Can Functional Magnetic Resonance Imaging at Rest Shed Light on the Pathophysiology of Migraine?

Since the first report on the blood oxygen leveldependent (BOLD) contrast by Ogawa et al, many researchers have applied this functional magnetic resonance imaging (fMRI) technique as a surrogate of neuronal activity to study pain and headacherelated activation changes in the human brain. In BOLD fMRI studies, a task or stimulation paradigm (eg, pain stimulation) is applied and the BOLD changes during the stimulation are contrasted to a baseline condition in between the stimulations. Such studies led to exciting insights into the organization of sensory and pain pathways and more generally of brain function in humans. More recently, it turned out that there are coherent slow spontaneous oscillations of the BOLD signal across spatially distributed brain areas at rest. These oscillations are more or less unrelated to specific tasks, but reproducible across humans, even across different species, and also across different states of consciousness. Interestingly, the spatial pattern of such networks defined by coherent BOLD oscillations mirrors the pattern of networks known from taskand stimulation-based fMRI experiments and reflects the structural architecture as determined by studies of fiber tracts in the human brain. Because they are conducted at rest without specific tasks, such studies are often termed restingstate functional connectivity MRI (rs-fcMRI). The 2 most commonly applied post-processing techniques for rs-fcMRI data are seed-based correlational approaches (SBC) and independent component analysis (ICA). The SBC is based on the correlation between the BOLD signal time course extracted from a specific brain region (seed) against the signal time course in all other brain voxels. This approach is based on an anatomical hypothesis regarding the location of the seed. ICA is a multivariate data-driven and hypothesis-free technique, which is decomposing the BOLD signal into a set of independent (usually in the spatial domain) components (networks). Either all or selected networks (based on a specific hypothesis) can then be studied, eg, for abnormalities in patients vs controls. Since the first observation of intrinsic connectivity in the sensory motor system reported by Biswal et al, many other resting-state networks (RSN) have been identified, eg, default mode network, salience network, visual network, auditory network, executive control network, and visuospatial network. Notably, the majority of these RSNs were confirmed using both methods described earlier indicating that they are quite robust. During the last few years, a growing number of studies investigated the integrity of RSNs in various neurological and psychiatric conditions suggesting alterations among others in Alzheimer’s disease and other neurodegenerative disorders, multiple sclerosis, schizophrenia, and bipolar disorder. More recently, these techniques were also applied in patients with primary head pain disorders. Three studies compared episodic migraine patients and healthy controls using ICA. Two studies applied a seed-based approach, and in 1 study, the amplitude of low frequency fluctuations was compared between patients and controls. Another study applied an unusual resting-state fMRI technique on the regional homogeneity of the fMRI signal time course. All these studies were mainly conducted in interictal patients with episodic migraine and they provided contradictory results. This observed variability between results could be due to different methodology used, different RSNs investigated, and somewhat ISSN 0017-8748 doi: 10.1111/head.12108 Published by Wiley Periodicals, Inc. Headache © 2013 American Headache Society