Proper glucose metabolism (CMRGlu), approximated by [18F]-fluorodeoxyglucose positron emission tomography (PET FDG), is essential for maintaining synaptic activity in the brain. Synaptic activity increases brain glucose and oxygen metabolism which is then reflected by CBF increases¹. The fMRI blood-oxygen-level dependent (BOLD) signal is related to CBF, CBV and oxygen metabolism in a complex fashion, so we sought to investigate its relationship with CMRGlu to understand the relation between BOLD and brain glucose consumption. We employed the best analytical approach (voxel vs structural or functional region based-analysis) to qualify fMRI measures as proxies of CMRGlu or biomarkers of aging.We collected MRI and PET-FDG datasets in cognitively normal young (N=25, 18-30 years old;) and older (N=32, 65-85 years old) healthy participants. Each session started with an anatomical T1-weighted 1 mm isotropic MPRAGE (TR/TE 1860/3.54 msec) acquisition, followed by a resting-state fMRI dataset of 5 min using a standard echoplanar imaging (EPI) sequence (35 axial image slices, 64 x 64 matrix, TR/TE 2730/40 msec, voxel size 3.438 x 3.438 x 4.2 mm). Brain PET scans were then performed on a Philips Gemini TF PET/CT scanner (voxel size of 2 x 2 x 2 mm, field of view of 250 x 250 x 180 mm, 60 min with time frames of 12 x 10 s, 8 x 30 s, 6 x 4 min, 3 x 10 min). CMRGlu (μmol/100 g/min) was computed using PMOD 3.3 in the Patlak graphical model² (lumped constant = 0.80) and defined as the product of the tracer uptake rate constant (K) multiplied by the plasma concentration of glucose. All fMRI analysis were carried out using a standard pipeline consisting of slice timing and motion correction and band-pass temporal filtering (0.005 to 0.1 Hz). Gaussian spatial smoothing was replaced by non-local mean denoising to improve signal SNR in subcortical areas³. Using AFNI⁴, we measured the amplitude of low frequencies fluctuations (ALFF)⁵ in the 0.01 – 0.1 Hz range as well as the regional local homogeneity (ReHo), the time series application of Kendall's W coefficient of concordance; For each voxel, a score of homogeneity between 27 neighbourhood voxels is computed in which a higher score reflects higher BOLD synchrony⁶. We also investigated the regional global homogeneity (closeness) computed as the sum of all correlations between parcellated regions. CMRGlu maps and voxel-based measures of ALFF and ReHo were registered to MNI standard space using ANTs⁷, allowing a direct comparison between FDG and fMRI datasets. For this, we parcellated the gray matter (GM) using Ward feature agglomeration clustering⁸ first based on the CMRGlu (50, 200 and 1000 clusters, respectively), then based on ReHo (50, 200 and 1000 clusters). For comparison, we subdivided the GM into 42 Freesurfer regions⁹ and no clustering (i.e. all voxels in the brain were pooled together). For both young and older groups, Fig. 1 shows a scatter plot of CMRGlu vs (a) ALFF (p >0.05), (b) ReHo (p<0.05) and (c) closeness (p<0.05) when using a 200 CMRGlu-based Ward clustering (results were similar using clusters of 50 and 1000 thus not shown here). Fig. 2 shows how different parcelization strategies affect the ReHo-CMRGlu relation. Although the correlation values change, ReHo is consistently better correlated to CMRGlu compared to ALFF, as shown on Fig. 3. Finally, Fig. 4 illustrates the potential of ReHo as a successful biomarker for aging since it can easily differentiate the younger and older group as effectively as CMRGlu.Using different gray matter parcelization strategies, we consistently observed that the amplitude of the BOLD signal (ALFF) is a poor predictor of CMRGlu (Fig. 3) whereas local temporal synchrony (ReHo) is strongly correlated to CMRGlu, and thus a potentially promising proxy of glucose intake (Fig. 1-2, Y: R=0.85, A: R=0.81) and biomarker of aging (Fig.4). However, the correlation between ReHo and CMRGlu varied depending on the manner in which the GM is parcellated (see Fig. 2). The physiological meaning of the strong ReHo-CMRGlu relationship is unclear; It has been proposed that resting-state BOLD fluctuations might be correlated to vasodilatation caused by CBF, possibly revealed by the local connectivity¹⁰ (ReHo). However, the fact that long-range connectivity (closeness or networks) is also observed in the same signal implies that vasomotion is homogeneous throughout the vasculature, an assumption that still requires verification. Since CMRGlu was shown to be strongly correlated to CBF¹, further investigation is needed to isolate the neurovascular underpinnings the local BOLD synchrony. Nevertheless, our data suggest that it can be used as an accurate marker for CMRGlu.