An empirical comparison of information-theoretic criteria in estimating the number of independent components of fMRI data.
BACKGROUND: Independent Component Analysis (ICA) has been widely applied to the analysis of fMRI data. Accurate estimation of the number of independent components of fMRI data is critical to reduce over/under fitting. Although various methods based on Information Theoretic Criteria (ITC) have been u...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2011-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3246467?pdf=render |
| _version_ | 1818953980363931648 |
|---|---|
| author | Mingqi Hui Juan Li Xiaotong Wen Li Yao Zhiying Long |
| author_facet | Mingqi Hui Juan Li Xiaotong Wen Li Yao Zhiying Long |
| author_sort | Mingqi Hui |
| collection | DOAJ |
| description | BACKGROUND: Independent Component Analysis (ICA) has been widely applied to the analysis of fMRI data. Accurate estimation of the number of independent components of fMRI data is critical to reduce over/under fitting. Although various methods based on Information Theoretic Criteria (ITC) have been used to estimate the intrinsic dimension of fMRI data, the relative performance of different ITC in the context of the ICA model hasn't been fully investigated, especially considering the properties of fMRI data. The present study explores and evaluates the performance of various ITC for the fMRI data with varied white noise levels, colored noise levels, temporal data sizes and spatial smoothness degrees. METHODOLOGY: Both simulated data and real fMRI data with varied Gaussian white noise levels, first-order auto-regressive (AR(1)) noise levels, temporal data sizes and spatial smoothness degrees were carried out to deeply explore and evaluate the performance of different traditional ITC. PRINCIPAL FINDINGS: Results indicate that the performance of ITCs depends on the noise level, temporal data size and spatial smoothness of fMRI data. 1) High white noise levels may lead to underestimation of all criteria and MDL/BIC has the severest underestimation at the higher Gaussian white noise level. 2) Colored noise may result in overestimation that can be intensified by the increase of AR(1) coefficient rather than the SD of AR(1) noise and MDL/BIC shows the least overestimation. 3) Larger temporal data size will be better for estimation for the model of white noise but tends to cause severer overestimation for the model of AR(1) noise. 4) Spatial smoothing will result in overestimation in both noise models. CONCLUSIONS: 1) None of ITC is perfect for all fMRI data due to its complicated noise structure. 2) If there is only white noise in data, AIC is preferred when the noise level is high and otherwise, Laplace approximation is a better choice. 3) When colored noise exists in data, MDL/BIC outperforms the other criteria. |
| first_indexed | 2024-12-20T10:14:53Z |
| format | Article |
| id | doaj.art-3bc606a5f7a046fc8dde4f80bf8c0dac |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-20T10:14:53Z |
| publishDate | 2011-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-3bc606a5f7a046fc8dde4f80bf8c0dac2022-12-21T19:44:06ZengPublic Library of Science (PLoS)PLoS ONE1932-62032011-01-01612e2927410.1371/journal.pone.0029274An empirical comparison of information-theoretic criteria in estimating the number of independent components of fMRI data.Mingqi HuiJuan LiXiaotong WenLi YaoZhiying LongBACKGROUND: Independent Component Analysis (ICA) has been widely applied to the analysis of fMRI data. Accurate estimation of the number of independent components of fMRI data is critical to reduce over/under fitting. Although various methods based on Information Theoretic Criteria (ITC) have been used to estimate the intrinsic dimension of fMRI data, the relative performance of different ITC in the context of the ICA model hasn't been fully investigated, especially considering the properties of fMRI data. The present study explores and evaluates the performance of various ITC for the fMRI data with varied white noise levels, colored noise levels, temporal data sizes and spatial smoothness degrees. METHODOLOGY: Both simulated data and real fMRI data with varied Gaussian white noise levels, first-order auto-regressive (AR(1)) noise levels, temporal data sizes and spatial smoothness degrees were carried out to deeply explore and evaluate the performance of different traditional ITC. PRINCIPAL FINDINGS: Results indicate that the performance of ITCs depends on the noise level, temporal data size and spatial smoothness of fMRI data. 1) High white noise levels may lead to underestimation of all criteria and MDL/BIC has the severest underestimation at the higher Gaussian white noise level. 2) Colored noise may result in overestimation that can be intensified by the increase of AR(1) coefficient rather than the SD of AR(1) noise and MDL/BIC shows the least overestimation. 3) Larger temporal data size will be better for estimation for the model of white noise but tends to cause severer overestimation for the model of AR(1) noise. 4) Spatial smoothing will result in overestimation in both noise models. CONCLUSIONS: 1) None of ITC is perfect for all fMRI data due to its complicated noise structure. 2) If there is only white noise in data, AIC is preferred when the noise level is high and otherwise, Laplace approximation is a better choice. 3) When colored noise exists in data, MDL/BIC outperforms the other criteria.http://europepmc.org/articles/PMC3246467?pdf=render |
| spellingShingle | Mingqi Hui Juan Li Xiaotong Wen Li Yao Zhiying Long An empirical comparison of information-theoretic criteria in estimating the number of independent components of fMRI data. PLoS ONE |
| title | An empirical comparison of information-theoretic criteria in estimating the number of independent components of fMRI data. |
| title_full | An empirical comparison of information-theoretic criteria in estimating the number of independent components of fMRI data. |
| title_fullStr | An empirical comparison of information-theoretic criteria in estimating the number of independent components of fMRI data. |
| title_full_unstemmed | An empirical comparison of information-theoretic criteria in estimating the number of independent components of fMRI data. |
| title_short | An empirical comparison of information-theoretic criteria in estimating the number of independent components of fMRI data. |
| title_sort | empirical comparison of information theoretic criteria in estimating the number of independent components of fmri data |
| url | http://europepmc.org/articles/PMC3246467?pdf=render |
| work_keys_str_mv | AT mingqihui anempiricalcomparisonofinformationtheoreticcriteriainestimatingthenumberofindependentcomponentsoffmridata AT juanli anempiricalcomparisonofinformationtheoreticcriteriainestimatingthenumberofindependentcomponentsoffmridata AT xiaotongwen anempiricalcomparisonofinformationtheoreticcriteriainestimatingthenumberofindependentcomponentsoffmridata AT liyao anempiricalcomparisonofinformationtheoreticcriteriainestimatingthenumberofindependentcomponentsoffmridata AT zhiyinglong anempiricalcomparisonofinformationtheoreticcriteriainestimatingthenumberofindependentcomponentsoffmridata AT mingqihui empiricalcomparisonofinformationtheoreticcriteriainestimatingthenumberofindependentcomponentsoffmridata AT juanli empiricalcomparisonofinformationtheoreticcriteriainestimatingthenumberofindependentcomponentsoffmridata AT xiaotongwen empiricalcomparisonofinformationtheoreticcriteriainestimatingthenumberofindependentcomponentsoffmridata AT liyao empiricalcomparisonofinformationtheoreticcriteriainestimatingthenumberofindependentcomponentsoffmridata AT zhiyinglong empiricalcomparisonofinformationtheoreticcriteriainestimatingthenumberofindependentcomponentsoffmridata |