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.

Rodent Whole-Brain fMRI Data Preprocessing Toolbox is a generally applicable and user-friendly fMRI preprocessing toolbox for whole-brain group datasets in rodents. Preprocessing of rodent fMRI data is challenging when using toolboxes designed for human data, so this toolbox contains procedures and parameters that are specific for mice and rats. This toolbox provides the standard preprocessing procedures for preprocessing rodent brains as described in (Chuang et al., 2018; Lee et al., 2019). It normalizes the group datasets to a standard template, and then extracts timeseries based on an atlas. Different from the existing preprocessing pipelines which are applicable only for a single animal model (e.g., either rats (Diao et al., 2021; Liu et al., 2020) or mice (Pallast et al., 2019)), this is the first software toolbox that is generalized for both mouse and rat group data. This is also the first software toolbox which allows a variety of combinations of preprocessing procedures and parameters that are specified by users depending on the applications. Moreover, a user-specified repressors file can be added for task pattern regressions in addition to the classical detrending, motion parameters, brain tissue or noise regressions, etc (see Figure 1).

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 dexmede tomidine. 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.

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 (30min, simulating induction and fMRI setup) and seven rats were anesthetized for a long period (3h, simulating surgical preparation). Following the initial anesthetic period, isoflurane was discontinued, and a dexmedetomidine bolus (0.025mg/kg) and continuous subcutaneous infusion (0.05mg/kg/h) were administered. Blood-oxygen-level dependent resting state imaging was performed every 30min from 0.75h post dexmedetomidine bolus until 5.75h 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 (0.05-0.3Hz) in both groups over the duration of resting state imaging. Greater low-band spectral power (0.05-0.15Hz) is present in the short isoflurane group for the first 2.75h, but the spectra become highly uniform at 3.25h. The emergence of a ~0.18Hz peak, beginning at the 3.75h 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 ~3.25h, 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.

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.

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.

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.

Functional magnetic resonance imaging (fMRI) is currently one of the most important neuroimaging methods in neuroscience. The image contrast in fMRI relies on the blood-oxygenation-level dependent (BOLD) signal, which indirectly reflects neural activity through neurovascular coupling. Because the mechanism that links the BOLD signal to neural activities involves multiple complicated processes, where neural activity, regional metabolism, hemodynamics, and the BOLD signal are all inter-connected, understanding the quantitative relationship between the BOLD signal and the underlying neural activities is crucial for interpreting fMRI data. Simultaneous local field potential (LFP) and fMRI recordings provide a method to study neurovascular coupling. There were a few studies that have shown non-linearities in stimulus related responses, but whether there is any non-linearity in LFP—BOLD relationship at rest has not been specifically quantified. In this study, we analyzed the simultaneous LFP and resting state-fMRI data acquired from rodents, and found that the relationship between LFP and BOLD is non-linear under isoflurane (ISO) anesthesia, but linear under dexmedetomidine (DMED) anesthesia. Subsequent analysis suggests that such non-linearity may come from the non-Gaussian distribution of LFP power and switching from LFP power to LFP amplitude can alleviate the problem to a degree. We also confirmed that, despite the non-linearity in the mean LFP—BOLD curve, the Pearson correlation between the two signals is relatively unaffected.

Resting state functional MRI (rs-fMRI) creates a rich four-dimensional data set that can be analyzed in a variety of ways. As more researchers come to view the brain as a complex dynamical system, tools are increasingly being drawn from other fields to characterize the complexity of the brain’s activity. However, given that the signal measured with rs-fMRI arises from the hemodynamic response to neural activity, the extent to which complexity metrics reflect neural complexity as compared to signal properties related to image quality remains unknown. To provide some insight into this question, correlation dimension, approximate entropy and Lyapunov exponent were calculated for different rs-fMRI scans from the same subject to examine their reliability. The metrics of complexity were then compared to several properties of the rs-fMRI signal from each brain area to determine if basic signal features could explain differences in the complexity metrics. Differences in complexity across brain areas were highly reliable and were closely linked to differences in the frequency profiles of the rs-fMRI signal. The spatial distributions of the complexity and frequency metrics suggest that they are both influenced by location-dependent signal properties that can obscure changes related to neural activity.