Spontaneous BOLD fluctuations are usually acquired to explore the brain’s functional connectivity of healthy subjects using the resting-state BOLD fMRI (rs-fMRI) analysis. Yet, a few recent reports have suggested that rs-fMRI could also be used to locate perfusion deficits in cerebrovascular disease patients^1-3. To further investigate the potential of rs-fMRI to distinguish different tissue types, we propose to examine the patterns of BOLD spontaneous fluctuations in different tissue types (healthy tissue, chronic perfusion deficit, acute diffusion core, perfusion-diffusion mismatch) in healthy volunteers, Moyamoya patients, and stroke patients. To avoid aliasing of cardiac and respiration frequencies, which might contain valuable information, we used Simultaneous MultiSlice (SMS) EPI with blipped CAIPI⁴ to achieve a high-temporal resolution and full brain coverage.The local IRB committee approved all studies. 10 volunteers (4 women; range 21-63yrs), 9 stroke patients (imaged between 7 hrs and 3 days post-stroke) (5 women; range 30-90yrs), and 8 Moyamoya patients (6 women; range 24-51yrs) were scanned at 3T (GE Healthcare Systems, Waukesha, WI) with an 8-channel GE head coil. Gradient echo SMS-EPI with blipped CAIPI (TE=30ms, TR=350-600ms, SMS acceleration factor 5, CAIPI FOV shift 3, FOV=20x20 cm, ST=5mm, matrix size=70x70, imaging time <3min) was used for rs-fMRI. 1 stroke patient was also scanned at 1.5T with both short and long repetition times (TR=350ms and 2000ms respectively). Resting-state data were corrected for head motion with SPM12 (least squares approach, 6 parameters) and the first 20 time points were discarded. A cross-correlation analysis was implemented with a superior sagittal sinus reference region, signal shifting between +/-20TR, with delay maps created by taking the time lag of the maximum of the correlation coefficient^1-3. In patients, diffusion-weighted imaging (DWI) and dynamic susceptibility contrast perfusion-weighted imaging (PWI) maps were also obtained⁵. Images were co-registered to the rs-fMRI data and regions-of-interest (healthy tissue [all subjects], chronic perfusion lesion [Moyamoya], acute diffusion lesion and mismatch [stroke]) were defined automatically through thresholding⁵.Figure 1 shows the signal fluctuations obtained in healthy tissues in 7 volunteers and 7 stroke patients (blue lines). As expected, the signals are specific to the inviduals. However, it can be seen that the patterns are comparable between the volunteers: same amplitude range, large variations at low frequencies (<0.1Hz) and smaller variations at respiration (~0.3Hz) and cardiac frequencies (~1Hz). Signals in the stroke patients are arranged according to increasing volume of infarcts (top-bottom). Larger variability between the patterns in healthy tissue can be observed, with S1 and S2 (small lesions) resembling those observed in volunteers, while other patients show reduced amplitude of fluctuations without slow component (S3 and S4), or large signal variations with chaotic fluctuations (S6-S7). When comparing the fluctuations obtained in healthy tissues (blue lines) to the corresponding DWI ROIs (red lines), one can observe that the signals become disordered in the lesions, even though the amplitude of fluctuations is similar. This is particularly evident in S4 and S5 (red arrows). In Moyamoya patients, where chronic PWI lesions without DWI lesions are present, the fluctuations in healthy tissues and PWI lesions were similar to those found in healthy volunteers (Fig.2). As expected, delays were found between slow fluctuations in the normal tissue and PWI lesions (Fig.2-Fig.3). The seed-based analysis (Fig.4) confirmed that healthy signals were well correlated to the PWI lesions. These findings are also present in stroke patients with mismatch (Fig.3). However, DWI positive regions were usually not highly correlated to healthy tissues. PWI, DWI, and rs-fMRI (with TR=2000ms) results from a stroke patient imaged at 1.5T are presented in Fig.5 to illustrate the versatility of the approach and the quality of the final estimates.This study suggests that rs-fMRI can be used to detect different types of lesions in patients with cerebrovascular diseases. While a cross-correlation delay analysis seems to be sufficient to locate perfusion deficits, the amplitude of signals fluctuations is not enough to locate the diffusion core. However, it might be possible to use the rs-fMRI correlation map to directly estimate the mismatch region. A measure of signal complexity such as local or multiscale entropy⁶ might also provide valuable information. Overall, this study suggests that a single, short rs-fMRI sequence may enable identification of both diffusion and perfusion lesions, potentially obviating the need for gadolinium and dramatically shortening the duration of an acute stroke MR study.