The intrinsic activity of the human brain, observed with resting-state fMRI (rsfMRI) and functional connectivity, exhibits macroscale spatial organization such as functional networks and gradients. ...Dynamic analysis techniques have shown that functional connectivity is a mere summary of time-varying patterns with distinct spatial and temporal characteristics. A better understanding of these patterns might provide insight into aspects of the brain's intrinsic activity that cannot be inferred by functional connectivity or the spatial maps derived from it, such as functional networks and gradients. Here, we describe three spatiotemporal patterns of coordinated activity across the whole brain obtained by averaging similar ~20-second-long segments of rsfMRI timeseries. In each of these patterns, activity propagates along a particular macroscale functional gradient, simultaneously across the cerebral cortex and in most other brain regions. In some regions, like the thalamus, the propagation suggests previously-undescribed gradients. The coordinated activity across areas is consistent with known tract-based connections, and nuanced differences in the timing of peak activity between regions point to plausible driving mechanisms. The magnitude of correlation within and particularly between functional networks is remarkably diminished when these patterns are regressed from the rsfMRI timeseries, a quantitative demonstration of their significant role in functional connectivity. Taken together, our results suggest that a few recurring patterns of propagating intrinsic activity along macroscale gradients give rise to and coordinate functional connections across the whole brain.
Dynamic network analysis based on resting-state magnetic resonance imaging (rsMRI) is a fairly new and potentially powerful tool for neuroscience and clinical research. Dynamic analysis can be ...sensitive to changes that occur in psychiatric or neurologic disorders and can detect variations related to performance on individual trials in healthy subjects. However, the appearance of time-varying connectivity can also arise in signals that share no temporal information, complicating the interpretation of dynamic functional connectivity studies. Researchers have begun utilizing simultaneous imaging and electrophysiological recording to elucidate the neural basis of the networks and their variability in animals and in humans. In this article, we review findings that link changes in electrically recorded brain states to changes in the networks obtained with rsMRI and discuss some of the challenges inherent in interpretation of these studies. The literature suggests that multiple brain processes may contribute to the dynamics observed, and we speculate that it may be possible to separate particular aspects of the rsMRI signal to enhance sensitivity to certain types of neural activity, providing new tools for basic neuroscience and clinical research.
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.
•Quasiperiodic patterns from individuals can be coarsely categorized into two types, and the type can be predicted by an individual’s global signal.•After global signal regression, QPPs become remarkably similar in their spatial extent of strong positive or negative correlation, strength and timing across individuals.•Motion and slow physiological fluctuations are not a major contributor to the QPPs.•QPPs are more similar for the same individuals scanned on different days than for different individuals.
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.
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.
►A novel pattern finding algorithm automatically detects reproducible spatiotemporal patterns of BOLD fluctuations previously observed in rats. ►Similar spatiotemporal patterns of BOLD fluctuations are also present in humans. ►These patterns are reproducible within and across subjects.
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.
Gradients in brain organization Bernhardt, Boris C.; Smallwood, Jonathan; Keilholz, Shella ...
NeuroImage,
05/2022, Volume:
251
Journal Article
Peer reviewed
Open access
In parallel, a mounting literature has shown evidence for similar gradients at the level of gene expression patterns (Burt et al., 2018; Goulas et al., 2019c; Seidlitz et al., 2019; Vogel et al., ...2020) as well as receptor architecture (Hansen et al., 2021), suggesting that gradients may offer a framework to interrogate the interplay between brain structure, metabolism, and function. ...by providing a continuous coordinate system to characterize topographic organization and structure-function relationships in humans and non-human animals (Buckner and Krienen, 2013; Fulcher et al., 2019; Huntenburg et al., 2018), the identification and analysis of brain gradients promises to advance theories and knowledge of brain evolution. A total of 36 articles (Fig. 1) cover (i) novel methods to study brain gradients and to evaluate their ability to capture inter-individual differences in brain organization, (ii) the ability of gradients to inform multiscale and multimodal anatomical investigations, also contextualizing gradient mapping with more established approaches to understand cortical arealization, (iii) the role of gradients in brain dynamics and cognition, and finally (iv) the utility of gradients as a framework to analyze and conceptualize brain evolution and development. (2020) present a virtual histology approach, relating different MRI measures with post mortem gene expression data.
•Variational autoencoder reveals characteristic spatiotemporal trajectories in rs-fMRI.•The trajectories are the building blocks for all spatiotemporal dynamics in rs-fMRI.•The trajectories were ...clustered into several groups based on spatial configurations.•The spatial groups agree with known networks and functional connectivity gradients.•The method reveals how activity flows along these spatial configuration of networks.
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.
The theory on the glymphatic convection mechanism of cerebrospinal fluid holds that cardiac pulsations in part pump cerebrospinal fluid from the peri-arterial spaces through the extracellular tissue ...into the peri-venous spaces facilitated by aquaporin water channels. Since cardiac pulses cannot be the sole mechanism of glymphatic propulsion, we searched for additional cerebrospinal fluid pulsations in the human brain with ultra-fast magnetic resonance encephalography. We detected three types of physiological mechanisms affecting cerebral cerebrospinal fluid pulsations: cardiac, respiratory, and very low frequency pulsations. The cardiac pulsations induce a negative magnetic resonance encephalography signal change in peri-arterial regions that extends centrifugally and covers the brain in ≈1 Hz cycles. The respiratory ≈0.3 Hz pulsations are centripetal periodical pulses that occur dominantly in peri-venous areas. The third type of pulsation was very low frequency (VLF 0.001–0.023 Hz) and low frequency (LF 0.023–0.73 Hz) waves that both propagate with unique spatiotemporal patterns. Our findings using critically sampled magnetic resonance encephalography open a new view into cerebral fluid dynamics. Since glymphatic system failure may precede protein accumulations in diseases such as Alzheimer's dementia, this methodological advance offers a novel approach to image brain fluid dynamics that potentially can enable early detection and intervention in neurodegenerative diseases.
The locus coeruleus (LC) has long been underappreciated for its role in the pathophysiology of Alzheimer's disease (AD), Parkinson's disease (PD), and other neurodegenerative disorders. While AD and ...PD are distinct in clinical presentation, both are characterized by prodromal protein aggregation in the LC, late-stage degeneration of the LC, and comorbid conditions indicative of LC dysfunction. Many of these early studies were limited to post-mortem histological techniques due to the LC's small size and location deep in the brainstem. Thus, there is a growing interest in utilizing
imaging of the LC as a predictor of preclinical neurodegenerative processes and biomarker of disease progression. Simultaneously, neuroimaging in animal models of neurodegenerative disease holds promise for identifying early alterations to LC circuits, but has thus far been underutilized. While still in its infancy, a handful of studies have reported effects of single gene mutations and pathology on LC function in disease using various neuroimaging techniques. Furthermore, combining imaging and optogenetics or chemogenetics allows for interrogation of network connectivity in response to changes in LC activity. The purpose of this article is twofold: (1) to review what magnetic resonance imaging (MRI) and positron emission tomography (PET) have revealed about LC dysfunction in neurodegenerative disease and its potential as a biomarker in humans, and (2) to explore how animal models can be used to test hypotheses derived from clinical data and establish a mechanistic framework to inform LC-focused therapeutic interventions to alleviate symptoms and impede disease progression.
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