在过去,由于动脉采样的侵入性,很少在临床常规中测定脑葡萄糖代谢率(CMRGlc)的绝对值。随着PET/MR联合成像技术的出现,CMRGlc值可以非侵入地获得,从而提供了在临床常规中利用完全定量数据的机会。然而,CMRGlc值显示出高生理变异性,可能是由于休息时大脑内在活动的波动。为了减少与这些波动相关的CMRGlc可变性,这项研究的目的是确定是否可以使用静息状态功能磁共振成像(rs-fMRI)的功能连接测量来校正这些内在脑活动的波动.
方法:我们研究了10名健康志愿者,他们使用完全集成的PET/MR系统(SiemensBiographmMR)进行了重新测试动态[18F]FDG-PET研究。为了验证基于PET和MR数据的组合分析的图像导出输入函数的非侵入性推导,获得动脉血样。使用动脉输入函数(AIF),使用Patlak图形方法确定代表CMRGlc的参数图像。在六个主要网络中的节点之间确定了定向功能连接(dFC)和非定向功能连接(uFC)(默认模式网络,显著性,L/R执行官,注意,和感觉运动网络)使用双变量相关(R系数)或多变量自回归(MVAR)模型。此外,区域连通性措施的性能,低频波动的分数振幅(fALFF),也被调查了。
结果:测试和再测试之间CMRGlc值的平均受试者内变异性被确定为(14±8%),其中在测试时平均受试者间变异性为25%,在再测试时平均受试者间变异性为15%。在测试时,所有网络的平均CMRGlc值(umol/100g/min)为39±10,并且在重新测试时略微增加至43±6。R,与受试者间变异性相比,MVAR和fALFF系数显示出相对较大的重测变异性,导致可靠性差(组内相关性在0.11-0.65范围内)。更重要的是,R系数(对于uFC)之间没有发现显著的关系,六个主要网络中任何一个的MVAR系数(用于dFC)或fALFF以及相应的CMRGlc值。
结论:在已建立的大脑网络中测量功能连接并不能提供一种降低CMRGlc值的受试者间或受试者内变异性的方法。因此,我们的结果表明,从与PET成像同时获得的rs-fMRI测得的连通性不适合校正与静息态脑活动内在波动相关的CMRGlc变异性.因此,鉴于观察到的CMRGlc值的受试者间和受试者内的实质性变异性,绝对定量与临床常规的相关性目前尚不确定。
In the past, determination of absolute values of cerebral metabolic rate of glucose (CMRGlc) in clinical routine was rarely carried out due to the invasive nature of arterial sampling. With the advent of combined PET/MR imaging technology, CMRGlc values can be obtained non-invasively, thereby providing the opportunity to take advantage of fully quantitative data in clinical routine. However, CMRGlc values display high physiological variability, presumably due to fluctuations in the intrinsic activity of the brain at rest. To reduce CMRGlc variability associated with these fluctuations, the objective of this study was to determine whether functional connectivity measures derived from resting-state fMRI (rs-fMRI) could be used to correct for these fluctuations in intrinsic brain activity.
METHODS: We studied 10 healthy volunteers who underwent a test-retest dynamic [18F]FDG-PET study using a fully integrated PET/MR system (Siemens Biograph mMR). To validate the non-invasive derivation of an image-derived input function based on combined analysis of PET and MR data, arterial blood samples were obtained. Using the arterial input function (AIF), parametric images representing CMRGlc were determined using the Patlak graphical approach. Both directed functional connectivity (dFC) and undirected functional connectivity (uFC) were determined between nodes in six major networks (Default mode network, Salience, L/R Executive, Attention, and Sensory-motor network) using either a bivariate-correlation (R coefficient) or a Multi-Variate AutoRegressive (MVAR) model. In addition, the performance of a regional connectivity measure, the fractional amplitude of low frequency fluctuations (fALFF), was also investigated.
RESULTS: The average intrasubject variability for CMRGlc values between test and retest was determined as (14 ±8%) with an average inter-subject variability of 25% at test and 15% at retest. The average CMRGlc value (umol/100 g/min) across all networks was 39 ±10 at test and increased slightly to 43 ±6 at retest. The R, MVAR and fALFF coefficients showed relatively large test-retest variability in comparison to the inter-subjects variability, resulting in poor reliability (intraclass correlation in the range of 0.11-0.65). More importantly, no significant relationship was found between the R coefficients (for uFC), MVAR coefficients (for dFC) or fALFF and corresponding CMRGlc values for any of the six major networks.
CONCLUSIONS: Measurement of functional connectivity within established brain networks did not provide a means to decrease the inter- or intrasubject variability of CMRGlc values. As such, our results indicate that connectivity measured derived from rs-fMRI acquired contemporaneously with PET imaging are not suited for correction of CMRGlc variability associated with intrinsic fluctuations of resting-state brain activity. Thus, given the observed substantial inter- and intrasubject variability of CMRGlc values, the relevance of absolute quantification for clinical routine is presently uncertain.