The blood oxygenation level dependent (BOLD) magnetic resonance imaging (MRI) fluctuations used to map functional connectivity contain a wealth of information about neural activity and physiological processes in the brain. Most functional connectivity studies wish to detect time-varying activity related to cognition and information processing, and view the presence of other contributors to the spontaneous BOLD fluctuations as a complication. However, evidence is growing that sources of “noise” in the BOLD signal contain clinically- relevant information about activity at different spatial and temporal scales. The challenge lies in separating contributions from different processes so that selective sensitivity to the process of interest can be achieved. We propose to combine spatial, spectral and temporal signal characteristics with multi-modal imaging to separate the BOLD fluctuations into four components with different spatial and temporal scales: 1) a quasiperiodic spatiotemporal pattern (QPP) linked to infraslow electrical activity; 2) oscillations that arise from properties of the vasculature; 3) global signal variations that do not reflect local neural processing; and 4) the remaining variability, which should have increased sensitivity to time-varying interactions between regions. The two key elements that make the isolation of BOLD components possible are the direct measurement of neural activity in conjunction with imaging experiments in the rat model, and dynamic analysis techniques that can capture spatial and temporal patterns in the imaging and recording data. While the foundational work described in this proposal will be performed in the rat, the tools we develop will be optimized and applied to standard resting state functional MRI (rs-fMRI) studies in humans. Our preliminary data shows that the BOLD signal contains contributions from two separable types of neural activity: infraslow activity, which produces quasiperiodic spatiotemporal patterns of BOLD activation; and activity in typical EEG bands, which is more closely tied to time-varying activity between areas. Using only analytical tools, we show that we can separate and identify similar processes in human data, a strong argument for the ultimate translatability of these techniques. We also show that the QPPs alone account for the differences in connectivity observed between patients with major depressive disorder and healthy controls, which demonstrates how selective analysis methods can aid in the diagnosis of psychiatric and neurological disorders and provide new insight into the alterations in connectivity that many disorders exhibit. We exhibit preliminary evidence for both neural and vascular contributions to the global BOLD signal, and describe a method for mapping the contribution of vascular oscillations. Specific aims are: 1.Determine the neural and hemodynamic correlates of the global BOLD signal; 2. Characterize the contributions of vascular oscillations; 3.Disting...