Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders

Functional brain networks demonstrate significant temporal variability and dynamic reconfiguration even in the resting state. Currently, most studies investigate temporal variability of brain networks at the scale of single (micro) or whole-brain (macro) connectivity. However, the mechanism underlying time-varying properties remains unclear, as the coupling between brain network variability and neural activity is not readily apparent when analysed at either micro or macroscales. We propose an intermediate 15 (meso) scale analysis and characterize temporal variability of the functional architecture associated with a particular region. This yields a topography of variability that reflects the whole-brain and, most importantly, creates an analytical framework to establish the fundamental relationship between variability of regional functional architecture and its neural activity or structural connectivity. We find that temporal variability reflects the dynamical reconfiguration of a brain region into distinct functional modules at different times and may be indicative of brain flexibility and adaptability. Primary and unimodal sensory-motor cortices demon- 20 strate low temporal variability, while transmodal areas, including heteromodal association areas and limbic system, demonstrate the high variability. In particular, regions with highest variability such as hippocampus/parahippocampus, inferior and middle temporal gyrus, olfactory gyrus and caudate are all related to learning, suggesting that the temporal variability may indicate the level of brain adaptability. With simultaneously recorded electroencephalography/functional magnetic resonance imaging and functional magnetic resonance imaging/diffusion tensor imaging data, we also find that variability of regional functional architec- 25 ture is modulated by local blood oxygen level-dependent activity and a-band oscillation, and is governed by the ratio of intra- to inter-community structural connectivity. Application of the mesoscale variability measure to multicentre datasets of three mental disorders and matched controls involving 1180 subjects reveals that those regions demonstrating extreme, i.e. highest/lowest variability in controls are most liable to change in mental disorders. Specifically, we draw attention to the identification of diametrically opposing patterns of variability changes between schizophrenia and attention deficit hyperactivity disorder/autism. 30 Regions of the default-mode network demonstrate lower variability in patients with schizophrenia, but high variability in patients with autism/attention deficit hyperactivity disorder, compared with respective controls. In contrast, subcortical regions, especially the thalamus, show higher variability in schizophrenia patients, but lower variability in patients with attention deficit hyperactivity disorder. The changes in variability of these regions are also closely related to symptom scores. Our work provides insights into the dynamic organization of the resting brain and how it changes in brain disorders. The nodal variability measure may also be 35 potentially useful as a predictor for learning and neural rehabilitation

Robust Reproducible Resting State Networks in the Awake Rodent Brain

Resting state networks (RSNs) have been studied extensively with functional MRI in humans in health and disease to reflect brain function in the un-stimulated state as well as reveal how the brain is altered with disease. Rodent models of disease have been used comprehensively to understand the biology of the disease as well as in the development of new therapies. RSN reported studies in rodents, however, are few, and most studies are performed with anesthetized rodents that might alter networks and differ from their non-anesthetized state. Acquiring RSN data in the awake rodent avoids the issues of anesthesia effects on brain function. Using high field fMRI we determined RSNs in awake rats using an independent component analysis (ICA) approach, however, ICA analysis can produce a large number of components, some with biological relevance (networks). We further have applied a novel method to determine networks that are robust and reproducible among all the components found with ICA. This analysis indicates that 7 networks are robust and reproducible in the rat and their putative role is discussed

Axonal Varicosity Density as an Index of Local Neuronal Interactions

Diffuse transmission is an important non-synaptic communication mode in the cerebral neocortex, in which neurotransmitters released from en passant varicosities interact with surrounding cells. In a previous study we have shown that the cholinergic axonal segments which were in the microproximity with dopaminergic fibers possessed a greater density of en passant varicosities compared to more distant segments, suggesting an activity-dependent level of en passant varicosities in the axonal zone of interaction. To further evaluate this plastic relationship, the density of cholinergic varicosities was quantified on fiber segments within the microproximity of activated or non-activated pyramidal cells of the prefrontal cortex (mPFC). Repetitive 14 days patterned visual stimulation paired with an electrical stimulation of the cholinergic fibers projecting to the mPFC from the HDB was performed to induce persistent axonal plastic changes. The c-Fos early gene immunoreactivity was used as a neuronal activity marker of layer V pyramidal cells, labelled with anti-glutamate transporter EAAC1. Cholinergic fibers were labeled with anti-ChAT (choline acetyltransferase) immunostaining. The density of ChAT+ varicosities on and the length of fiber segments within the 3 µm microproximity of c-Fos positive/negative pyramidal cells were evaluated on confocal images. More than 50% of the pyramidal cells in the mPFC were c-Fos immunoreactive. Density of ChAT+ varicosities was significantly increased within 3 µm vicinity of activated pyramidal cells (0.50±0.01 per µm of ChAT+ fiber length) compared to non-activated cells in this group (0.34±0.001; p≤0.05) or control rats (0.32±0.02; p≤0.05). Different types of stimulation (visual, HDB or visual/HDB) induced similar increase of the density of ChAT+ varicosities within microproximity of activated pyramidal cells. This study demonstrated at the subcellular level an activity-dependent enrichment of ChAT+ varicosities in the axonal zone of interaction with other neuronal elements

THE EFFECTS OF MODERATE PRENATAL ALCOHOL EXPOSURE ON RESTING STATE FUNCTIONAL CONNECTIVITY AND ITS RELATIONSHIP TO BEHAVIOR

It is well established that heavy ethanol exposure during prenatal brain development leads to drastic morphological, cognitive and behavioral consequences. In contrast to heavy exposure, the effects of moderate prenatal alcohol exposure (PAE) are less severe yet continue well into adulthood. Animal models of PAE have been of immense importance to researchers for their ability to control for extraneous variables such as socio economic status, age, nutrition, stress, and co-exposure to other substances. Studies of moderate PAE have investigated several discrete brain regions, neurotransmitter systems, and animal behaviors. However, the effects of moderate PAE on resting state functional network connectivity (FNC) have not been well characterized. Moreover, the relationship between PAE, functional connectivity, and animal behavior has not been previously investigated. The present study determined whether moderate PAE alters whole brain FNC. Furthermore, the relationship between hippocampal FNC and behavior was investigated. Long-Evans rats were exposed to 5% ethanol or saccharin throughout the entire gestational period. In adulthood, rats were anesthetized (1.0-2.3% isoflurane) and BOLD signals were acquired during a 10 min echoplanar imaging sequence in a 4.7T Bruker Biospin scanner. Following motion correction, spatial normalization and smoothing, spatial group independent component analysis (gICA) was performed using the Infomax algorithm implemented in the GIFT toolbox. A total of 17 non-artifactual components were retained for analysis of spectral power and connectivity. Components were observed in cortical, hippocampal, striatal, thalamic, and cerebellar structures. Cortical, hippocampal, and midbrain regions frequently stood out as areas that displayed more significant prenatal treatment differences. PAE animals displayed reductions in low frequency spectral power for several components. PAE animals often displayed a loss of strength in connectivity. Furthermore, analyses of social behaviors with hippocampal related connectivity showed that cortex-to-hippocampus (Cx-H) connectivity is most sensitive to alcohol exposure. PAE females and males displayed more negative correlations compared to their respective saccharin comparison groups. The results indicated that moderate fetal ethanol exposure can have long-lasting consequences on functional connectivity and that hippocampal-containing connectivity was linked to alterations in social behavior

Identifying Respiration-Related Aliasing Artifacts in the Rodent Resting-State fMRI

Resting-state functional magnetic resonance imaging (rs-fMRI) combined with optogenetics and electrophysiological/calcium recordings in animal models is becoming a popular platform to investigate brain dynamics under specific neurological states. Physiological noise originating from the cardiac and respiration signal is the dominant interference in human rs-fMRI and extensive efforts have been made to reduce these artifacts from the human data. In animal fMRI studies, physiological noise sources including the respiratory and cardiorespiratory artifacts to the rs-fMRI signal fluctuation have typically been less investigated. In this article, we demonstrate evidence of aliasing effects into the low-frequency rs-fMRI signal fluctuation mainly due to respiration-induced B0 offsets in anesthetized rats. This aliased signal was examined by systematically altering the fMRI sampling rate, i.e., the time of repetition (TR), in free-breathing conditions and by adjusting the rate of ventilation. Anesthetized rats under ventilation showed a significantly narrower frequency bandwidth of the aliasing effect than free-breathing animals. It was found that the aliasing effect could be further reduced in ventilated animals with a muscle relaxant. This work elucidates the respiration-related aliasing effects on the rs-fMRI signal fluctuation from anesthetized rats, indicating non-negligible physiological noise needed to be taken care of in both awake and anesthetized animal rs-fMRI studies

Sampling Rate Effects on Resting State fMRI Metrics

Low image sampling rates used in resting state functional magnetic resonance imaging (rs-fMRI) may cause aliasing of the cardiorespiratory pulsations over the very low frequency (VLF) BOLD signal fluctuations which reflects to functional connectivity (FC). In this study, we examine the effect of sampling rate on currently used rs-fMRI FC metrics. Ultra-fast fMRI magnetic resonance encephalography (MREG) data, sampled with TR 0.1 s, was downsampled to different subsampled repetition times (sTR, range 0.3–3 s) for comparisons. Echo planar k-space sampling (TR 2.15 s) and interleaved slice collection schemes were also compared against the 3D single shot trajectory at 2.2 s sTR. The quantified connectivity metrics included stationary spatial, time, and frequency domains, as well as dynamic analyses. Time domain methods included analyses of seed-based functional connectivity, regional homogeneity (ReHo), coefficient of variation, and spatial domain group level probabilistic independent component analysis (ICA). In frequency domain analyses, we examined fractional and amplitude of low frequency fluctuations. Aliasing effects were spatially and spectrally analyzed by comparing VLF (0.01–0.1 Hz), respiratory (0.12–0.35 Hz) and cardiac power (0.9–1.3 Hz) FFT maps at different sTRs. Quasi-periodic pattern (QPP) of VLF events were analyzed for effects on dynamic FC methods. The results in conventional time and spatial domain analyses remained virtually unchanged by the different sampling rates. In frequency domain, the aliasing occurred mainly in higher sTR (1–2 s) where cardiac power aliases over respiratory power. The VLF power maps suffered minimally from increasing sTRs. Interleaved data reconstruction induced lower ReHo compared to 3D sampling (p < 0.001). Gradient recalled echo-planar imaging (EPI BOLD) data produced both better and worse metrics. In QPP analyses, the repeatability of the VLF pulse detection becomes linearly reduced with increasing sTR. In conclusion, the conventional resting state metrics (e.g., FC, ICA) were not markedly affected by different TRs (0.1–3 s). However, cardiorespiratory signals showed strongest aliasing in central brain regions in sTR 1–2 s. Pulsatile QPP and other dynamic analyses benefit linearly from short TR scanning

The Effects of Chronic Sleep Deprivation on Sustained Attention: A Study of Brain Dynamic Functional Connectivity

It is estimated that about 35-40% of adults in the U.S. suffer from insufficient sleep. Chronic sleep deprivation has become a prevalent phenomenon because of contemporary lifestyle and work-related factors. Sleep deprivation can reduce the capabilities and efficiency of attentional performance by impairing perception, increasing effort to maintain concentration, as well as introducing vision disturbance. Thus, it is important to understand the neural mechanisms behind how chronic sleep deprivation impairs sustained attention. In recent years, more attention has been paid to the study of the integration between anatomically distributed and functionally connected brain regions. Functional connectivity has been widely used to characterize brain functional integration, which measures the statistical dependency between neurophysiological events of the human brain. Further, evidence from recent studies has shown the non-stationary nature of brain functional connectivity, which may reveal more information about the human brain. Thus, the objective of this thesis is to investigate the effects of chronic sleep deprivation on sustained attention from the perspective of dynamic functional connectivity. A modified spatial cueing paradigm was used to assess human sustained attention in rested wakefulness and chronic sleep deprivation conditions. Partial least squares approach was applied to distinguish brain functional connectivity for the experimental conditions. With the integration of a sliding-window approach, dynamic patterns of brain functional connectivity were identified in two experimental conditions. The brain was modeled as a series of dynamic functional networks in each experimental condition. Graph theoretic analysis was performed to investigate the dynamic properties of brain functional networks, using network measures of clustering coefficient and characteristics path length. In the chronic sleep deprivation condition, a compensation mechanism between highly clustered organization and ineffective adaptability of brain functional networks was observed. Specifically, a highly clustered organization of brain functional networks was illustrated with a large clustering coefficient. This organization suggested that brain utilizes more connections to maintain attention in the chronic sleep deprivation condition. A smaller impact of clustering coefficient variation on characteristics path lengths indicated an ineffective adaptability of brain functional networks in the chronic sleep deprivation condition. In the rested wakefulness condition, brain functional networks showed the small-world topology in general, with the average small-world topology index larger than one. Small-world topology was identified as an optimal network structure with the balance between local information processing and global integration. Given the fluctuating values of the index over time, small-world brain networks were observed in most cases, indicating an effective adaptability of the human brain to maintain the dominance of small-world networks in the rested wakefulness condition. On the contrary, given that the average small-world topology index was smaller than one, brain functional networks generally exhibited random network structure. From the perspective of dynamic functional networks, even though there were few cases showing small-world brain networks, brain functional networks failed to maintain the dominance of small-world topology in the chronic sleep deprivation condition. In conclusion, to the best of our knowledge this thesis was the first to investigate the effects of chronic sleep deprivation on sustained attention from the perspective of dynamic brain functional connectivity. A compensation mechanism between highly clustered organization and ineffective adaptability of brain functional networks was observed in the chronic sleep deprivation condition. Furthermore, chronic sleep deprivation impaired sustained attention by reducing the effectiveness of brain functional networks\u27 adaptability, resulting in the disrupted dominance of small-world brain networks

On Arousal and the Internal Regulation of Brain Function: Theory and Evidence across Modalities and Species

The brain is an organ. It is subject to the same physiological regulatory processes that engage the rest of the body’s organs, sculpted over hundreds of millions of years to sustain life so effectively. The central message of this thesis is that the holistic functioning of the brain, rather than operating at some level above or independent from these systemic regulatory processes, is deeply related to them. In short, as our limited attention spans might suggest: brain function is internally regulated. I propose that this internal regulation is a primary function of intrinsic brain activity. Chapter 2 provides a theoretical treatment of this issue, recasting intrinsic activity as an internal regulatory process operating on the brain’s temporal “states” and spatial “networks”. After establishing this framework, Chapters 3 and 4 provide tests of specific predictions. Thus, Chapter 3 confirms, in humans and macaque monkeys, the presence of topographically organized traveling waves occurring in synchrony with ongoing arousal fluctuations, with propagation occurring in parallel within the neocortex, striatum, thalamus, and cerebellum. This process is argued to provide a heretofore lacking physiological account of “resting-state functional connectivity” and related phenomenology. Chapter 4 extends this observation by demonstrating a continuous and tightly coordinated temporal evolution of brain, body, and behavioral states along a latent arousal cycle. Across multiple recording techniques and species, this cyclic trajectory is shown to be coupled to the traveling wave process described in Chapter 3, thus providing a parsimonious and integrative account of intrinsic brain activity and its spatiotemporal dynamics. Taken together, this thesis argues for the existence of an intrinsic regulatory process for global brain function

The (un)conscious mouse as a model for human brain functions: key principles of anesthesia and their impact on translational neuroimaging

In recent years, technical and procedural advances have brought functional magnetic resonance imaging (fMRI) to the field of murine neuroscience. Due to its unique capacity to measure functional activity non-invasively, across the entire brain, fMRI allows for the direct comparison of large-scale murine and human brain functions. This opens an avenue for bidirectional translational strategies to address fundamental questions ranging from neurological disorders to the nature of consciousness. The key challenges of murine fMRI are: (1) to generate and maintain functional brain states that approximate those of calm and relaxed human volunteers, while (2) preserving neurovascular coupling and physiological baseline conditions. Low-dose anesthetic protocols are commonly applied in murine functional brain studies to prevent stress and facilitate a calm and relaxed condition among animals. Yet, current mono-anesthesia has been shown to impair neural transmission and hemodynamic integrity. By linking the current state of murine electrophysiology, Ca(2+) imaging and fMRI of anesthetic effects to findings from human studies, this systematic review proposes general principles to design, apply and monitor anesthetic protocols in a more sophisticated way. The further development of balanced multimodal anesthesia, combining two or more drugs with complementary modes of action helps to shape and maintain specific brain states and relevant aspects of murine physiology. Functional connectivity and its dynamic repertoire as assessed by fMRI can be used to make inferences about cortical states and provide additional information about whole-brain functional dynamics. Based on this, a simple and comprehensive functional neurosignature pattern can be determined for use in defining brain states and anesthetic depth in rest and in response to stimuli. Such a signature can be evaluated and shared between labs to indicate the brain state of a mouse during experiments, an important step toward translating findings across species

Quasi-periodic patterns of brain intrinsic activity coordinate the functional connections in humans

The brain is a complex self-organizing biophysical system and intrinsically very active. How such intrinsic activity organizes the brain in humans is widely being studied during resting-state using functional magnetic resonance imaging (rsfMRI) and the functional connectivity (FC) metric. FC, calculated as the Pearson correlation between rsfMRI timeseries from different brain areas, indicates coherent activity on average over time, and can reflect some spatial aspects of the brain’s intrinsic organization. For example, based on the FC profile of each area, the cerebral cortex can be parcellated into a few resting-state networks (RSNs) or exhibit a few functional connectivity gradients (FCGs). Brain is a complex system and exhibits varied dynamic spatiotemporal regimes of coherent activity, which are still poorly understood. A subset of such regimes should be giving rise to FC, yet they might entail significantly insightful aspects about the brain’s self-organizing processes, which cannot be captured by FC. Among such dynamic regimes is the quasi-periodic pattern (QPP), obtained by identifying and averaging similar ~20s-long segments of rsfMRI timeseries. QPP involves a cycle of activation and deactivation of different areas with different timings, such that the overall activity within QPP resembles RSNs and FCGs, suggesting QPP might be contributing to FC. To robustly detect multiple QPPs, method improvements were implemented and three primary QPPs were thoroughly characterized. Within these QPPs activity propagates along the functional gradients at the cerebral cortex and most subcortical regions, in a well-coordinated way, because of the consistencies and synchronies across all brain regions which reasonably accord with the consensus on the structural connections. Nuanced timing differences between regions and the closed flow of activity throughout the brain suggest drivers for these patterns. When three QPPs are removed from rsfMRI timeseries, FC within and particularly between RSNs remarkably reduces, illustrating their dominant contribution. Together, our results suggest a few recurring spatiotemporal patterns of intrinsic activity might be dominantly coordinating the functional connections across the whole brain and serving self-organization. These intrinsic patterns possibly interact with the external tasks, affecting performance, or might provide more sensitive biomarkers in certain disorders and diseases.Ph.D