Resting state MRI reveals spontaneous physiological fluctuations in the kidney and tracks diabetic nephropathy in rats

The kidneys maintain fluid-electrolyte balance and excrete waste in the presence of constant fluctuations in plasma volume and systemic blood pressure. The kidneys perform these functions to control capillary perfusion and glomerular filtration by modulating the mechanisms of autoregulation. An effect of these modulations are spontaneous, natural fluctuations in nephron perfusion. Numerous other mechanisms can lead to fluctuations in perfusion and flow. The ability to monitor these spontaneous physiological fluctuations in vivo could facilitate the early detection of kidney disease. The goal of this work was to investigate the use of resting- state magnetic resonance imaging (rsMRI) to detect spontaneous physiological fluctuations in the kidney. We performed rsMRI of rat kidneys in vivo over 10 minutes, applying motion correction to resolve time series in each voxel. We observed spatially variable, spontaneous fluctuations in rsMRI signal between 0-0.3 Hz, in frequency bands also associated with autoregulatory mechanisms. We further applied rsMRI to investigate changes in these fluctuations in a rat model of diabetic nephropathy. Spectral analysis was performed on time series of rsMRI signal in kidney cortex and medulla. Power from spectra in specific frequency bands from kidney cortex correlated with severity of glomerular pathology caused by diabetic nephropathy. Finally, we investigated the feasibility of using rsMRI of the human kidney in two participants, observing the presence of similar, spatially variable fluctuations. This approach may enable a range of preclinical and clinical investigations of kidney function, and facilitate the development of new therapies to improve outcomes in patients with kidney disease.

Time-varying functional connectivity predicts fluctuations in sustained attention in a serial tapping task

The mechanisms for how large-scale brain networks contribute to sustained attention are unknown. Attention fluctuates from moment to moment, and this continuous change is consistent with dynamic changes in functional connectivity between brain networks involved in the internal and external allocation of attention. In this study, we investigated how brain network activity varied across different levels of attentional focus (i.e., "zones"). Participants performed a finger-tapping task, and guided by previous research, in-the-zone performance or state was identified by low reaction time variability and out-of-the-zone as the inverse. In-the-zone sessions tended to occur earlier in the session than out-of-the-zone blocks. This is unsurprising given the way attention fluctuates over time. Employing a novel method of time-varying functional connectivity, called the quasi-periodic pattern analysis (i.e., reliable, network-level low-frequency fluctuations), we found that the activity between the default mode network (DMN) and task positive network (TPN) is significantly more anti-correlated during in-the-zone states versus out-of-the-zone states. Furthermore, it is the frontoparietal control network (FPCN) switch that differentiates the two zone states. Activity in the dorsal attention network (DAN) and DMN were desynchronized across both zone states. During out-of-the-zone periods, FPCN synchronized with DMN, while during in-the-zone periods, FPCN switched to synchronized with DAN. In contrast, the ventral attention network (VAN) synchronized more closely with DMN during in-the-zone periods compared with out-of-the-zone periods. These findings demonstrate that time-varying functional connectivity of low frequency fluctuations across different brain networks varies with fluctuations in sustained attention or other processes that change over time.

The interaction between random and systematic visual stimulation and infraslow quasiperiodic spatiotemporal patterns of whole brain activity

One prominent feature of the infraslow BOLD signal during rest or task is quasi-periodic spatiotemporal pattern (QPP) of signal changes that involves an alternation of activity in key functional networks and propagation of activity across brain areas, and that is known to tie to the infraslow neural activity involved in attention and arousal fluctuations. This ongoing whole-brain pattern of activity might potentially modify the response to incoming stimuli or be modified itself by the induced neural activity. To investigate this, we presented checkerboard sequences flashing at 6Hz to subjects. This is a salient visual stimulus that is known to produce a strong response in visual processing regions. Two different visual stimulation sequences were employed, a systematic stimulation sequence in which the visual stimulus appeared every 20.3 secs and a random stimulation sequence in which the visual stimulus occurred randomly every 14~62.3 secs. Three central observations emerged. First, the two different stimulation conditions affect the QPP waveform in different aspects, i.e., systematic stimulation has greater effects on its phase and random stimulation has greater effects on its magnitude. Second, the QPP was more frequent in the systematic condition with significantly shorter intervals between consecutive QPPs compared to the random condition. Third, the BOLD signal response to the visual stimulus across both conditions was swamped by the QPP at the stimulus onset. These results provide novel insights into the relationship between intrinsic patterns and stimulated brain activity.

The interaction between random and systematic visual stimulation and infraslow quasiperiodic spatiotemporal patterns of whole brain activity

One prominent feature of the infraslow BOLD signal during rest or task is quasi-periodic spatiotemporal pattern (QPP) of signal changes that involves an alternation of activity in key functional networks and propagation of activity across brain areas, and that is known to tie to the infraslow neural activity involved in attention and arousal fluctuations. This ongoing whole-brain pattern of activity might potentially modify the response to incoming stimuli or be modified itself by the induced neural activity. To investigate this, we presented checkerboard sequences flashing at 6Hz to subjects. This is a salient visual stimulus that is known to produce a strong response in visual processing regions. Two different visual stimulation sequences were employed, a systematic stimulation sequence in which the visual stimulus appeared every 20.3 secs and a random stimulation sequence in which the visual stimulus occurred randomly every 14∼62.3 secs. Three central observations emerged. First, the two different stimulation conditions affect the QPP waveform in different aspects, i.e., systematic stimulation has greater effects on its phase and random stimulation has greater effects on its magnitude. Second, the QPP was more frequent in the systematic condition with significantly shorter intervals between consecutive QPPs compared to the random condition. Third, the BOLD signal response to the visual stimulus across both conditions was swamped by the QPP at the stimulus onset. These results provide novel insights into the relationship between intrinsic patterns and stimulated brain activity.

A consensus protocol for functional connectivity analysis in the rat brain

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows for comparisons with data modalities collected under invasive or terminal procedures. Currently, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. Here we introduce StandardRat, a consensus rat functional magnetic resonance imaging acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired from rats across 46 centers. We developed a reproducible pipeline for analyzing rat data acquired with diverse protocols and determined experimental and processing parameters associated with the robust detection of functional connectivity across centers. We show that the standardized protocol enhances biologically plausible functional connectivity patterns relative to previous acquisitions. The protocol and processing pipeline described here is openly shared with the neuroimaging community to promote interoperability and cooperation toward tackling the most important challenges in neuroscience.

Selective blockade of rat brain T-type calcium channels provides insights on neurophysiological basis of arousal dependent resting state functional magnetic resonance imaging signals

A number of studies point to slow (0.1–2 Hz) brain rhythms as the basis for the resting-state functional magnetic resonance imaging (rsfMRI) signal. Slow waves exist in the absence of stimulation, propagate across the cortex, and are strongly modulated by vigilance similar to large portions of the rsfMRI signal. However, it is not clear if slow rhythms serve as the basis of all neural activity reflected in rsfMRI signals, or just the vigilance-dependent components. The rsfMRI data exhibit quasi-periodic patterns (QPPs) that appear to increase in strength with decreasing vigilance and propagate across the brain similar to slow rhythms. These QPPs can complicate the estimation of functional connectivity (FC) via rsfMRI, either by existing as unmodeled signal or by inducing additional wide-spread correlation between voxel-time courses of functionally connected brain regions. In this study, we examined the relationship between cortical slow rhythms and the rsfMRI signal, using a well-established pharmacological model of slow wave suppression. Suppression of cortical slow rhythms led to significant reduction in the amplitude of QPPs but increased rsfMRI measures of intrinsic FC in rats. The results suggest that cortical slow rhythms serve as the basis of only the vigilance-dependent components (e.g., QPPs) of rsfMRI signals. Further attenuation of these non-specific signals enhances delineation of brain functional networks.

A parsimonious description of global functional brain organization in three spatiotemporal patterns

Resting-state functional magnetic resonance imaging (MRI) has yielded seemingly disparate insights into large-scale organization of the human brain. The brain's large-scale organization can be divided into two broad categories: zero-lag representations of functional connectivity structure and time-lag representations of traveling wave or propagation structure. In this study, we sought to unify observed phenomena across these two categories in the form of three low-frequency spatiotemporal patterns composed of a mixture of standing and traveling wave dynamics. We showed that a range of empirical phenomena, including functional connectivity gradients, the task-positive/task-negative anti-correlation pattern, the global signal, time-lag propagation patterns, the quasiperiodic pattern and the functional connectome network structure, are manifestations of these three spatiotemporal patterns. These patterns account for much of the global spatial structure that underlies functional connectivity analyses and unifies phenomena in resting-state functional MRI previously thought distinct.

Spatiotemporal trajectories in resting-state FMRI revealed by convolutional variational autoencoder

Recent resting-state fMRI studies have shown that brain activity exhibits temporal variations in functional connectivity by using various approaches including sliding window correlation, co-activation patterns, independent component analysis, quasi-periodic patterns, and hidden Markov models. These methods often model the brain activity as a discretized hopping among several brain states that are defined by the spatial configurations of network activity. However, the discretized states are merely a simplification of what is likely to be a continuous process, where each network evolves over time following its unique path. To model these characteristic spatiotemporal trajectories, we trained a variational autoencoder using rs-fMRI data and evaluated the spatiotemporal features of the latent variables obtained from the trained networks. Our results suggest that there are a relatively small number of approximately orthogonal whole-brain spatiotemporal patterns that capture the most prominent features of rs-fMRI data, which can serve as the building blocks to construct all possible spatiotemporal dynamics in resting state fMRI. These spatiotemporal patterns provide insight into how activity flows across the brain in concordance with known network structures and functional connectivity gradients.

Complexity Analysis of Resting-State and Task fMRI Using Multiscale Sample Entropy

Functional magnetic resonance imaging (fMRI) is a powerful tool that allows for analysis of neural activity via the measurement of blood-oxygenation-level-dependent (BOLD) signal. The BOLD fluctuations can exhibit different levels of complexity, depending upon the conditions under which they are measured. We examined the complexity of both resting-state and task-based fMRI using sample entropy (SampEn) as a surrogate for signal predictability. We found that within most tasks, regions of the brain that were deemed task-relevant displayed significantly low levels of SampEn, and there was a strong negative correlation between parcel entropy and amplitude.

Spatiotemporal functional interactivity among large-scale brain networks

The macro-scale intrinsic functional network architecture of the human brain has been well characterized. Early studies revealed robust and enduring patterns of static connectivity, while more recent work has begun to explore the temporal dynamics of these large-scale brain networks. Little work to date has investigated directed connectivity within and between these networks, or the temporal patterns of afferent (input) and efferent (output) connections between network nodes. Leveraging a novel analytic approach, prediction correlation, we investigated the causal interactions within and between large-scale networks of the brain using resting state fMRI. This technique allows us to characterize information transfer between brain regions in both the spatial (direction) and temporal (duration) scales. Using data from the Human Connectome Project (N = 200) we applied prediction correlation techniques to four resting-state fMRI scans (each scan has TRs = 1200). Three central observations emerged. First, the strongest and longest duration connections were observed within the somatomotor, visual, and dorsal attention networks. Second, the short duration connections were observed for high-degree nodes in the visual and default networks, as well as in the hippocampus. Specifically, the connectivity profile of the highest-degree nodes was dominated by efferent connections to multiple cortical areas. Moderate high-degree nodes, particularly in hippocampal regions, showed an afferent connectivity profile. Finally, multimodal association nodes in lateral prefrontal brain regions demonstrated a short duration, bidirectional connectivity profile, consistent with this region's role in integrative and modulatory processing. These results provide novel insights into the spatiotemporal dynamics of human brain function.

Resting Brain Fluctuations Are Intrinsically Coupled to Visual Response Dynamics

How do intrinsic brain dynamics interact with processing of external sensory stimuli? We sought new insights using functional magnetic resonance imaging to track spatiotemporal activity patterns at the whole brain level in lightly anesthetized mice, during both resting conditions and visual stimulation trials. Our results provide evidence that quasiperiodic patterns (QPPs) are the most prominent component of mouse resting brain dynamics. These QPPs captured the temporal alignment of anticorrelation between the default mode (DMN)- and task-positive (TPN)-like networks, with global brain fluctuations, and activity in neuromodulatory nuclei of the reticular formation. Specifically, the phase of QPPs prior to stimulation could significantly stratify subsequent visual response magnitude, suggesting QPPs relate to brain state fluctuations. This is the first observation in mice that dynamics of the DMN- and TPN-like networks, and particularly their anticorrelation, capture a brain state dynamic that affects sensory processing. Interestingly, QPPs also displayed transient onset response properties during visual stimulation, which covaried with deactivations in the reticular formation. We conclude that QPPs appear to capture a brain state fluctuation that may be orchestrated through neuromodulation. Our findings provide new frontiers to understand the neural processes that shape functional brain states and modulate sensory input processing.

Functional MRI Signal Complexity Analysis Using Sample Entropy

Resting-state functional magnetic resonance imaging (rs-fMRI) is an immensely powerful method in neuroscience that uses the blood oxygenation level-dependent (BOLD) signal to record and analyze neural activity in the brain. We examined the complexity of brain activity acquired by rs-fMRI to determine whether it exhibits variation across brain regions. In this study the complexity of regional brain activity was analyzed by calculating the sample entropy of 200 whole-brain BOLD volumes as well as of distinct brain networks, cortical regions, and subcortical regions of these brain volumes. It can be seen that different brain regions and networks exhibit distinctly different levels of entropy/complexity, and that entropy in the brain significantly differs between brains at rest and during task performance.

Animal Functional Magnetic Resonance Imaging: Trends and Path Toward Standardization

Animal whole-brain functional magnetic resonance imaging (fMRI) provides a non-invasive window into brain activity. A collection of associated methods aims to replicate observations made in humans and to identify the mechanisms underlying the distributed neuronal activity in the healthy and disordered brain. Animal fMRI studies have developed rapidly over the past years, fueled by the development of resting-state fMRI connectivity and genetically encoded neuromodulatory tools. Yet, comparisons between sites remain hampered by lack of standardization. Recently, we highlighted that mouse resting-state functional connectivity converges across centers, although large discrepancies in sensitivity and specificity remained. Here, we explore past and present trends within the animal fMRI community and highlight critical aspects in study design, data acquisition, and post-processing operations, that may affect the results and influence the comparability between studies. We also suggest practices aimed to promote the adoption of standards within the community and improve between-lab reproducibility. The implementation of standardized animal neuroimaging protocols will facilitate animal population imaging efforts as well as meta-analysis and replication studies, the gold standards in evidence-based science.

Author Correction: Quasi-Periodic Patterns of Neural Activity improve Classification of Alzheimer's Disease in Mice

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Investigating the Intersession Reliability of Dynamic Brain-State Properties

Dynamic functional connectivity metrics have much to offer to the neuroscience of individual differences of cognition. Yet, despite the recent expansion in dynamic connectivity research, limited resources have been devoted to the study of the reliability of these connectivity measures. To address this, resting-state functional magnetic resonance imaging data from 100 Human Connectome Project subjects were compared across 2 scan days. Brain states (i.e., patterns of coactivity across regions) were identified by classifying each time frame using k means clustering. This was done with and without global signal regression (GSR). Multiple gauges of reliability indicated consistency in the brain-state properties across days and GSR attenuated the reliability of the brain states. Changes in the brain-state properties across the course of the scan were investigated as well. The results demonstrate that summary metrics describing the clustering of individual time frames have adequate test/retest reliability, and thus, these patterns of brain activation may hold promise for individual-difference research.

Dynamic resting state fMRI analysis in mice reveals a set of Quasi-Periodic Patterns and illustrates their relationship with the global signal

Time-resolved 'dynamic' over whole-period 'static' analysis of low frequency (LF) blood-oxygen level dependent (BOLD) fluctuations provides many additional insights into the macroscale organization and dynamics of neural activity. Although there has been considerable advancement in the development of mouse resting state fMRI (rsfMRI), very little remains known about its dynamic repertoire. Here, we report for the first time the detection of a set of recurring spatiotemporal Quasi-Periodic Patterns (QPPs) in mice, which show spatial similarity with known resting state networks. Furthermore, we establish a close relationship between several of these patterns and the global signal. We acquired high temporal rsfMRI scans under conditions of low (LA) and high (HA) medetomidine-isoflurane anesthesia. We then employed the algorithm developed by Majeed et al. (2011), previously applied in rats and humans, which detects and averages recurring spatiotemporal patterns in the LF BOLD signal. One type of observed patterns in mice was highly similar to those originally observed in rats, displaying propagation from lateral to medial cortical regions, which suggestively pertain to a mouse Task-Positive like network (TPN) and Default Mode like network (DMN). Other QPPs showed more widespread or striatal involvement and were no longer detected after global signal regression (GSR). This was further supported by diminished detection of subcortical dynamics after GSR, with cortical dynamics predominating. Observed QPPs were both qualitatively and quantitatively determined to be consistent across both anesthesia conditions, with GSR producing the same outcome. Under LA, QPPs were consistently detected at both group and single subject level. Under HA, consistency and pattern occurrence rate decreased, whilst cortical contribution to the patterns diminished. These findings confirm the robustness of QPPs across species and demonstrate a new approach to study mouse LF BOLD spatiotemporal dynamics and mechanisms underlying functional connectivity. The observed impact of GSR on QPPs might help better comprehend its controversial role in conventional resting state studies. Finally, consistent detection of QPPs at single subject level under LA promises a step forward towards more reliable mouse rsfMRI and further confirms the importance of selecting an optimal anesthesia regime.

Quasi-Periodic Patterns of Neural Activity improve Classification of Alzheimer's Disease in Mice

Resting state (rs)fMRI allows measurement of brain functional connectivity and has identified default mode (DMN) and task positive (TPN) network disruptions as promising biomarkers for Alzheimer's disease (AD). Quasi-periodic patterns (QPPs) of neural activity describe recurring spatiotemporal patterns that display DMN with TPN anti-correlation. We reasoned that QPPs could provide new insights into AD network dysfunction and improve disease diagnosis. We therefore used rsfMRI to investigate QPPs in old TG2576 mice, a model of amyloidosis, and age-matched controls. Multiple QPPs were determined and compared across groups. Using linear regression, we removed their contribution from the functional scans and assessed how they reflected functional connectivity. Lastly, we used elastic net regression to determine if QPPs improved disease classification. We present three prominent findings: (1) Compared to controls, TG2576 mice were marked by opposing neural dynamics in which DMN areas were anti-correlated and displayed diminished anti-correlation with the TPN. (2) QPPs reflected lowered DMN functional connectivity in TG2576 mice and revealed significantly decreased DMN-TPN anti-correlations. (3) QPP-derived measures significantly improved classification compared to conventional functional connectivity measures. Altogether, our findings provide insight into the neural dynamics of aberrant network connectivity in AD and indicate that QPPs might serve as a translational diagnostic tool.

Infraslow Electroencephalographic and Dynamic Resting State Network Activity

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 (<1 Hz) has been little studied. Previously, work in rodents showed that infraslow local field potentials play a role in functional connectivity, particularly in the dynamic organization of large-scale networks. To examine the relationship between infraslow activity and network dynamics in humans, direct current (DC) EEG and resting state magnetic resonance imaging data were acquired simultaneously. The DC EEG signals were correlated with the BOLD signal in patterns that resembled resting state networks. Subsequent dynamic analysis showed that the correlation between DC EEG and the BOLD signal varied substantially over time, even within individual subjects. The variation in DC EEG appears to reflect the time-varying contribution of different resting state networks. Furthermore, some of the patterns of DC EEG and BOLD correlation are consistent with previous work demonstrating quasiperiodic spatiotemporal patterns of large-scale network activity in resting state. These findings demonstrate that infraslow electrical activity is linked to BOLD fluctuations in humans and that it may provide a basis for large-scale organization comparable to that observed in animal studies.

Quasi-periodic patterns of intrinsic brain activity in individuals and their relationship to global signal

Quasiperiodic patterns (QPPs) as reported by Majeed et al., 2011 are prominent features of the brain's intrinsic activity that involve important large-scale networks (default mode, DMN; task positive, TPN) and are likely to be major contributors to widely used measures of functional connectivity. We examined the variability of these patterns in 470 individuals from the Human Connectome Project resting state functional MRI dataset. The QPPs from individuals can be coarsely categorized into two types: one where strong anti-correlation between the DMN and TPN is present, and another where most areas are strongly correlated. QPP type could be predicted by an individual's global signal, with lower global signal corresponding to QPPs with strong anti-correlation. After regression of global signal, all QPPs showed strong anti-correlation between DMN and TPN. QPP occurrence and type was similar between a subgroup of individuals with extremely low motion and the rest of the sample, which shows that motion is not a major contributor to the QPPs. After regression of estimates of slow respiratory and cardiac induced signal fluctuations, more QPPs showed strong anti-correlation between DMN and TPN, an indication that while physiological noise influences the QPP type, it is not the primary source of the QPP itself. QPPs were more similar for the same subjects scanned on different days than for different subjects. These results provide the first assessment of the variability in individual QPPs and their relationship to physiological parameters.

Parametric Dependencies of Sliding Window Correlation

OBJECTIVE

In this paper, we explore the dependence of sliding window correlation (SWC) results on different parameters of correlating signals. The SWC is extensively used to explore the dynamics of functional connectivity (FC) networks using resting-state functional MRI (rsfMRI) scans. These scanned signals often contain multiple amplitudes, frequencies, and phases. However, the exact values of these parameters are unknown. Two recent studies explored the relationship of window length and frequencies (minimum/maximum) in the correlating signals.

METHODS

We extend the findings of these studies by using two deterministic signals with multiple amplitudes, frequencies, and phases. Afterward, we modulate one of the signals to introduce dynamics (nonstationarity) in their relationship. We also explore the relationship of window length and frequency band for real rsfMRI data.

RESULTS

For deterministic signals, the spurious fluctuations due to the method itself minimize, and the SWC estimates the stationary correlation when frequencies in the signals have specific relationship. For dynamic relationship also, the undesirable frequencies were removed under specific conditions for the frequencies. For real rsfMRI data, the SWC results varied with frequencies and window length.

CONCLUSION

In the absence of any "ground truth" for different parameters in real rsfMRI signals, the SWC with a constant window size may not be a reliable method to study the dynamics of the FC.

SIGNIFICANCE

This study reveals the parametric dependencies of the SWC and its limitation as a method to analyze dynamics of FC networks in the absence of any ground truth.

Multiscale network activity in resting state fMRI

The brain is inherently multiscalar in both space and time. We argue that this multiscalar nature is reflected in the blood oxygenation level dependent (BOLD) fluctuations used to map functional connectivity. We present evidence that global fluctuations in activity, quasiperiodic spatiotemporal patterns, and aperiodic time-varying activity coexist within the BOLD signal. These processes can be separated using careful analysis and appear to reflect electrical activity on similar scales, suggesting that the BOLD signal fluctuations can provide novel insight into the functional architecture of the brain.

Noise and non-neuronal contributions to the BOLD signal: applications to and insights from animal studies

The BOLD signal reflects hemodynamic events within the brain, which in turn are driven by metabolic changes and neural activity. However, the link between BOLD changes and neural activity is indirect and can be influenced by a number of non-neuronal processes. Motion and physiological cycles have long been known to affect the BOLD signal and are present in both humans and animal models. Differences in physiological baseline can also contribute to intra- and inter-subject variability. The use of anesthesia, common in animal studies, alters neural activity, vascular tone, and neurovascular coupling. Most intriguing, perhaps, are the contributions from other processes that do not appear to be neural in origin but which may provide information about other aspects of neurophysiology. This review discusses different types of noise and non-neuronal contributors to the BOLD signal, sources of variability for animal studies, and insights to be gained from animal models.

Cluster-based analysis for characterizing dynamic functional connectivity

Different regions in the resting brain exhibit non-stationary functional connectivity (FC) over time. In this paper, a simple and efficient framework of clustering the variability in FC of a rat's brain at rest is proposed. This clustering process reveals areas that are always connected with a chosen region, called seed voxel, along with the areas exhibiting variability in the FC. This addresses an issue common to most dynamic FC analysis techniques, which is the assumption that the spatial extent of a given network remains constant over time. We increase the voxel size and reduce the spatial resolution to analyze variable FC of the whole resting brain. We hypothesize that the adjacent voxels in resting state functional magnetic resonance imaging (rsfMRI), just as in task-based fMRI, exhibit similar intensities, so they can be averaged to obtain larger voxels without any significant loss of information. Sliding window correlation is used to compute variable patterns of the rat's whole brain FC with the seed voxel in the sensorimotor cortex. These patterns are grouped based on their spatial similarities using binary transformed feature vectors in k-means clustering, not only revealing the variable and nonvariable portions of FC in the resting brain but also detecting the extent of the variability of these patterns.

Considerations for resting state functional MRI and functional connectivity studies in rodents

Resting state functional MRI (rs-fMRI) and functional connectivity mapping have become widely used tools in the human neuroimaging community and their use is rapidly spreading into the realm of rodent research as well. One of the many attractive features of rs-fMRI is that it is readily translatable from humans to animals and back again. Changes in functional connectivity observed in human studies can be followed by more invasive animal experiments to determine the neurophysiological basis for the alterations, while exploratory work in animal models can identify possible biomarkers for further investigation in human studies. These types of interwoven human and animal experiments have a potentially large impact on neuroscience and clinical practice. However, impediments exist to the optimal application of rs-fMRI in small animals, some similar to those encountered in humans and some quite different. In this review we identify the most prominent of these barriers, discuss differences between rs-fMRI in rodents and in humans, highlight best practices for animal studies, and review selected applications of rs-fMRI in rodents. Our goal is to facilitate the integration of human and animal work to the benefit of both fields.

A comparative analysis of mouse and human medial geniculate nucleus connectivity: a DTI and anterograde tracing study

Understanding the function and connectivity of thalamic nuclei is critical for understanding normal and pathological brain function. The medial geniculate nucleus (MGN) has been studied mostly in the context of auditory processing and its connection to the auditory cortex. However, there is a growing body of evidence that the MGN and surrounding associated areas ('MGN/S') have a diversity of projections including those to the globus pallidus, caudate/putamen, amygdala, hypothalamus, and thalamus. Concomitantly, pathways projecting to the medial geniculate include not only the inferior colliculus but also the auditory cortex, insula, cerebellum, and globus pallidus. Here we expand our understanding of the connectivity of the MGN/S by using comparative diffusion weighted imaging with probabilistic tractography in both human and mouse brains (most previous work was in rats). In doing so, we provide the first report that attempts to match probabilistic tractography results between human and mice. Additionally, we provide anterograde tracing results for the mouse brain, which corroborate the probabilistic tractography findings. Overall, the study provides evidence for the homology of MGN/S patterns of connectivity across species for understanding translational approaches to thalamic connectivity and function. Further, it points to the utility of DTI in both human studies and small animal modeling, and it suggests potential roles of these connections in human cognition, behavior, and disease.

Phase-amplitude coupling and infraslow (<1 Hz) frequencies in the rat brain: relationship to resting state fMRI

Resting state functional magnetic resonance imaging (fMRI) can identify network alterations that occur in complex psychiatric diseases and behaviors, but its interpretation is difficult because the neural basis of the infraslow BOLD fluctuations is poorly understood. Previous results link dynamic activity during the resting state to both infraslow frequencies in local field potentials (LFP) (<1 Hz) and band-limited power in higher frequency LFP (>1 Hz). To investigate the relationship between these frequencies, LFPs were recorded from rats under two anesthetics: isoflurane and dexmedetomidine. Signal phases were calculated from low-frequency LFP and compared to signal amplitudes from high-frequency LFP to determine if modulation existed between the two frequency bands (phase-amplitude coupling). Isoflurane showed significant, consistent phase-amplitude coupling at nearly all pairs of frequencies, likely due to the burst-suppression pattern of activity that it induces. However, no consistent phase-amplitude coupling was observed in rats that were anesthetized with dexmedetomidine. fMRI-LFP correlations under isoflurane using high frequency LFP were reduced when the low frequency LFP's influence was accounted for, but not vice-versa, or in any condition under dexmedetomidine. The lack of consistent phase-amplitude coupling under dexmedetomidine and lack of shared variance between high frequency and low frequency LFP as it relates to fMRI suggests that high and low frequency neural electrical signals may contribute differently, possibly even independently, to resting state fMRI. This finding suggests that researchers take care in interpreting the neural basis of resting state fMRI, as multiple dynamic factors in the underlying electrophysiology could be driving any particular observation.

Microembolism induces anhedonia but no detectable changes in white matter integrity in aged rats

Microvascular disease leads to alterations of cerebral vasculature including the formation of microembolic (ME) strokes. Though ME are associated with changes in mood and the severity and progression of cognitive decline, the effect of ME strokes on cerebral microstructure and its relationship to behavioral endpoints is unknown. Here, we used adult and aged male rats to test the hypotheses that ME lesions result in subtle changes to white and gray matter integrity as detected by high-throughput diffusion tensor imaging (DTI) and that these structural disruptions correspond to behavioral deficits. Two weeks post-surgery, aged animals showed depressive-like behaviors in the sucrose consumption test in the absence of altered cerebral diffusivity as assessed by ex-vivo DTI. Furthermore, DTI indices did not correlate with the degree of behavioral disruption in aged animals or in a subset of animals with observed tissue cavitation and subtle DTI alterations. Together, data suggest that behavioral deficits are not the result of damage to brain regions or white matter tracts, rather the activity of other systems may underlie functional disruption and recovery.

Effects of severing the corpus callosum on electrical and BOLD functional connectivity and spontaneous dynamic activity in the rat brain

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.

Abstract TP261: Spontaneous Strokes In Sickle Cell Transgenic Mice Captured Ex Vivo With Magnetic Resonance Imaging

With 11% of children with sickle cell disease (SCD) having strokes, many as early as 1 to 5 years old, the underlying mechanisms are still unclear, and it is still unknown why this subpopulation has greater risk. More will suffer from subclinical silent strokes that cause cognitive rather than physical impairments. Sickling of red blood cells due to a point mutation in hemoglobin induces chronic inflammation and vascular remodeling in SCD. We have previously shown that powerful proteases, cathepsins, are activated in SCD to promote vascular remodeling that could lead to stroke. However, a mouse model of detectable sickle cell stroke is required to study mechanism and intervention strategies. Here, we used Townes sickle transgenic mouse, with murine hemoglobin knocked out and replaced with human, sickle hemoglobin, to test the hypothesis that sickle transgenic mice have MRI detectable strokes and increased cathepsin proteolytic activity. Three mice homozygous for sickle mutation (SS) and 6 heterozygous littermate controls (AS) were perfused at 8 weeks. Brains were isolated, embedded in gadolinium-agarose, and scanned using a Bruker 9.4 Tesla magnet with a T2-weighted protocol (TE = 13.4 ms. TR= 10,000, 512 x 512 x 70). Of the 3 SS mice, 2 had abnormalities suggestive of stroke (Figure 1, red arrows), with none noted in the AS controls. Separately, immunohistochemistry with anti-cathepsin K antibody indicated a gene dosing effect of sickle allele on cathepsin K expression (SS>AS>AA), verified in aorta homogenates by multiplex cathepsin zymography assays (n=3, p<.05). To conclude, we have identified strokes in sickle transgenic mice with T2 images. These mouse stroke scans, without occlusive or chemical intervention, will be an improved model to test spontaneous stroke interventions specific to sickle cell disease including targeting upregulated cathepsins.

Dynamic properties of functional connectivity in the rodent

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.

Functional connectivity in blood oxygenation level-dependent and cerebral blood volume-weighted resting state functional magnetic resonance imaging in the rat brain

PURPOSE

To directly compare functional connectivity and spatiotemporal dynamics acquired with blood oxygenation level-dependent (BOLD) and cerebral blood volume (CBV)-weighted functional magnetic resonance imaging (fMRI) in anesthetized rats.

MATERIALS AND METHODS

A series of BOLD images were acquired in 10 rats followed by CBV-weighted images created by injection of ultrasmall iron oxide particles. Functional connectivity, spectral information, and spatiotemporal dynamics were compared for the BOLD and CBV-weighted resting state scans.

RESULTS

BOLD scans exhibited higher cross-correlation values compared to CBV-weighted scans, but the spatial patterns of correlation were similar. The BOLD spectrum contains power evenly distributed throughout the low-frequency range while the CBV power spectrum exhibited a high power peak localized to approximately 0.2 Hz. Both BOLD and CBV resting state scans showed similar propagating waves of activity along the cortex from the SII toward MI; however, these waves were detected more often in BOLD scans than in CBV scans.

CONCLUSION

While the power spectrum of the CBV signal is different from that of the BOLD signal, both connectivity maps and spatiotemporal dynamics are similar for the two modalities. Further experiments should address the relationship between spontaneous neural activity, local changes in metabolism, and hemodynamic fluctuations to elucidate the origins of the BOLD and CBV signals.

Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans

Most studies involving spontaneous fluctuations in the BOLD signal extract connectivity patterns that show relationships between brain areas that are maintained over the length of the scanning session. In this study, however, we examine the spatiotemporal dynamics of the BOLD fluctuations to identify common patterns of propagation within a scan. A novel pattern finding algorithm was developed for detecting repeated spatiotemporal patterns in BOLD fMRI data. The algorithm was applied to high temporal resolution T2*-weighted multislice images obtained from rats and humans in the absence of any task or stimulation. In rats, the primary pattern consisted of waves of high signal intensity, propagating in a lateral to medial direction across the cortex, replicating our previous findings (Majeed et al., 2009a). These waves were observed primarily in sensorimotor cortex, but also extended to visual and parietal association areas. A secondary pattern, confined to subcortical regions consisted of an initial increase and subsequent decrease in signal intensity in the caudate-putamen. In humans, the most common spatiotemporal pattern consisted of an alteration between activation of areas comprising the "default-mode" (e.g., posterior cingulate and anterior medial prefrontal cortices) and the "task-positive" (e.g., superior parietal and premotor cortices) networks. Signal propagation from focal starting points was also observed. The pattern finding algorithm was shown to be reasonably insensitive to the variation in user-defined parameters, and the results were consistent within and between subjects. This novel approach for probing the spontaneous network activity of the brain has implications for the interpretation of conventional functional connectivity studies, and may increase the amount of information that can be obtained from neuroimaging data.

Comparison of alpha-chloralose, medetomidine and isoflurane anesthesia for functional connectivity mapping in the rat

Functional connectivity measures based upon low-frequency blood-oxygenation-level-dependent functional magnetic resonance imaging (BOLD fMRI) signal fluctuations have become a widely used tool for investigating spontaneous brain activity in humans. Still unknown, however, is the precise relationship between neural activity, the hemodynamic response and fluctuations in the MRI signal. Recent work from several groups had shown that correlated low-frequency fluctuations in the BOLD signal can be detected in the anesthetized rat - a first step toward elucidating this relationship. Building on this preliminary work, through this study, we demonstrate that functional connectivity observed in the rat depends strongly on the type of anesthesia used. Power spectra of spontaneous fluctuations and the cross-correlation-based connectivity maps from rats anesthetized with alpha-chloralose, medetomidine or isoflurane are presented using a high-temporal-resolution imaging sequence that ensures minimal contamination from physiological noise. The results show less localized correlation in rats anesthetized with isoflurane as compared with rats anesthetized with alpha-chloralose or medetomidine. These experiments highlight the utility of using different types of anesthesia to explore the fundamental physiological relationships of the BOLD signal and suggest that the mechanisms contributing to functional connectivity involve a complicated relationship between changes in neural activity, neurovascular coupling and vascular reactivity.

MR diagnosis of a pulmonary embolism: comparison of P792 and Gd-DOTA for first-pass perfusion MRI and contrast-enhanced 3D MRA in a rabbit model

Objective

To compare P792 (gadomelitol, a rapid clearance blood pool MR contrast agent) with gadolinium-tetraazacyclododecanetetraacetic acid (Gd-DOTA), a standard extracellular agent, for their suitability to diagnose a pulmonary embolism (PE) during a first-pass perfusion MRI and 3D contrast-enhanced (CE) MR angiography (MRA).

Materials and Methods

A perfusion MRI or CE-MRA was performed in a rabbit PE model following the intravenous injection of a single dose of contrast agent. The time course of the pulmonary vascular and parenchymal enhancement was assessed by measuring the signal in the aorta, pulmonary artery, and lung parenchyma as a function of time to determine whether there is a significant difference between the techniques. CE-MRA studies were evaluated by their ability to depict the pulmonary vasculature and following defects between 3 seconds and 15 minutes after a triple dose intravenous injection of the contrast agents.

Results

The P792 and Gd-DOTA were equivalent in their ability to demonstrate PE as perfusion defects on first pass imaging. The signal from P792 was significantly higher in vasculature than that from Gd-DOTA between the first and the tenth minutes after injection. The results suggest that a CE-MRA PE could be reliably diagnosed up to 15 minutes after injection.

Conclusion

P792 is superior to Gd-DOTA for the MR diagnosis of PE.

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

PURPOSE

To examine spatiotemporal dynamics of low frequency fluctuations in rat cortex.

MATERIALS AND METHODS

Gradient-echo echo-planar imaging images were acquired from anesthetized rats (repetition time = 100 ms). Power spectral analysis was performed to detect different frequency peaks. Functional connectivity maps were obtained for the frequency peaks of interest. The images in the filtered time-series were displayed as a movie to study spatiotemporal patterns in the data for frequency bands of interest.

RESULTS

High temporal and spectral resolution allowed separation of primary components of physiological noise and visualization of spectral details. Two low frequency peaks with distinct characteristics were observed. Selective visualization of the second low frequency peak revealed waves of activity that typically began in the secondary somatosensory cortex and propagated to the primary motor cortex.

CONCLUSION

To date, analysis of these fluctuations has focused on the detection of functional networks assuming steady state conditions. These results suggest that detailed examination of the spatiotemporal dynamics of the low frequency fluctuations may provide more insight into brain function, and add a new perspective to the analysis of resting state fMRI data.

BOLD and CBV-weighted functional magnetic resonance imaging of the rat somatosensory system

A multislice spin echo EPI sequence was used to obtain functional MR images of the entire rat brain with blood oxygenation level dependent (BOLD) and cerebral blood volume (CBV) contrast at 11.7 T. Maps of activation incidence were created by warping each image to the Paxinos rat brain atlas and marking the extent of the activated area. Incidence maps for BOLD and CBV were similar, but activation in draining veins was more prominent in the BOLD images than in the CBV images. Cerebellar activation was observed along the surface in BOLD images, but in deeper regions in the CBV images. Both effects may be explained by increased signal dropout and distortion in the EPI images after administration of the ferumoxtran-10 contrast agent for CBV fMRI. CBV-weighted incidence maps were also created for 10, 20, and 30 mg Fe/kg doses of ferumoxtran-10. The magnitude of the average percentage change during stimulation increased from 4.9% with the 10 mg Fe/kg dose to 8.7% with the 30-mg Fe/kg dose. Incidence of activation followed a similar trend.

Functional MRI of the rodent somatosensory pathway using multislice echo planar imaging

A multislice EPI sequence was used to obtain functional MR images of the entire rat brain with BOLD contrast at 11.7 T. Ten to 11 slices covering the rat brain, with an in-plane resolution of 300 microm, provided enough sensitivity to detect activation in brain regions known to be involved in the somatosensory pathway during stimulation of the forelimbs. These regions were identified by warping a digitized rat brain atlas to each set of images. Data analysis was constrained to four major areas of the somatosensory pathway: primary and secondary somatosensory cortices, thalamus, and cerebellum. Incidence maps were generated. Electrical stimulation at 3 Hz led to significant activation in the primary sensory cortex in all rats. Activation in the secondary sensory cortex and cerebellum was observed in 70% of the studies, while thalamic activation was observed in 40%. The amplitude of activation was measured for each area, and average response time courses were calculated. Finally, the frequency dependence of the response to forepaw stimulation was measured in each of the activated areas. Optimal activation occurred in all areas at 3 Hz. These results demonstrate that whole-brain fMRI can be performed on rodents at 11.7 T to probe a well-defined neural network.

Comparison of first-pass Gd-DOTA and FAIRER MR perfusion imaging in a rabbit model of pulmonary embolism

PURPOSE

To compare the sensitivity of contrast-enhanced magnetic resonance imaging (MRI) and arterial spin labeling to perfusion deficits in the lung.

MATERIALS AND METHODS

A rabbit model of pulmonary embolism was imaged with both flow-sensitive alternating inversion recovery with an extra radiofrequency pulse (FAIRER) arterial spin labeling and Gd-DOTA enhanced MRI. The signal-to-noise ratio (SNR) was measured in the area of the perfusion deficit and the normal lung for both techniques.

RESULTS

The defect was readily visible in all images. The normal lung had an average of 3.8 +/- 1.2 times the SNR of the unperfused lung with the arterial spin labeling technique, and approximately 13.7 +/- 3.3 times the SNR with the contrast-enhanced technique.

CONCLUSION

Gd-DOTA enhanced MRI provides higher SNR in pulmonary perfusion imaging; however, arterial spin labeling is also adequate and may be used when repeated studies are indicated.

Gravity-dependent perfusion of the lung demonstrated with the FAIRER arterial spin tagging method

Magnetic resonance imaging of lung perfusion using an arterial spin tagging (AST) sequence called flow sensitive alternating inversion recovery with an extra RF pulse (FAIRER) was performed in the left and right lateral positions in five volunteers. Coronal slices were obtained and the average intensity of each lung was measured. In both positions, an increase in the intensity of the dependent lung was found (229% for left lateral, 40% for right lateral). No change was seen along an isogravitational plane. Lung volumes were measured in each position to account for the compression of the lungs by the heart. This effect was found to be symmetric and did not contribute to the perfusion gradient. This demonstrates that AST is sensitive to gravity-dependent perfusion gradients in the lung.