Spatiotemporal dynamics of low frequency fluctuations in BOLD fMRI of the rat.

LOW FREQUENCY FLUCTUATIONS (LFFs) in the blood oxygenation level dependent (BOLD) functional MRI (fMRI) signal from areas known to be strongly connected anatomically are correlated even in the absence of a task or stimulus (1). These spontaneous fluctuations appear to originate from the same source as the task-related BOLD response, leading to the hypothesis that correlated fluctuations reflect coordinated variations in neural activity (2). The possibility that correlated LFFs reflect the spontaneous “functional connectivity” of the brain has excited the interest of the fMRI and neuroscience communities, and the number of studies using the technique is growing rapidly. Most research performed on LFFs falls into three categories. The first involves mapping functional networks in the brain. Networks associated with different systems including visual, motor, auditory, memory, and language have been detected in resting state fMRI studies (3,4). In addition, researchers have identified two widely present networks, one containing areas that are typically deactivated during a task (the “default mode” network) and another containing areas that are active during a wide variety of tasks and that may be related to attention (5). Studies in the second category focus on the effect of different interventions or pathological conditions on functional connectivity and LFFs. Anesthesia, hypercapnia, and cocaine administration have been reported to disrupt functional connectivity (6-8). Also, changes in functional connectivity have been observed in conditions including autism, Alzheimer’s disease, schizophrenia, and blindness (9-12). The third group of studies addresses the origin of the LFFs. Physiological processes such as respiration can introduce spatially localized correlations in functional MRI data, and several groups have examined the effects of physiological noise on functional connectivity (13-16). Networks of correlated areas persisted despite significant attenuation of physiological noise, indicating that the noise is not the primary source of the correlation (15,17). Recent work by several groups has provided additional support for the neural basis of functional connectivity. A strong reduction of functional connectivity between hemispheres has been reported after surgical severance of the corpus callosum, the primary interhemispheric fiber tract. Intrahemispheric connectivity, however, was retained (18). It has also been shown that functional connectivity and the relationship between correlated networks is related to behavioral variability and the ability to learn new muscle synergies, more evidence that the correlated signal fluctuations are closely related to functional changes in the brain (19,20). Additional support comes from combined electroencephalography (EEG) -fMRI studies, although the findings are not consistent between the studies (21-24), possibly due to differences in the areas examined or recording methods used. To better understand the relationship between neural activity and functional connectivity, several groups have recently turned to animal models (25-30). The use of animal models allows for more invasive experimental designs, which can give deeper insight into the LFFs. These studies have provided additional evidence of a link between coordinated neural activity and functional connectivity. Anesthesia-dependent changes in delta power correlation were shown to co-vary with changes in functional connectivity in rats, although the measurements were not performed simultaneously due to technical limitations (30). Shmuel and Leopold reported correlations between spontaneous neural activity and BOLD fluctuations in the visual cortex of monkeys in the absence of any stimulus (27). These results are an extension of previous work showing very slow (< 0.1 Hz) coherent oscillations in the band limited power of local field potentials obtained from monkey visual cortex (31). Current functional connectivity analysis techniques such as cross-correlation of time-courses (1) or independent component analysis (ICA) (32) provide only steady-state information about networks where the relationship between the areas involved is assumed to be maintained over the entire length of the scan. These analysis methods do not give any information about the spatiotemporal dynamics of the LFFs. From EEG and magnetoencephalography (MEG) studies, it is known that behaviorally relevant changes in network activity occur on much shorter time scales than the minutes required for the acquisition of functional connectivity data. If the LFFs used to map functional connectivity have a neural basis, it should be possible to identify dynamic changes in network activity using MRI. In support of this idea, the envelope of EEG is shown to predict fluctuations in cerebral blood flow measured using laser Doppler flowmetry in rats (33). The spatiotemporal dynamics of the LFFs might, therefore, enhance our interpretation of functional connectivity data and provide us with clues to the origin of the low frequency fluctuations. In this study, we report a preliminary investigation into the spatiotemporal dynamics of LFFs in the rodent model. The use of an animal model is conducive to future experiments combining functional connectivity MRI and electrophysiology. In addition, acquisition with very short repetition time (TR) and high spatial resolution is possible with the high-field animal scanners, which is necessary for obtaining spatiotemporal information from the data while avoiding aliasing. This will also allow us to address questions that remain unanswered for animal studies, including the relative contribution of different frequency components and the extent of physiological noise contamination. In previous animal studies, acquisition parameters did not allow the visualization of fine spectral details needed to address this issue. In addition, most animal studies were conducted using a fairly long repetition time and no physiological correction was performed. The contributions of respiration and the cardiac cycle have not been examined in the rodent model, and could be more severe than in humans due to the widespread use of high-field scanners (34). In this study, we describe the acquisition of functional connectivity data from the rat with high temporal resolution that allows separation of the primary cardiac and respiratory components from the frequencies of interest. High spectral resolution allows the identification of two low frequency peaks in the data acquired from the rat cortex. The peaks exhibit different functional connectivity specificity. We further characterize spatiotemporal characteristics of both peaks and identify patterns of LFF propagation, which may not be deduced from typical functional connectivity analysis methods.