RESUMO
Functional imaging of intrinsic signals allows minimally invasive spatiotemporal mapping of stimulus representations in the cortex, but representations are often corrupted by stimulus-independent spatial artifacts, especially those originating from the blood vessels. In this paper, we present novel algorithms for unsupervised identification of cerebral vascularization, allowing blind separation of stimulus representations from noise. These algorithms commonly take advantage of the temporal fluctuations in global reflectance to extract anatomic information. More specifically, the phase of low-frequency oscillations relative to global fluctuations reveals local vascular identity. Arterioles can be reconstructed using their characteristically high power in those frequencies corresponding to respiration, heartbeat, and vasomotion signals. By treating the vasculature as a dynamic flow network, we finally demonstrate that direction of blood perfusion can be quantitatively visualized. Application of these methods for removal of stimulus-independent changes in reflectance permits isolation of stimulus-evoked representations even if the representation spatially overlaps with blood vessels. The algorithms can be expanded further to extract temporal information on blood flow, monitor revascularization following a focal stroke, and distinguish arterioles from venules and parenchyma.