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Quantifying effects of temporal regression techniques on resting state fMRI parameters in patients with brain tumor
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Ahmed, Mohamed A.. Quantifying effects of temporal regression techniques on resting state fMRI parameters in patients with brain tumor. Retrieved from https://doi.org/doi:10.7282/t3-h1j6-e875

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TitleQuantifying effects of temporal regression techniques on resting state fMRI parameters in patients with brain tumor
NameAhmed, Mohamed A. (author); Gohel, Suril (chair); Gohel, Suril (member); Vachha, Behroze (member); Srinivasan, Shankar (member); Mitrofanova, Antonina (member); Rutgers University; School of Health Professions
Date Created2022
Other Date2022-08 (degree)
SubjectBioinformatics, Health sciences, Default mode network, fMRI, Functional connectivity, Resting state, Sensorimotor network, Temporal regression
Extent121 pages : illustrations
DescriptionResting state functional magnetic resonance imaging (rs-fMRI) employs blood oxygen level dependent (BOLD) signals as an indirect measure of the brain's intrinsic neuronal activities. Studies in the healthy control (HC) population have revealed that temporal regression techniques utilizing cerebrospinal fluid (CSF) and white matter (WM) signals as covariates are being used to enhance the signal to noise ratio (SNR) of BOLD signals. However, in brain tumor data, tumors may cause alteration in boundaries of brain tissues such as CSF, WM, and gray matter (GM). This could lead to covariates in CSF and WM signals representing GM signals and vice versa. The goal of this study is to undertake a quantitative investigation of resting state fMRI temporal regression techniques and their influence on local and global resting state parameters in patients with brain tumor. The current work evaluates changes in resting state parameters as a result of several temporal regression approaches in brain tumor patients and healthy control (HC) populations. Five different temporal regression strategies including motion parameters, motion scrubbing, and physiological noise regressors were implemented. We investigated the influence of those temporal regression techniques on local and global rs-fMRI data in tumor patients and HC population. As a result, the impact of adding nuisance covariates on local and global resting state parameters is different between healthy control (HC) subjects and brain tumor patients. Among the five strategies, Inclusion of PCA of CSF/WM regressors affected local and global parameters differently and it has the largest impact on parameters in healthy control (HC) and tumor patients. There was increase in mALFF values and decrease in fALFF and ReHo values, in addition to introducing connectivity between DMN and SMN in Grade II tumor population that had not been discovered in the other strategies. Also, there is a significant impact of tumor grade on local and global resting state parameters.
NotePh.D.
NoteIncludes bibliographical references
Genretheses
Persistent URLhttps://doi.org/doi:10.7282/t3-h1j6-e875
LanguageEnglish
CollectionSchool of Health Professions ETD Collection
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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