Dynamic reconfiguration of cortical functional connectivity across brain states

Chronic full-band recordings with graphene microtransistors as neural interfaces for discrimination of brain states

Brain states such as sleep, anesthesia, wakefulness, or coma are characterized by specific patterns of cortical activity dynamics, from local circuits to full-brain emergent properties. We previously demonstrated that full-spectrum signals, including the infraslow component (DC, direct current-coupled), can be recorded acutely in multiple sites using flexible arrays of graphene solution-gated field-effect transistors (gSGFETs). Here, we performed chronic implantation of 16-channel gSGFET arrays over the rat cerebral cortex and recorded full-band neuronal activity with two objectives: (1) to test the long-term stability of implanted devices; and (2) to investigate full-band activity during the transition across different levels of anesthesia. First, we demonstrate it is possible to record full-band signals with stability, fidelity, and spatiotemporal resolution for up to 5.5 months using chronic epicortical gSGFET implants. Second, brain states generated by progressive variation of levels of anesthesia could be identified as traditionally using the high-pass filtered (AC, alternating current-coupled) spectrogram: from synchronous slow oscillations in deep anesthesia through to asynchronous activity in the awake state. However, the DC signal introduced a highly significant improvement for brain-state discrimination: the DC band provided an almost linear information prediction of the depth of anesthesia, with about 85% precision, using a trained algorithm. This prediction rose to about 95% precision when the full-band (AC + DC) spectrogram was taken into account. We conclude that recording infraslow activity using gSGFET interfaces is superior for the identification of brain states, and further supports the preclinical and clinical use of graphene neural interfaces for long-term recordings of cortical activity.

Functional hierarchies in brain dynamics characterized by signal reversibility in ferret cortex

Brain signal irreversibility has been shown to be a promising approach to study neural dynamics. Nevertheless, the relation with cortical hierarchy and the influence of different electrophysiological features is not completely understood. In this study, we recorded local field potentials (LFPs) during spontaneous behavior, including awake and sleep periods, using custom micro-electrocorticographic (μECoG) arrays implanted in ferrets. In contrast to humans, ferrets remain less time in each state across the sleep-wake cycle. We deployed a diverse set of metrics in order to measure the levels of complexity of the different behavioral states. In particular, brain irreversibility, which is a signature of non-equilibrium dynamics, captured by the arrow of time of the signal, revealed the hierarchical organization of the ferret's cortex. We found different signatures of irreversibility and functional hierarchy of large-scale dynamics in three different brain states (active awake, quiet awake, and deep sleep), showing a lower level of irreversibility in the deep sleep stage, compared to the other. Irreversibility also allowed us to disentangle the influence of different cortical areas and frequency bands in this process, showing a predominance of the parietal cortex and the theta band. Furthermore, when inspecting the embedded dynamic through a Hidden Markov Model, the deep sleep stage was revealed to have a lower switching rate and lower entropy production. These results suggest functional hierarchies in organization that can be revealed through thermodynamic features and information theory metrics.

Structural basis of envelope and phase intrinsic coupling modes in the cerebral cortex

Intrinsic coupling modes (ICMs) can be observed in ongoing brain activity at multiple spatial and temporal scales. Two families of ICMs can be distinguished: phase and envelope ICMs. The principles that shape these ICMs remain partly elusive, in particular their relation to the underlying brain structure. Here we explored structure-function relationships in the ferret brain between ICMs quantified from ongoing brain activity recorded with chronically implanted micro-ECoG arrays and structural connectivity (SC) obtained from high-resolution diffusion MRI tractography. Large-scale computational models were used to explore the ability to predict both types of ICMs. Importantly, all investigations were conducted with ICM measures that are sensitive or insensitive to volume conduction effects. The results show that both types of ICMs are significantly related to SC, except for phase ICMs when using measures removing zero-lag coupling. The correlation between SC and ICMs increases with increasing frequency which is accompanied by reduced delays. Computational models produced results that were highly dependent on the specific parameter settings. The most consistent predictions were derived from measures solely based on SC. Overall, the results demonstrate that patterns of cortical functional coupling as reflected in both phase and envelope ICMs are both related, albeit to different degrees, to the underlying structural connectivity in the cerebral cortex.

Coupled Dynamics of Stimulus-Evoked Gustatory Cortical and Basolateral Amygdalar Activity

Gustatory cortical (GC) single-neuron taste responses reflect taste quality and palatability in successive epochs. Ensemble analyses reveal epoch-to-epoch firing-rate changes in these responses to be sudden, coherent transitions. Such nonlinear dynamics suggest that GC is part of a recurrent network, producing these dynamics in concert with other structures. Basolateral amygdala (BLA), which is reciprocally connected to GC and central to hedonic processing, is a strong candidate partner for GC, in that BLA taste responses evolve on the same general clock as GC and because inhibition of activity in the BLA→GC pathway degrades the sharpness of GC transitions. These facts motivate, but do not test, our overarching hypothesis that BLA and GC act as a single, comodulated network during taste processing. Here, we provide just this test of simultaneous (BLA and GC) extracellular taste responses in female rats, probing the multiregional dynamics of activity to directly test whether BLA and GC responses contain coupled dynamics. We show that BLA and GC response magnitudes covary across trials and within single responses, and that changes in BLA-GC local field potential phase coherence are epoch specific. Such classic coherence analyses, however, obscure the most salient facet of BLA-GC coupling: sudden transitions in and out of the epoch known to be involved in driving gaping behavior happen near simultaneously in the two regions, despite huge trial-to-trial variability in transition latencies. This novel form of inter-regional coupling, which we show is easily replicated in model networks, suggests collective processing in a distributed neural network.SIGNIFICANCE STATEMENT There has been little investigation into real-time communication between brain regions during taste processing, a fact reflecting the dominant belief that taste circuitry is largely feedforward. Here, we perform an in-depth analysis of real-time interactions between GC and BLA in response to passive taste deliveries, using both conventional coherence metrics and a novel methodology that explicitly considers trial-to-trial variability and fast single-trial dynamics in evoked responses. Our results demonstrate that BLA-GC coherence changes as the taste response unfolds, and that BLA and GC specifically couple for the sudden transition into (and out of) the behaviorally relevant neural response epoch, suggesting (although not proving) that: (1) recurrent interactions subserve the function of the dyad as (2) a putative attractor network.

Optimal Control Costs of Brain State Transitions in Linear Stochastic Systems

The brain is a system that performs numerous functions by controlling its states. Quantifying the cost of this control is essential as it reveals how the brain can be controlled based on the minimization of the control cost, and which brain regions are most important to the optimal control of transitions. Despite its great potential, the current control paradigm in neuroscience uses a deterministic framework and is therefore unable to consider stochasticity, severely limiting its application to neural data. Here, to resolve this limitation, we propose a novel framework for the evaluation of control costs based on a linear stochastic model. Following our previous work, we quantified the optimal control cost as the minimal Kullback-Leibler divergence between the uncontrolled and controlled processes. In the linear model, we established an analytical expression for minimal cost and showed that we can decompose it into the cost for controlling the mean and covariance of brain activity. To evaluate the utility of our novel framework, we examined the significant brain regions in the optimal control of transitions from the resting state to seven cognitive task states in human whole-brain imaging data of either sex. We found that, in realizing the different transitions, the lower visual areas commonly played a significant role in controlling the means, while the posterior cingulate cortex commonly played a significant role in controlling the covariances.SIGNIFICANCE STATEMENT The brain performs many cognitive functions by controlling its states. Quantifying the cost of this control is essential as it reveals how the brain can be optimally controlled in terms of the cost, and which brain regions are most important to the optimal control of transitions. Here, we built a novel framework to quantify control cost that takes account of stochasticity of neural activity, which is ignored in previous studies. We established the analytical expression of the stochastic control cost, which enables us to compute the cost in high-dimensional neural data. We identified the significant brain regions for the optimal control in cognitive tasks in human whole-brain imaging data.

Directed functional and structural connectivity in a large-scale model for the mouse cortex

Inferring the structural connectivity from electrophysiological measurements is a fundamental challenge in systems neuroscience. Directed functional connectivity measures, such as the generalized partial directed coherence (GPDC), provide estimates of the causal influence between areas. However, the relation between causality estimates and structural connectivity is still not clear. We analyzed this problem by evaluating the effectiveness of GPDC to estimate the connectivity of a ground-truth, data-constrained computational model of a large-scale network model of the mouse cortex. The model contains 19 cortical areas composed of spiking neurons, with areas connected by long-range projections with weights obtained from a tract-tracing cortical connectome. We show that GPDC values provide a reasonable estimate of structural connectivity, with an average Pearson correlation over simulations of 0.74. Moreover, even in a typical electrophysiological recording scenario containing five areas, the mean correlation was above 0.6. These results suggest that it may be possible to empirically estimate structural connectivity from functional connectivity even when detailed whole-brain recordings are not achievable.

We analyzed the relationship between structural and directed functional connectivity by evaluating the effectiveness of generalized partial directed coherence (GPDC) to estimate the connectivity of a ground-truth, data-constrained computational model of a large-scale network model of the mouse cortex. We show that GPDC values provide a reasonable estimate of structural connectivity even in a typical electrophysiological recording scenario containing few areas. These results suggest that it may be possible to empirically estimate structural connectivity from functional connectivity even when detailed whole-brain recordings are not achievable.

Quantifying brain state transition cost via Schrödinger Bridge

Quantifying brain state transition cost is a fundamental problem in systems neuroscience. Previous studies utilized network control theory to measure the cost by considering a neural system as a deterministic dynamical system. However, this approach does not capture the stochasticity of neural systems, which is important for accurately quantifying brain state transition cost. Here, we propose a novel framework based on optimal control in stochastic systems. In our framework, we quantify the transition cost as the Kullback-Leibler divergence from an uncontrolled transition path to the optimally controlled path, which is known as Schrödinger Bridge. To test its utility, we applied this framework to functional magnetic resonance imaging data from the Human Connectome Project and computed the brain state transition cost in cognitive tasks. We demonstrate correspondence between brain state transition cost and the difficulty of tasks. The results suggest that our framework provides a general theoretical tool for investigating cognitive functions from the viewpoint of transition cost.

Revealing the Relevant Spatiotemporal Scale Underlying Whole-Brain Dynamics

The brain rapidly processes and adapts to new information by dynamically transitioning between whole-brain functional networks. In this whole-brain modeling study we investigate the relevance of spatiotemporal scale in whole-brain functional networks. This is achieved through estimating brain parcellations at different spatial scales (100-900 regions) and time series at different temporal scales (from milliseconds to seconds) generated by a whole-brain model fitted to fMRI data. We quantify the richness of the dynamic repertoire at each spatiotemporal scale by computing the entropy of transitions between whole-brain functional networks. The results show that the optimal relevant spatial scale is around 300 regions and a temporal scale of around 150 ms. Overall, this study provides much needed evidence for the relevant spatiotemporal scales and recommendations for analyses of brain dynamics.

Combined resting state-fMRI and calcium recordings show stable brain states for task-induced fMRI in mice under combined ISO/MED anesthesia

For fMRI in animal models, the combination of low-dose anesthetic, isoflurane (ISO), and the sedative medetomidine (MED) has recently become an advocated regimen to achieve stable neuronal states and brain networks in rats that are required for reliable task-induced BOLD fMRI. However, in mice the temporal stability of neuronal states and networks in resting-state (rs)-fMRI experiments during the combined ISO/MED regimen has not been systematically investigated. Using a multimodal approach with optical calcium (Ca²⁺) recordings and rs-fMRI, we investigated cortical neuronal/astrocytic Ca²⁺activity states and brain networks at multiple time points while switching from anesthesia with 1% ISO to a combined ISO/MED regimen. We found that cortical activity states reached a steady-state 45 min following start of MED infusion as indicated by stable Ca²⁺ transients. Similarly, rs-networks were not statistically different between anesthesia with ISO and the combined ISO/MED regimen 45 and 100 min after start of MED. Importantly, during the transition time we identified changed rs-network signatures that likely reflect the different mode of action of the respective anesthetic; these included a dose-dependent increase in cortico-cortical functional connectivity (FC) presumably caused by reduction of ISO concentration and decreased FC in subcortical arousal nuclei due to MED infusion. Furthermore, we report detection of visual stimulation-induced BOLD fMRI during the stable ISO/MED neuronal state 45 min after induction. Based on our findings, we recommend a 45-minute waiting period after switching from ISO anesthesia to the combined ISO/MED regimen before performing rs- or task-induced fMRI experiments.

Awakening: Predicting external stimulation to force transitions between different brain states

We describe a quantitative and robust definition of a brain state as an ensemble of “metastable substates,” each with a probabilistic stability and occurrence frequency. Fitting this to a generative whole-brain model provides an innovative avenue for predicting where simulated brain stimulation can force transitions between different brain states. We provide proof-of-concept by systematically applying this model framework to neuroimaging data of the human sleep cycle and show where to stimulate to awaken the human sleeping brain and vice versa. These results suggest an avenue for using causal whole-brain models to discover in silico where to force a transition between brain states, which may potentially support recovery in disease.

A fundamental problem in systems neuroscience is how to force a transition from one brain state to another by external driven stimulation in, for example, wakefulness, sleep, coma, or neuropsychiatric diseases. This requires a quantitative and robust definition of a brain state, which has so far proven elusive. Here, we provide such a definition, which, together with whole-brain modeling, permits the systematic study in silico of how simulated brain stimulation can force transitions between different brain states in humans. Specifically, we use a unique neuroimaging dataset of human sleep to systematically investigate where to stimulate the brain to force an awakening of the human sleeping brain and vice versa. We show where this is possible using a definition of a brain state as an ensemble of “metastable substates,” each with a probabilistic stability and occurrence frequency fitted by a generative whole-brain model, fine-tuned on the basis of the effective connectivity. Given the biophysical limitations of direct electrical stimulation (DES) of microcircuits, this opens exciting possibilities for discovering stimulation targets and selecting connectivity patterns that can ensure propagation of DES-induced neural excitation, potentially making it possible to create awakenings from complex cases of brain injury.

A systematic review on the quantitative relationship between structural and functional network connectivity strength in mammalian brains

The mammalian brain is composed of densely connected and interacting regions, which form structural and functional networks. An improved understanding of the structure-function relation is crucial to understand the structural underpinnings of brain function and brain plasticity after injury. It is currently unclear how functional connectivity strength relates to structural connectivity strength. We obtained an overview of recent papers that report on correspondences between quantitative functional and structural connectivity measures in the mammalian brain. We included network studies in which functional connectivity was measured with resting-state fMRI, and structural connectivity with either diffusion-weighted MRI or neuronal tract tracers. Twenty-seven of the 28 included studies showed a positive structure-function relationship. Large inter-study variations were found comparing functional connectivity strength with either quantitative diffusion-based (correlation coefficient (r) ranges: 0.18-0.82) or neuronal tracer-based structural connectivity measures (r = 0.24-0.74). Two functional datasets demonstrated lower structure-function correlations with neuronal tracer-based (r = 0.22 and r = 0.30) than with diffusion-based measures (r = 0.49 and r = 0.65). The robust positive quantitative structure-function relationship supports the hypothesis that structural connectivity provides the hardware from which functional connectivity emerges. However, methodological differences between the included studies complicate the comparison across studies, which emphasize the need for validation and standardization in brain structure-function studies.

Human cognition involves the dynamic integration of neural activity and neuromodulatory systems

The human brain integrates diverse cognitive processes into a coherent whole, shifting fluidly as a function of changing environmental demands. Despite recent progress, the neurobiological mechanisms responsible for this dynamic system-level integration remain poorly understood. Here we investigated the spatial, dynamic, and molecular signatures of system-wide neural activity across a range of cognitive tasks. We found that neuronal activity converged onto a low-dimensional manifold that facilitates the execution of diverse task states. Flow within this attractor space was associated with dissociable cognitive functions, unique patterns of network-level topology, and individual differences in fluid intelligence. The axes of the low-dimensional neurocognitive architecture aligned with regional differences in the density of neuromodulatory receptors, which in turn relate to distinct signatures of network controllability estimated from the structural connectome. These results advance our understanding of functional brain organization by emphasizing the interface between neural activity, neuromodulatory systems, and cognitive function.

Developing Next-generation Brain Sensing Technologies - A Review

Advances in sensing technology raise the possibility of creating neural interfaces that can more effectively restore or repair neural function and reveal fundamental properties of neural information processing. To realize the potential of these bioelectronic devices, it is necessary to understand the capabilities of emerging technologies and identify the best strategies to translate these technologies into products and therapies that will improve the lives of patients with neurological and other disorders. Here we discuss emerging technologies for sensing brain activity, anticipated challenges for translation, and perspectives for how to best transition these technologies from academic research labs to useful products for neuroscience researchers and human patients.

Functional Brain Connectivity Revealed by Sparse Coding of Large-Scale Local Field Potential Dynamics

Exploration of brain dynamics patterns has attracted increasing attention due to its fundamental significance in understanding the working mechanism of the brain. However, due to the lack of effective modeling methods, how the simultaneously recorded LFP can inform us about the brain dynamics remains a general challenge. In this paper, we propose a novel sparse coding based method to investigate brain dynamics of freely-behaving mice from the perspective of functional connectivity, using super-long local field potential (LFP) recordings from 13 distinct regions of the mouse brain. Compared with surrogate datasets, six and four reproducible common functional connectivities were discovered to represent the space of brain dynamics in the frequency bands of alpha and theta respectively. Modeled by a finite state machine, temporal transition framework of functional connectivities was inferred for each frequency band, and evident preference was discovered. Our results offer a novel perspective for analyzing neural recording data at such high temporal resolution and recording length, as common functional connectivities and their transition framework discovered in this work reveal the nature of the brain dynamics in freely behaving mice.

Sensory Processing Across Conscious and Nonconscious Brain States: From Single Neurons to Distributed Networks for Inferential Representation

Neuronal activity is markedly different across brain states: it varies from desynchronized activity during wakefulness to the synchronous alternation between active and silent states characteristic of deep sleep. Surprisingly, limited attention has been paid to investigating how brain states affect sensory processing. While it was long assumed that the brain was mostly disconnected from external stimuli during sleep, an increasing number of studies indicates that sensory stimuli continue to be processed across all brain states-albeit differently. In this review article, we first discuss what constitutes a brain state. We argue that-next to global, behavioral states such as wakefulness and sleep-there is a concomitant need to distinguish bouts of oscillatory dynamics with specific global/local activity patterns and lasting for a few hundreds of milliseconds, as these can lead to the same sensory stimulus being either perceived or not. We define these short-lasting bouts as micro-states. We proceed to characterize how sensory-evoked neural responses vary between conscious and nonconscious states. We focus on two complementary aspects: neuronal ensembles and inter-areal communication. First, we review which features of ensemble activity are conducive to perception, and how these features vary across brain states. Properties such as heterogeneity, sparsity and synchronicity in neuronal ensembles will especially be considered as essential correlates of conscious processing. Second, we discuss how inter-areal communication varies across brain states and how this may affect brain operations and sensory processing. Finally, we discuss predictive coding (PC) and the concept of multi-level representations as a key framework for understanding conscious sensory processing. In this framework the brain implements conscious representations as inferences about world states across multiple representational levels. In this representational hierarchy, low-level inference may be carried out nonconsciously, whereas high levels integrate across different sensory modalities and larger spatial scales, correlating with conscious processing. This inferential framework is used to interpret several cellular and population-level findings in the context of brain states, and we briefly compare its implications to two other theories of consciousness. In conclusion, this review article, provides foundations to guide future studies aiming to uncover the mechanisms of sensory processing and perception across brain states.

Intrinsic Functional Connectivity Resembles Cortical Architecture at Various Levels of Isoflurane Anesthesia

Cortical single neuron activity and local field potential patterns change at different depths of general anesthesia. Here, we investigate the associated network level changes of functional connectivity. We recorded ongoing electrocorticographic (ECoG) activity from temporo-parieto-occipital cortex of 6 ferrets at various levels of isoflurane/nitrous oxide anesthesia and determined functional connectivity by computing amplitude envelope correlations. Through hierarchical clustering, we derived typical connectivity patterns corresponding to light, intermediate and deep anesthesia. Generally, amplitude correlation strength increased strongly with depth of anesthesia across all cortical areas and frequency bands. This was accompanied, at the deepest level, by the emergence of burst-suppression activity in the ECoG signal and a change of the spectrum of the amplitude envelope. Normalization of functional connectivity to the distribution of correlation coefficients showed that the topographical patterns remained similar across depths of anesthesia, reflecting the functional association of the underlying cortical areas. Thus, while strength and temporal properties of amplitude co-modulation vary depending on the activity of local neural circuits, their network-level interaction pattern is presumably most strongly determined by the underlying structural connectivity.

Putting the "dynamic" back into dynamic functional connectivity

The study of fluctuations in time-resolved functional connectivity is a topic of substantial current interest. As the term "dynamic functional connectivity" implies, such fluctuations are believed to arise from dynamics in the neuronal systems generating these signals. While considerable activity currently attends to methodological and statistical issues regarding dynamic functional connectivity, less attention has been paid toward its candidate causes. Here, we review candidate scenarios for dynamic (functional) connectivity that arise in dynamical systems with two or more subsystems; generalized synchronization, itinerancy (a form of metastability), and multistability. Each of these scenarios arises under different configurations of local dynamics and intersystem coupling: We show how they generate time series data with nonlinear and/or nonstationary multivariate statistics. The key issue is that time series generated by coupled nonlinear systems contain a richer temporal structure than matched multivariate (linear) stochastic processes. In turn, this temporal structure yields many of the phenomena proposed as important to large-scale communication and computation in the brain, such as phase-amplitude coupling, complexity, and flexibility. The code for simulating these dynamics is available in a freeware software platform, the Brain Dynamics Toolbox.

Functional integration across oscillation frequencies by cross-frequency phase synchronization

Neuronal oscillations and their inter-areal synchronization may be instrumental in regulating neuronal communication in distributed networks. Several lines of research have, however, shown that cognitive tasks engage neuronal oscillations simultaneously in multiple frequency bands that have distinct functional roles in cognitive processing. Gamma oscillations (30-120 Hz) are associated with bottom-up processing, while slower oscillations in delta (1-4 Hz), theta (4-7 Hz), alpha (8-14 Hz) and beta (14-30 Hz) frequency bands may have roles in executive or top-down controlling functions, although also other distinctions have been made. Identification of the mechanisms that integrate such spectrally distributed processing and govern neuronal communication among these networks is crucial for understanding how cognitive functions are achieved in neuronal circuits. Cross-frequency interactions among oscillations have been recognized as a likely candidate mechanism for such integration. We advance here the hypothesis that phase-phase synchronization of neuronal oscillations in two different frequency bands, cross-frequency phase synchrony (CFS), could serve to integrate, coordinate and regulate neuronal processing distributed into neuronal assemblies concurrently in multiple frequency bands. A trail of studies over the past decade has revealed the presence of CFS among cortical oscillations and linked CFS with roles in cognitive integration. We propose that CFS could connect fast and slow oscillatory networks and thereby integrate distributed cognitive functions such as representation of sensory information with attentional and executive functions.