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
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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.

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镇静临床静息状态 fMRI 的成像和麻醉方案优化。
背景和目的:麻醉下静息状态功能磁共振成像(rs-fMRI)的质量参差不齐,目前尚无关于最佳图像采集或麻醉方案的指南。我们旨在找出可能导致麻醉下临床 rs-fMRI 质量下降的因素:在这项横断面研究中,我们分析了马萨诸塞州总医院 2009-2023 年在麻醉状态下获得的临床 rs-fMRI 数据。我们对每位患者的独立成分分析驱动的静息状态网络(RSN)进行了定性和定量评估,并将其分为强健型和弱型。采用定性方法对整体网络进行评估,采用定量方法对运动和语言网络进行评估。RSN 的稳健性按 4 个结果类别进行分析:整体网络、运动-语言组合网络、单个运动网络和语言网络。预测变量包括 rs-fMRI 采集参数、麻醉药物、潜在的大脑结构异常、年龄和性别。采用逻辑回归法检验研究变量对 RSN 稳健性的影响:结果:共确定了 69 名患者。定性评估结果显示,40 名患者的 RSN 整体稳健,29 名患者的 RSN 整体较弱。定量评估结果显示,45 名患者的运动语言网络健全,24 名患者的运动语言网络薄弱。在所有预测变量中,只有七氟烷对结果有显著影响,使用七氟烷降低了总体 RSN 强化的几率(Odds Radio (OR)= 0.2,95% 置信区间 (CI) = [0.05;0.79],p = .02)、运动语言(OR = 0.18,95% CI = [0.04;0.80],p = .02)和单个运动(OR= 0.1,95% CI = [0.02;0.64],p= .02)类别中具有稳健 RSN 的几率。个人语言网络的稳健性与测试的预测变量无关:结论:七氟烷麻醉可能会影响fMRI静息态网络的可见性,尤其是对运动网络的影响。这一发现表明,麻醉类型是影响 rs-fMRI 质量的关键因素。我们没有观察到磁共振采集技术或潜在结构异常与RSN鲁棒性的关联:缩写:BOLD = 血氧水平依赖性;ICA = 独立成分分析;Rs-fMRI = 静息状态功能磁共振成像;RSN = 静息状态网络;SNR = 信噪比。
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