TY - JOUR
T1 - Optimal approaches to analyzing functional MRI data in glioma patients
AU - Park, Ki Yun
AU - Shimony, Joshua S.
AU - Chakrabarty, Satrajit
AU - Tanenbaum, Aaron B.
AU - Hacker, Carl D.
AU - Donovan, Kara M.
AU - Luckett, Patrick H.
AU - Milchenko, Mikhail
AU - Sotiras, Aristeidis
AU - Marcus, Daniel S.
AU - Leuthardt, Eric C.
AU - Snyder, Abraham Z.
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2024/2
Y1 - 2024/2
N2 - Background: Resting-state fMRI is increasingly used to study the effects of gliomas on the functional organization of the brain. A variety of preprocessing techniques and functional connectivity analyses are represented in the literature. However, there so far has been no systematic comparison of how alternative methods impact observed results. New method: We first surveyed current literature and identified alternative analytical approaches commonly used in the field. Following, we systematically compared alternative approaches to atlas registration, parcellation scheme, and choice of graph-theoretical measure as regards differentiating glioma patients (N = 59) from age-matched reference subjects (N = 163). Results: Our results suggest that non-linear, as opposed to affine registration, improves structural match to an atlas, as well as measures of functional connectivity. Functionally- as opposed to anatomically-derived parcellation schemes maximized the contrast between glioma patients and reference subjects. We also demonstrate that graph-theoretic measures strongly depend on parcellation granularity, parcellation scheme, and graph density. Comparison with existing methods and conclusions: Our current work primarily focuses on technical optimization of rs-fMRI analysis in glioma patients and, therefore, is fundamentally different from the bulk of papers discussing glioma-induced functional network changes. We report that the evaluation of glioma-induced alterations in the functional connectome strongly depends on analytical approaches including atlas registration, choice of parcellation scheme, and graph-theoretical measures.
AB - Background: Resting-state fMRI is increasingly used to study the effects of gliomas on the functional organization of the brain. A variety of preprocessing techniques and functional connectivity analyses are represented in the literature. However, there so far has been no systematic comparison of how alternative methods impact observed results. New method: We first surveyed current literature and identified alternative analytical approaches commonly used in the field. Following, we systematically compared alternative approaches to atlas registration, parcellation scheme, and choice of graph-theoretical measure as regards differentiating glioma patients (N = 59) from age-matched reference subjects (N = 163). Results: Our results suggest that non-linear, as opposed to affine registration, improves structural match to an atlas, as well as measures of functional connectivity. Functionally- as opposed to anatomically-derived parcellation schemes maximized the contrast between glioma patients and reference subjects. We also demonstrate that graph-theoretic measures strongly depend on parcellation granularity, parcellation scheme, and graph density. Comparison with existing methods and conclusions: Our current work primarily focuses on technical optimization of rs-fMRI analysis in glioma patients and, therefore, is fundamentally different from the bulk of papers discussing glioma-induced functional network changes. We report that the evaluation of glioma-induced alterations in the functional connectome strongly depends on analytical approaches including atlas registration, choice of parcellation scheme, and graph-theoretical measures.
KW - Atlas registration
KW - Glioma
KW - Graph-theoretic measures
KW - Parcellation granularity
KW - Parcellation schemes
KW - Resting-state fMRI
UR - http://www.scopus.com/inward/record.url?scp=85177775252&partnerID=8YFLogxK
U2 - 10.1016/j.jneumeth.2023.110011
DO - 10.1016/j.jneumeth.2023.110011
M3 - Article
C2 - 37981126
AN - SCOPUS:85177775252
SN - 0165-0270
VL - 402
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
M1 - 110011
ER -