Temporal scaling properties and spatial synchronization of spontaneous blood oxygenation level‐dependent (BOLD) signal fluctuations in rat sensorimotor network at different levels of isoflurane anesthesia

Abstract

Spontaneous fluctuations in the blood oxygenation level-dependent (BOLD) MRI signal during the resting state are increasingly being studied in healthy and diseased brain in humans and animal models. Yet, the relationship between functional brain status and the characteristics of spontaneous BOLD fluctuations remains poorly understood. In order to obtain more insights into this relationship and, in particular, the effects of anesthesia thereupon, we investigated the spatial and temporal correlations of spontaneous BOLD fluctuations in somatosensory and motor regions of rat brain at different inhalation levels of the frequently applied anesthetic isoflurane. We found that the temporal scaling, characterized by the Hurst exponent (H), showed persistent behavior (H > 0.5) at 0.5-1.0% isoflurane. Furthermore, low-pass-filtered spontaneous BOLD oscillations were correlated significantly in bilateral somatosensory and bilateral motor cortices, reflective of interhemispheric functional connectivity. Under 2.9% isoflurane anesthesia, the temporal scaling characteristics approached those of Gaussian white noise (H = 0.5), the relative amplitude of BOLD low-frequency fluctuations declined, and cross-correlations of these oscillations between functionally connected regions decreased significantly. Loss of interhemispheric functional connectivity at 2.9% isoflurane anesthesia was stronger between bilateral motor regions than between bilateral somatosensory regions, which points to distinct effects of anesthesia on differentially organized neuronal networks. Although we cannot completely rule out a possible contribution from hemodynamic signals with a non-neuronal origin, our results emphasize that spatiotemporal characteristics of spontaneous BOLD fluctuations are related to the brain's specific functional status and network organization, and demonstrate that these are largely preserved under light to mild anesthesia with isoflurane.

Extent of Bilateral Neuronal Network Reorganization and Functional Recovery in Relation to Stroke Severity

“…Our findings of early loss followed by recovery of interhemispheric functional connectivity are in line with pioneering studies in patients (He et al, 2007) and animal models (van Meer et al, 2010b) that have explored the impact of acute ischemic injury and subsequent recovery on resting-state fMRI signals. Although in animal studies anesthesia may affect functional connectivity measurements, we and others have shown that coherence of low-frequency BOLD signal fluctuations between bilateral homologous sensorimotor regions is preserved at 1% isoflurane (Wang et al, 2011) and correlates with slow power modulations of local field potentials in rats (Pan et al, 2011). We here confirm the neurophysiological basis of strokeinduced functional connectivity changes by showing parallel shifts in the correlation coefficient of interhemispheric lowfrequency BOLD signals and interhemispheric low-frequency delta EEG signals.…”

“…Interregional functional connectivity was determined as zЈ between the mean low-frequency BOLD signal fluctuations in left and right sensorimotor cortices. Intraregional signal coherence in left and right sensorimotor cortices was calculated as the mean zЈ between the low-frequency BOLD signal fluctuations of each voxel within the ROI and the average low-frequency BOLD signal time series of that ROI (Wang et al, 2011).…”

Dynamic Repertoire of Intrinsic Brain States Is Reduced in Propofol-Induced Unconsciousness

“…Several fMRI studies have demonstrated resting-state brain networks in awake and anesthetized rodents (Becerra et al, 2011;Hutchison et al, 2010;Kalthoff et al, 2011;Liang et al, 2011;Liu et al, 2011;Lu et al, 2012;Nasrallah et al, 2012;Pawela et al, 2008;Tu et al, 2011;Upadhyay et al, 2011;Wang et al, 2011;Williams et al, 2010;Zhang et al, 2010;Zhao et al, 2008). However, only a few investigators have examined dynamic connectivity in anesthetized rodents (Keilholz et al, 2013;Magnuson et al, 2010;Majeed et al, 2009) or primates (Hutchison et al, 2013b;Liu et al, 2013a;Vincent et al, 2007).…”

“…However, only a few investigators have examined dynamic connectivity in anesthetized rodents (Keilholz et al, 2013;Magnuson et al, 2010;Majeed et al, 2009) or primates (Hutchison et al, 2013b;Liu et al, 2013a;Vincent et al, 2007). Dose-dependent comparisons across anesthetic levels relevant for the loss of consciousness in rodents have been particularly scarce (Liu et al, 2013d;Tu et al, 2011;Wang et al, 2011).…”

Role of Ongoing, Intrinsic Activity of Neuronal Populations for Quantitative Neuroimaging of Functional Magnetic Resonance Imaging–Based Networks

“…In contrast, fractal analysis, which is a scale-free examination of fluctuating data (Mandelbrot and van Ness, 1968), calculates a fractal parameter independently for each BOLD signal time series (i.e., from each voxel). Thus, a fractal parameter map itself can be used to define subtle changes across brain states (Wang et al, 2011) and/or regional variations (Herman et al, 2011). Since the fractal parameter captures the unique behavior of the fluctuating signal that is governed by physiological processes (see Eke et al, 2002 for details on fractal analysis), it could potentially be used for network identification without experimental bias of visual inspection.…”