Jacob C. Billings

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.

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 LFPs 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.

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

&NA; 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. HighlightsImage noise, time course noise, intra‐animal variability and inter‐group variability all affect sensitivity to changes in neural activity using MRISources of variability in animal studies differ from those in human studies, primarily due to the use of anesthesiaMultimodal experiments in animal studies might identify neurophysiological sources for nonneuronal contributions to the BOLD signal

Multiscale FC analysis refines functional connectivity networks in individual brains

Recent advances in functional connectivity (FC) analysis of functional magnetic resonance imaging (fMRI) data facilitate the characterization of the brains intrinsic functional networks (FC-fMRI). Because the fMRI signal does not provides a perfect representation of neuronal activity, the potential for FC-fMRI to identify functionally relevant networks critically depends upon separating overlapping signals from one another and from external noise. As a step in data preconditioning, researchers often band-pass filter fMRI signals to the range from 0.01 Hz to 0.1 Hz. However, coordinated network oscillations operate across multiple frequencies. Thus, it is not clear that the view of FC-fMRI networks within a single spectral range produces the fullest characterization of brains multiple and overlapping systems. The following study addresses this limitation by advancing a multiscale fractionation of FC-fMRI networks, as well methods for quantifying cross-spectral network similarity. These methods clearly and consistently represent group-level brains as composed of well-known functional networks.

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.

Instantaneous brain dynamics mapped to a continuous state space

&NA; Measures of whole‐brain activity, from techniques such as functional Magnetic Resonance Imaging, provide a means to observe the brains dynamical operations. However, interpretation of whole‐brain dynamics has been stymied by the inherently high‐dimensional structure of brain activity. The present research addresses this challenge through a series of scale transformations in the spectral, spatial, and relational domains. Instantaneous multispectral dynamics are first developed from input data via a wavelet filter bank. Voxel‐level signals are then projected onto a representative set of spatially independent components. The correlation distance over the instantaneous wavelet‐ICA state vectors is a graph that may be embedded onto a lower‐dimensional space to assist the interpretation of state‐space dynamics. Applying this procedure to a large sample of resting‐state and task‐active data (acquired through the Human Connectome Project), we segment the empirical state space into a continuum of stimulus‐dependent brain states. Upon observing the local neighborhood of brain‐states adopted subsequent to each stimulus, we may conclude that resting brain activity includes brain states that are, at times, similar to those adopted during tasks, but that are at other times distinct from task‐active brain states. As task‐active brain states often populate a local neighborhood, back‐projection of segments of the dynamical state space onto the brains surface reveals the patterns of brain activity that support many experimentally‐defined states. HighlightsWe demonstrate the construction and interrogation of a continuous, two‐dimensional map of fMRI dynamics.Map points represent an individuals multispectral, and multispectral BOLD state centered at a single point in time.Task‐based scans occupy focal state‐spaces, reinforcing the utility of study methods to capture salient BOLD dynamics.Resting‐state scans occupy a broad state‐space, reinforcing the view that the resting mind is highly active.

Multiscale functional networks in human resting state functional MRI

Recent advent of fast imaging techniques for MRI application allow whole brain coverage with sub-second resolution, opening the door for new data-driven computational techniques that can harvest the information contained in the data. This paper examines the use of wavelet based spectral decomposition and hierarchical clustering for resting state functional MRI. Wavelet packets naturally enable short time spectral decomposition with minimal temporal window lengths across multiple frequency ranges, while hierarchical clustering is used for organizing broadband and filtered fMRI data into functional network. This method was applied to human group data from five volunteers from the 1000 Functional Connectomes database.

Agglomerative clustering for resting state MRI

Methods to interpret data obtained from resting state functional magnetic imaging (rs-fMRI) must be developed to more thoroughly understand how network structure of the brain supports the body and the mind. To this end, we examine the use of agglomerative clustering (AC) as a method for rs-fMRI analysis. AC is a data driven approach for organizing spatially distinct clusters of temporally similar activity. Its application to rs-fMRI data produces spatial parcellation of brain areas that share similar temporal characteristics. The technique is scalable, enabling identification of local to widespread organization. Using a wavelet based filter bank, the technique is made amenable to frequency domain scaling as well. Comparisons drawn between AC and two alternative rs-fMRI analytics - seed-based correlation, and spatial independent component analysis - highlight the ability of the proposed technique to recognize well known functional brain regions.

Quasi-periodic patterns contribute to functional connectivity in the brain

Functional connectivity is widely used to study the coordination of activity between brain regions over time. Functional connectivity in the default mode and task positive networks is particularly important for normal brain function. However, the processes that give rise to functional connectivity in the brain are not fully understood. It has been postulated that low-frequency neural activity plays a key role in establishing the functional architecture of the brain. Quasi-periodic patterns (QPPs) are a reliably observable form of low-frequency neural activity that involve the default mode and task positive networks. Here, QPPs from resting-state and working memory task-performing individuals were acquired. The spatial pattern and the temporal frequency of the QPPs between the two groups was compared and their contribution to functional connectivity in the brain was measured. In task-performing individuals, the spatial pattern of the QPP changes, particularly in task-relevant regions; and the QPP tends to occur with greater strength and frequency. Differences in the QPPs between the two groups could partially account for the variance in functional connectivity between resting-state and task-performing individuals. The QPPs contribute strongly to connectivity in the default mode and task positive networks and to the degree of anti-correlation seen between the two networks. Many of the connections affected by QPPs are also disrupted during several neurological disorders. These findings help towards understanding the dynamic neural processes that give rise to functional connectivity in the brain and how they may be disrupted during disease.

Detection of neural light-scattering activity in vivo: optical transmittance studies in the rat brain

&NA; Optical studies of ex vivo brain slices where blood is absent show that neural activity is accompanied by significant intrinsic optical signals (IOS) related to activity‐dependent scattering changes in neural tissue. However, the neural scattering signals have been largely ignored in vivo in widely‐used IOS methods where absorption contrast from hemoglobin was employed. Changes in scattering were observed on a time scale of seconds in previous brain slice IOS studies, similar to the time scale for the hemodynamic response. Therefore, potential crosstalk between the scattering and absorption changes may not be ignored if they have comparable contributions to IOS. In vivo, the IOS changes linked to neural scattering have been elusive. To isolate neural scattering signals in vivo, we employed 2 implantable optodes for small‐separation (2 mm) transmission measurements of local brain tissue in anesthetized rats. This unique geometry enables us to separate neuronal activity‐related changes in neural tissue scattering from changes in blood absorption based upon the direction of the signal change. The changes in IOS scattering and absorption in response to up‐states of spontaneous neuronal activity in cortical or subcortical structures have strong correlation to local field potentials, but significantly different response latencies. We conclude that activity‐dependent neural tissue scattering in vivo may be an additional source of contrast for functional brain studies that provides complementary information to other optical or MR‐based systems that are sensitive to hemodynamic contrast. HighlightsInvestigated intrinsic optical signals (IOS) linked to local field potentials in the rat brain in vivo.First transmission measurement of IOS in localized brain areas.Transmittance geometry enables separation of IOS scattering from absorption.Time scales of seconds for both neuro‐scattering and hemoglobin absorption signals.Shorter latency of neuro‐scattering response relative to neurovascular response.