods
Conventional blind source separation (BSS) methods have become widely adopted tools for
neurophysiological data analysis. However, the increasing availability of multiset and …
neurophysiological data analysis. However, the increasing availability of multiset and …
Multimodal data fusion using source separation: Two effective models based on ICA and IVA and their properties
Fusion of information from multiple sets of data in order to extract a set of features that are
most useful and relevant for the given task is inherent to many problems we deal with today …
most useful and relevant for the given task is inherent to many problems we deal with today …
Spectral–spatial classification of hyperspectral images using ICA and edge-preserving filter via an ensemble strategy
To obtain accurate classification results of hyperspectral images, both spectral and spatial
information should be fully exploited in the classification process. In this paper, we propose …
information should be fully exploited in the classification process. In this paper, we propose …
A new blind source separation framework for signal analysis and artifact rejection in functional near-infrared spectroscopy
In the analysis of functional Near-Infrared Spectroscopy (fNIRS) signals from real-world
scenarios, artifact rejection is essential. However, currently there exists no gold-standard …
scenarios, artifact rejection is essential. However, currently there exists no gold-standard …
Big data analytics enabled by feature extraction based on partial independence
Complex cells in primary visual cortex (V1) selectively respond to bars and edges at a
particular location and orientation. Namely, they are relatively invariant to the phase as well …
particular location and orientation. Namely, they are relatively invariant to the phase as well …
Extraction of time-varying spatiotemporal networks using parameter-tuned constrained IVA
Dynamic functional connectivity analysis is an effective way to capture the networks that are
functionally associated and continuously changing over the scanning period. However …
functionally associated and continuously changing over the scanning period. However …