Modeling resting-state functional networks when the cortex falls asleep: local and global changes

Generative whole-brain dynamics models from healthy subjects predict functional alterations in stroke at the level of individual patients

Computational whole-brain models describe the resting activity of each brain region based on a local model, inter-regional functional interactions, and a structural connectome that specifies the strength of inter-regional connections. Strokes damage the healthy structural connectome that forms the backbone of these models and produce large alterations in inter-regional functional interactions. These interactions are typically measured by correlating the time series of the activity between two brain regions in a process, called resting functional connectivity. We show that adding information about the structural disconnections produced by a patient's lesion to a whole-brain model previously trained on structural and functional data from a large cohort of healthy subjects enables the prediction of the resting functional connectivity of the patient and fits the model directly to the patient's data (Pearson correlation = 0.37; mean square error = 0.005). Furthermore, the model dynamics reproduce functional connectivity-based measures that are typically abnormal in stroke patients and measures that specifically isolate these abnormalities. Therefore, although whole-brain models typically involve a large number of free parameters, the results show that, even after fixing those parameters, the model reproduces results from a population very different than that on which the model was trained. In addition to validating the model, these results show that the model mechanistically captures the relationships between the anatomical structure and the functional activity of the human brain.

Frequency-dependent connectivity in large-scale resting-state brain networks during sleep

Functional connectivity (FC) during sleep has been shown to break down as non-rapid eye movement (NREM) sleep deepens before returning to a state closer to wakefulness during rapid eye movement (REM) sleep. However, the specific spatial and temporal signatures of these fluctuations in connectivity patterns remain poorly understood. This study aimed to investigate how frequency-dependent network-level FC fluctuates during nocturnal sleep in healthy young adults using high-density electroencephalography (hdEEG). Specifically, we examined source-localized FC in resting-state networks during NREM2, NREM3 and REM sleep (sleep stages scored using a semi-automatic procedure) in the first three sleep cycles of 29 participants. Our results showed that FC within and between all resting-state networks decreased from NREM2 to NREM3 sleep in multiple frequency bands and all sleep cycles. The data also highlighted a complex modulation of connectivity patterns during the transition to REM sleep whereby delta and sigma bands hosted a persistence of the connectivity breakdown in all networks. In contrast, a reconnection occurred in the default mode and the attentional networks in frequency bands characterizing their organization during wake (i.e., alpha and beta bands, respectively). Finally, all network pairs (except the visual network) showed higher gamma-band FC during REM sleep in cycle three compared to earlier sleep cycles. Altogether, our results unravel the spatial and temporal characteristics of the well-known breakdown in connectivity observed as NREM sleep deepens. They also illustrate a complex pattern of connectivity during REM sleep that is consistent with network- and frequency-specific breakdown and reconnection processes.

Influence of vigilance-related arousal on brain dynamics: Potentials of new approaches

Growing research has focused on how mesoscopic activity in the brain develops over time and space. Recent influential studies using functional imaging have characterized brain dynamics in terms of the spread of activation across the brain following a unimodal to transmodal axis. In parallel, a number of studies have assessed changes of brain connectivity in terms of vigilance-linked arousal. Here I offer a view on how these two lines of research can lead to a deeper understanding of how arousal shapes the brain's dynamic behavior. This knowledge could have great impact on the investigation of mental disease.

Whole-brain modeling explains the context-dependent effects of cholinergic neuromodulation

Integration and segregation are two fundamental principles of brain organization. The brain manages the transitions and balance between different functional segregated or integrated states through neuromodulatory systems. Recently, computational and experimental studies suggest a pro-segregation effect of cholinergic neuromodulation. Here, we studied the effects of the cholinergic system on brain functional connectivity using both empirical fMRI data and computational modeling. First, we analyzed the effects of nicotine on functional connectivity and network topology in healthy subjects during resting-state conditions and during an attentional task. Then, we employed a whole-brain neural mass model interconnected using a human connectome to simulate the effects of nicotine and investigate causal mechanisms for these changes. The drug effect was modeled decreasing both the global coupling and local feedback inhibition parameters, consistent with the known cellular effects of acetylcholine. We found that nicotine incremented functional segregation in both empirical and simulated data, and the effects are context-dependent: observed during the task, but not in the resting state. In-task performance correlates with functional segregation, establishing a link between functional network topology and behavior. Furthermore, we found in the empirical data that the regional density of the nicotinic acetylcholine α₄β₂ correlates with the decrease in functional nodal strength by nicotine during the task. Our results confirm that cholinergic neuromodulation promotes functional segregation in a context-dependent fashion, and suggest that this segregation is suited for simple visual-attentional tasks.

Altered dynamic amplitude of low-frequency fluctuation between bipolar type I and type II in the depressive state

BACKGROUND

Bipolar disorder is a chronic and highly recurrent mental disorder that can be classified as bipolar type I (BD I) and bipolar type II (BD II). BD II is sometimes taken as a milder form of BD I or even doubted as an independent subtype. However, the fact that symptoms and severity differ in patients with BD I and BD II suggests different pathophysiologies and underlying neurobiological mechanisms. In this study, we aimed to explore the shared and unique functional abnormalities between subtypes.

METHODS

The dynamic amplitude of low-frequency fluctuation (dALFF) was performed to compare 31 patients with BD I, 32 with BD II, and 79 healthy controls (HCs). Global dALFF was calculated using sliding-window analysis. Group differences in dALFF among the 3 groups were compared using analysis of covariance (ANCOVA), with covariates of age, sex, years of education, and mean FD, and Bonferroni correction was applied for post hoc analysis. Pearson and Spearman's correlations were conducted between clusters with significant differences and clinical features in the BD I and BD II groups, after which false error rate (FDR) was used for correction.

RESULTS

We found a significant decrease in dALFF values in BD patients compared with HCs in the following brain regions: the bilateral-side inferior frontal gyrus (including the triangular, orbital, and opercular parts), inferior temporal gyrus, the medial part of the superior frontal gyrus, middle frontal gyrus, anterior cingulum, insula gyrus, lingual gyrus, calcarine gyrus, precuneus gyrus, cuneus gyrus, left-side precentral gyrus, postcentral gyrus, inferior parietal gyrus, superior temporal pole gyrus, middle temporal gyrus, middle occipital gyrus, superior occipital gyrus and right-side fusiform gyrus, parahippocampal gyrus, hippocampus, middle cingulum, orbital part of the medial frontal gyrus and superior frontal gyrus. Unique alterations in BD I were observed in the right-side supramarginal gyrus and postcentral gyrus. In addition, dALFF values in BD II were significantly higher than those in BD I in the right superior temporal gyrus and middle temporal gyrus. The variables of dALFF correlated with clinical characteristics differently according to the subtypes, but no correlations survived after FDR correction.

LIMITATIONS

Our study was cross-sectional. Most of our patients were on medication, and the sample was limited.

CONCLUSIONS

Our findings demonstrated neurobiological characteristics of BD subtypes, providing evidence for BD II as an independent existence, which could be the underlying explanation for the specific symptoms and/or severity and point to potential biomarkers for the differential diagnosis of bipolar subtypes.

The effect of acquisition duration on cerebral blood flow-based resting-state functional connectivity

Resting-state functional connectivity (rs-FC) is widely used to examine the functional architecture of the brain, and the blood-oxygenation-level-dependent (BOLD) signal is often utilized for determining rs-FC. However, the BOLD signal is susceptible to various factors that have less influence on the cerebral blood flow (CBF). Therefore, CBF could comprise an alternative for determining rs-FC. Since acquisition duration is one of the essential parameters for obtaining reliable rs-FC, we investigated the effect of acquisition duration on CBF-based rs-FC to examine the reliability of CBF-based rs-FC. Nineteen participants underwent CBF scanning for a total duration of 50 min. Variance of CBF-based rs-FC within the whole brain and 13 large-scale brain networks at various acquisition durations was compared to that with a 50-min duration using the Levene's test. Variance of CBF-based rs-FC at any durations did not differ from that at a 50-min duration (p > .05). Regarding variance of rs-FC within each large-scale brain network, the acquisition duration required to obtain reliable estimates of CBF-based rs-FC was shorter than 10 min and varied across large-scale brain networks. Altogether, an acquisition duration of at least 10 min is required to obtain reliable CBF-based rs-FC. These results indicate that CBF-based resting-state functional magnetic resonance imaging (rs-fMRI) with more than 10 min of total acquisition duration could be an alternative method to BOLD-based rs-fMRI to obtain reliable rs-FC.

Slow waves form expanding, memory-rich mesostates steered by local excitability in fading anesthesia

In the arousal process, the brain restores its integrative activity from the synchronized state of slow wave activity (SWA). The mechanisms underpinning this state transition remain, however, to be elucidated. Here we simultaneously probed neuronal assemblies throughout the whole cortex with micro-electrocorticographic recordings in mice. We investigated the progressive shaping of propagating SWA at different levels of isoflurane. We found a form of memory of the wavefront shapes at deep anesthesia, tightly alternating posterior-anterior-posterior patterns. At low isoflurane, metastable patterns propagated in more directions, reflecting an increased complexity. The wandering across these mesostates progressively increased its randomness, as predicted by simulations of a network of spiking neurons, and confirmed in our experimental data. The complexity increase is explained by the elevated excitability of local assemblies with no modifications of the network connectivity. These results shed new light on the functional reorganization of the cortical network as anesthesia fades out.

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Complexity of isoflurane-induced slow waves reliably determines anesthesia level

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In deep anesthesia, the propagation strictly alternates between front-back-front patterns

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In light anesthesia, there is a continuum of directions and faster propagation

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Local excitability underpins the cortical reorganization in fading anesthesia

Spatiotemporal Patterns of Adaptation-Induced Slow Oscillations in a Whole-Brain Model of Slow-Wave Sleep

During slow-wave sleep, the brain is in a self-organized regime in which slow oscillations (SOs) between up- and down-states travel across the cortex. While an isolated piece of cortex can produce SOs, the brain-wide propagation of these oscillations are thought to be mediated by the long-range axonal connections. We address the mechanism of how SOs emerge and recruit large parts of the brain using a whole-brain model constructed from empirical connectivity data in which SOs are induced independently in each brain area by a local adaptation mechanism. Using an evolutionary optimization approach, good fits to human resting-state fMRI data and sleep EEG data are found at values of the adaptation strength close to a bifurcation where the model produces a balance between local and global SOs with realistic spatiotemporal statistics. Local oscillations are more frequent, last shorter, and have a lower amplitude. Global oscillations spread as waves of silence across the undirected brain graph, traveling from anterior to posterior regions. These traveling waves are caused by heterogeneities in the brain network in which the connection strengths between brain areas determine which areas transition to a down-state first, and thus initiate traveling waves across the cortex. Our results demonstrate the utility of whole-brain models for explaining the origin of large-scale cortical oscillations and how they are shaped by the connectome.

On the influence of prior information evaluated by fully Bayesian criteria in a personalized whole-brain model of epilepsy spread

Individualized anatomical information has been used as prior knowledge in Bayesian inference paradigms of whole-brain network models. However, the actual sensitivity to such personalized information in priors is still unknown. In this study, we introduce the use of fully Bayesian information criteria and leave-one-out cross-validation technique on the subject-specific information to assess different epileptogenicity hypotheses regarding the location of pathological brain areas based on a priori knowledge from dynamical system properties. The Bayesian Virtual Epileptic Patient (BVEP) model, which relies on the fusion of structural data of individuals, a generative model of epileptiform discharges, and a self-tuning Monte Carlo sampling algorithm, is used to infer the spatial map of epileptogenicity across different brain areas. Our results indicate that measuring the out-of-sample prediction accuracy of the BVEP model with informative priors enables reliable and efficient evaluation of potential hypotheses regarding the degree of epileptogenicity across different brain regions. In contrast, while using uninformative priors, the information criteria are unable to provide strong evidence about the epileptogenicity of brain areas. We also show that the fully Bayesian criteria correctly assess different hypotheses about both structural and functional components of whole-brain models that differ across individuals. The fully Bayesian information-theory based approach used in this study suggests a patient-specific strategy for epileptogenicity hypothesis testing in generative brain network models of epilepsy to improve surgical outcomes.

Attractor competition enriches cortical dynamics during awakening from anesthesia

Slow oscillations (≲ 1 Hz), a hallmark of slow-wave sleep and deep anesthesia across species, arise from spatiotemporal patterns of activity whose complexity increases as wakefulness is approached and cognitive functions emerge. The arousal process constitutes an open window to the unknown mechanisms underlying the emergence of such dynamical richness in awake cortical networks. Here, we investigate the changes in network dynamics as anesthesia fades out in the rat visual cortex. Starting from deep anesthesia, slow oscillations gradually increase their frequency, eventually expressing maximum regularity. This stage is followed by the abrupt onset of an infra-slow (~0.2 Hz) alternation between sleep-like oscillations and activated states. A population rate model reproduces this transition driven by an increased excitability that brings it to periodically cross a critical point. Based on our model, dynamical richness emerges as a competition between two metastable attractor states, a conclusion strongly supported by the data.

Cortex-Wide Dynamics of Intrinsic Electrical Activities: Propagating Waves and Their Interactions

Cortical circuits generate patterned activities that reflect intrinsic brain dynamics that lay the foundation for any, including stimuli-evoked, cognition and behavior. However, the spatiotemporal organization properties and principles of this intrinsic activity have only been partially elucidated because of previous poor resolution of experimental data and limited analysis methods. Here we investigated continuous wave patterns in the 0.5-4 Hz (delta band) frequency range on data from high-spatiotemporal resolution optical voltage imaging of the upper cortical layers in anesthetized mice. Waves of population activities propagate in heterogeneous directions to coordinate neuronal activities between different brain regions. The complex wave patterns show characteristics of both stereotypy and variety. The location and type of wave patterns determine the dynamical evolution when different waves interact with each other. Local wave patterns of source, sink, or saddle emerge at preferred spatial locations. Specifically, "source" patterns are predominantly found in cortical regions with low multimodal hierarchy such as the primary somatosensory cortex. Our findings reveal principles that govern the spatiotemporal dynamics of spontaneous cortical activities and associate them with the structural architecture across the cortex.SIGNIFICANCE STATEMENT Intrinsic brain activities, as opposed to external stimulus-evoked responses, have increasingly gained attention, but it remains unclear how these intrinsic activities are spatiotemporally organized at the cortex-wide scale. By taking advantage of the high spatiotemporal resolution of optical voltage imaging, we identified five wave pattern types, and revealed the organization properties of different wave patterns and the dynamical mechanisms when they interact with each other. Moreover, we found a relationship between the emergence probability of local wave patterns and the multimodal structure hierarchy across cortical areas. Our findings reveal the principles of spatiotemporal wave dynamics of spontaneous activities and associate them with the underlying hierarchical architecture across the cortex.

Functional thalamocortical connectivity at term equivalent age and outcome at 2 years in infants born preterm

Infants born preterm are at high risk of long-term motor and neurocognitive deficits. In the majority of these infants structural MRI at the time of normal birth does not predict motor or cognitive outcomes accurately, and many infants without apparent brain lesions later develop motor and cognitive deficits. Thalamocortical connections are known to be necessary for normal brain function; they develop during late fetal life and are vulnerable to perinatal adversity. This study addressed the hypothesis that abnormalities in the functional connectivity between cortex and thalamus underlie neurocognitive impairments seen after preterm birth. Using resting state functional connectivity magnetic resonance imaging (fMRI) in a group of 102 very preterm infants without major focal brain lesions, we used partial correlations between thalamus and functionally-derived cortical areas to determine significant connectivity between cortical areas and thalamus, and correlated the parameter estimates of these connections with standardised neurocognitive assessments in each infant at 20 months of age. Pre-motor association cortex connectivity to thalamus correlates with motor function, while connectivity between primary sensory-motor cortex and thalamus correlates with cognitive scores. These results demonstrate the importance and vulnerability of functional thalamocortical connectivity development in the perinatal period for later neurocognitive functioning.

Brain functional connectivity during the first day of coma reflects long-term outcome

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Coma patients show different connectivity patterns depending on long-term outcome.

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Time-variance of functional connectivity is an early prognostic marker for coma patients.

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Connectivity patterns observed in chronic patients may develop early after coma onset.

Objective

In patients with disorders of consciousness (DOC), properties of functional brain networks at rest are informative of the degree of consciousness impairment and of long-term outcome. Here we investigate whether connectivity differences between patients with favorable and unfavorable outcome are already present within 24 h of coma onset.

Methods

We prospectively recorded 63-channel electroencephalography (EEG) at rest during the first day of coma after cardiac arrest. We analyzed 98 adults, of whom 57 survived beyond unresponsive wakefulness. Functional connectivity was estimated by computing the ‘debiased weighted phase lag index’ over epochs of five seconds duration. We evaluated the network’s topological features, including clustering coefficient, path length, modularity and participation coefficient and computed their variance over time. Finally, we estimated the predictive value of these topological features for patients’ outcomes by splitting the patient sample in training and test datasets.

Results

Group-level analysis revealed lower clustering coefficient, higher modularity and path length variance in patients with favorable compared to those with unfavorable outcomes (p < 0.01). Within all features, the path length variance in the network provided the best positive predictive value (PPV) for favorable outcome and specificity for unfavorable outcome in the test dataset (PPV: 0.83, p < 0.01; specificity: 0.86, p < 0.01) with above-chance negative predictive value and accuracy. Of note, the exclusion of patients with epileptiform activity (20 in total) eliminates all false positive predictions (n = 6) for path length variance.

Interpretation

Topological features of functional connectivity differ as a function of long-term outcome in patients on the first day of coma. These differences are not interpretable in terms of consciousness levels as all patients were in a deep unconscious state. The time variance of path length is informative of comatose patients’ outcome, as patients with favorable outcome exhibit a richer repertoire of path length than those with unfavorable outcomes.

Beyond the Epileptic Focus: Functional Epileptic Networks in Focal Epilepsy

Focal epilepsy can be conceptualized as a network disorder, and the functional epileptic network can be described as a complex system of multiple brain areas that interact dynamically to generate epileptic activity. However, we still do not fully understand the functional architecture of epileptic networks. We studied a cohort of 21 patients with extratemporal focal epilepsy. We used independent component analysis of functional magnetic resonance imaging (fMRI) data. In order to identify the epilepsy-related components, we examined the general linear model-derived electroencephalography-fMRI (EEG–fMRI) time courses associated with interictal epileptic activity as intrinsic hemodynamic epileptic biomarkers. Independent component analysis revealed components related to the epileptic time courses in all 21 patients. Each epilepsy-related component described a network of spatially distributed brain areas that corresponded to the specific epileptic network in each patient. We also provided evidence for the interaction between the epileptic activity generated at the epileptic network and the physiological resting state networks. Our findings suggest that independent component analysis, guided by EEG–fMRI epileptic time courses, have the potential to define the functional architecture of the epileptic network in a noninvasive way. These data could be useful in planning invasive EEG electrode placement, guiding surgical resections, and more effective therapeutic interventions.

Functional connectivity dynamics slow with descent from wakefulness to sleep

The transition from wakefulness to sleep is accompanied by widespread changes in brain functioning. Here we investigate the implications of this transition for interregional functional connectivity and their dynamic changes over time. Simultaneous EEG-fMRI was used to measure brain functional activity of 21 healthy participants as they transitioned from wakefulness into sleep. fMRI volumes were independent component analysis (ICA)-decomposed, yielding 42 neurophysiological sources. Static functional connectivity (FC) was estimated from independent component time courses. A sliding window method and k-means clustering (k = 7, L2-norm) were used to estimate dynamic FC. Static FC in Wake and Stage-2 Sleep (NREM2) were largely similar. By contrast, FC dynamics across wake and sleep differed, with transitions between FC states occurring more frequently during wakefulness than during NREM2. Evidence of slower FC dynamics during sleep is discussed in relation to sleep-related reductions in effective connectivity and synaptic strength.

New perspectives on dimensionality and variability from large-scale cortical dynamics

The neocortex is a multi-scale network, with intricate local circuitry interwoven into a global mesh of long-range connections. Neural activity propagates within this network on a wide range of temporal and spatial scales. At the micro scale, neurophysiological recordings reveal coordinated dynamics in local neural populations, which support behaviorally relevant computations. At the macro scale, neuroimaging modalities measure global activity fluctuations organized into spatiotemporal patterns across the entire brain. Here we review recent advances linking the local and global scales of cortical dynamics and their relationship to behavior. We argue that diverse experimental observations on the dimensionality and variability of neural activity can be reconciled by considering how activity propagates in space and time on multiple spatial scales.

Quantitative relations between transient BOLD responses, cortical energetics, and impulse firing in different cortical regions

The blood oxygen level-dependent (BOLD) functional magnetic resonance imaging signal arises as a consequence of changes in blood flow (cerebral blood flow) and oxygen usage (cerebral metabolic rate of oxygen) that in turn are modulated by changes in neuronal activity. Much attention has been given to both theoretical and experimental aspects of the energetics but not to the neuronal activity. Here we use our previous theory relating the steady-state BOLD signal to neuronal activity and amalgamate it with the standard dynamic causal model (DCM, Friston) theory to produce a quantitative model relating the time-dependent BOLD signal to the underlying neuronal activity. Unlike existing treatments, this new theory incorporates a nonzero baseline activity in a completely consistent way and is thus able to account for both positive and negative BOLD signals. It can reproduce a wide variety of experimental BOLD signals reported in the literature solely by adjusting the neuronal input activity. In this way it provides support for the claim that the main features of the signals, including poststimulus undershoot and overshoot, are principally a result of changes in neuronal activity.NEW & NOTEWORTHY A previous model relating the steady-state blood oxygen level-dependent (BOLD) signal to neuronal activity, both above and below baseline, is extended to account for transient BOLD signals. This allows for a detailed investigation of the role neuronal activity can play in such signals and also encompasses poststimulus undershoot and overshoot. A wide variety of experimental BOLD signals are reproduced solely by adjusting the neuronal input activity, including recent results regarding the BOLD signal in patients with schizophrenia.

The relationship between heart rate and functional connectivity of brain regions involved in autonomic control

The peripheral autonomic nervous system (ANS) adjusts the heart rate (HR) to intrinsic and extrinsic demands. It is controlled by a group of functionally connected brain regions assembling the so-called central autonomic network (CAN). More specifically, forebrain cortical regions, limbic and brainstem structures within the CAN have been identified as important components of circuits involved in HR regulation. The present study aimed to investigate whether functional connectivity (FC) between these regions varies in subjects with different heart rates. Thus, 84 healthy subjects were separated according to their HR in slow, medium and fast. We observed a direct association between HR and FC in CAN regions, where stronger FC was related to slower HR. This relationship, however, is non-linear, follows an exponential course and is not restricted to CAN areas only. The network-based analysis (NBS) using time series from 262 independent anatomical ROIs revealed significantly increased functional connectivity in subjects with slow HR compared to subjects with fast HR mainly in regions being part of the salience network, but also of the default-mode network. We additionally simulated the effect of aliasing on the functional connectivity using several TRs and heart rates to exclude the possibility that FC differences might be due to different aliasing effects in the data. The result of the simulation indicated that aliasing cannot explain our findings. Thus, present results imply a functionally meaningful coupling between FC and HR that need to be accounted for in future studies. Moreover, given the established link between HR and emotional, cognitive and social processes, present findings may also be considered to explain individual differences in brain activation or connectivity when using corresponding paradigms in the MR scanner to investigate such processes.

General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks

Intrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displayed higher estimates of heritability than resting-state functional connectivity. We also found that predictions of cognitive ability from GFC generalized across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.

Sleep Modelling across Physiological Levels

Sleep and circadian rhythms are regulated across multiple functional, spatial and temporal levels: from genes to networks of coupled neurons and glial cells, to large scale brain dynamics and behaviour. The dynamics at each of these levels are complex and the interaction between the levels is even more so, so research have mostly focused on interactions within the levels to understand the underlying mechanisms—the so-called reductionist approach. Mathematical models were developed to test theories of sleep regulation and guide new experiments at each of these levels and have become an integral part of the field. The advantage of modelling, however, is that it allows us to simulate and test the dynamics of complex biological systems and thus provides a tool to investigate the connections between the different levels and study the system as a whole. In this paper I review key models of sleep developed at different physiological levels and discuss the potential for an integrated systems biology approach for sleep regulation across these levels. I also highlight the necessity of building mechanistic connections between models of sleep and circadian rhythms across these levels.

Global fluctuations of cerebral blood flow indicate a global brain network independent of systemic factors

Global synchronization across specialized brain networks is a common feature of network models and in-vivo electrical measurements. Although the imaging of specialized brain networks with blood oxygenation sensitive resting state functional magnetic resonance imaging (rsfMRI) has enabled detailed study of regional networks, the study of globally correlated fluctuations with rsfMRI is confounded by spurious contributions to the global signal from systemic physiologic factors and other noise sources. Here we use an alternative rsfMRI method, arterial spin labeled perfusion MRI, to characterize global correlations and their relationship to correlations and anti-correlations between regional networks. Global fluctuations that cannot be explained by systemic factors dominate the fluctuations in cerebral blood flow. Power spectra of these fluctuations are band limited to below 0.05 Hz, similar to prior measurements of regional network fluctuations in the brain. Removal of these global fluctuations prior to measurement of regional networks reduces all regional network fluctuation amplitudes to below the global fluctuation amplitude and changes the strength and sign of inter network correlations. Our findings support large amplitude, globally synchronized activity across networks that require a reassessment of regional network amplitude and correlation measures.

Role of Network Science in the Study of Anesthetic State Transitions

The heterogeneity of molecular mechanisms, target neural circuits, and neurophysiologic effects of general anesthetics makes it difficult to develop a reliable and drug-invariant index of general anesthesia. No single brain region or mechanism has been identified as the neural correlate of consciousness, suggesting that consciousness might emerge through complex interactions of spatially and temporally distributed brain functions. The goal of this review article is to introduce the basic concepts of networks and explain why the application of network science to general anesthesia could be a pathway to discover a fundamental mechanism of anesthetic-induced unconsciousness. This article reviews data suggesting that reduced network efficiency, constrained network repertoires, and changes in cortical dynamics create inhospitable conditions for information processing and transfer, which lead to unconsciousness. This review proposes that network science is not just a useful tool but a necessary theoretical framework and method to uncover common principles of anesthetic-induced unconsciousness.

Quantitative relations between BOLD responses, cortical energetics, and impulse firing

The blood oxygen level-dependent (BOLD) functional magnetic resonance imaging signal arises as a consequence of changes in blood flow and oxygen usage that in turn are modulated by changes in neural activity. Much attention has been given to both theoretical and experimental aspects of the energetics but not to the neural activity. Here we identify the best energetic theory for the steady-state BOLD signal on the basis of correct predictions of experimental observations. This theory is then used, together with the recently determined relationship between energetics and neural activity, to predict how the BOLD signal changes with activity. Unlike existing treatments, this new theory incorporates a nonzero baseline activity in a completely consistent way and is thus able to account for both sustained positive and negative BOLD signals. We also show that the increase in BOLD signal for a given increase in activity is significantly smaller the larger the baseline activity, as is experimentally observed. Furthermore, the decline of the positive BOLD signal arising from deeper cortical laminae in response to an increase in neural firing is shown to arise as a consequence of the larger baseline activity in deeper laminae. Finally, we provide quantitative relations integrating BOLD responses, energetics, and impulse firing, which among other predictions give the same results as existing theories when the baseline activity is zero. NEW & NOTEWORTHY We use a recently established relation between energetics and neural activity to give a quantitative account of BOLD dependence on neural activity. The incorporation of a nonzero baseline neural activity accounts for positive and negative BOLD signals, shows that changes in neural activity give BOLD changes that are smaller the larger the baseline, and provides a basis for the observed inverse relation between BOLD responses and the depth of cortical laminae giving rise to them.

Dynamical Signatures of Structural Connectivity Damage to a Model of the Brain Posed at Criticality

Synchronization of brain activity fluctuations is believed to represent communication between spatially distant neural processes. These interareal functional interactions develop in the background of a complex network of axonal connections linking cortical and subcortical neurons, termed the human "structural connectome." Theoretical considerations and experimental evidence support the view that the human brain can be modeled as a system operating at a critical point between ordered (subcritical) and disordered (supercritical) phases. Here, we explore the hypothesis that pathologies resulting from brain injury of different etiologies are related to this model of a critical brain. For this purpose, we investigate how damage to the integrity of the structural connectome impacts on the signatures of critical dynamics. Adopting a hybrid modeling approach combining an empirical weighted network of human structural connections with a conceptual model of critical dynamics, we show that lesions located at highly transited connections progressively displace the model toward the subcritical regime. The topological properties of the nodes and links are of less importance when considered independently of their weight in the network. We observe that damage to midline hubs such as the middle and posterior cingulate cortex is most crucial for the disruption of criticality in the model. However, a similar effect can be achieved by targeting less transited nodes and links whose connection weights add up to an equivalent amount. This implies that brain pathology does not necessarily arise due to insult targeted at well-connected areas and that intersubject variability could obscure lesions located at nonhub regions. Finally, we discuss the predictions of our model in the context of clinical studies of traumatic brain injury and neurodegenerative disorders.

Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at "rest." Here, we introduce the concept of harmonic brain modes-fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

How structure sculpts function: Unveiling the contribution of anatomical connectivity to the brain's spontaneous correlation structure

Intrinsic brain activity is characterized by highly organized co-activations between different regions, forming clustered spatial patterns referred to as resting-state networks. The observed co-activation patterns are sustained by the intricate fabric of millions of interconnected neurons constituting the brain's wiring diagram. However, as for other real networks, the relationship between the connectional structure and the emergent collective dynamics still evades complete understanding. Here, we show that it is possible to estimate the expected pair-wise correlations that a network tends to generate thanks to the underlying path structure. We start from the assumption that in order for two nodes to exhibit correlated activity, they must be exposed to similar input patterns from the entire network. We then acknowledge that information rarely spreads only along a unique route but rather travels along all possible paths. In real networks, the strength of local perturbations tends to decay as they propagate away from the sources, leading to a progressive attenuation of the original information content and, thus, of their influence. Accordingly, we define a novel graph measure, topological similarity, which quantifies the propensity of two nodes to dynamically correlate as a function of the resemblance of the overall influences they are expected to receive due to the underlying structure of the network. Applied to the human brain, we find that the similarity of whole-network inputs, estimated from the topology of the anatomical connectome, plays an important role in sculpting the backbone pattern of time-average correlations observed at rest.

Functional reorganization in obstructive sleep apnoea and insomnia: A systematic review of the resting-state fMRI

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Resting state functional MRI studies is a promising non-invasive tool for better understanding of the pathophysiology of sleep disorders.

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The salience network is involved in hyperarousal and affective symptoms in insomnia.

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The posterior default mode network appears to underlie cognitive and depressive symptoms of obstructive sleep apnoea.

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Disruption of intrinsic networks have been demonstrated in major depression, which is a common co-morbidity of sleep disorders.

Functional neuroimaging techniques have accelerated progress in the study of sleep disorders. Considering the striking prevalence of these disorders in the general population, however, as well as their strong bidirectional relationship with major neuropsychiatric disorders, including major depressive disorder, their numbers are still surprisingly low. This review examines the contribution of resting state functional MRI to current understanding of two major sleep disorders, insomnia disorder and obstructive sleep apnoea. An attempt is made to learn from parallels of previous resting state functional neuroimaging findings in major depressive disorder. Moreover, shared connectivity biomarkers are suggested for each of the sleep disorders. Taken together, despite some inconsistencies, the synthesis of findings to date highlights the importance of the salience network in hyperarousal and affective symptoms in insomnia. Conversely, dysfunctional connectivity of the posterior default mode network appears to underlie cognitive and depressive symptoms of obstructive sleep apnoea.

The global signal in fMRI: Nuisance or Information?

The global signal is widely used as a regressor or normalization factor for removing the effects of global variations in the analysis of functional magnetic resonance imaging (fMRI) studies. However, there is considerable controversy over its use because of the potential bias that can be introduced when it is applied to the analysis of both task-related and resting-state fMRI studies. In this paper we take a closer look at the global signal, examining in detail the various sources that can contribute to the signal. For the most part, the global signal has been treated as a nuisance term, but there is growing evidence that it may also contain valuable information. We also examine the various ways that the global signal has been used in the analysis of fMRI data, including global signal regression, global signal subtraction, and global signal normalization. Furthermore, we describe new ways for understanding the effects of global signal regression and its relation to the other approaches.

Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs

The large-scale structural ingredients of the brain and neural connectomes have been identified in recent years. These are, similar to the features found in many other real networks: the arrangement of brain regions into modules and the presence of highly connected regions (hubs) forming rich-clubs. Here, we examine how modules and hubs shape the collective dynamics on networks and we find that both ingredients lead to the emergence of complex dynamics. Comparing the connectomes of C. elegans, cats, macaques and humans to surrogate networks in which either modules or hubs are destroyed, we find that functional complexity always decreases in the perturbed networks. A comparison between simulated and empirically obtained resting-state functional connectivity indicates that the human brain, at rest, lies in a dynamical state that reflects the largest complexity its anatomical connectome can host. Last, we generalise the topology of neural connectomes into a new hierarchical network model that successfully combines modular organisation with rich-club forming hubs. This is achieved by centralising the cross-modular connections through a preferential attachment rule. Our network model hosts more complex dynamics than other hierarchical models widely used as benchmarks.

Nicotinic receptors in mouse prefrontal cortex modulate ultraslow fluctuations related to conscious processing

The prefrontal cortex (PFC) plays an important role in cognitive processes, including access to consciousness. The PFC receives significant cholinergic innervation and nicotinic acetylcholine receptors (nAChRs) contribute greatly to the effects of acetylcholine signaling. Using in vivo two-photon imaging of both awake and anesthetized mice, we recorded spontaneous, ongoing neuronal activity in layer II/III in the PFC of WT mice and mice deleted for different nAChR subunits. As in humans, this activity is characterized by synchronous ultraslow fluctuations and neuronal synchronicity is disrupted by light general anesthesia. Both the α7 and β2 nAChR subunits play an important role in the generation of ultraslow fluctuations that occur to a different extent during quiet wakefulness and light general anesthesia. The β2 subunit is specifically required for synchronized activity patterns. Furthermore, chronic application of mecamylamine, an antagonist of nAChRs, disrupts the generation of ultraslow fluctuations. Our findings provide new insight into the ongoing spontaneous activity in the awake and anesthetized state, and the role of cholinergic neurotransmission in the orchestration of cognitive functions.

Can the macro beat the micro? Integrated information across spatiotemporal scales

Causal interactions within complex systems such as the brain can be analyzed at multiple spatiotemporal levels. It is widely assumed that the micro level is causally complete, thus excluding causation at the macro level. However, by measuring effective information-how much a system's mechanisms constrain its past and future states-we recently showed that causal power can be stronger at macro rather than micro levels. In this work, we go beyond effective information and consider additional requirements of a proper measure of causal power from the intrinsic perspective of a system: composition (the cause-effect power of the parts), state-dependency (the cause-effect power of the system in a specific state); integration (the causal irreducibility of the whole to its parts), and exclusion (the causal borders of the system). A measure satisfying these requirements, called Φ Max, was developed in the context of integrated information theory. Here, we evaluate Φ Max systematically at micro and macro levels in space and time using simplified neuronal-like systems. We show that for systems characterized by indeterminism and/or degeneracy, Φ can indeed peak at a macro level. This happens if coarse-graining micro elements produces macro mechanisms with high irreducible causal selectivity. These results are relevant to a theoretical account of consciousness, because for integrated information theory the spatiotemporal maximum of integrated information fixes the spatiotemporal scale of consciousness. More generally, these results show that the notions of macro causal emergence and micro causal exclusion hold when causal power is assessed in full and from the intrinsic perspective of a system.

Propofol anesthesia reduces Lempel-Ziv complexity of spontaneous brain activity in rats

Consciousness is thought to scale with brain complexity, and it may be diminished in anesthesia. Lempel-Ziv complexity (LZC) of field potentials has been shown to be a promising measure of the level of consciousness in anesthetized human subjects, neurological patients, and across the sleep-wake states in rats. Whether this relationship holds for intrinsic networks obtained by functional brain imaging has not been tested. To fill this gap of knowledge, we estimated LZC from large-scale dynamic analysis of functional magnetic resonance images (fMRI) in conscious sedated and unconscious anesthetized rats. Blood oxygen dependent (BOLD) signals were obtained from 30-min whole-brain resting-state scans while the anesthetic propofol was infused intravenously at constant infusion rates of 20mg/kg/h (conscious sedated) and 40mg/kg/h (unconscious). Dynamic brain networks were defined at voxel level by sliding window analysis of regional homogeneity (ReHo) of the BOLD signal. From scans performed at low to high propofol dose, the LZC was significantly reduced by 110%. The results suggest that the difference in LZC between conscious sedated and anesthetized unconscious subjects is conserved in rats and this effect is detectable in large-scale brain network obtained from fMRI.

Tracking brain arousal fluctuations with fMRI

Changes in brain activity accompanying shifts in vigilance and arousal can interfere with the study of other intrinsic and task-evoked characteristics of brain function. However, the difficulty of tracking and modeling the arousal state during functional MRI (fMRI) typically precludes the assessment of arousal-dependent influences on fMRI signals. Here we combine fMRI, electrophysiology, and the monitoring of eyelid behavior to demonstrate an approach for tracking continuous variations in arousal level from fMRI data. We first characterize the spatial distribution of fMRI signal fluctuations that track a measure of behavioral arousal; taking this pattern as a template, and using the local field potential as a simultaneous and independent measure of cortical activity, we observe that the time-varying expression level of this template in fMRI data provides a close approximation of electrophysiological arousal. We discuss the potential benefit of these findings for increasing the sensitivity of fMRI as a cognitive and clinical biomarker.

Metastability and Coherence: Extending the Communication through Coherence Hypothesis Using A Whole-Brain Computational Perspective

Understanding the mechanisms for communication in the brain remains one of the most challenging scientific questions. The communication through coherence (CTC) hypothesis was originally proposed 10 years ago, stating that two groups of neurons communicate most effectively when their excitability fluctuations are coordinated in time (i.e., coherent), and this control by cortical coherence is a fundamental brain mechanism for large-scale, distant communication. In light of new evidence from whole-brain computational modelling of multimodal neuroimaging data, we link CTC to the concept of metastability, which refers to a rich exploration of the functional repertoire made possible by the underlying structural whole-brain connectivity.

On the origins of the 'global signal' determined by functional magnetic resonance imaging in the resting state

OBJECTIVE

Functional magnetic resonance imaging blood oxygen level dependent (BOLD) determinations of correlations between 'resting-state' neuronal activity in different regions of cortex have generated much interest. Determination of these correlations requires regressing out signals that are correlated in all parts of the cortex and are taken to be artefactual, such as those due to movement, respiration and cardiovascular activity. However when these are removed there still remains a 'global signal' (GS), which is taken to be of unknown physiological origin, and is regressed out by some researchers but not by others.

APPROACH

We have investigated the origin of this GS using cortical models consisting of coupled networks of modules representing regions of interest.

MAIN RESULTS

We show that the GS has an amplitude that is linearly related to the average correlation between the modules/voxels in the network over a large range of such correlations. The GS arises as a consequence of feedback between the modules/voxels leading to correlations in their BOLD signals. Given the relationship between the GS and the average correlations it might be anticipated that regressing out the GS during preprocessing will significantly modify the correlations subsequently determined. This is shown to be the case when comparing the connections of individual modules with that predicted by the correlations.

SIGNIFICANCE

The present model shows that such correlations can arise as a consequence of the intermodular feedback connectivity without recourse to imposing a GS independent of the connectivity. Our model indicates that the GS reflects the extent of feedback pathways provided by the intermodular/inter-regional connections and hence the average correlation between modules or regions of cortex. However the model has not been used to elucidate the possible contributions of a GS independent of the connectivity, which might indeed contribute to the GS of the cortex.

Cortical Network Models of Firing Rates in the Resting and Active States Predict BOLD Responses

Measurements of blood oxygenation level dependent (BOLD) signals have produced some surprising observations. One is that their amplitude is proportional to the entire activity in a region of interest and not just the fluctuations in this activity. Another is that during sleep and anesthesia the average BOLD correlations between regions of interest decline as the activity declines. Mechanistic explanations of these phenomena are described here using a cortical network model consisting of modules with excitatory and inhibitory neurons, taken as regions of cortical interest, each receiving excitatory inputs from outside the network, taken as subcortical driving inputs in addition to extrinsic (intermodular) connections, such as provided by associational fibers. The model shows that the standard deviation of the firing rate is proportional to the mean frequency of the firing when the extrinsic connections are decreased, so that the mean BOLD signal is proportional to both as is observed experimentally. The model also shows that if these extrinsic connections are decreased or the frequency of firing reaching the network from the subcortical driving inputs is decreased, or both decline, there is a decrease in the mean firing rate in the modules accompanied by decreases in the mean BOLD correlations between the modules, consistent with the observed changes during NREM sleep and under anesthesia. Finally, the model explains why a transient increase in the BOLD signal in a cortical area, due to a transient subcortical input, gives rises to responses throughout the cortex as observed, with these responses mediated by the extrinsic (intermodular) connections.

Frequency-Dependent Modulation of Regional Synchrony in the Human Brain by Eyes Open and Eyes Closed Resting-States

The eyes-open (EO) and eyes-closed (EC) states have differential effects on BOLD-fMRI signal dynamics, affecting both the BOLD oscillation frequency of a single voxel and the regional homogeneity (ReHo) of several neighboring voxels. To explore how the two resting-states modulate the local synchrony through different frequency bands, we decomposed the time series of each voxel into several components that fell into distinct frequency bands. The ReHo in each of the bands was calculated and compared between the EO and EC conditions. The cross-voxel correlations between the mean frequency and the overall ReHo of each voxel’s original BOLD series in different brain areas were also calculated and compared between the two states. Compared with the EC state, ReHo decreased with EO in a wide frequency band of 0.01–0.25 Hz in the bilateral thalamus, sensorimotor network, and superior temporal gyrus, while ReHo increased significantly in the band of 0–0.01 Hz in the primary visual cortex, and in a higher frequency band of 0.02–0.1 Hz in the higher order visual areas. The cross-voxel correlations between the frequency and overall ReHo were negative in all the brain areas but varied from region to region. These correlations were stronger with EO in the visual network and the default mode network. Our results suggested that different frequency bands of ReHo showed different sensitivity to the modulation of EO-EC states. The better spatial consistency between the frequency and overall ReHo maps indicated that the brain might adopt a stricter frequency-dependent configuration with EO than with EC.

Toward a standardized structural-functional group connectome in MNI space

The analysis of the structural architecture of the human brain in terms of connectivity between its subregions has provided profound insights into its underlying functional organization and has coined the concept of the "connectome", a structural description of the elements forming the human brain and the connections among them. Here, as a proof of concept, we introduce a novel group connectome in standard space based on a large sample of 169 subjects from the Enhanced Nathan Kline Institute-Rockland Sample (eNKI-RS). Whole brain structural connectomes of each subject were estimated with a global tracking approach, and the resulting fiber tracts were warped into standard stereotactic (MNI) space using DARTEL. Employing this group connectome, the results of published tracking studies (i.e., the JHU white matter and Oxford thalamic connectivity atlas) could be largely reproduced directly within MNI space. In a second analysis, a study that examined structural connectivity between regions of a functional network, namely the default mode network, was reproduced. Voxel-wise structural centrality was then calculated and compared to others' findings. Furthermore, including additional resting-state fMRI data from the same subjects, structural and functional connectivity matrices between approximately forty thousand nodes of the brain were calculated. This was done to estimate structure-function agreement indices of voxel-wise whole brain connectivity. Taken together, the combination of a novel whole brain fiber tracking approach and an advanced normalization method led to a group connectome that allowed (at least heuristically) performing fiber tracking directly within MNI space. Such an approach may be used for various purposes like the analysis of structural connectivity and modeling experiments that aim at studying the structure-function relationship of the human connectome. Moreover, it may even represent a first step toward a standard DTI template of the human brain in stereotactic space. The standardized group connectome might thus be a promising new resource to better understand and further analyze the anatomical architecture of the human brain on a population level.

A Biphasic Change of Regional Blood Volume in the Frontal Cortex during Non-Rapid Eye Movement Sleep: A Near-Infrared Spectroscopy Study

STUDY OBJECTIVES

Current knowledge on hemodynamics in sleep is limited because available techniques do not allow continuous recordings and mainly focus on cerebral blood flow while neglecting other important parameters, such as blood volume (BV) and vasomotor activity.

DESIGN

Observational study.

PARTICIPANTS AND SETTINGS

Continuous measures of hemodynamics over the left forehead and biceps were performed using near-infrared spectroscopy (NIRS) during nocturnal polysomnography in 16 healthy participants in sleep laboratory.

MEASUREMENTS AND RESULTS

Temporal dynamics and mean values of cerebral and muscular oxygenated hemoglobin (HbO2), deoxygenated hemoglobin (HHb), and BV during different sleep stages were compared. A biphasic change of cerebral BV was observed which contrasted a monotonic increase of muscular BV during non-rapid eye movement sleep. A significant decrement in cerebral HbO2 and BV accompanied by an increase of HHb was recorded at sleep onset (Phase I). Prior to slow wave sleep (SWS) HbO2 and BV turned to increase whereas HHb began to decrease in subsequent Phase II suggested increased brain perfusion during SWS. The cerebral HbO2 slope correlated to BV slope in Phase I and II, but it only correlated to HHb slope in Phase II. The occurrence time of inflection points correlated to SWS latencies.

CONCLUSION

Initial decrease of brain perfusion with decreased blood volume (BV) and oxygenated hemoglobin (HbO2) together with increasing muscular BV fit thermoregulation process at sleep onset. The uncorrelated and correlated slopes of HbO2 and deoxygenated hemoglobin indicate different mechanisms underlying the biphasic hemodynamic process in light sleep and slow wave sleep (SWS). In SWS, changes in vasomotor activity (i.e., increased vasodilatation) may mediate increasing cerebral and muscular BV.

Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints

This paper studies the link between resting-state functional connectivity (FC), measured by the correlations of fMRI BOLD time courses, and structural connectivity (SC), estimated through fiber tractography. Instead of a static analysis based on the correlation between SC and FC averaged over the entire fMRI time series, we propose a dynamic analysis, based on the time evolution of the correlation between SC and a suitably windowed FC. Assessing the statistical significance of the time series against random phase permutations, our data show a pronounced peak of significance for time window widths around 20-30 TR (40-60 s). Using the appropriate window width, we show that FC patterns oscillate between phases of high modularity, primarily shaped by anatomy, and phases of low modularity, primarily shaped by inter-network connectivity. Building upon recent results in dynamic FC, this emphasizes the potential role of SC as a transitory architecture between different highly connected resting-state FC patterns. Finally, we show that the regions contributing the most to these whole-brain level fluctuations of FC on the supporting anatomical architecture belong to the default mode and the executive control networks suggesting that they could be capturing consciousness-related processes such as mind wandering.

Can apparent resting state connectivity arise from systemic fluctuations?

It is widely accepted that the fluctuations in resting state blood oxygenation level dependent (BOLD) functional MRI (fMRI) reflect baseline neuronal activation through neurovascular coupling; this data is used to infer functional connectivity in the human brain during rest. Consistent activation patterns, i.e., resting state networks (RSN) are seen across groups, conditions, and even species. In this study, we show that some of these patterns can also be generated from the dynamic, systemic, non-neuronal physiological low frequency oscillations (sLFOs) in the BOLD signal alone. We have previously used multimodal imaging to demonstrate the wide presence of the same sLFOs in the brain (BOLD) and periphery with different time delays. This study shows that these sLFOs from BOLD signals alone can give rise to stable spatial patterns, which can be detected during resting state analyses. We generated synthetic resting state data for 11 subjects based only on subject-specific, dynamic sLFO information obtained from resting state data using concurrent peripheral optical imaging or a novel recursive procedure. We compared the results obtained by performing a group independent component analysis (ICA) on this synthetic data (i.e., the result from simulation) to the results obtained from analysis of the real data. ICA detected most of the eight well-known RSNs, including visual, motor, and default mode networks (DMNs), in both the real and the synthetic data sets. These findings suggest that RSNs may reflect, to some extent, vascular anatomy associated with systemic fluctuations, rather than neuronal connectivity.

Cortical network models of impulse firing in the resting and active states predict cortical energetics

Measurements of the cortical metabolic rate of glucose oxidation [CMR(glc(ox))] have provided a number of interesting and, in some cases, surprising observations. One is the decline in CMR(glc(ox)) during anesthesia and non-rapid eye movement (NREM) sleep, and another, the inverse relationship between the resting-state CMR(glc(ox)) and the transient following input from the thalamus. The recent establishment of a quantitative relationship between synaptic and action potential activity on the one hand and CMR(glc(ox)) on the other allows neural network models of such activity to probe for possible mechanistic explanations of these phenomena. We have carried out such investigations using cortical models consisting of networks of modules with excitatory and inhibitory neurons, each receiving excitatory inputs from outside the network in addition to intermodular connections. Modules may be taken as regions of cortical interest, the inputs from outside the network as arising from the thalamus, and the intermodular connections as long associational fibers. The model shows that the impulse frequency of different modules can differ from each other by less than 10%, consistent with the relatively uniform CMR(glc(ox)) observed across different regions of cortex. The model also shows that, if correlations of the average impulse rate between different modules decreases, there is a concomitant decrease in the average impulse rate in the modules, consistent with the observed drop in CMR(glc(ox)) in NREM sleep and under anesthesia. The model also explains why a transient thalamic input to sensory cortex gives rise to responses with amplitudes inversely dependent on the resting-state frequency, and therefore resting-state CMR(glc(ox)).

Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders

The study of human brain networks with in vivo neuroimaging has given rise to the field of connectomics, furthered by advances in network science and graph theory informing our understanding of the topology and function of the healthy brain. Here our focus is on the disruption in neuropsychiatric disorders (pathoconnectomics) and how whole-brain computational models can help generate and predict the dynamical interactions and consequences of brain networks over many timescales. We review methods and emerging results that exhibit remarkable accuracy in mapping and predicting both spontaneous and task-based healthy network dynamics. This raises great expectations that whole-brain modeling and computational connectomics may provide an entry point for understanding brain disorders at a causal mechanistic level, and that computational neuropsychiatry can ultimately be leveraged to provide novel, more effective therapeutic interventions, e.g., through drug discovery and new targets for deep brain stimulation.

Spin-glass model predicts metastable brain states that diminish in anesthesia

Patterns of resting state connectivity change dynamically and may represent modes of cognitive information processing. The diversity of connectivity patterns (global brain states) reflects the information capacity of the brain and determines the state of consciousness. In this work, computer simulation was used to explore the repertoire of global brain states as a function of cortical activation level. We implemented a modified spin glass model to describe UP/DOWN state transitions of neuronal populations at a mesoscopic scale based on resting state BOLD fMRI data. Resting state fMRI was recorded in 20 participants and mapped to 10,000 cortical regions (sites) defined on a group-aligned cortical surface map. Each site represented the population activity of a ~20 mm(2) area of the cortex. Cross-correlation matrices of the mapped BOLD time courses of the set of sites were calculated and averaged across subjects. In the model, each cortical site was allowed to interact with the 16 other sites that had the highest pair-wise correlation values. All sites stochastically transitioned between UP and DOWN states under the net influence of their 16 pairs. The probability of local state transitions was controlled by a single parameter T corresponding to the level of global cortical activation. To estimate the number of distinct global states, first we ran 10,000 simulations at T = 0. Simulations were started from random configurations that converged to one of several distinct patterns. Using hierarchical clustering, at 99% similarity, close to 300 distinct states were found. At intermediate T, metastable state configurations were formed suggesting critical behavior with a sharp increase in the number of metastable states at an optimal T. Both reduced activation (anesthesia, sleep) and increased activation (hyper-activation) moved the system away from equilibrium, presumably incompatible with conscious mentation. During equilibrium, the diversity of large-scale brain states was maximum, compatible with maximum information capacity-a presumed condition of consciousness.