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G-protein-coupled receptors (GPCRs) play important roles in cellular functions. However, their intracellular organization is largely unknown. Through investigation of the cannabinoid receptor 1 (CB1), we discovered periodically repeating clusters of CB1 hotspots within the axons of neurons. We observed these CB1 hotspots interact with the membrane-associated periodic skeleton (MPS) forming a complex crucial in the regulation of CB1 signaling. Furthermore, we found that CB1 hotspot periodicity increased upon CB1 agonist application, and these activated CB1 displayed less dynamic movement compared to non-activated CB1. Our results suggest that CB1 forms periodic hotspots organized by the MPS as a mechanism to increase signaling efficacy upon activation., Despite the importance of G-protein-coupled receptors in many cellular functions, their intracellular organisation is largely unknown. The authors identified periodically repeating clusters of cannabinoid receptor 1 hotspots within neuronal axons that are dynamically regulated by CB1 agonists.

The investigation of neural circuits is important for interpreting both healthy brain function and psychiatric disorders. Currently, the architecture of neural circuits is always investigated with fluorescent protein encoding neurotropic virus and ex vivo fluorescent imaging technology. However, it is difficult to obtain a whole-brain neural circuit connection in living animals, due to the limited fluorescent imaging depth. Herein, the non-invasive, whole-brain imaging technique of MRI and the hypotoxicity virus vector AAV (adeno-associated virus) were combined to investigate the whole-brain neural circuits in vivo. AAV2-retro are an artificially-evolved virus vector that permits access to the terminal of neurons and retrograde transport to their cell bodies. By expressing the ferritin protein which could accumulate iron ions and influence the MRI contrast, the neurotropic virus can cause MRI signal changes in the infected regions. For mice injected with the ferritin-encoding virus vector (rAAV2-retro-CAG-Ferritin) in the caudate putamen (CPu), several regions showed significant changes in MRI contrasts, such as PFC (prefrontal cortex), HIP (hippocampus), Ins (insular cortex) and BLA (basolateral amygdala). The expression of ferritin in those regions were also verified with ex vivo fluorescence imaging. In addition, we demonstrated that changes in T2 relaxation time could be used to identify the spread area of the virus in the brain over time. Thus, the neural connections could be longitudinally detected with the in vivo MRI method. This novel technique could be utilized to observe the viral infection long-term and detect the neural circuits in a living animal. \ud Keywords: Neural circuit; Ferritin; In vivo MRI; rAAV2-retro; Immunohistochemistry.

Odorants can reach olfactory receptor neurons (ORNs) by two routes: orthonasally, when volatiles enter the nasal cavity during inhalation/sniffing; and retronasally, when food volatiles released in the mouth pass into the nasal cavity during exhalation/eating. Previous work in humans has shown that orthonasal and retronasal delivery of the same odorant can evoke distinct perceptions and patterns of neural responses. Each delivery route is known to influence specific responses across the glomerular sheet of the dorsal parts of the olfactory bulb (OB), but spatial distributions across the entire glomerular sheet throughout the whole OB remain largely unexplored. We used functional MRI (fMRI) to measure and compare activations across the entire glomerular sheet in rat OB resulting from both orthonasal and retronasal stimulations of the same odors. We observed reproducible fMRI activation maps of the whole OB during both orthonasal and retronasal stimuli. However, retronasal stimuli required double the orthonasal odor concentration for similar response amplitudes in the OB. Regardless, both the magnitude and spatial extent of activity were larger during orthonasal versus retronasal stimuli for the same odor. Orthonasal and retronasal response patterns show overlap as well as some route-specific dominance. Orthonasal maps were dominant in dorsal-medial regions, whereas retronasal maps were dominant in caudal and lateral regions. These different whole OB encodings likely underlie differences in odor perception between these biologically important routes for odorants among mammals. These results establish the relationships between orthonasal and retronasal odor representations in the rat OB.

G-protein-coupled receptors (GPCRs) play important roles in cellular functions. However, their intracellular organization is largely unknown. Through investigation of the cannabinoid receptor 1 (CB1), we discovered periodically repeating clusters of CB1 hotspots within the axons of neurons. We observed these CB1 hotspots interact with the membrane-associated periodic skeleton (MPS) forming a complex crucial in the regulation of CB1 signaling. Furthermore, we found that CB1 hotspot periodicity increased upon CB1 agonist application, and these activated CB1 displayed less dynamic movement compared to non-activated CB1. Our results suggest that CB1 forms periodic hotspots organized by the MPS as a mechanism to increase signaling efficacy when being activated.

Resting state fMRI (rsfMRI) as a technique showed much initial promise for use in psychiatric and neurological diseases where diagnosis and treatment were difficult. To realize this promise, many groups have moved towards examining “dynamic rsfMRI,” which relies on the assumption that rsfMRI measurements on short time scales remain relevant to the underlying neural and metabolic activity. Many dynamic rsfMRI studies have demonstrated differences between clinical or behavioral groups beyond what static rsfMRI measured, suggesting a neurometabolic basis. Correlative studies combining dynamic rsfMRI and other physiological measurements have supported this. However, they also indicate multiple mechanisms and, if using correlation alone, it is difficult to separate cause and effect. Hypothesis-driven studies are needed, a few of which have begun to illuminate the underlying neurometabolic mechanisms that shape observed differences in dynamic rsfMRI. While the number of potential noise sources, potential actual neurometabolic sources, and methodological considerations can seem overwhelming, dynamic rsfMRI provides a rich opportunity in systems neuroscience. Even an incrementally better understanding of the neurometabolic basis of dynamic rsfMRI would expand rsfMRI's research and clinical utility, and the studies described herein take the first steps on that path forward.

Fluctuations in spontaneous activity have been observed by many neuroimaging techniques, but because these resting-state changes are not evoked by stimuli, it is difficult to determine how they relate to task-evoked activations. We conducted multi-modal neuroimaging scans of the rat olfactory bulb, both with and without odor, to examine interaction between spontaneous and evoked activities. Independent component analysis of spontaneous fluctuations revealed resting-state networks, and odor-evoked changes revealed activation maps. We constructed simulated activation maps using resting-state networks that were highly correlated to evoked activation maps. Simulated activation maps derived by intrinsic optical signal (IOS), which covers the dorsal portion of the glomerular sheet, significantly differentiated one odor's evoked activation map from the other two. To test the hypothesis that spontaneous activity of the entire glomerular sheet is relevant for representing odor-evoked activations, we used functional magnetic resonance imaging (fMRI) to map the entire glomerular sheet. In contrast to the IOS results, the fMRI-derived simulated activation maps significantly differentiated all three odors' evoked activation maps. Importantly, no evoked activation maps could be significantly differentiated using simulated activation maps produced using phase-randomized resting-state networks. Given that some highly organized resting-state networks did not correlate with any odors' evoked activation maps, we posit that these resting-state networks may characterize evoked activation maps associated with odors not studied. These results emphasize that fluctuations in spontaneous activity form a foundation for active processing, signifying the relevance of resting-state mapping to functional neuroimaging.

Childhood maltreatment is associated with a wide range of psychopathologies including anxiety that emerge in childhood and in many cases persist in adulthood. Increased amygdala activation in response to threat and abnormal amygdala connectivity with frontolimbic brain regions, such as the hippocampus and the prefrontal cortex, are some of the most consistent findings seen in individuals exposed to childhood maltreatment. The underlying mechanisms responsible for these changes are difficult to study in humans but can be elucidated using animal models of early-life stress. Such studies are especially powerful in the mouse where precise control of the genetic background and the stress paradigm can be coupled with resting-state fMRI (rsfMRI) to map abnormal connectivity in circuits that regulate anxiety. To address this issue we first compared the effects of two models of early-life stress, limited bedding (LB) and unpredictable postnatal stress (UPS), on anxiety-like behavior in juvenile and adult mice. We found that UPS, but not LB, causes a robust increase in anxiety in juvenile and adult male mice. Next, we used rsfMRI to compare frontolimbic connectivity in control and UPS adult male mice. We found increased amygdala–prefrontal cortex and amygdala–hippocampus connectivity in UPS. The strength of the amygdala–hippocampal and amygdala–prefrontal cortex connectivity was highly correlated with anxiety-like behavior in the open-field test and elevated plus maze. These findings are the first to link hyperconnectivity in frontolimbic circuits and increased anxiety in a mouse model of early-life stress, allowing for more mechanistic understanding of parallel findings in humans.

Neuroimaging studies typically consider white matter as unchanging in different neural and metabolic states. However, a recent study demonstrated that white matter signal regression (WMSR) produced a similar loss of neurometabolic information to global (whole-brain) signal regression (GSR) in resting-state functional magnetic resonance imaging (R-fMRI) data. This was unexpected as the loss of information would normally be attributed to neural activity within gray matter correlating with the global R-fMRI signal. Indeed, WMSR has been suggested as an alternative to avoid such pitfalls in GSR. To address these concerns about tissue-specific regression in R-fMRI data analysis, we performed GSR, WMSR, and gray matter signal regression (GMSR) on R-fMRI data from the 1000 Functional Connectomes Project. We describe several regional and motion-related differences between different types of regressions. However, the overall effects of concern, particularly network-specific alteration of correlation coefficients, are present for all regressions. This suggests that tissue-specific regression is not an adequate strategy to counter pitfalls of GSR. Conversely, if GSR is desired, but the studied disease state excludes either gray matter or white matter from analysis (e.g., due to tissue atrophy), our results indicate that WMSR or GMSR may reproduce the gross effects of GSR.

While functional connectivity has typically been calculated over the entire length of the scan (5-10 min), interest has been growing in dynamic analysis methods that can detect changes in connectivity on the order of cognitive processes (seconds). Previous work with sliding window correlation has shown that changes in functional connectivity can be observed on these time scales in the awake human and in anesthetized animals. This exciting advance creates a need for improved approaches to characterize dynamic functional networks in the brain. Previous studies were performed using sliding window analysis on regions of interest defined based on anatomy or obtained from traditional steady-state analysis methods. The parcellation of the brain may therefore be suboptimal, and the characteristics of the time-varying connectivity between regions are dependent upon the length of the sliding window chosen. This manuscript describes an algorithm based on wavelet decomposition that allows data-driven clustering of voxels into functional regions based on temporal and spectral properties. Previous work has shown that different networks have characteristic frequency fingerprints, and the use of wavelets ensures that both the frequency and the timing of the BOLD fluctuations are considered during the clustering process. The method was applied to resting state data acquired from anesthetized rats, and the resulting clusters agreed well with known anatomical areas. Clusters were highly reproducible across subjects. Wavelet cross-correlation values between clusters from a single scan were significantly higher than the values from randomly-matched clusters that shared no temporal information, indicating that wavelet-based analysis is sensitive to the relationship between areas.

The field of brain connectomics develops our understanding of the brain's intrinsic organization by characterizing trends in spontaneous brain activity. Linear correlations in spontaneous blood-oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI) fluctuations are often used as measures of functional connectivity (FC), that is, as a quantity describing how similarly two brain regions behave over time. Given the natural spectral scaling of BOLD-fMRI signals, it may be useful to represent BOLD-fMRI as multiple processes occurring over multiple scales. The wavelet domain presents a transform space well suited to the examination of multiscale systems as the wavelet basis set is constructed from a self-similar rescaling of a time and frequency delimited kernel. In the present study, we utilize wavelet transforms to examine fluctuations in whole-brain BOLD-fMRI connectivity as a function of wavelet spectral scale in a sample (N = 31) of resting healthy human volunteers. Information theoretic criteria measure relatedness between spectrally-delimited FC graphs. Voxelwise comparisons of between-spectra graph structures illustrate the development of preferential functional networks across spectral bands.

Because the human brain consumes a disproportionate fraction of the resting body’s energy, positron emission tomography (PET) measurements of absolute glucose metabolism (CMRglc) can serve as disease biomarkers. Global mean normalization (GMN) of PET data reveals disease-based differences from healthy individuals as fractional changes across regions relative to a global mean. To assess the impact of GMN applied to metabolic data, we compared CMRglc with and without GMN in healthy awake volunteers with eyes closed (i.e., control) against specific physiological/clinical states, including healthy/awake with eyes open, healthy/awake but congenitally blind, healthy/sedated with anesthetics, and patients with disorders of consciousness. Without GMN, global CMRglc alterations compared to control were detected in all conditions except in congenitally blind where regional CMRglc variations were detected in the visual cortex. However, GMN introduced regional and bidirectional CMRglc changes at smaller fractions of the quantitative delocalized changes. While global information was lost with GMN, the quantitative approach (i.e., a validated method for quantitative baseline metabolic activity without GMN) not only preserved global CMRglc alterations induced by opening eyes, sedation, and varying consciousness but also detected regional CMRglc variations in the congenitally blind. These results caution the use of GMN upon PET-measured CMRglc data in health and disease.

The brain is organized into networks composed of spatially separated anatomical regions exhibiting coherent functional activity over time. Two of these networks (the default mode network, DMN, and the task positive network, TPN) have been implicated in the performance of a number of cognitive tasks. To directly examine the stable relationship between network connectivity and behavioral performance, high temporal resolution functional magnetic resonance imaging (fMRI) data were collected during the resting state, and behavioral data were collected from 15 subjects on different days, exploring verbal working memory, spatial working memory, and fluid intelligence. Sustained attention performance was also evaluated in a task interleaved between resting state scans. Functional connectivity within and between the DMN and TPN was related to performance on these tasks. Decreased TPN resting state connectivity was found to significantly correlate with fewer errors on an interrupter task presented during a spatial working memory paradigm and decreased DMN/TPN anti-correlation was significantly correlated with fewer errors on an interrupter task presented during a verbal working memory paradigm. A trend for increased DMN resting state connectivity to correlate to measures of fluid intelligence was also observed. These results provide additional evidence of the relationship between resting state networks and behavioral performance, and show that such results can be observed with high temporal resolution fMRI. Because cognitive scores and functional connectivity were collected on nonconsecutive days, these results highlight the stability of functional connectivity/cognitive performance coupling.

A number of studies have linked the blood oxygenation level dependent (BOLD) signal to electroencephalographic (EEG) signals in traditional frequency bands (δ, θ, α, β, and γ), but the relationship between BOLD and its direct frequency correlates in the infraslow band (

Anesthesia is often necessary to perform fMRI experiments in the rodent model; however, commonly used anesthetic protocols may manifest changing brain conditions over the duration of the study. This possibility was explored in the current work. Eleven rats were anesthetized with 2% isoflurane anesthesia; four rats were anesthetized for a short period (30 min, simulating induction and fMRI setup) and seven rats were anesthetized for a long period (3 h, simulating surgical preparation). Following the initial anesthetic period, isoflurane was discontinued, and a dexmedetomidine bolus (0.025 mg/kg) and continuous subcutaneous infusion (0.05 mg/kg/h) were administered. Blood-oxygen-level dependent resting state imaging was performed every 30 min from 0.75 h post dexmedetomidine bolus until 5.75 h post-bolus. Evaluation of power spectra obtained from time courses in the primary somatosensory cortex revealed, in general, a monotonic increase in low-frequency power (0.05-0.3 Hz) in both groups over the duration of resting state imaging. Greater low-band spectral power (0.05-0.15 Hz) is present in the short isoflurane group for the first 2.75 h, but the spectra become highly uniform at 3.25 h. The emergence of a ~0.18 Hz peak, beginning at the 3.75 h time point, exists in both groups and evolves similarly, increasing in strength as the duration of dexmedetomidine sedation (and time since isoflurane cessation) extends. In the long isoflurane group only, bilateral functional connectivity strengthens with anesthetic duration, and correlation is linearly linked to low-band spectral power. Convergence of connectivity and spectral metrics between the short and long isoflurane groups occurs at ~3.25 h, suggesting the effects of isoflurane have subsided. Researchers using dexmedetomidine following isoflurane for functional studies should be aware of the duration specific effects of the pre-scan isoflurane durations as well as the continuing influences of long-term imaging under dexmedetomidine.

Functional connectivity measurements from resting state blood-oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) are proving a powerful tool to probe both normal brain function and neuropsychiatric disorders. However, the neural mechanisms that coordinate these large networks are poorly understood, particularly in the context of the growing interest in network dynamics. Recent work in anesthetized rats has shown that the spontaneous BOLD fluctuations are tightly linked to infraslow local field potentials (LFPs) that are seldom recorded but comparable in frequency to the slow BOLD fluctuations. These findings support the hypothesis that long-range coordination involves low frequency neural oscillations and establishes infraslow LFPs as an excellent candidate for probing the neural underpinnings of the BOLD spatiotemporal patterns observed in both rats and humans. To further examine the link between large-scale network dynamics and infraslow LFPs, simultaneous fMRI and microelectrode recording were performed in anesthetized rats. Using an optimized filter to isolate shared components of the signals, we found that time-lagged correlation between infraslow LFPs and BOLD is comparable in spatial extent and timing to a quasi-periodic pattern (QPP) found from BOLD alone, suggesting that fMRI-measured QPPs and the infraslow LFPs share a common mechanism. As fMRI allows spatial resolution and whole brain coverage not available with electroencephalography, QPPs can be used to better understand the role of infraslow oscillations in normal brain function and neurological or psychiatric disorders.

Functional connectivity between brain regions, measured with resting state functional magnetic resonance imaging, holds great potential for understanding the basis of behavior and neuropsychiatric diseases. Recently it has become clear that correlations between the blood oxygenation level dependent (BOLD) signals from different areas vary over the course of a typical scan (6–10 min in length), though the changes are obscured by standard methods of analysis that assume the relationships are stationary. Unfortunately, because similar variability is observed in signals that share no temporal information, it is unclear which dynamic changes are related to underlying neural events. To examine this question, BOLD data were recorded simultaneously with local field potentials (LFP) from interhemispheric primary somatosensory cortex (SI) in anesthetized rats. LFP signals were converted into band-limited power (BLP) signals including delta, theta, alpha, beta and gamma. Correlation between signals from interhemispheric SI was performed in sliding windows to produce signals of correlation over time for BOLD and each BLP band. Both BOLD and BLP signals showed large changes in correlation over time and the changes in BOLD were significantly correlated to the changes in BLP. The strongest relationship was seen when using the theta, beta and gamma bands. Interestingly, while steady-state BOLD and BLP correlate with the global fMRI signal, dynamic BOLD becomes more like dynamic BLP after the global signal is regressed. As BOLD sliding window connectivity is partially reflecting underlying LFP changes, the present study suggests it may be a valuable method of studying dynamic changes in brain states.

The slow fluctuations of the blood-oxygenation-level dependent (BOLD) signal in resting-state fMRI are widely utilized as a surrogate marker of ongoing neural activity. Spontaneous neural activity includes a broad range of frequencies, from infraslow (< 0.5 Hz) fluctuations to fast action potentials. Recent studies have demonstrated a correlative relationship between the BOLD fluctuations and power modulations of the local field potential (LFP), particularly in the gamma band. However, the relationship between the BOLD signal and the infraslow components of the LFP, which are directly comparable in frequency to the BOLD fluctuations, has not been directly investigated. Here we report a first examination of the temporal relation between the resting-state BOLD signal and infraslow LFPs using simultaneous fMRI and full-band LFP recording in rat. The spontaneous BOLD signal at the recording sites exhibited significant localized correlation with the infraslow LFP signals as well as with the slow power modulations of higher-frequency LFPs (1–100 Hz) at a delay comparable to the hemodynamic response time under anesthesia. Infraslow electrical activity has been postulated to play a role in attentional processes, and the findings reported here suggest that infraslow LFP coordination may share a mechanism with the large-scale BOLD-based networks previously implicated in task performance, providing new insight into the mechanisms contributing to the resting state fMRI signal.

A better understanding of how behavioral performance emerges from interacting brain systems may come from analysis of functional networks using functional magnetic resonance imaging. Recent studies comparing such networks with human behavior have begun to identify these relationships, but few have used a time scale small enough to relate their findings to variation within a single individual's behavior. In the present experiment we examined the relationship between a psychomotor vigilance task and the interacting default mode and task positive networks. Two time-localized comparative metrics were calculated: difference between the two networks' signals at various time points around each instance of the stimulus (peristimulus times) and correlation within a 12.3-s window centered at each peristimulus time. Correlation between networks was also calculated within entire resting-state functional imaging runs from the same individuals. These metrics were compared with response speed on both an intraindividual and an interindividual basis. In most cases, a greater difference or more anticorrelation between networks was significantly related to faster performance. While interindividual analysis showed this result generally, using intraindividual analysis it was isolated to peristimulus times 4 to 8 s before the detected target. Within that peristimulus time span, the effect was stronger for individuals who tended to have faster response times. These results suggest that the relationship between functional networks and behavior can be better understood by using shorter time windows and also by considering both intraindividual and interindividual variability. Hum Brain Mapp 34:3280–3298, 2013. © 2012 Wiley Periodicals, Inc.

Correlated low frequency fluctuations in the blood oxygenation level dependent signal have been widely observed in highly connected brain regions and are considered to be indicative of coordinated activity within those regions. A typical functional connectivity MRI study consists of hundreds of time points acquired from thousands of image voxels, and thus exploratory data analysis is a significant challenge. This paper investigates the utilization of analytical methods based upon graph theory that can potentially provide a data-driven approach to examining the relationships between and within groups of voxels. Three algorithms, based on reachable groups, path-length analysis, and hierarchical clustering, are described and evaluated in the relatively simple context of the rodent brain. Analysis indicates that (based on the cross-correlation coefficient) cortical voxels are the most strongly connected network nodes. These voxels exhibit stronger clustering than would be expected in a randomly connected graph, and the amount of clustering is dependent on the cross-correlation threshold chosen. The analysis algorithms identify core groups in somatosensory areas and indicate that left and right somatosensory regions are more strongly connected to each other than to midline cortical areas. The results show that algorithms based on graph theory are well-suited for the data-driven analysis of functional connectivity studies.

Resting state functional magnetic resonance imaging (rsfMRI) results have indicated that network mapping can contribute to understanding behavior and disease, but it has been difficult to translate the maps created with rsfMRI to neuroelectrical states in the brain. Recently, dynamic analyses have revealed multiple patterns in the rsfMRI signal that are strongly associated with particular bands of neural activity. To further investigate these findings, simultaneously recorded invasive electrophysiology and rsfMRI from rats were used to examine two types of electrical activity (directly measured low-frequency/infraslow activity and band-limited power of higher frequencies) and two types of dynamic rsfMRI (quasi-periodic patterns or QPP, and sliding window correlation or SWC). The relationship between neural activity and dynamic rsfMRI was tested under three anesthetic states in rats: dexmedetomidine and high and low doses of isoflurane. Under dexmedetomidine, the lightest anesthetic, infraslow electrophysiology correlated with QPP but not SWC, whereas band-limited power in higher frequencies correlated with SWC but not QPP. Results were similar under isoflurane; however, the QPP was also correlated to band-limited power, possibly due to the burst-suppression state induced by the anesthetic agent. The results provide additional support for the hypothesis that the two types of dynamic rsfMRI are linked to different frequencies of neural activity, but isoflurane anesthesia may make this relationship more complicated. Understanding which neural frequency bands appear as particular dynamic patterns in rsfMRI may ultimately help isolate components of the rsfMRI signal that are of interest to disorders such as schizophrenia and attention deficit disorder.

Functional connectivity mapping with resting-state magnetic resonance imaging (MRI) has become an immensely powerful technique that provides insight into both normal cognitive function and disruptions linked to neurological disorders. Traditionally, connectivity is mapped using data from an entire scan (minutes), but it is well known that cognitive processes occur on much shorter time scales (seconds). Recent studies have demonstrated that the correlation between the blood oxygenation level-dependent (BOLD) MRI signal from different areas varies over time, motivating a further exploration of these fluctuations in apparent connectivity. However, it has also been shown that similar changes in correlation can arise when the timing relationships between voxels are randomized (Handwerker et al., 2012 ). In this work, we show that functional connectivity in the anesthetized rat exhibits dynamic properties that are similar to those previously observed in awake humans (Chang and Glover, 2010 ) and anesthetized monkeys (Hutchison et al., 2012 ). Sliding window correlation between BOLD time courses obtained from bilateral cortical and subcortical regions of interest results in periods of variable positive and negative correlation for most pairs of areas except homologous areas in opposite hemispheres, which exhibit a primarily positive correlation. A comparison with sliding window correlation of randomly matched time courses suggests that with the exception of homologous areas and sensorimotor connections, the dynamics cannot be distinguished from random fluctuations in correlation over time, supporting the idea that some of these dynamic patterns may be due to inherent properties of the signal rather than variations in neural coherence. Within the pairs of areas where the dynamics are most different from those of randomly matched time courses, ten common patterns of connectivity are identified, and their occurrence as a function of time is plotted for all animals. The observation of time-varying correlation in the rodent model will facilitate the future multimodal experiments needed to determine whether the changes in apparent connectivity are linked to underlying neural variability.

Resting-state functional magnetic resonance imaging (fMRI) is widely used for exploring spontaneous brain activity and large-scale networks; however, the neural processes underlying the observed resting-state fMRI signals are not fully understood. To investigate the neural correlates of spontaneous low-frequency fMRI fluctuations and functional connectivity, we developed a rat model of simultaneous fMRI and multiple-site intracortical neural recordings. This allowed a direct comparison to be made between the spontaneous signals and interhemispheric connectivity measured with the two modalities. Results show that low-frequency blood oxygen level-dependent (BOLD) fluctuations (

To examine the neural basis of the blood oxygenation level dependent (BOLD) magnetic resonance imaging (MRI) signal, we have developed a rodent model in which functional MRI data and in vivo intracortical recording can be performed simultaneously. The combination of MRI and electrical recording is technically challenging because the electrodes used for recording distort the MRI images and the MRI acquisition induces noise in the electrical recording. To minimize the mutual interference of the two modalities, glass microelectrodes were used rather than metal and a noise removal algorithm was implemented for the electrophysiology data. In our studies, two microelectrodes were separately implanted in bilateral primary somatosensory cortices (SI) of the rat and fixed in place. One coronal slice covering the electrode tips was selected for functional MRI. Electrode shafts and fixation positions were not included in the image slice to avoid imaging artifacts. The removed scalp was replaced with toothpaste to reduce susceptibility mismatch and prevent Gibbs ringing artifacts in the images. The artifact structure induced in the electrical recordings by the rapidly-switching magnetic fields during image acquisition was characterized by averaging all cycles of scans for each run. The noise structure during imaging was then subtracted from original recordings. The denoised time courses were then used for further analysis in combination with the fMRI data. As an example, the simultaneous acquisition was used to determine the relationship between spontaneous fMRI BOLD signals and band-limited intracortical electrical activity. Simultaneous fMRI and electrophysiological recording in the rodent will provide a platform for many exciting applications in neuroscience in addition to elucidating the relationship between the fMRI BOLD signal and neuronal activity.

Functional networks, defined by synchronous spontaneous blood oxygenation level-dependent (BOLD) oscillations between spatially distinct brain regions, appear to be essential to brain function and have been implicated in disease states, cognitive capacity, and sensing and motor processes. While the topographical extent and behavioral function of these networks has been extensively investigated, the neural functions that create and maintain these synchronizations remain mysterious. In this work callosotomized rodents are examined, providing a unique platform for evaluating the influence of structural connectivity via the corpus callosum on bilateral resting state functional connectivity. Two experimental groups were assessed, a full callosotomy group, in which the corpus callosum was completely sectioned, and a sham callosotomy group, in which the gray matter was sectioned but the corpus callosum remained intact. Results indicated a significant reduction in interhemispheric connectivity in the full callosotomy group as compared with the sham group in primary somatosensory cortex and caudate-putamen regions. Similarly, electrophysiology revealed significantly reduced bilateral correlation in band limited power. Bilateral gamma Band-limited power connectivity was most strongly affected by the full callosotomy procedure. This work represents a robust finding indicating the corpus callosum's influence on maintaining integrity in bilateral functional networks; further, functional magnetic resonance imaging (fMRI) and electrophysiological connectivity share a similar decrease in connectivity as a result of the callosotomy, suggesting that fMRI-measured functional connectivity reflects underlying changes in large-scale coordinated electrical activity. Finally, spatiotemporal dynamic patterns were evaluated in both groups; the full callosotomy rodents displayed a striking loss of bilaterally synchronous propagating waves of cortical activity.

Recently, researchers have taken interest in simultaneously recording electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to explore the relationship between the blood oxygen level dependent (BOLD) signal and underlying neuronal activity[1]. Many studies investigate BOLD signal relationship to high-pass (> 1 Hz) filtered EEG data, but little work on has been done on slow (< 1Hz) cortical potential correlates of fMRI because low frequency EEG data are commonly discarded as drift artifacts[1]. Nevertheless, much slower (< 0.1 Hz) components of the fMRI signal are used to establish functional resting state networks (RSNs) within the brain[2]. In this study, five subjects underwent simultaneous recording of EEG and fMRI in a resting state, lying quietly with eyes open. Two ten minute scans were acquired per subject over the entire brain (TR=2 seconds, TE=30 milliseconds, 64x64 voxels, 33 slices). After standard functional connectivity preprocessing[3], the BOLD signals were band-pass filtered between 0.01 – 0.08 Hz. EEG data were obtained from a 64-channel electrode montage in a standard International 10-10 System configuration. Each signal was then filtered between 0.01 – 0.08 Hz and de-noised of scanner and ballistocardiographic artifacts. Channels were clustered using k-means and each cluster’s signal was calculated in order to be regressed from its respective electrodes. All EEG data were resampled to 0.5 Hz for comparison with functional data. Pearson correlation was then computed between pairs of individual EEG electrodes. Two electrode channels (AF3 and PO8) were chosen to be cross-correlated with BOLD signals at various time shifts between -10 – 20 seconds, due to their consistent presence in anticorrelated clusters from the paired EEG clustering. This was performed both with and without EEG cluster signal removal, the former allowing better visualization of correlation results. Finally, all subjects’ electrode-specific EEG-fMRI correlations were averaged together and two maps of correlation were produced (Figure ​(Figure1).1). These maps were corrected for multiple comparisons using a false discovery rate of 0.05, assuming a normal distribution of correlation values. The relationship between BOLD signals and slow cortical potentials can be observed over the sensorimotor cortex for both AF3 and PO8, demonstrating a correlation between EEG and a known RSN[2]. The time delays in correlation may also be related to previously observed BOLD signal propagations[3]. Figure 1 fMRI to EEG correlation for two electrodes. Slice number and time shift are shown on the abscissa and ordinate, respectively. Approximate electrode location is indicated by a white circle in the upper left slice. Arrows point to areas of significant correlation. ...

In the previous decade, interest in the “functional connectivity” of the brain has greatly increased, but the nature of the signal underlying derived predictive metrics remains poorly understood [1]. A typical study uses functional magnetic resonance imaging (fMRI) and calculates regions of correlated low-frequency activity or “functional networks” when no task is being performed, the “resting state”. However, unlike traditional block/event based fMRI, the spontaneous fluctuations that determine such networks may not relate to a standard “hemodynamic response” to neural activity [2] and may be task and brain region dependent [1]. Ten rats were anesthetized with either isoflurane (iso) or dexmedetomidine (med). Each rat had simultaneous local field potentials (LFP) [3] recorded from implanted electrodes in bilateral primary somatosensory cortex (SI) simultaneously with single-slice fMRI of SI [4]. After preprocessing, signals were filtered to regions of significant spectral coherence (0.04-0.18Hz iso, 0.05-0.3Hz med). Pearson correlation (rt) was calculated between LFP signals at time shifts -10s to 10s relative to fMRI, at every fMRI voxel (Figure ​(Figure1B).1B). Instead of a simple hemodynamic response, the LFP correlates appeared both to have a component of spatial propagation (Figure ​(Figure1B,1B, white arrows), and alternation between positive and negative correlation. This was observed using both anesthesias and suggests that LFPs in coherent frequencies do not simply reflect local activation, but may instead be part of a large scale dynamic process. Using an fMRI-based algorithm validated in both anesthetized rats and awake humans [5], a spatiotemporal dynamic was produced that was highly similar to rt (Figure ​(Figure1C).1C). Spatial correlation (rs) between the two types of pattern reached a maximum at approximately the same shift between patterns in all rats, mean rs = 0.25 (med) and mean rs = 0.23 (iso), with mean rs > 0.10 indicating significance at p < 0.05 when using boot-strapping and correcting for multiple comparisons [6]. These results suggest that the neural basis of functional networks may be more complex than a simple hemodynamic response and possibly contains contributions from large-scale neuromodulatory processes. Figure 1 A. A coronal image of a rat’s brain in the same plane as the fMRI images used in this study. B. (med) rt between LFP and fMRI at each voxel, times listed are the time shift of LFP prior to fMRI. C. (med) fMRI pattern from Majeed et al. algorithm ...