Imaging and anesthesia protocol optimization in sedated clinical resting state fMRI

Elmira Hassanzadeh; Alyssa Ailion; Masoud Hassanzadeh; Alena Hornak; Noam Peled; Dana Martino; Simon K Warfield; Zhou Lan; Taha Gholipour; Steven M Stufflebeam

doi:10.3174/ajnr.A8438

Imaging and anesthesia protocol optimization in sedated clinical resting state fMRI

AJNR Am J Neuroradiol. 2024 Aug 12:ajnr.A8438. doi: 10.3174/ajnr.A8438. Online ahead of print.

Affiliation

¹ From the Department of Radiology (E.H., Z.L.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Neurology (A.A., A.H., D.M.), Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Surgery (M.H.), Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA, Department of Radiology (S.K.W.) Boston Children's Hospital Harvard Medical School, Boston, MA, USA; and Comprehensive Epilepsy Center (T.G.), University of California San Diego, San Diego, Ca, USA; Department of Radiology (E.H, S.M.S, N.P.), Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

PMID: 39134370
DOI: 10.3174/ajnr.A8438

Abstract

Background and purpose: The quality of resting-state functional MRI (rs-fMRI) under anesthesia is variable and there are no guidelines on optimal image acquisition or anesthesia protocol. We aim to identify the factors that may lead to compromised clinical rs-fMRI under anesthesia.

Materials and methods: In this cross-sectional study, we analyzed clinical rs-fMRI data acquired under anesthesia from 2009-2023 at Massachusetts General Hospital. Independent component analysis driven resting state networks (RSN) of each patient were evaluated qualitatively and quantitatively and grouped as robust or weak. Overall networks were evaluated using the qualitative method, and motor and language networks were evaluated using the quantitative method. RSN robustness was analyzed in 4 outcome categories: overall, combined Motor-Language, individual motor, and language networks. Predictor variables included rs-fMRI acquisition parameters, anesthesia medications, underlying brain structural abnormalities, age, and sex. Logistic regression was used to examine the effect of the study variables on RSN robustness.

Results: Sixty-nine patients were identified. With qualitative assessment, 40 had robust and 29 had weak overall RSN. Quantitatively, 45 patients had robust, while 24 had weak Motor-Language networks. Among all the predictor variables, only sevoflurane significantly contributed to the outcomes, with sevoflurane administration reducing the odds of having robust RSN in overall (Odds Radio (OR)= 0.2, 95% Confidence Interval (CI) = [0.05;0.79], p = .02), Motor-Language (OR = 0.18, 95% CI = [0.04;0.80], p = .02) and individual motor (OR= 0.1, 95% CI = [0.02;0.64], p= .02) categories. Individual language network robustness was not associated with the tested predictor variables.

Conclusions: Sevoflurane anesthesia may compromise the visibility of fMRI resting state networks, particularly impacting motor networks. This finding suggests that the type of anesthesia is a critical factor in rs-fMRI quality. We did not observe the association of the MR acquisition technique or underlying structural abnormality with the RSN robustness.

Abbreviations: BOLD = Blood Oxygen Level-Dependent; ICA = Independent Component Analysis; Rs-fMRI = Resting-State Functional Magnetic Resonance Imaging; RSN = Resting-State Networks; SNR = Signal-to-Noise Ratio.