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A Baseline for the Multivariate Comparison of Resting-State Networks

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects...

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Autores principales: Allen, Elena A., Erhardt, Erik B., Damaraju, Eswar, Gruner, William, Segall, Judith M., Silva, Rogers F., Havlicek, Martin, Rachakonda, Srinivas, Fries, Jill, Kalyanam, Ravi, Michael, Andrew M., Caprihan, Arvind, Turner, Jessica A., Eichele, Tom, Adelsheim, Steven, Bryan, Angela D., Bustillo, Juan, Clark, Vincent P., Feldstein Ewing, Sarah W., Filbey, Francesca, Ford, Corey C., Hutchison, Kent, Jung, Rex E., Kiehl, Kent A., Kodituwakku, Piyadasa, Komesu, Yuko M., Mayer, Andrew R., Pearlson, Godfrey D., Phillips, John P., Sadek, Joseph R., Stevens, Michael, Teuscher, Ursina, Thoma, Robert J., Calhoun, Vince D.
Formato: Texto
Lenguaje:English
Publicado: Frontiers Research Foundation 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3051178/
https://www.ncbi.nlm.nih.gov/pubmed/21442040
http://dx.doi.org/10.3389/fnsys.2011.00002
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author Allen, Elena A.
Erhardt, Erik B.
Damaraju, Eswar
Gruner, William
Segall, Judith M.
Silva, Rogers F.
Havlicek, Martin
Rachakonda, Srinivas
Fries, Jill
Kalyanam, Ravi
Michael, Andrew M.
Caprihan, Arvind
Turner, Jessica A.
Eichele, Tom
Adelsheim, Steven
Bryan, Angela D.
Bustillo, Juan
Clark, Vincent P.
Feldstein Ewing, Sarah W.
Filbey, Francesca
Ford, Corey C.
Hutchison, Kent
Jung, Rex E.
Kiehl, Kent A.
Kodituwakku, Piyadasa
Komesu, Yuko M.
Mayer, Andrew R.
Pearlson, Godfrey D.
Phillips, John P.
Sadek, Joseph R.
Stevens, Michael
Teuscher, Ursina
Thoma, Robert J.
Calhoun, Vince D.
author_facet Allen, Elena A.
Erhardt, Erik B.
Damaraju, Eswar
Gruner, William
Segall, Judith M.
Silva, Rogers F.
Havlicek, Martin
Rachakonda, Srinivas
Fries, Jill
Kalyanam, Ravi
Michael, Andrew M.
Caprihan, Arvind
Turner, Jessica A.
Eichele, Tom
Adelsheim, Steven
Bryan, Angela D.
Bustillo, Juan
Clark, Vincent P.
Feldstein Ewing, Sarah W.
Filbey, Francesca
Ford, Corey C.
Hutchison, Kent
Jung, Rex E.
Kiehl, Kent A.
Kodituwakku, Piyadasa
Komesu, Yuko M.
Mayer, Andrew R.
Pearlson, Godfrey D.
Phillips, John P.
Sadek, Joseph R.
Stevens, Michael
Teuscher, Ursina
Thoma, Robert J.
Calhoun, Vince D.
author_sort Allen, Elena A.
collection PubMed
description As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.
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spelling pubmed-30511782011-03-25 A Baseline for the Multivariate Comparison of Resting-State Networks Allen, Elena A. Erhardt, Erik B. Damaraju, Eswar Gruner, William Segall, Judith M. Silva, Rogers F. Havlicek, Martin Rachakonda, Srinivas Fries, Jill Kalyanam, Ravi Michael, Andrew M. Caprihan, Arvind Turner, Jessica A. Eichele, Tom Adelsheim, Steven Bryan, Angela D. Bustillo, Juan Clark, Vincent P. Feldstein Ewing, Sarah W. Filbey, Francesca Ford, Corey C. Hutchison, Kent Jung, Rex E. Kiehl, Kent A. Kodituwakku, Piyadasa Komesu, Yuko M. Mayer, Andrew R. Pearlson, Godfrey D. Phillips, John P. Sadek, Joseph R. Stevens, Michael Teuscher, Ursina Thoma, Robert J. Calhoun, Vince D. Front Syst Neurosci Neuroscience As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease. Frontiers Research Foundation 2011-02-04 /pmc/articles/PMC3051178/ /pubmed/21442040 http://dx.doi.org/10.3389/fnsys.2011.00002 Text en Copyright © 2011 Allen, Erhardt, Damaraju, Gruner, Segall, Silva, Havlicek, Rachakonda, Fries, Kalyanam, Michael, Caprihan, Turner, Eichele, Adelsheim, Bryan, Bustillo, Clark, Feldstein Ewing, Filbey, Ford, Hutchison, Jung, Kiehl, Kodituwakku, Komesu, Mayer, Pearlson, Phillips, Sadek, Stevens, Teuscher, Thoma and Calhoun. http://www.frontiersin.org/licenseagreement This is an open-access article subject to an exclusive license agreement between the authors and Frontiers Media SA, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.
spellingShingle Neuroscience
Allen, Elena A.
Erhardt, Erik B.
Damaraju, Eswar
Gruner, William
Segall, Judith M.
Silva, Rogers F.
Havlicek, Martin
Rachakonda, Srinivas
Fries, Jill
Kalyanam, Ravi
Michael, Andrew M.
Caprihan, Arvind
Turner, Jessica A.
Eichele, Tom
Adelsheim, Steven
Bryan, Angela D.
Bustillo, Juan
Clark, Vincent P.
Feldstein Ewing, Sarah W.
Filbey, Francesca
Ford, Corey C.
Hutchison, Kent
Jung, Rex E.
Kiehl, Kent A.
Kodituwakku, Piyadasa
Komesu, Yuko M.
Mayer, Andrew R.
Pearlson, Godfrey D.
Phillips, John P.
Sadek, Joseph R.
Stevens, Michael
Teuscher, Ursina
Thoma, Robert J.
Calhoun, Vince D.
A Baseline for the Multivariate Comparison of Resting-State Networks
title A Baseline for the Multivariate Comparison of Resting-State Networks
title_full A Baseline for the Multivariate Comparison of Resting-State Networks
title_fullStr A Baseline for the Multivariate Comparison of Resting-State Networks
title_full_unstemmed A Baseline for the Multivariate Comparison of Resting-State Networks
title_short A Baseline for the Multivariate Comparison of Resting-State Networks
title_sort baseline for the multivariate comparison of resting-state networks
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3051178/
https://www.ncbi.nlm.nih.gov/pubmed/21442040
http://dx.doi.org/10.3389/fnsys.2011.00002
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