Estimating the number of independent components for functional magnetic resonance imaging data.
Reads0
Chats0
TLDR
This work uses the software package ICASSO to analyze the independent component estimates at different orders and shows that, when ICA is performed at overestimated orders, the stability of the IC estimates decreases and the estimation of task related brain activations show degradation.Abstract:
Multivariate analysis methods such as independent component analysis (ICA) have been applied to the analysis of functional magnetic resonance imaging (fMRI) data to study brain function. Because of the high dimensionality and high noise level of the fMRI data, order selection, i.e., estimation of the number of informative components, is critical to reduce over/underfitting in such methods. De- pendence among fMRI data samples in the spatial and temporal domain limits the usefulness of the practical formulations of information-theoretic criteria (ITC) for order selection, since they are based on likelihood of independent and identically distributed (i.i.d.) data samples. To address this issue, we pro- pose a subsampling scheme to obtain a set of effectively i.i.d. samples from the dependent data samples and apply the ITC formulas to the effectively i.i.d. sample set for order selection. We apply the proposed method on the simulated data and show that it significantly improves the accuracy of order selection from dependent data. We also perform order selection on fMRI data from a visuomotor task and show that the proposed method alleviates the over-estimation on the number of brain sources due to the intrin- sic smoothness and the smooth preprocessing of fMRI data. We use the software package ICASSO (Him- berg et al. (2004): Neuroimage 22:1214-1222) to analyze the independent component (IC) estimates at dif- ferent orders and show that, when ICA is performed at overestimated orders, the stability of the IC esti- mates decreases and the estimation of task related brain activations show degradation. Hum Brain Mappread more
Citations
More filters
Journal ArticleDOI
Aberrant "default mode" functional connectivity in schizophrenia.
Abigail G. Garrity,Godfrey D. Pearlson,Kristen McKiernan,Dan Lloyd,Kent A. Kiehl,Vince D. Calhoun +5 more
TL;DR: Schizophrenia is associated with altered temporal frequency and spatial location of the default mode network, and this network may be under- or overmodulated by key regions, including the anterior and posterior cingulate cortex.
Journal ArticleDOI
A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data.
TL;DR: An overview of current approaches for utilizing ICA to make group inferences with a focus upon the group ICA approach implemented in the GIFT software and an overview of the use of I CA to combine or fuse multimodal data are provided.
Journal ArticleDOI
Selective changes of resting-state networks in individuals at risk for Alzheimer's disease
Christian Sorg,Valentin Riedl,Valentin Riedl,Mark Mühlau,Vince D. Calhoun,Tom Eichele,Leonhard Läer,Alexander Drzezga,Hans Förstl,Alexander Kurz,Claus Zimmer,Afra M. Wohlschläger +11 more
TL;DR: This work analyzes functional and structural MRI data from healthy elderly and patients with amnestic mild cognitive impairment and concludes that in individuals at risk for AD, a specific subset of RSNs is altered, likely representing effects of ongoing early neurodegeneration.
Journal ArticleDOI
A method for functional network connectivity among spatially independent resting-state components in schizophrenia.
TL;DR: A novel approach for quantifying functional connectivity among brain networks identified with spatial ICA is presented and applied to functional magnetic resonance imaging (fMRI) data collected from persons with schizophrenia and healthy controls.
Journal ArticleDOI
Reliable intrinsic connectivity networks: test-retest evaluation using ICA and dual regression approach.
Xi-Nian Zuo,Clare Kelly,Jonathan S. Adelstein,Donald F. Klein,Donald F. Klein,Donald F. Klein,F. Xavier Castellanos,F. Xavier Castellanos,Michael P. Milham +8 more
TL;DR: The present work systematically evaluated the test-retest reliability of TC-GICA derived RSFC measures over the short-term (<45 min) and long-term (5-16 months) and found moderate-to-high short-and-long-term test
References
More filters
Journal ArticleDOI
Estimating the Dimension of a Model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Estimating the dimension of a model
TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
Proceedings Article
Information Theory and an Extention of the Maximum Likelihood Principle
TL;DR: The classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion to provide answers to many practical problems of statistical model fitting.
Book ChapterDOI
Information Theory and an Extension of the Maximum Likelihood Principle
TL;DR: In this paper, it is shown that the classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion.
Book
Probability, random variables and stochastic processes
TL;DR: This chapter discusses the concept of a Random Variable, the meaning of Probability, and the axioms of probability in terms of Markov Chains and Queueing Theory.