Bayesian longitudinal spectral estimation with application to resting-state fMRI data analysis

Ning Dai; Galin L. Jones; Mark Fiecas

doi:10.1016/j.ecosta.2019.01.002

Bayesian longitudinal spectral estimation with application to resting-state fMRI data analysis

Ning Dai, Galin L. Jones, Mark Fiecas

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

The amplitude of the oscillatory patterns present in spontaneous fluctuations of brain signals obtained from resting-state functional magnetic resonance imaging (fMRI), measured using an index called the fractional amplitude of low-frequency fluctuation (fALFF), is a well-known measure of brain activity with potential to serve as a marker for brain dysfunction. With the rise of longitudinal neuroimaging studies, there is a great need for methodologies that take advantage of the longitudinal design in modeling the impact of aging or disease progression. Motivated by the longitudinal design of the Alzheimer's Disease Neuroimaging Initiative (ADNI), a novel Bayesian longitudinal model is developed in order to estimate the spectra of resting-state fMRI time courses, from which one can extract estimates of fALFF that are potentially associated with aging. The model incorporates within-subject correlation to improve estimates of the spectra, in addition to the variability that naturally arises between subjects. The model is validated using simulated data to show the gains in performance for estimating fALFF by taking advantage of the longitudinal design. Finally, a longitudinal analysis on fALFF from the resting-state fMRI data from ADNI is conducted, where the impact of both Alzheimer's disease and aging on the spontaneous fluctuations of brain activity is shown.

Original language	English (US)
Pages (from-to)	104-116
Number of pages	13
Journal	Econometrics and Statistics
Volume	15
DOIs	https://doi.org/10.1016/j.ecosta.2019.01.002
State	Published - Jul 2020

Bibliographical note

Funding Information:
Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI; Principal Investigator Michael W. Weiner, MD) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co. Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for NeuroImaging at the University of Southern California. The authors would like to thank Timothy D. Johnson, James S. Hodges, and Brian B. Hart for helpful conversations.

Funding Information:
Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI; Principal Investigator Michael W. Weiner, MD) ( National Institutes of Health Grant U01 AG024904 ) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for NeuroImaging at the University of Southern California.

Publisher Copyright:
© 2019 EcoSta Econometrics and Statistics

Keywords

Alzheimer's disease
Amplitude of low-frequency fluctuation
Longitudinal data analysis
Markov chain Monte Carlo
Resting-state fMRI
Spectral estimation

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10.1016/j.ecosta.2019.01.002

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@article{e73584f8258e418496c1f6351c9cc679,

title = "Bayesian longitudinal spectral estimation with application to resting-state fMRI data analysis",

abstract = "The amplitude of the oscillatory patterns present in spontaneous fluctuations of brain signals obtained from resting-state functional magnetic resonance imaging (fMRI), measured using an index called the fractional amplitude of low-frequency fluctuation (fALFF), is a well-known measure of brain activity with potential to serve as a marker for brain dysfunction. With the rise of longitudinal neuroimaging studies, there is a great need for methodologies that take advantage of the longitudinal design in modeling the impact of aging or disease progression. Motivated by the longitudinal design of the Alzheimer's Disease Neuroimaging Initiative (ADNI), a novel Bayesian longitudinal model is developed in order to estimate the spectra of resting-state fMRI time courses, from which one can extract estimates of fALFF that are potentially associated with aging. The model incorporates within-subject correlation to improve estimates of the spectra, in addition to the variability that naturally arises between subjects. The model is validated using simulated data to show the gains in performance for estimating fALFF by taking advantage of the longitudinal design. Finally, a longitudinal analysis on fALFF from the resting-state fMRI data from ADNI is conducted, where the impact of both Alzheimer's disease and aging on the spontaneous fluctuations of brain activity is shown.",

keywords = "Alzheimer's disease, Amplitude of low-frequency fluctuation, Longitudinal data analysis, Markov chain Monte Carlo, Resting-state fMRI, Spectral estimation",

author = "Ning Dai and Jones, {Galin L.} and Mark Fiecas",

note = "Funding Information: Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI; Principal Investigator Michael W. Weiner, MD) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co. Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for NeuroImaging at the University of Southern California. The authors would like to thank Timothy D. Johnson, James S. Hodges, and Brian B. Hart for helpful conversations. Funding Information: Data collection and sharing for this project was funded by the Alzheimer{\textquoteright}s Disease Neuroimaging Initiative (ADNI; Principal Investigator Michael W. Weiner, MD) ( National Institutes of Health Grant U01 AG024904 ) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer{\textquoteright}s Association; Alzheimer{\textquoteright}s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer{\textquoteright}s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for NeuroImaging at the University of Southern California. Publisher Copyright: {\textcopyright} 2019 EcoSta Econometrics and Statistics",

year = "2020",

month = jul,

doi = "10.1016/j.ecosta.2019.01.002",

language = "English (US)",

volume = "15",

pages = "104--116",

journal = "Econometrics and Statistics",

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T1 - Bayesian longitudinal spectral estimation with application to resting-state fMRI data analysis

AU - Dai, Ning

AU - Jones, Galin L.

AU - Fiecas, Mark

N1 - Funding Information: Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI; Principal Investigator Michael W. Weiner, MD) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co. Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for NeuroImaging at the University of Southern California. The authors would like to thank Timothy D. Johnson, James S. Hodges, and Brian B. Hart for helpful conversations. Funding Information: Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI; Principal Investigator Michael W. Weiner, MD) ( National Institutes of Health Grant U01 AG024904 ) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for NeuroImaging at the University of Southern California. Publisher Copyright: © 2019 EcoSta Econometrics and Statistics

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N2 - The amplitude of the oscillatory patterns present in spontaneous fluctuations of brain signals obtained from resting-state functional magnetic resonance imaging (fMRI), measured using an index called the fractional amplitude of low-frequency fluctuation (fALFF), is a well-known measure of brain activity with potential to serve as a marker for brain dysfunction. With the rise of longitudinal neuroimaging studies, there is a great need for methodologies that take advantage of the longitudinal design in modeling the impact of aging or disease progression. Motivated by the longitudinal design of the Alzheimer's Disease Neuroimaging Initiative (ADNI), a novel Bayesian longitudinal model is developed in order to estimate the spectra of resting-state fMRI time courses, from which one can extract estimates of fALFF that are potentially associated with aging. The model incorporates within-subject correlation to improve estimates of the spectra, in addition to the variability that naturally arises between subjects. The model is validated using simulated data to show the gains in performance for estimating fALFF by taking advantage of the longitudinal design. Finally, a longitudinal analysis on fALFF from the resting-state fMRI data from ADNI is conducted, where the impact of both Alzheimer's disease and aging on the spontaneous fluctuations of brain activity is shown.

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KW - Markov chain Monte Carlo

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KW - Spectral estimation

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Bayesian longitudinal spectral estimation with application to resting-state fMRI data analysis

Abstract

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Keywords

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