A parcellation based nonparametric algorithm for independent component analysis with application to fMRI data

Shanshan Li, Shaojie Chen, Chen Yue, Brian S Caffo

Research output: Contribution to journalArticle

Abstract

Independent Component analysis (ICA) is a widely used technique for separating signals that have been mixed together. In this manuscript, we propose a novel ICA algorithm using density estimation and maximum likelihood, where the densities of the signals are estimated via p-spline based histogram smoothing and the mixing matrix is simultaneously estimated using an optimization algorithm. The algorithm is exceedingly simple, easy to implement and blind to the underlying distributions of the source signals. To relax the identically distributed assumption in the density function, a modified algorithm is proposed to allow for different density functions on different regions. The performance of the proposed algorithm is evaluated in different simulation settings. For illustration, the algorithm is applied to a research investigation with a large collection of resting state fMRI datasets. The results show that the algorithm successfully recovers the established brain networks.

Original languageEnglish (US)
Article number15
JournalFrontiers in Neuroscience
Volume10
Issue numberJAN
DOIs
StatePublished - 2016

Fingerprint

Magnetic Resonance Imaging
Brain
Research

Keywords

  • Blind source separation
  • Density estimation
  • Functional MRI
  • P-spline bases
  • Signal processing

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

A parcellation based nonparametric algorithm for independent component analysis with application to fMRI data. / Li, Shanshan; Chen, Shaojie; Yue, Chen; Caffo, Brian S.

In: Frontiers in Neuroscience, Vol. 10, No. JAN, 15, 2016.

Research output: Contribution to journalArticle

@article{d475e9ca6579467d8575b5b18d23caa0,
title = "A parcellation based nonparametric algorithm for independent component analysis with application to fMRI data",
abstract = "Independent Component analysis (ICA) is a widely used technique for separating signals that have been mixed together. In this manuscript, we propose a novel ICA algorithm using density estimation and maximum likelihood, where the densities of the signals are estimated via p-spline based histogram smoothing and the mixing matrix is simultaneously estimated using an optimization algorithm. The algorithm is exceedingly simple, easy to implement and blind to the underlying distributions of the source signals. To relax the identically distributed assumption in the density function, a modified algorithm is proposed to allow for different density functions on different regions. The performance of the proposed algorithm is evaluated in different simulation settings. For illustration, the algorithm is applied to a research investigation with a large collection of resting state fMRI datasets. The results show that the algorithm successfully recovers the established brain networks.",
keywords = "Blind source separation, Density estimation, Functional MRI, P-spline bases, Signal processing",
author = "Shanshan Li and Shaojie Chen and Chen Yue and Caffo, {Brian S}",
year = "2016",
doi = "10.3389/fnins.2016.00015",
language = "English (US)",
volume = "10",
journal = "Frontiers in Neuroscience",
issn = "1662-4548",
publisher = "Frontiers Research Foundation",
number = "JAN",

}

TY - JOUR

T1 - A parcellation based nonparametric algorithm for independent component analysis with application to fMRI data

AU - Li, Shanshan

AU - Chen, Shaojie

AU - Yue, Chen

AU - Caffo, Brian S

PY - 2016

Y1 - 2016

N2 - Independent Component analysis (ICA) is a widely used technique for separating signals that have been mixed together. In this manuscript, we propose a novel ICA algorithm using density estimation and maximum likelihood, where the densities of the signals are estimated via p-spline based histogram smoothing and the mixing matrix is simultaneously estimated using an optimization algorithm. The algorithm is exceedingly simple, easy to implement and blind to the underlying distributions of the source signals. To relax the identically distributed assumption in the density function, a modified algorithm is proposed to allow for different density functions on different regions. The performance of the proposed algorithm is evaluated in different simulation settings. For illustration, the algorithm is applied to a research investigation with a large collection of resting state fMRI datasets. The results show that the algorithm successfully recovers the established brain networks.

AB - Independent Component analysis (ICA) is a widely used technique for separating signals that have been mixed together. In this manuscript, we propose a novel ICA algorithm using density estimation and maximum likelihood, where the densities of the signals are estimated via p-spline based histogram smoothing and the mixing matrix is simultaneously estimated using an optimization algorithm. The algorithm is exceedingly simple, easy to implement and blind to the underlying distributions of the source signals. To relax the identically distributed assumption in the density function, a modified algorithm is proposed to allow for different density functions on different regions. The performance of the proposed algorithm is evaluated in different simulation settings. For illustration, the algorithm is applied to a research investigation with a large collection of resting state fMRI datasets. The results show that the algorithm successfully recovers the established brain networks.

KW - Blind source separation

KW - Density estimation

KW - Functional MRI

KW - P-spline bases

KW - Signal processing

UR - http://www.scopus.com/inward/record.url?scp=84958038655&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84958038655&partnerID=8YFLogxK

U2 - 10.3389/fnins.2016.00015

DO - 10.3389/fnins.2016.00015

M3 - Article

AN - SCOPUS:84958038655

VL - 10

JO - Frontiers in Neuroscience

JF - Frontiers in Neuroscience

SN - 1662-4548

IS - JAN

M1 - 15

ER -