MR hybrid scanner study

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Article history: Received 7 November 2014 Accepted 9 March 2015 Available online xxxx

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Keywords: PET/MRI [18F]FDG Functional connectivity Resting-state fMRI Networks

IRCCS SDN, Istituto di Ricerca Diagnostica e Nucleare, Via E. Gianturco 113, 80143 Naples, Italy Department of Neurosciences, Reproductive Sciences and Odontostomatology, Federico II University, via S. Pansini 5-ed. 17, I-80131 Naples, Italy c INSERM U894, Université Paris Descartes, Centre Hospitalier Sainte-Anne, Sorbonne Paris Cité, Paris, France d Biostructure and Bioimaging Institute, National Research Council, Via T. De Amicis 95, 80145 Naples, Italy b

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Marco Aiello a,⁎, Elena Salvatore b,1, Arnaud Cachia c,1, Sabina Pappatà d, Carlo Cavaliere a, Anna Prinster d, Emanuele Nicolai a, Marco Salvatore a, Jean-Claude Baron c,2, Mario Quarantelli d,2

a b s t r a c t

Recently introduced hybrid PET/MR scanners provide the opportunity to measure simultaneously, and in direct spatial correspondence, both metabolic demand and functional activity of the brain, hence capturing complementary information on the brain's physiological state. Here we exploited PET/MR simultaneous imaging to explore the relationship between the metabolic information provided by resting-state fluorodeoxyglucosePET (FDG-PET) and fMRI (rs-fMRI) in neurologically healthy subjects. Regional homogeneity (ReHo), fractional amplitude of low frequency fluctuations (fALFF), and degree of centrality (DC) maps were generated from the rs-fMRI data in 23 subjects, and voxel-wise comparison to glucose uptake distribution provided by simultaneously acquired FDG-PET was performed. The mutual relationships among each couple of these four metrics were explored in terms of similarity, both of spatial distribution across the brain and the whole group, and voxel-wise across subjects, taking into account partial volume effects by adjusting for grey matter (GM) volume. Although a significant correlation between the spatial distribution of glucose uptake and rs-fMRI derived metrics was present, only a limited percentage of GM voxels correlated with PET across subjects. Moreover, the correlation between the spatial distributions of PET and RS-fMRIderived metrics is spatially heterogeneous across both anatomic regions and functional networks, with lowest correlation strength in the limbic network (Spearman rho around −0.11 for DC), and strongest correlation for the default-mode network (up to 0.89 for ReHo and 0.86 for fALFF). Overall, ReHo and fALFF provided significantly higher correlation coefficients with PET (p = 10− 8 and 10− 7, respectively) as compared to DC, while no significant differences were present between ReHo and fALFF. Local GM volume variations introduced a limited overestimation of the rs-fMRI to FDG correlation between the modalities under investigation through partial volume effects. These novel results provide the basis for future studies of alterations of the coupling between brain metabolism and functional connectivity in pathologic conditions. © 2015 Elsevier Inc. All rights reserved.

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Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: A PET/MR hybrid scanner study

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Abbreviations:AAL, automated anatomicallabelling; BOLD, blood-oxygen-level-dependent; CBF, cerebral blood flow; DC, degree of centrality; DMN, default mode network; fALFF, fractional amplitude of low frequency fluctuations; FC, functional connectivity; FDG, fluorodeoxyglucose; MRI, magnetic resonance imaging; PET, positron emission tomography; ReHo, regional homogeneity; rGU, relative glucose uptake; rs-fMRI, resting-state functional MRI; RSN, resting-state network ⁎ Corresponding author. E-mail address: [email protected] (M. Aiello). 1 These two authors share 2nd author position. 2 These two authors share senior authorship.

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Introduction

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Resting-state glucose and oxygen metabolism are closely linked (Jueptner and Weiller, 1995; Baron et al., 1984) and physiologically related to neural activity. Moreover, both processes are in turn strongly related to resting cerebral blood flow (CBF) that delivers O2 and glucose to the tissue (Attwell and Iadecola, 2002; Baron et al., 1984). The mechanisms underlying neurovascular coupling and neuronal function are presently incompletely understood and being investigated by a transdisciplinary research community (Leithner and Royl, 2014). Studying the neurovascular structure is complex due to the need for both neuronal interconnectivity and vascular networks to be intact.

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http://dx.doi.org/10.1016/j.neuroimage.2015.03.017 1053-8119/© 2015 Elsevier Inc. All rights reserved.

Please cite this article as: Aiello, M., et al., Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: A PET/MR hybrid scanner study, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.017

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Material and methods

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connectivity processes; on the other hand, ReHo can be considered as a measurements of short-range functional connectivity (limited to the neighbourhood extent) while DC, by considering that distant voxels are much more numerous than the neighbouring ones, is essentially weighted by long-range functional connectivity. For each couple of the four assessed metrics, namely rGU, ReHo, fALFF and DC, relationships among them were explored both in terms of similarity of spatial distribution across the brain voxels (by assessing the correlation between voxel values of the maps of each couple of modalities), and voxel-wise across subjects (by assessing voxel-wise the correlation across subjects). The first analysis has been carried out both on the single-subject maps, and on the maps obtained by averaging, for each of the four metrics, the maps from all the subjects, to obtain measures of the similarity of the patterns of the voxel values which characterize the different metrics. On the other hand, the second analysis assesses, at voxel level, the capability of the two techniques to rank the subjects in a similar order. These two analyses were carried out considering both the whole brain, and individual brain structures derived from functional and anatomical parcellation methods.

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The present study was approved by the local Institutional Review Board of the SDN Foundation, and informed written consent was obtained from all participants. Consecutive patients N 40 yrs of age referred for whole-body FDGPET for non-CNS lesions (b2 cm in diameter in all cases), which resulted negative at FDG-PET scan, with normal neurological examination, were approached for participation in this study and enrolled if they met the inclusion and exclusion criteria. Exclusion criteria were a current or past history of psychiatric or neurological disorders, or of substance abuse, or treatment with medications active on the CNS. Mini mental state examination (MMSE) (Folstein et al., 1975) and the Italian version of the Brief Symptom Inventory (De Leo et al., 1993) were also administered at the time of the scan, and subjects who scored positive for cognitive impairment (MMSE score ≤27), and/or with signs or symptoms of depression or anxiety were excluded from the study. In addition, subjects were excluded from the analysis if any abnormality was present on the PET and/or MRI scan, apart from mild age-related leukoaraiosis (Fazekas score ≤1). A total of 26 subjects (16 females; age range 40– 78 yrs) were enrolled, including 18 breast lesions, 2 colorectal lesions, and 6 with lung lesions. The use of a control group with negative PET scan performed for suspected minimal localized oncologic disease was elected by default in this study because the Italian law discourages the use of healthy individuals in exploratory research studies using PET, due to the nonnegligible radiation exposure. Previously published studies similar to ours in design have also used the same sort of clinical material as controls (Pagani et al., 2014).

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Acquisition protocol

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PET/MR data were simultaneously acquired in the resting state (subjects asked to relax and keep their eyes open without falling asleep) using a Biograph mMR tomograph (Siemens Healthcare, Erlangen, Germany) designed with a multi-ring LSO detector block embedded into a 3 T magnetic resonance scanner. Nominal axial and transverse resolution of the PET system was 4.4 mm and 4.1 mm FWHM, respectively, at 1 cm from the isocenter. Additional technical details on the scanner are reported elsewhere (Delso et al., 2011). The PET procedure was in compliance with the European Association of Nuclear Medicine procedure guidelines for both tumour

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In-vivo imaging allows the complete neurovascular structure to be observed from different standpoints, as well as its link to clinical measures 61 to be assessed. Recent studies have investigated the relationship be62 tween resting-state energy consumption and functional activity using 63 in-vivo imaging techniques (Nishida et al., 2008; Tomasi et al., 2013; 64 Q9 Li et al., 2012a, 2012b; Liang et al., 2013). However, a well-known limi65 tation of the majority of these studies is that the measurements of both 66 functional variables were obtained at different time points, hence likely 67 during different physiological states. 68 Recently introduced hybrid PET/MR scanners provide the oppor69 tunity to overcome these limitations, allowing to simultaneously obtain 70 both the metabolic information, provided by imaging, as well as the 71 functional and structural information provided by magnetic resonance 72 imaging (MRI). 73 More generally, beyond the mere combination of functional and 74 morphological imaging, the information offered by MRI and PET may 75 complement each another, considering the wide range of MRI tech76 niques and PET radiotracers presently available (Herzog et al., 2010; 77 Judenhofer et al., 2008; Wehrl et al., 2013). 78 In the present work, we exploited the capacity of PET/MR simulta79 neous imaging to estimate concomitant resting-state cerebral glucose 80 metabolism by means of [18F] fluorodeoxyglucose positron emission 81 tomography (FDG-PET) and the functional brain activity, as measured 82 by the hemodynamic response detected by the blood-oxygen-level83 dependent (BOLD) signal during resting-state functional MRI (rs-fMRI). 84 The aim of this study was to explore the relationship between the 85 functional information provided by both modalities in neurologically 86 healthy subjects. To this purpose, we compared over the whole brain 87 and voxel-wise the relative glucose uptake (rGU) assessed by FDG-PET 88 and three metrics of functional connectivity (FC) derived from rs89 fMRI – namely regional homogeneity (ReHo), fractional amplitude of 90 low frequency fluctuations (fALFF), and degree of centrality (DC) – 91 previously used by several groups for the characterisation of brain dis92 orders (Liu et al., 2006; Wu et al., 2009; He et al., 2007; Han et al., 93 Q10 2011; Palaniyappan and Liddle, 2014). 94 Briefly, regional homogeneity (ReHo) is a voxel-based measure of 95 brain activity that estimates the degree of synchronization between 96 the time series of a given voxel and its nearest neighbours (Zang et al., 97 2004). The value of this measure rests on the assumption that intrinsic 98 brain activity is strongly related to the simultaneous activation of clus99 ters of voxels, as opposed to single voxels. Higher ReHo may thus indi100 cate higher synchronization of local field potential of neuronal activity 101 in the human brain (Li et al., 2013). Fractional amplitude of low frequen102 cy fluctuations (fALFF, (Zou et al., 2008; Zuo et al., 2010)) quantifies the 103 amplitude of low frequency oscillations (LFOs), and is defined, for the 104 time course of each voxel, as the power within the low-frequency 105 range (0.01–0.1 Hz) divided by the total power in the entire detectable 106 frequency range, thus representing the relative contribution of LFOs to 107 the whole frequency range. The rationale for the use of fALFF is based 108 on the assumption that slow fluctuations in activity are a fundamental 109 feature of the resting brain, and their presence is key in determining 110 correlated activity between brain regions, and to define resting state 111 networks. The relative magnitude of these fluctuations can differ be112 tween brain regions (Han et al., 2011) and between subjects, and thus 113 may serve as a marker of individual differences or dysfunction. The 114 degree of centrality (DC, (Buckner et al., 2009)), also called Intrinsic 115 Connectivity Contrast (Martuzzi et al., 2011), or global Functional Con116 Q11 nectivity Density (Tomasi and Volkow, 2012), considers Pearson's 117 correlation coefficient as metric for functional connectivity estimation 118 between each pair of voxels, assigning to each voxel the global number 119 of functional connections between it and all other voxels across the 120 brain. In its more diffuse implementations, it involves the use of a 121 threshold over which a pair of voxels is considered connected. 122 The spatial extent of the physiological phenomena probed by these 123 measures is also different. fALFF contrast is only due to single voxel sig124 nal, and is thus independent of the spatial range of the underlying

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Please cite this article as: Aiello, M., et al., Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: A PET/MR hybrid scanner study, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.017

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i) Three-dimensional T1-weighted magnetization-prepared rapid acquisition gradient-echo sequence (MPRAGE, 176 sagittal planes, 256 × 240 mm2 field of view, voxel size 1 × 1 × 1 mm3, TR/TE/TI 2300/2.96/900 ms, flip angle 9°, TA = 5′14″) ii) T2*-weighted single-shot EPI sequence (voxel-size 4 × 4 × 4 mm3, TR/TE = 1920/32 ms, flip angle = 90°, 240 time points, FOV read = 256, distance factor = 0, TA = 7′40″) for rs-fMRI.

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In addition, following the simultaneous PET/MRI acquisition and during the same scanning session, axial T2-FLAIR, T2-TSE and diffusion weighted images were also acquired for completeness.

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MRI data were pre-processed using the REST toolkit [Son11], a 223 Matlab (Mathworks Inc.) toolbox containing libraries for fMRI analysis 224 that relies on the Statistical Parametric Mapping 8 package (SPM8, the 225 Q13 Wellcome Department of Neurology, London U.K. (Friston and Frith, 226 Q14 1995)). rs-fMRI pre-processing is described hereinafter. The first 10 227 time points were removed to avoid non-equilibrium effects of magneti228 zation. The remaining 230 volumes of functional BOLD images were 229 corrected for slice timing effects and motion correction was performed 230 by coregistering all the subsequent volumes to the first time point 231 Q15 (Friston and Frith, 1995). Studies with an estimated maximum head 232 motion larger than 3.0 mm and/or 3.0° were excluded. Subsequently, 233 all EPI volumes were spatially normalized to the Montreal Neurological 234 Institute (MNI) template and resampled to a voxel size of 3 × 3 × 235 3 mm3. 236 Considering that all the data acquired during the same session are 237 intrinsically co-registered, the transformation from single subject 238 space to MNI was derived from T1-weighted high resolution data by 239 means of the diffeomorphic normalisation step performed during the 240 segmentation procedure (Ashburner, 2007) implemented in SPM8 241 (see below), and a smoothing equivalent to a convolution with an iso242 tropic gaussian kernel of 6 mm was applied, according to the estimated 243 smoothness of PET data. 244 In order to remove BOLD signal fluctuations unrelated to neuronal 245 activity, the white matter and cerebrospinal fluid mean signals were 246 preliminarily regressed out as nuisance variables (Zuo et al., 2013). In 247 addition, to take into account signal drifts that arise from scanner

Statistical analysis

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instability or other possible causes, linear trend was analogously removed from each voxel's time course. Before ReHo and DC maps calculation, a temporal filter (0.01– 0.08 Hz) was performed to minimise the effects of undesired fluctuations, including higher frequency noise and the effects of physiological movements. The ReHo measure relies on rank correlations of time series to assess the homogeneity of signal changes over time in a given centre voxel and its neighbouring voxels. For the present study, ReHo was computed using Kendall's coefficient of concordance (KCC) as homogeneity metric (Kendall and Gibbons, 1990) over a cluster of 27 neighbouring voxels as suggested in Li et al. (2012a, 2012b). fALFF, defined as the power within the low-frequency range (0.01– 0.1 Hz) divided by the total power of the entire detectable frequency range, represents the relative contribution of specific low frequency oscillations to the whole frequency range of signal variations (Zou et al., 2008). Accordingly, fALFF maps were calculated before temporal filtering. DC is a long-range measure of local network connectivity and identifies the nodes connected to the highest number of voxels by summing up, for each voxel, the significance of the “connection” to every other voxel. For the present work we used Pearson's correlation coefficient (r) as measure of connectivity strength between each pair of voxel's time series. Pairs of voxels with r-values greater than 0.25 were considered connected, and the DC value of each voxel was calculated as the sum of the r values of all its connections (Buckner et al., 2009). Considering previous works (Fransson et al., 2011; Zuo et al., 2012), the cutoff for correlation was chosen at 0.25 as estimated with statistical significance of p = 0.0001 for a sample of 230 time points. In addition, for each subject the normalized grey matter (GM) map was obtained by segmentation of the MPRAGE volume using SPM unified segmentation (Ashburner and Friston, 2005) procedure and Diffeomorphic Anatomical Registration through Exponentiated Lie Algebra (Ashburner, 2007). Such map is usually interpreted as an estimation of the probability of presence of GM within each voxel. PET and FC data of each subject, both intrinsically registered with the corresponding MPRAGE volumes, were also spatially normalized to the MNI space for group comparisons using the same spatial transformation parameters as for the GM data, and smoothed with same kernel size of PET and FC maps (6 mm along each direction). In order to simplify the across-subject and between-modality comparisons (Chételat et al., 2008; Buckner et al., 2009), all individual rs-fMRI and PET maps were linearly standardized into Z-values by subtracting from each voxel value the mean voxel value from the individual's whole-brain mask (obtained by applying a 50% threshold onto the SPM8's a priori brain mask), and then dividing this difference by the standard deviation pooled across brain mask voxels of each subject. Finally, two separate parcellation methods were applied to normalized FC and rGU maps, to assess possible regional specificities of the correlation between the functional and metabolic information. Such heterogeneities may indeed be expected due to possible regional differences in the neurovascular coupling (Devonshire et al., 2012), aerobic glycolysis (Vaishnavi et al., 2010) and/or in the hemodynamic function (Badillo et al., 2013), which link functional activation and BOLD signal changes. To this purpose, an anatomical parcellation, obtained applying the automated anatomical labelling (AAL), a digital brain atlas (TzourioMazoyer et al., 2002) which divides the MNI space into a set of 116 volumes of interest, and a functional parcellation obtained pooling, for each of the 7 main resting state networks (Yeo et al., 2011), the corresponding AAL regions were applied.

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(Boellaard et al., 2009) and brain (Varrone et al., 2009) PET imaging. Accordingly, the patients rested in a quiet and warm dark room for 15 min before FDG administration and during the uptake period. Simultaneous PET and rs-fMRI data acquisition started 30 min following the i.v. injection of 5 MBq/Kg of 18F-FDG, at the beginning of the whole body scan. Subjects were not allowed to consume any food or sugar for at least 6 h prior to FDG injection. Blood glucose was measured at arrival at the PET centre in all cases, and FDG was injected only if glycaemia was below 120 mg/dl. The PET data were acquired in sinogram mode for 15 min; matrix size was 256 × 256. PET emission data were reconstructed with ordered subset-expectation maximization (OSEM) algorithm (21 subsets, 4 iterations) and post-filtered with a three-dimensional isotropic gaussian of 4 mm at FWHM, resulting in a final spatial resolution of approximately 6 mm along each direction. Attenuation correction was performed using MR-based attenuation maps derived from a dual echo (TE = 1.23– 2.46 ms) Dixon-based sequence (repetition time 3.60 ms), allowing for reconstruction of fat-only, water-only and of fat–water images. The resulting segmentation of background, fat, and muscles is used to estimate head profiles needed for calculation of attenuation (MartinezMöller et al., 2009; Berker et al., 2012). During PET acquisition, the following MRI sequences were run sequentially:

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Spatial distribution analysis 309 In order to investigate the relationship between the spatial distribu- 310 tion of the four metrics obtained by the processing pipeline described 311

Please cite this article as: Aiello, M., et al., Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: A PET/MR hybrid scanner study, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.017

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Across-subjects analysis The across-subjects analysis was performed calculating a voxel-wise multimodal correlation between each couple of metrics as implemented in Biological Parametric Mapping toolbox (Casanova et al., 2007). Essentially, for each couple of metrics, maps were generated calculating voxelwise the correlation coefficient ρ between the values provided by the two metrics across the subjects. These maps reflect at each voxel the degree to which the different modalities show the same ranking among the subjects. In addition, to explore the effect of partial volume from non-GM voxels, the corresponding partial correlation maps were generated using the percentage of the voxel volume occupied by GM as nuisance covariate. The degree of correspondence for each couple of metrics was measured by the percentage of voxels showing a significant (p b 0.05 FWE-corrected at cluster level) positive correlation across subjects, and significance of the differences between the proportions was tested pairwise by Z-test for proportions in dependent groups. This analysis was carried out only on voxels containing N50% grey matter and the results were assessed both for the whole brain and for the functional parcellation ROIs.

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To provide an estimate of the inter-subject variability of the correlation between rGU and FC metrics, these same analyses were carried out separately on each subject, using the GM threshold providing the best results on the mean maps, and differences in correlation with PET of the three FC metrics were assessed pairwise by paired t-test.

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above, the correlations among the Z-scores of each modality were assessed pairwise by Spearman's correlation coefficient (ρ), calculated on the mean maps obtained by averaging across all subjects the voxelwise maps of each modality. A non-parametric correlation coefficient was used in all cases to avoid any assumption of linearity in the relationship between the assessed metrics. Since both the BOLD contrast and FDG-PET signal arise mainly from grey matter, partial volume effect from non-grey matter voxels may potentially influence the voxel-wise relationship between these variables. Therefore, in order to assess as far as possible the ‘pure’ correlation between FC and PET maps, partial correlation analysis, in which the grey matter fraction was regressed out voxel-wise from the Z-scores for each modality, was also performed. Specifically, the normalized GM maps, further smoothed to match the spatial resolution of the PET and FC metrics, were used as measures of the percentage of each voxel occupied by GM. Therefore, partial correlation coefficients were measured as Spearman ρ between the residuals resulting from the linear regression of each PET and FC map, and the GM map. For both simple and partial correlation, the correlation was assessed only for GM voxels selected applying a threshold to the GM probability maps. To investigate the impact of this threshold on the correlation analysis, seven equally spaced GM thresholds, ranging from 0.3 to 0.9, were tested. This analysis was performed also region-wise: for anatomical parcellation, the correlations among the mean Z-value of each small region of the AAL atlas were calculated; for functional parcellation, in which each network has a sizable spatial extent (and is likely to have a specific metabolic-functional pattern), the correlation analysis was performed voxelwise within each region.

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Fig. 1. Representative slices of the GU and FC metrics masked over the whole intracranial volume. All the images were normalized to the MNI space and averaged across all subjects. For all images, Z-scores scaled to the same range are used.

Please cite this article as: Aiello, M., et al., Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: A PET/MR hybrid scanner study, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.017

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Results

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Spatial distribution analysis: whole brain assessment Fig. 1 shows PET and FC metrics maps calculated over the whole intracranial volume averaged across all 23 subjects. On visual inspection, over and above the overall distribution similarities, some clear differences are also present, notably the dampened cortical ‘landscape’ on ReHo and fALFF, and the excess hypoactivity of the frontal areas on DC and of the inferior temporal cortex on DC and fALFF. In addition, an overall higher GM/WM contrast is apparent in the PET compared to the FC maps. Both full and partial correlation coefficients between PET and the three FC maps are reported in Table 1 for the seven GM probability thresholds. All the FC contrasts positively and significantly correlated with PET contrast, achieving the best partial correlation coefficients with PET at a GM threshold of 0.7. In addition, the results obtained at this GM threshold from single subjects (resulting in lower correlation values, due to the higher noise of single-subject data compared to averaged maps) are summarized as means and standard deviations across all subjects in the same table. A significantly lower correlation with PET of DC, compared to both ReHo (p b 10−7) and fALFF (p b 10−6), emerged for both full and partial correlation analysis, when probing pairwise these data by paired t-test. No significant differences emerged between ReHo and fALFF. Increasing the GM threshold influenced significantly and nonlinearly the strength of the partial correlation. It can however be observed that in general the correlation coefficients appear to be only modestly affected by the GM partial volume, as differences between the full and partial correlation coefficients are relatively small. Overall, the correlation of ReHo and fALFF (both full and partial) with PET increased with higher GM thresholds, up to 0.8, while DC showed relatively lower correlation coefficients, relatively stable up to the same GM threshold. All the FC maps showed reduced correlation for GM thresholds above 0.8. Figs. 2 and 3 depict the scatterplots of pairwise correlations between maps, calculated on brain voxel values defined using GM thresholds of 0.3 and 0.9, respectively. In Fig. 4 pairwise scatterplots of the mean Z-values pooled over each region of the AAL atlas (GM threshold 0.7) are reported. The results of the correlation analysis over each of the seven selected resting-state networks are reported in Table 2. Overall, ReHo provided significantly higher correlation coefficients with PET, compared to DC (p = 0.005 and p = 0.008 for full and partial correlation, respectively, at paired t-test). No significant differences were present between ReHo and fALFF, the latter showing only a trend for a stronger correlation with PET compared to DC (p = 0.07 and 0.09, respectively). The highest (both full and partial) correlation coefficients between PET and FC maps were achieved for all the FC maps in the default-mode network (DMN, Fig. 5), followed by the visual and fronto-parietal ones. The limbic network was the only network showing a heterogeneous pattern of correlation of the PET with the three FC metrics, ranging from slightly negative correlation coefficients for ReHo and DC, to a significantly positive correlation with fALFF. Otherwise, ReHo and fALFF provided very close patterns of activity, while DC showed consistently lower correlation coefficients with FDG-PET for all the tested networks, except for the somatomotor and default-mode networks.

Across-subjects analysis: whole brain assessment Results of across-subject correlations between the four modalities are reported in Table 3 and depicted voxelwise in Fig. 6 for the whole brain. In general only a limited percentage (up to 7.4% when comparing PET vs. fALFF) of voxels showed a significant correlation across subjects between PET and the three modalities, with higher percentages for fALFF and ReHo, while DC showed an even weaker correlation with rGU. Partialling out GM volume only slightly reduced these values.

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Three subjects were excluded due to estimated head motion within the EPI acquisition exceeding the exclusion criteria. The results presented hereafter were thus obtained from the remaining 23 subjects.

Across-subjects analysis: regional assessment Results of across-subject correlations between the four modalities are reported in Table 4 separately for the assessed RSNs. The ranking of the correlations of the three FC metrics with rGU obtained on whole brain was consistent across the main RSNs, although for all modalities consistently higher values where obtained for Ventral attention, limbic, and default-mode networks, for which when correlating rGU to fALFF values above 10% of voxels showed a significant direct correlation between the metrics across subjects. When assessing pairwise the correlations between the three MRIderived measures, correlation coefficients consistently above 70% (with the exception of the fronto-parietal network) were obtained between fALFF and ReHo, while DC showed significantly lower correlations with fALFF, with intermediate values when comparing DC with ReHo. In all cases, the correction for GM volume by partial correlation only moderately reduced the significance of the direct correlations, slightly increasing the number of voxels showing significant inverse correlations.

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Table 1 Spearman correlation coefficients (full and partial) between PET and FC metrics averaged across all subjects. Spearman correlation coefficients are reported, calculated over GM voxels defined using different GM probability thresholds. In addition, for the GM threshold providing overall the highest correlation coefficients over the mean maps (GM N 0.7), the mean and standard deviation (across all subjects) of the correlation coefficients obtained from the spatial distribution analysis at single-subject level are reported.

t1:1 Q2 t1:2 t1:3 t1:4 t1:5 t1:6 t1:7 t1:8 t1:9

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Correlation

PET vs ReHO

PET vs DC

PET vs fALFF

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GM N 0.3 (58794 voxels) GM N 0.4 (49403 voxels) GM N 0.5 (37915 voxels) GM N 0.6 (23535 voxels) GM N 0.7 (12077 voxels) GM N 0.8 (3870 voxels) GM N 0.9 (583 voxels) Single subject analysis (GM N 0.7)

0.53 0.54 0.58 0.66 0.73 0.72 0.59 0.478 ± 0.090⁎⁎

0.50 0.48 0.50 0.56 0.57 0.43 0.08 0.268 ± 0.128

0.56 0.54 0.57 0.65 0.74 0.77 0.73 0.493 ± 0.100⁎

Partial Correlation

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GM N 0.3 (58794 voxels) GM N 0.4 (49403 voxels) GM N 0.5 (37915 voxels) GM N 0.6 (23535 voxels) GM N 0.7 (12077 voxels) GM N 0.8 (3870 voxels) GM N 0.9 (583 voxels) Single subject analysis (Mean ± SD) GM N 0.7

0.49 0.52 0.57 0.66 0.74 0.75 0.65 0.476 ± 0.090⁎⁎

0.47 0.47 0.49 0.55 0.58 0.50 0.28 0.266 ± 0.128

0.55 0.55 0.57 0.65 0.74 0.79 0.76 0.491 ± 0.100⁎

t1:22 t1:23 t1:24 t1:25 t1:26 t1:27 t1:28 t1:29 t1:30

⁎ Significantly (p b 10 − 6) different across subjects at paired t-test compared to the PET Vs DC. ⁎⁎ Significantly (p b 10 − 7) different across subjects at paired t-test compared to the PET Vs DC.

t1:31 t1:32 t1:33 t1:34

Please cite this article as: Aiello, M., et al., Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: A PET/MR hybrid scanner study, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.017

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Fig. 2. Scatterplots of pair-wise correlations between voxel-wise maps (averaged across all subjects) over grey matter voxels with probability greater than 0.3. Lower diagonal: full correlation; upper diagonal: partial correlation. Histograms show the distributions of Z-values for each modality.

Fig. 3. Scatterplots of pair-wise correlations between voxel-wise maps (averaged across all subjects) over grey matter voxels with probability greater than 0.9. Lower diagonal: full correlation; upper diagonal: partial correlation. Histograms show the distributions of Z-values for each modality.

Please cite this article as: Aiello, M., et al., Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: A PET/MR hybrid scanner study, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.017

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Discussion

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To the best of our knowledge, this is the first study that assessed across the whole brain the correlation between resting-state rGU, as assessed by FDG-PET, and brain activity, as assessed by rs-fMRI, in humans, taking advantage of simultaneous PET/MR imaging. The main findings are as follows:

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The above findings are overall in line with the only previous work that has directly correlated FC metrics and rGU across the whole brain in normal subjects (Tomasi et al., 2013), with the major differences that the data were acquired in separate imaging sessions, and that ReHo was not assessed. In this study, measures of the mean lowfrequency signal oscillations (ALFF, not normalized by the total frequency spectrum) and of DC (both global and restricted to local connectivity of the voxel) were used, revealing that higher glucose metabolism was significantly associated with higher ALFF and DC in cerebellum, occipital and parietal cortices, with DC showing significant correlations also in temporal and frontal clusters, and with a substantially linear relationship between rGU and both ALFF and DC across subjects. Our findings are also consistent with those reported by Li et al. (2012a, 2012b), who correlated voxel-wise ReHo and ALFF with resting CBF measured by ASL, finding a significant correlation between the two metrics throughout the brain, although more extensive for ALFF, led by strong correlations within the DMN structures, although in that case no metrics of the strength of the correlation were provided.

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• A clearly significant but somewhat limited correlation between the spatial distribution of rGU as measured by PET and all the FC metrics, higher for fALFF and ReHo as compared to DC, which implies that they provide partly independent and potentially complementary information; • A significantly heterogeneous pattern of correlations with PET across both functional networks and anatomic regions; • Limited across-subjects correlation of the three MRI-derived metrics with rGU throughout the brain, not directly linked to the spatial distribution of the correlations between the two modalities (e.g. within the limbic system there was no correlation between the PET values and the three MRI-derived metrics, while the across-subject correlation coefficients were among the highest among the assessed RSNs); • A substantial similarity of the spatial distribution of fALFF and ReHo, despite their totally different spatial extent (the spatial variable being absent for fALFF, while ReHo reflects strictly local connectivity), with the possible exception of the limbic structures and the frontal lobes;

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481 482 483 484 485 486 487

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• A variable impact of partial volume effects on the correlations between the different modalities, resulting in artificially increased r values when using lower GM thresholds (below 0.5) for masking, and slightly decreased r values for GM thresholds above 0.7, possibly due to a dilution of the metrics leading to a reduced range of values.

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Fig. 4. Scatter plots of pair-wise correlations over each region of the AAL atlas. Each data point coordinate on the scatterplots represents the mean Z-value of the considered map (averaged across all subjects) pooled over each region of the AAL atlas. Histograms show the distributions of Z-values for each modality.

Please cite this article as: Aiello, M., et al., Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: A PET/MR hybrid scanner study, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.017

488 489 490 491 492 493 494 495 496 497 498 499 500 Q18 501 502 503 504 505

Table 2 Spearman's correlation coefficients (full and partial) between PET and FC metrics voxelwise maps within the regions assigned by functional parcellation. Correlation

PET vs ReHO

PET vs DC

PET vs fALFF

t2:5 t2:6 t2:7 t2:8 t2:9 t2:10 t2:11 t2:12 t2:13

Visual Somatomotor Dorsal attention Ventral attention Limbic Frontoparietal Default-mode

0.82 0.53 0.54 0.70 −0.09 0.76 0.89

0.70 0.52 0.41 0.42 −0.11 0.61 0.82

0.77 0.37 0.57 0.65 0.61 0.72 0.87

Partial correlation

PET vs ReHO

PET vs DC

PET vs fALFF

t2:14 t2:15 t2:16 t2:17 t2:18 t2:19 t2:20

Visual Somatomotor Dorsal attention Ventral attention Limbic Frontoparietal Default-mode

0.79 0.44 0.56 0.68 −0.08 0.78 0.88

0.70 0.49 0.40 0.46 −0.07 0.62 0.80

0.72 0.25 0.61 0.62 0.61 0.74 0.86

509 510 511 512 513 514

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515

The heterogeneity, among the tested functional networks, of the correlations between PET and FC metrics, as well as between the three FC metrics, is likely to be due to the concurrent effects of different factors. These may include the different spatial extent of the networks (ranging from rather localized networks, such as the visual, to more widely distributed ones, such as the DMN), which may present differences in the degree of neurometabolic coupling (Liang et al., 2013). In addition, the different degree of activity of the networks during rest (e.g. more sustained activity of the DMN, compared to other networks), coupled to the differences in the timescale of the phenomena probed by the

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two techniques (metabolic activity integrated over minutes for PET, FC sampled with sampling period of a few seconds for rs-fMRI), may have played a role. In contrast to the overall similarity of the spatial distribution of PET and FC maps, the across-subject analysis showed substantial differences at voxel-level in terms of ranking of the subjects, with only a small percentage of GM voxels showing a significant correlation between PET and FC maps. However, this discrepancy between the two analyses (spatial versus across-subject correlation) is not surprising due to their inherently different nature. Furthermore, it should be pointed out that only a limited correlation is to be expected across subjects when, like in this case, only healthy controls in the resting state are analyzed, since it is likely that the between-subject differences at voxel level are within the range of the noise of the measure. Of note, a similarly limited degree of correlation has been found previously when testing across-subjects correlations between imaging modalities of physiological functions that are known to be tightly coupled, like GU and CBF (e.g. Cha et al., 2013; Wong et al., 2006). Studies including both healthy subjects under sensory or behavioural stimulation and patients with pathologies characterized by reductions of cerebral glucose metabolism will clarify whether these modalities overlap also in terms of the stratification of subjects. We found partial voluming from non-GM tissues to introduce a limited overestimation of correlation coefficients, when these are calculated including also voxels occupied by less than 50% by GM. Non linearity of this effect is likely to be due to the simultaneous presence of competing effects. Increasing the GM thresholds limits the calculation to increasingly “purer” GM voxels, possibly improving the accuracy of calculation of the FC parameters (which may increase the correlation if indeed FC is linked to glucose consumption), but also reducing the partial volume effect, which may be a source of spurious

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Fig. 5. Scatterplots of pair-wise correlations between voxel-wise maps (averaged across all subjects) over voxels belonging to the default mode network. Lower diagonal: full correlation; upper diagonal: partial correlation. Histograms show the distributions of Z-values for each modality.

Please cite this article as: Aiello, M., et al., Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: A PET/MR hybrid scanner study, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.017

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M. Aiello et al. / NeuroImage xxx (2015) xxx–xxx Table 3 Percentage of grey matter voxels (occupied by ≥50% of grey matter, n = 37915 voxels), showing a significant (p b 0.05, FWE corrected at cluster level) direct correlation between the values of each couples of modalities across subjects. In parenthesis, the corresponding correlation coefficients (mean ± SD) of significant voxels are reported. Results obtained both using the original maps, and after partialling out grey matter volume using partial correlation analysis, are reported for the whole brain. No cluster of inverse correlation survived FWE correction. All proportions significantly differ from each other, with the exception of DC vs. ReHo and fALFF vs. ReHo.

558 559 560 561 562

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correlation between PET and FC maps. The use of voxels with high percentage of WM and/or CSF (compartments which are characterized by lower or null values of both rGU and FC metrics, compared to GM) artificially increased the correlation between FC maps and PET, as voxels with high WM or CSF contribution show apparently reduced both glucose metabolism and FC values. In addition, when increasing the GM threshold this phenomenon was reduced, although at the expense of a reduction of the number of voxels over which the coefficients are calculated, which increases the noise in the measure, thus reducing the significance of the correlation. Similar to our data, in a recent study (Liang et al., 2013), correlating DC with regional CBF (measured by cASL), the correction for GM volume using partial correlation decreased the coefficient of the voxel-wise correlation across the whole brain between CBF and FC by b 10%, demonstrating, in the same vein as our data, a limited effect of partial volume on the correlation between perfusion and FC maps. However, it should

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(0.40 ± 0.08) (0.42 ± 0.09) (0.41 ± 0.09) (0.56 ± 0.12) (0.50 ± 0.11) (0.58 ± 0.12)

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5.0% 1.0% 5.6% 87.5% 75.6% 88.3%

(0.40 ± 0.08) (0.42 ± 0.09) (0.41 ± 0.09) (0.56 ± 0.12) (0.50 ± 0.11) (0.58 ± 0.12)

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GM-corrected

6.9% 0.9% 7.4% 87.5% 75.4% 87.6%

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PET vs. ReHo PET vs. DC PET vs. fALFF DC vs. ReHo DC vs. fALFF fALFF vs. ReHo

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be noted that, while covarying for voxel GM percentage removes the variability in the data caused by variable cortical thickness in different parts of the brain (a major component of partial volume effects in PET data, and reasonably so also in fMRI-derived metrics), spill-in of activity from neighbouring voxels is not taken into account using this approach. Accordingly, future work using full partial volume correction strategies will have to clarify the full impact of partial volume effect on the correlation between FDG uptake and fMRI-derived metrics. We did not include age as a nuisance covariate in our partial correlation analysis, as we were interested in the correlations between FC metrics and rGU, independent of the physiological conditions such as age that may modify their values. However, because GM density and age are known to be closely related throughout the brain, any residual age effect in the present correlations is likely to be minimal. Of note, in Liang et al. article (Liang et al., 2013), DC Z-scores averaged for each Brodmann area, were strongly correlated with the corresponding data from a database of previous FDG-PET and CMRO2 studies (performed in different subjects), providing correlation values similar to ours. When comparing these results with ours, besides the regional differences between CBF and glucose metabolism, potential limitations of ASL in measuring rCBF should be considered, which include variability deriving from variable labelling efficiency (as function of differences in subjects' head position, shimming conditions, and efficiency of background suppression), as well as potential quantification errors caused by changes in arterial transit time, leading to apparent differences in blood flow in the watershed zone between anterior and posterior arterial circulations (Wong et al., 1999). In addition, it is worth mentioning here that current MR-based attenuation correction (AC) techniques such as the one used in the present work produce slightly spatially biased metabolic patterns relative to CT-based AC (Hitz et al., 2014; Andersen et al., 2014), with significantly lower PET values in fronto-parietal portions of the neocortex, and significantly higher values in subcortical and basal regions of the brain. These differences, besides biasing spatially the relationship

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Fig. 6. Voxels showing a significant (p b 0.05, FWE corrected at cluster level) correlation between the different modalities (the color scale, representing r values ranging from 0 to 1, is reported on the right), overlaid on the mean GM map thresholded at 0.5 (in white). No clusters of inverse correlation survived FWE correction.

Please cite this article as: Aiello, M., et al., Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: A PET/MR hybrid scanner study, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.017

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Table 4 Percentage of grey matter voxels (occupied by ≥50% of grey matter, n = 37915 voxels) showing a significant (p b 0.05, FWE corrected at cluster level) direct correlation between the values of each couple of modalities across subjects. Results obtained both using the original maps, and after partialling out grey matter volume, are reported for seven major functional resting-state networks. No cluster of inverse correlation survived FWE correction. Limbic

Fronto-parietal

Default mode

5.6% 0.3% 3.5% 80.2% 71.0% 81.6% 5.2% 0.3% 3.2% 79.7% 71.7% 82.9%

2.7% 0.0% 0.7% 97.7% 90.6% 94.5% 1.3% 0.0% 0.5% 97.9% 91.9% 94.8%

6.8% 1.5% 8.5% 98.7% 89.7% 95.6% 6.8% 1.7% 7.5% 98.7% 88.8% 95.4%

9.7% 0.0% 12.3% 95.1% 74.4% 95.7% 4.3% 0.0% 5.3% 95.0% 73.0% 95.6%

16.0% 6.1% 18.9% 92.1% 79.4% 90.1% 12.0% 6.2% 16.5% 91.3% 79.0% 90.7%

1.4% 0.0% 3.1% 70.0% 60.4% 62.6% 2.0% 0.0% 1.4% 71.4% 63.9% 66.3%

10.4% 1.4% 13.1% 87.8% 65.0% 91.4% 4.6% 1.4% 10.8% 87.9% 63.5% 91.6%

597

between PET and FC maps, must be taken into account when comparing our results to those obtained using PET studies employing CT-based attenuation correction. 600 It is interesting to note that, although they apparently represent phe601 nomena with different spatial extents, fALFF and ReHo metrics strongly 602 correlate with PET over the whole brain except for the voxels belonging 603 to the limbic system network. However, it should be pointed out here 604 that the cortical areas composing the limbic network as defined in Yeo 605 et al. (2011) are mainly located in fronto-basal and inferior medial 606 temporal regions, which are among the most affected by magnetic sus607 ceptibility artefacts. These regions are in fact located close to brain/ 608 bone/air interfaces (related to the adjacent sphenoid sinus and 609 mastoids), possibly leading to deformations and reduced signal/noise 610 ratio in the EPI images used to obtain the BOLD contrast. These artefacts 611 may contribute in turn to a reduced homogeneity of the signal changes 612 in these regions (leading to lower apparent ReHo, and to some extent 613 fALFF, which can be seen in Fig. 1), and to a reduced capability of detect614 ing FC with other regions (resulting in lower apparent DC). In addition, 615 in these same regions the calculated MRI-based attenuation correction 616 used in the present work, which does not include bone density infor617 mation, may contribute to a reduced accuracy in these regions, com618 pared to more cranial structures, where geometric conditions are 619 more homogeneous. 620 One limitation of the present exploratory study lies in the use of a 621 direct correlation index, which clearly cannot fully reflect the complex 622 and potentially non-linear interactions involved in the function/ 623 metabolism relationship (Tomasi et al., 2013). Indeed, both entities 624 reflect multifaceted biological processes, including for example 625 both local information processing and glial metabolic demands, 626 which may result in composite relationships between connectivity 627 and metabolism. In addition, it should be considered that FC only 628 probes the effective communications among brain structures, with629 out taking into account wiring efficiency and robustness demands, 630 which may contribute to the energetic costs of the communications, 631 and in turn to glucose consumption. 632 A further limitation of the present study lies in the fact that FDG-PET 633 and rs-fMRI were acquired simultaneously during the plateau phase of 634 the FDG uptake curve, so that differences in the timing of the neuronal 635 activity represented by the two techniques may ensue, the FDG data 636 mainly representing the neuronal activity occurred during the preced637 ing early uptake phase. However, a recent study (Chonde et al., 2013), 638 probing FDG uptake when the tracer is injected during rs-fMRI acquisi639 tion, compared to i.v. administration under truly unstimulated rest con640 ditions, has shown a substantially reproducible uptake, with less than 641 3% increase in the FDG uptake, limited to the primary acoustic area 642 and due to the EPI noise in the early uptake phase. This degree of vari643 ability, which is below the 5% variability demonstrated in test–retest 644 Q20 FDG studies (Schmidt et al., 1997), mitigates against this shortcoming.

However, further studies are warranted to assess the effect of scan timing on the correlation between the information provided by the two imaging modalities. The aim of the present study was not to assess changes in the relationship between these modalities during task-related activations or in subjects with neurological conditions. Preclinical studies using a hybrid scanner (Wehrl et al., 2013) have shown a significantly different pattern of cortical activation detected by FDG-PET and BOLD fMRI using sensory stimuli in rats. However, the only available study that has used a hybrid PET/MRI scanner to acquire simultaneously rsfMRI and FDG-PET data in humans during visual stimulation (Riedl et al., 2014) has shown a relationship between rGU and FC changes in regions activated by visual stimuli. In this work a seed-based analysis of the RS-fMRI data demonstrated a significant voxelwise relationship between changes in BOLD synchronization and rGU, when tested in regions with increased metabolism in subjects undergoing open-eyes acquisitions, compared to those scanned with eyes closed. Similarly, in the aforementioned work by Liang et al. (2013), the relationship between DC and rCBF strengthened with increasing task load during an N-back working-memory task. Accordingly, the relationship between the MRI-derived metrics assessed here (fALFF, ReHo and DC) and glucose metabolism may change under activation conditions, and further studies are needed to assess these specific aspects. Future extensions of the present work should explore the relationship between FC and glucose metabolism in the diseased brain, especially in neurodegenerative disorders such as Alzheimer's disease, where both functional connectivity and glucose metabolism are extensively altered. This would allow one to assess possible changes in the interrelationships among these metrics, adding novel insight into the current understanding of the functional impairment underlying these conditions. In addition, whether DC represents a clinically useful surrogate to FDG-PET obtained without exposure to ionizing radiation, is an important issue to address in future studies.

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FC showed a significant correlation with GU across the brain, significantly stronger for ReHo and fALFF than for DC, although the strength of this correlation substantially varied across functional networks. Despite the similar spatial distributions of FC metrics and GU, only a small percentage of GM voxels showed a significant correlation across subjects in normal conditions, unrelated to the similarities in the spatial distributions of these metrics, possibly also due to the limited inter-subject variability present in normal subjects. Partial volume effects lead to a very limited overestimation of these correlations, provided voxels containing at least 50% by GM are considered.

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Dorsal attention

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Please cite this article as: Aiello, M., et al., Relationship between simultaneously acquired resting-state regional cerebral glucose metabolism and functional MRI: A PET/MR hybrid scanner study, NeuroImage (2015), http://dx.doi.org/10.1016/j.neuroimage.2015.03.017

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These novel results provide the basis for future studies of alterations of the coupling between brain metabolism and functional connectivity in pathologic conditions.

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