A Parcellation Based Nonparametric Algorithm for Independent Component Analysis with Application to fMRI Data
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 sm...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2016-01-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnins.2016.00015/full |
| _version_ | 1818900335551315968 |
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| author | Shanshan eLi Shaojie eChen Chen eYue Brian eCaffo |
| author_facet | Shanshan eLi Shaojie eChen Chen eYue Brian eCaffo |
| author_sort | Shanshan eLi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T20:02:14Z |
| format | Article |
| id | doaj.art-208004ae9f7446dcb95c3df564478e19 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-19T20:02:14Z |
| publishDate | 2016-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-208004ae9f7446dcb95c3df564478e192022-12-21T20:07:39ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2016-01-011010.3389/fnins.2016.00015152050A Parcellation Based Nonparametric Algorithm for Independent Component Analysis with Application to fMRI DataShanshan eLi0Shaojie eChen1Chen eYue2Brian eCaffo3Indiana University Fairbanks School of Public HealthJohns Hopkins Bloomberg School of Public HealthJohns Hopkins Bloomberg School of Public HealthJohns Hopkins Bloomberg School of Public HealthIndependent 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.http://journal.frontiersin.org/Journal/10.3389/fnins.2016.00015/fullfunctional MRISignal processingblind source separationdensity estimationP-spline bases |
| spellingShingle | Shanshan eLi Shaojie eChen Chen eYue Brian eCaffo A Parcellation Based Nonparametric Algorithm for Independent Component Analysis with Application to fMRI Data Frontiers in Neuroscience functional MRI Signal processing blind source separation density estimation P-spline bases |
| title | A Parcellation Based Nonparametric Algorithm for Independent Component Analysis with Application to fMRI Data |
| title_full | A Parcellation Based Nonparametric Algorithm for Independent Component Analysis with Application to fMRI Data |
| title_fullStr | A Parcellation Based Nonparametric Algorithm for Independent Component Analysis with Application to fMRI Data |
| title_full_unstemmed | A Parcellation Based Nonparametric Algorithm for Independent Component Analysis with Application to fMRI Data |
| title_short | A Parcellation Based Nonparametric Algorithm for Independent Component Analysis with Application to fMRI Data |
| title_sort | parcellation based nonparametric algorithm for independent component analysis with application to fmri data |
| topic | functional MRI Signal processing blind source separation density estimation P-spline bases |
| url | http://journal.frontiersin.org/Journal/10.3389/fnins.2016.00015/full |
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