Cargando…
Hybrid ICA-Seed-Based Methods for fMRI Functional Connectivity Assessment: A Feasibility Study
Brain functional connectivity (FC) is often assessed from fMRI data using seed-based methods, such as those of detecting temporal correlation between a predefined region (seed) and all other regions in the brain; or using multivariate methods, such as independent component analysis (ICA). ICA is a u...
Autores principales: | , , , , , , , , , |
---|---|
Formato: | Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2010
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2905944/ https://www.ncbi.nlm.nih.gov/pubmed/20689712 http://dx.doi.org/10.1155/2010/868976 |
_version_ | 1782184008345452544 |
---|---|
author | Kelly, Robert E. Wang, Zhishun Alexopoulos, George S. Gunning, Faith M. Murphy, Christopher F. Morimoto, Sarah Shizuko Kanellopoulos, Dora Jia, Zhiru Lim, Kelvin O. Hoptman, Matthew J. |
author_facet | Kelly, Robert E. Wang, Zhishun Alexopoulos, George S. Gunning, Faith M. Murphy, Christopher F. Morimoto, Sarah Shizuko Kanellopoulos, Dora Jia, Zhiru Lim, Kelvin O. Hoptman, Matthew J. |
author_sort | Kelly, Robert E. |
collection | PubMed |
description | Brain functional connectivity (FC) is often assessed from fMRI data using seed-based methods, such as those of detecting temporal correlation between a predefined region (seed) and all other regions in the brain; or using multivariate methods, such as independent component analysis (ICA). ICA is a useful data-driven tool, but reproducibility issues complicate group inferences based on FC maps derived with ICA. These reproducibility issues can be circumvented with hybrid methods that use information from ICA-derived spatial maps as seeds to produce seed-based FC maps. We report results from five experiments to demonstrate the potential advantages of hybrid ICA-seed-based FC methods, comparing results from regressing fMRI data against task-related a priori time courses, with “back-reconstruction” from a group ICA, and with five hybrid ICA-seed-based FC methods: ROI-based with (1) single-voxel, (2) few-voxel, and (3) many-voxel seed; and dual-regression-based with (4) single ICA map and (5) multiple ICA map seed. |
format | Text |
id | pubmed-2905944 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-29059442010-08-05 Hybrid ICA-Seed-Based Methods for fMRI Functional Connectivity Assessment: A Feasibility Study Kelly, Robert E. Wang, Zhishun Alexopoulos, George S. Gunning, Faith M. Murphy, Christopher F. Morimoto, Sarah Shizuko Kanellopoulos, Dora Jia, Zhiru Lim, Kelvin O. Hoptman, Matthew J. Int J Biomed Imaging Research Article Brain functional connectivity (FC) is often assessed from fMRI data using seed-based methods, such as those of detecting temporal correlation between a predefined region (seed) and all other regions in the brain; or using multivariate methods, such as independent component analysis (ICA). ICA is a useful data-driven tool, but reproducibility issues complicate group inferences based on FC maps derived with ICA. These reproducibility issues can be circumvented with hybrid methods that use information from ICA-derived spatial maps as seeds to produce seed-based FC maps. We report results from five experiments to demonstrate the potential advantages of hybrid ICA-seed-based FC methods, comparing results from regressing fMRI data against task-related a priori time courses, with “back-reconstruction” from a group ICA, and with five hybrid ICA-seed-based FC methods: ROI-based with (1) single-voxel, (2) few-voxel, and (3) many-voxel seed; and dual-regression-based with (4) single ICA map and (5) multiple ICA map seed. Hindawi Publishing Corporation 2010 2010-06-28 /pmc/articles/PMC2905944/ /pubmed/20689712 http://dx.doi.org/10.1155/2010/868976 Text en Copyright © 2010 Robert E. Kelly et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Kelly, Robert E. Wang, Zhishun Alexopoulos, George S. Gunning, Faith M. Murphy, Christopher F. Morimoto, Sarah Shizuko Kanellopoulos, Dora Jia, Zhiru Lim, Kelvin O. Hoptman, Matthew J. Hybrid ICA-Seed-Based Methods for fMRI Functional Connectivity Assessment: A Feasibility Study |
title | Hybrid ICA-Seed-Based Methods for fMRI Functional Connectivity Assessment: A Feasibility Study |
title_full | Hybrid ICA-Seed-Based Methods for fMRI Functional Connectivity Assessment: A Feasibility Study |
title_fullStr | Hybrid ICA-Seed-Based Methods for fMRI Functional Connectivity Assessment: A Feasibility Study |
title_full_unstemmed | Hybrid ICA-Seed-Based Methods for fMRI Functional Connectivity Assessment: A Feasibility Study |
title_short | Hybrid ICA-Seed-Based Methods for fMRI Functional Connectivity Assessment: A Feasibility Study |
title_sort | hybrid ica-seed-based methods for fmri functional connectivity assessment: a feasibility study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2905944/ https://www.ncbi.nlm.nih.gov/pubmed/20689712 http://dx.doi.org/10.1155/2010/868976 |
work_keys_str_mv | AT kellyroberte hybridicaseedbasedmethodsforfmrifunctionalconnectivityassessmentafeasibilitystudy AT wangzhishun hybridicaseedbasedmethodsforfmrifunctionalconnectivityassessmentafeasibilitystudy AT alexopoulosgeorges hybridicaseedbasedmethodsforfmrifunctionalconnectivityassessmentafeasibilitystudy AT gunningfaithm hybridicaseedbasedmethodsforfmrifunctionalconnectivityassessmentafeasibilitystudy AT murphychristopherf hybridicaseedbasedmethodsforfmrifunctionalconnectivityassessmentafeasibilitystudy AT morimotosarahshizuko hybridicaseedbasedmethodsforfmrifunctionalconnectivityassessmentafeasibilitystudy AT kanellopoulosdora hybridicaseedbasedmethodsforfmrifunctionalconnectivityassessmentafeasibilitystudy AT jiazhiru hybridicaseedbasedmethodsforfmrifunctionalconnectivityassessmentafeasibilitystudy AT limkelvino hybridicaseedbasedmethodsforfmrifunctionalconnectivityassessmentafeasibilitystudy AT hoptmanmatthewj hybridicaseedbasedmethodsforfmrifunctionalconnectivityassessmentafeasibilitystudy |