Previous work has shown that low frequency fluctuations (LFFs) of the functional MRI signal are related to neuronal activity [1,2]. Within individual datasets, LFFs show differences between functional networks and state-based changes [1,3]. In this work, we investigate LFFs in scan-rescan data, showing that LFFs are reproducible in a variety of cortical areas.Five healthy controls were scanned (Scan 1), repositioned and re-scanned (Scan 2) on a Siemens 7T Magnetom with SC72 gradient (Siemens Medical Solutions, Erlangen) using a 32-channel head coil (Nova Medical). Rs-fMRI data were acquired using slice-accelerated EPI sequence (WIP 770, Siemens). The scan parameters were as follows: 132 repetitions of 81 axial slices acquired with TE/TR=21ms/2800ms, matrix 160x160, FOV 192mm x 192mm, 1.2×1.2×1.5mm3 voxels, bandwidth = 1562 Hz/pixel, slice-accelerate factor = 3 and grappa factor = 2. Subjects were instructed to keep their eyes closed during scans. The first four volumes were discarded and scans were corrected for motion and physiologic noise, detrended, and lowpass filtered [4,5]. In each dataset, anatomic and functional information was used to place 9-voxel in-plane ROIs bilaterally in the following regions: posterior cingulate cortex (PCC), dorsal lateral prefrontal cortex (DLPFC), hippocampus, and four different regions of the primary motor cortex (M1). In addition, ROIs were placed in cerebrospinal fluid (CSF) and white matter (WM). For each ROI, the mean timeseries was extracted for Scan 1 (TS1) and Scan 2 (TS2). A modified cross-correlation was conducted by calculating the correlation coefficient between TS1 and step-wise volume-shifted TS2. Volume-shifted TS2 was recirculated with the end of the timeseries wrapped to the beginning. The resulting correlations were Fourier transformed and maximum and standard deviation of distributed frequency components for each anatomical region was calculated.Figure 1A shows the relationship of left motor cortex TS1 and TS2 at each shifted time point in a representative subject, and the Fourier transform with a clear peak at 0.02 Hz. Figure 1B shows the relationship of TS1 and TS2 in CSF from the same subject, with the associated transform. Most, though not all, subjects exhibited clear periodicity in the relationship of TS1 and TS2 in all cortical ROIs. Figure 3 shows the mean distribution of maximum and standard deviation for all ROIs. One-ways ANOVAs showed the maximum frequency was higher (F = 6.35, p = 0.002) and the standard deviation larger (F = 18.92, p = 6.6x10-7) in the hippocampus than in all other cortical regions. All cortical regions except the hippocampus showed significantly smaller standard deviations when compared to WM (F = 16.7, p = 9.6x10-8) and CSF (F = 21.39, p = 5.3x10-9). Motor and DLPFC showed a trend toward lower maximum frequency than WM and CSF, though this comparison did not reach significance.This work shows consistent periodicity of LFFs across scans and across cortical regions. We found no difference between hippocampal measures and those of WM and CSF, suggesting that hippocampal measures might be contaminated by sources other than neuronal activity. Future work will focus on assessing the spatial specificity of the periodicity and investigating individual differences.