A thalamocortical substrate for integrated information via critical synchronous bursting.

Understanding the neurobiological mechanisms underlying consciousness remains a significant challenge. Recent evidence suggests that the coupling between distal-apical and basal-somatic dendrites in thick-tufted layer 5 pyramidal neurons (L5PN), regulated by the nonspecific-projecting thalamus, is crucial for consciousness. Yet, it is uncertain whether this thalamocortical mechanism can support emergent signatures of consciousness, such as integrated information. To address this question, we constructed a biophysical network of dual-compartment thick-tufted L5PN, with dendrosomatic coupling controlled by thalamic inputs. Our findings demonstrate that integrated information is maximized when nonspecific thalamic inputs drive the system into a regime of time-varying synchronous bursting. Here, the system exhibits variable spiking dynamics with broad pairwise correlations, supporting the enhanced integrated information. Further, the observed peak in integrated information aligns with criticality signatures and empirically observed layer 5 pyramidal bursting rates. These results suggest that the thalamocortical core of the mammalian brain may be evolutionarily configured to optimize effective information processing, providing a potential neuronal mechanism that integrates microscale theories with macroscale signatures of consciousness.

Brain state kinematics and the trajectory of task performance improvement.

Dimensionality reduction techniques offer a unique perspective on brain state dynamics, in which systems-level activity can be tracked through the engagement of a small number of component trajectories. Used in combination with neuroimaging data collected during the performance of cognitive tasks, these approaches can expose the otherwise latent dimensions upon which the brain reconfigures in order to facilitate cognitive performance. Here, we utilized Principal Component Analysis to transform parcellated BOLD timeseries from an fMRI dataset in which 70 human subjects performed an instruction based visuomotor learning task into orthogonal low-dimensional components. We then used Linear Discriminant Analysis to maximise the mean differences between the low-dimensional signatures of fast-and-slow reaction times and early-and-late learners, while also conserving variance present within these groups. The resultant basis set allowed us to describe meaningful differences between these groups and, importantly, to detail the patterns of brain activity which underpin these differences. Our results demonstrate non-linear interactions between three key brain activation maps with convergent trajectories observed at higher task repetitions consistent with optimization. Furthermore, we show subjects with the greatest reaction time improvements have delayed recruitment of left dorsal and lateral prefrontal cortex, as well as deactivation in parts of the occipital lobe and motor cortex, and that the slowest performers have weaker recruitment of somatosensory association cortex and left ventral visual stream, as well as weaker deactivation in the dorsal lateral prefrontal cortex. Overall our results highlight the utility of a kinematic description of brain states, whereby reformatting data into low-dimensional trajectories sensitive to the subtleties of a task can capture non-linear trends in a tractable manner and permit hypothesis generation at the level of brain states.

Fractal spike dynamics and neuronal coupling in the primate visual system.

KEY POINTS: We measured fractal (self-similar) fluctuations in ongoing spiking activity in subcortical (lateral geniculate nucleus, LGN) and cortical (area MT) visual areas in anaesthetised marmosets. Cells in the evolutionary ancient koniocellular LGN pathway and in area MT show high-amplitude fractal fluctuations, whereas evolutionarily newer parvocellular and magnocellular LGN cells do not. Spiking activity in koniocellular cells and MT cells shows substantial correlation to the local population activity, whereas activity in parvocellular and magnocellular cells is less correlated with local activity. We develop a model consisting of a fractal process and a global rate modulation which can reproduce and explain the fundamental relationship between fractal fluctuations and population coupling in LGN and MT. The model provides a unified account of apparently disparate aspects of neural spiking activity and can improve our understanding of information processing in evolutionary ancient and modern visual pathways. ABSTRACT: The brain represents and processes information through patterns of spiking activity, which is influenced by local and widescale brain circuits as well as intrinsic neural dynamics. Whether these influences have independent or linked effects on spiking activity is, however, not known. Here we measured spiking activity in two visual centres, the lateral geniculate nucleus (LGN) and cortical area MT, in marmoset monkeys. By combining the Fano-factor time curve, power spectral analysis and rescaled range analysis, we reveal inherent fractal fluctuations of spiking activity in LGN and MT. We found that the evolutionary ancient koniocellular (K) pathway in LGN and area MT exhibits strong fractal fluctuations at short (<1 s) time scales. Parvocellular (P) and magnocellular (M) LGN cells show weaker fractal fluctuations at longer (multi-second) time scales. In both LGN and MT, the amplitude and time scale of fractal fluctuations can explain short and long time scale spiking dynamics. We further show differential neuronal coupling of LGN and MT cells to local population spiking activity. The population coupling is intrinsically linked to fractal fluctuations: neurons showing stronger fluctuations are more strongly correlated to the local population activity. To understand this relationship, we modelled spiking activity using a fractal inhomogeneous Poisson process with dynamic rate, which is the product of an intrinsic stochastic fractal rate and a global modulatory gain. Our model explains the intrinsic links between neuronal spike rate and population coupling in LGN and MT, and establishes a unified account of dynamic spiking properties in afferent visual pathways.

Core and matrix thalamic sub-populations relate to spatio-temporal cortical connectivity gradients.

Recent neuroimaging experiments have defined low-dimensional gradients of functional connectivity in the cerebral cortex that subserve a spectrum of capacities that span from sensation to cognition. Despite well-known anatomical connections to the cortex, the subcortical areas that support cortical functional organization have been relatively overlooked. One such structure is the thalamus, which maintains extensive anatomical and functional connections with the cerebral cortex across the cortical mantle. The thalamus has a heterogeneous cytoarchitecture, with at least two distinct cell classes that send differential projections to the cortex: granular-projecting 'Core' cells and supragranular-projecting 'Matrix' cells. Here we use high-resolution 7T resting-state fMRI data and the relative amount of two calcium-binding proteins, parvalbumin and calbindin, to infer the relative distribution of these two cell-types (Core and Matrix, respectively) in the thalamus. First, we demonstrate that thalamocortical connectivity recapitulates large-scale, low-dimensional connectivity gradients within the cerebral cortex. Next, we show that diffusely-projecting Matrix regions preferentially correlate with cortical regions with longer intrinsic fMRI timescales. We then show that the Core-Matrix architecture of the thalamus is important for understanding network topology in a manner that supports dynamic integration of signals distributed across the brain. Finally, we replicate our main results in a distinct 3T resting-state fMRI dataset. Linking molecular and functional neuroimaging data, our findings highlight the importance of the thalamic organization for understanding low-dimensional gradients of cortical connectivity.

Critical Dynamics of Natural Time-Varying Images.

A deep understanding of the dynamical properties of natural time-varying images is essential for interpreting how they are efficiently processed in the brain. Here we examine natural time-varying images from the perspective of their spatiotemporal patterns and find evidence of dynamical thermodynamic criticality. We further demonstrate that these spatiotemporal patterns ubiquitously organize as localized, propagating patterns. By studying these propagating patterns and their spreading dynamics over time, we demonstrate that the critical dynamics of natural time-varying images belong to the universality class of directed percolation. These critical dynamics have important implications for understanding neural processing of time-varying stimuli.

Relationship between cortical state and spiking activity in the lateral geniculate nucleus of marmosets.

KEY POINTS: How parallel are the primate visual pathways? In the present study, we demonstrate that parallel visual pathways in the dorsal lateral geniculate nucleus (LGN) show distinct patterns of interaction with rhythmic activity in the primary visual cortex (V1). In the V1 of anaesthetized marmosets, the EEG frequency spectrum undergoes transient changes that are characterized by fluctuations in delta-band EEG power. We show that, on multisecond timescales, spiking activity in an evolutionary primitive (koniocellular) LGN pathway is specifically linked to these slow EEG spectrum changes. By contrast, on subsecond (delta frequency) timescales, cortical oscillations can entrain spiking activity throughout the entire LGN. Our results are consistent with the hypothesis that, in waking animals, the koniocellular pathway selectively participates in brain circuits controlling vigilance and attention. ABSTRACT: The major afferent cortical pathway in the visual system passes through the dorsal lateral geniculate nucleus (LGN), where nerve signals originating in the eye can first interact with brain circuits regulating visual processing, vigilance and attention. In the present study, we investigated how ongoing and visually driven activity in magnocellular (M), parvocellular (P) and koniocellular (K) layers of the LGN are related to cortical state. We recorded extracellular spiking activity in the LGN simultaneously with local field potentials (LFP) in primary visual cortex, in sufentanil-anaesthetized marmoset monkeys. We found that asynchronous cortical states (marked by low power in delta-band LFPs) are linked to high spike rates in K cells (but not P cells or M cells), on multisecond timescales. Cortical asynchrony precedes the increases in K cell spike rates by 1-3 s, implying causality. At subsecond timescales, the spiking activity in many cells of all (M, P and K) classes is phase-locked to delta waves in the cortical LFP, and more cells are phase-locked during synchronous cortical states than during asynchronous cortical states. The switch from low-to-high spike rates in K cells does not degrade their visual signalling capacity. By contrast, during asynchronous cortical states, the fidelity of visual signals transmitted by K cells is improved, probably because K cell responses become less rectified. Overall, the data show that slow fluctuations in cortical state are selectively linked to K pathway spiking activity, whereas delta-frequency cortical oscillations entrain spiking activity throughout the entire LGN, in anaesthetized marmosets.

Brain-wide impacts of sedation on spontaneous activity and auditory processing in larval zebrafish.

Despite their widespread use, we have limited knowledge of the mechanisms by which sedatives mediate their effects on brain-wide networks. This is, in part, due to the technical challenge of observing activity across large populations of neurons in normal and sedated brains. In this study, we examined the effects of the sedative dexmedetomidine, and its antagonist atipamezole, on spontaneous brain dynamics and auditory processing in zebrafish larvae. Our brain-wide, cellular-resolution calcium imaging reveals, for the first time, the brain regions involved in these network-scale dynamics and the individual neurons that are affected within those regions. Further analysis reveals a variety of dynamic changes in the brain at baseline, including marked reductions in spontaneous activity, correlation, and variance. The reductions in activity and variance represent a "quieter" brain state during sedation, an effect that causes highly correlated evoked activity in the auditory system to stand out more than it does in un-sedated brains. We also observe a reduction in auditory response latencies across the brain during sedation, suggesting that the removal of spontaneous activity leaves the core auditory pathway free of impingement from other non-auditory information. Finally, we describe a less dynamic brain-wide network during sedation, with a higher energy barrier and a lower probability of brain state transitions during sedation. In total, our brain-wide, cellular-resolution analysis shows that sedation leads to quieter, more stable, and less dynamic brain, and that against this background, responses across the auditory processing pathway become sharper and more prominent. Significance Statement: Animals' brain states constantly fluctuate in response to their environment and context, leading to changes in perception and behavioral choices. Alterations in perception, sensorimotor gating, and behavioral selection are hallmarks of numerous neuropsychiatric disorders, but the circuit- and network-level underpinnings of these alterations are poorly understood.Pharmacological sedation alters perception and responsiveness and provides a controlled and repeatable manipulation for studying brain states and their underlying circuitry. Here, we show that sedation of larval zebrafish with dexmedetomidine reduces brain-wide spontaneous activity and locomotion but leaves portions of brain-wide auditory processing and behavior intact. We describe and computationally model changes at the levels of individual neurons, local circuits, and brain-wide networks that lead to altered brain states and sensory processing during sedation.

Cortical acetylcholine dynamics are predicted by cholinergic axon activity and behavior state.

Acetylcholine (ACh) is thought to play a role in driving the rapid, spontaneous brain-state transitions that occur during wakefulness; however, the spatiotemporal properties of cortical ACh activity during these state changes are still unclear. We perform simultaneous imaging of GRAB-ACh sensors, GCaMP-expressing basal forebrain axons, and behavior to address this question. We observed a high correlation between axon and GRAB-ACh activity around periods of locomotion and pupil dilation. GRAB-ACh fluorescence could be accurately predicted from axonal activity alone, and local ACh activity decreased at farther distances from an axon. Deconvolution of GRAB-ACh traces allowed us to account for sensor kinetics and emphasized rapid clearance of small ACh transients. We trained a model to predict ACh from pupil size and running speed, which generalized well to unseen data. These results contribute to a growing understanding of the precise timing and spatial characteristics of cortical ACh during fast brain-state transitions.

Cortical acetylcholine dynamics are predicted by cholinergic axon activity and behavior state.

Even under spontaneous conditions and in the absence of changing environmental demands, awake animals alternate between increased or decreased periods of alertness. These changes in brain state can occur rapidly, on a timescale of seconds, and neuromodulators such as acetylcholine (ACh) are thought to play an important role in driving these spontaneous state transitions. Here, we perform the first simultaneous imaging of ACh sensors and GCaMP-expressing axons in vivo, to examine the spatiotemporal properties of cortical ACh activity and release during spontaneous changes in behavioral state. We observed a high correlation between simultaneously recorded basal forebrain axon activity and neuromodulator sensor fluorescence around periods of locomotion and pupil dilation. Consistent with volume transmission of ACh, increases in axon activity were accompanied by increases in local ACh levels that fell off with the distance from the nearest axon. GRAB-ACh fluorescence could be accurately predicted from axonal activity alone, providing the first validation that neuromodulator axon activity is a reliable proxy for nearby neuromodulator levels. Deconvolution of fluorescence traces allowed us to account for the kinetics of the GRAB-ACh sensor and emphasized the rapid clearance of ACh for smaller transients outside of running periods. Finally, we trained a predictive model of ACh fluctuations from the combination of pupil size and running speed; this model performed better than using either variable alone, and generalized well to unseen data. Overall, these results contribute to a growing understanding of the precise timing and spatial characteristics of cortical ACh during fast brain state transitions.

Tonic and burst-like locus coeruleus stimulation distinctly shift network activity across the cortical hierarchy.

Noradrenaline (NA) release from the locus coeruleus (LC) changes activity and connectivity in neuronal networks across the brain, modulating multiple behavioral states. NA release is mediated by both tonic and burst-like LC activity. However, it is unknown whether the functional changes in target areas depend on these firing patterns. Using optogenetics, photometry, electrophysiology and functional magnetic resonance imaging in mice, we show that tonic and burst-like LC firing patterns elicit brain responses that hinge on their distinct NA release dynamics. During moderate tonic LC activation, NA release engages regions associated with associative processing, while burst-like stimulation biases the brain toward sensory processing. These activation patterns locally couple with increased astrocytic and inhibitory activity and change the brain's topological configuration in line with the hierarchical organization of the cerebral cortex. Together, these findings reveal how the LC-NA system achieves a nuanced regulation of global circuit operations.

Causal evidence for cholinergic stabilization of attractor landscape dynamics.

There is substantial evidence that neuromodulatory systems critically influence brain state dynamics; however, most work has been purely descriptive. Here, we quantify, using data combining local inactivation of the basal forebrain with simultaneous measurement of resting-state fMRI activity in the macaque, the causal role of long-range cholinergic input to the stabilization of brain states in the cerebral cortex. Local inactivation of the nucleus basalis of Meynert (nbM) leads to a decrease in the energy barriers required for an fMRI state transition in cortical ongoing activity. Moreover, the inactivation of particular nbM sub-regions predominantly affects information transfer in cortical regions known to receive direct anatomical projections. We demonstrate these results in a simple neurodynamical model of cholinergic impact on neuronal firing rates and slow hyperpolarizing adaptation currents. We conclude that the cholinergic system plays a critical role in stabilizing macroscale brain state dynamics.

Neuronal connected burst cascades bridge macroscale adaptive signatures across arousal states.

The human brain displays a rich repertoire of states that emerge from the microscopic interactions of cortical and subcortical neurons. Difficulties inherent within large-scale simultaneous neuronal recording limit our ability to link biophysical processes at the microscale to emergent macroscopic brain states. Here we introduce a microscale biophysical network model of layer-5 pyramidal neurons that display graded coarse-sampled dynamics matching those observed in macroscale electrophysiological recordings from macaques and humans. We invert our model to identify the neuronal spike and burst dynamics that differentiate unconscious, dreaming, and awake arousal states and provide insights into their functional signatures. We further show that neuromodulatory arousal can mediate different modes of neuronal dynamics around a low-dimensional energy landscape, which in turn changes the response of the model to external stimuli. Our results highlight the promise of multiscale modelling to bridge theories of consciousness across spatiotemporal scales.

The non-specific matrix thalamus facilitates the cortical information processing modes relevant for conscious awareness.

The neurobiological mechanisms of arousal and anesthesia remain poorly understood. Recent evidence highlights the key role of interactions between the cerebral cortex and the diffusely projecting matrix thalamic nuclei. Here, we interrogate these processes in a whole-brain corticothalamic neural mass model endowed with targeted and diffusely projecting thalamocortical nuclei inferred from empirical data. This model captures key features seen in propofol anesthesia, including diminished network integration, lowered state diversity, impaired susceptibility to perturbation, and decreased corticocortical coherence. Collectively, these signatures reflect a suppression of information transfer across the cerebral cortex. We recover these signatures of conscious arousal by selectively stimulating the matrix thalamus, recapitulating empirical results in macaque, as well as wake-like information processing states that reflect the thalamic modulation of large-scale cortical attractor dynamics. Our results highlight the role of matrix thalamocortical projections in shaping many features of complex cortical dynamics to facilitate the unique communication states supporting conscious awareness.

The music of the hemispheres: Cortical eigenmodes as a physical basis for large-scale brain activity and connectivity patterns.

Neuroscience has had access to high-resolution recordings of large-scale cortical activity and structure for decades, but still lacks a generally adopted basis to analyze and interrelate results from different individuals and experiments. Here it is argued that the natural oscillatory modes of the cortex-cortical eigenmodes-provide a physically preferred framework for systematic comparisons across experimental conditions and imaging modalities. In this framework, eigenmodes are analogous to notes of a musical instrument, while commonly used statistical patterns parallel frequently played chords. This intuitive perspective avoids problems that often arise in neuroimaging analyses, and connects to underlying mechanisms of brain activity. We envisage this approach will lead to novel insights into whole-brain function, both in existing and prospective datasets, and facilitate a unification of empirical findings across presently disparate analysis paradigms and measurement modalities.

The role of the locus coeruleus in shaping adaptive cortical melodies.

Neural dynamics are shaped and constrained by the projections of a small nucleus in the pons: the noradrenergic locus coeruleus (LC). Much like a bow to the brain's violin, activity in the LC lacks content specificity, but instead dynamically shapes the excitability and receptivity of neurons across the brain. In this review, we explain how the style of the bowing technique, which is analogous to different firing modes in the LC, affects distinct activity patterns in the rest of the brain. Through this analogical lens, we provide intuitive insights into how the complex activity of the LC acts to coordinate adaptive neural dynamics.

It's about time: Linking dynamical systems with human neuroimaging to understand the brain.

Most human neuroscience research to date has focused on statistical approaches that describe stationary patterns of localized neural activity or blood flow. While these patterns are often interpreted in light of dynamic, information-processing concepts, the static, local, and inferential nature of the statistical approach makes it challenging to directly link neuroimaging results to plausible underlying neural mechanisms. Here, we argue that dynamical systems theory provides the crucial mechanistic framework for characterizing both the brain's time-varying quality and its partial stability in the face of perturbations, and hence, that this perspective can have a profound impact on the interpretation of human neuroimaging results and their relationship with behavior. After briefly reviewing some key terminology, we identify three key ways in which neuroimaging analyses can embrace a dynamical systems perspective: by shifting from a local to a more global perspective, by focusing on dynamics instead of static snapshots of neural activity, and by embracing modeling approaches that map neural dynamics using "forward" models. Through this approach, we envisage ample opportunities for neuroimaging researchers to enrich their understanding of the dynamic neural mechanisms that support a wide array of brain functions, both in health and in the setting of psychopathology.

The ascending arousal system shapes neural dynamics to mediate awareness of cognitive states.

Models of cognitive function typically focus on the cerebral cortex and hence overlook functional links to subcortical structures. This view does not consider the role of the highly-conserved ascending arousal system's role and the computational capacities it provides the brain. We test the hypothesis that the ascending arousal system modulates cortical neural gain to alter the low-dimensional energy landscape of cortical dynamics. Here we use spontaneous functional magnetic resonance imaging data to study phasic bursts in both locus coeruleus and basal forebrain, demonstrating precise time-locked relationships between brainstem activity, low-dimensional energy landscapes, network topology, and spatiotemporal travelling waves. We extend our analysis to a cohort of experienced meditators and demonstrate locus coeruleus-mediated network dynamics were associated with internal shifts in conscious awareness. Together, these results present a view of brain organization that highlights the ascending arousal system's role in shaping both the dynamics of the cerebral cortex and conscious awareness.

Computational models link cellular mechanisms of neuromodulation to large-scale neural dynamics.

Decades of neurobiological research have disclosed the diverse manners in which the response properties of neurons are dynamically modulated to support adaptive cognitive functions. This neuromodulation is achieved through alterations in the biophysical properties of the neuron. However, changes in cognitive function do not arise directly from the modulation of individual neurons, but are mediated by population dynamics in mesoscopic neural ensembles. Understanding this multiscale mapping is an important but nontrivial issue. Here, we bridge these different levels of description by showing how computational models parametrically map classic neuromodulatory processes onto systems-level models of neural activity. The ensuing critical balance of systems-level activity supports perception and action, although our knowledge of this mapping remains incomplete. In this way, quantitative models that link microscale neuronal neuromodulation to systems-level brain function highlight gaps in knowledge and suggest new directions for integrating theoretical and experimental work.

Diffuse neural coupling mediates complex network dynamics through the formation of quasi-critical brain states.

The biological mechanisms that allow the brain to balance flexibility and integration remain poorly understood. A potential solution may lie in a unique aspect of neurobiology, which is that numerous brain systems contain diffuse synaptic connectivity. Here, we demonstrate that increasing diffuse cortical coupling within a validated biophysical corticothalamic model traverses the system through a quasi-critical regime in which spatial heterogeneities in input noise support transient critical dynamics in distributed subregions. The presence of quasi-critical states coincides with known signatures of complex, adaptive brain network dynamics. Finally, we demonstrate the presence of similar dynamic signatures in empirical whole-brain human neuroimaging data. Together, our results establish that modulating the balance between local and diffuse synaptic coupling in a thalamocortical model subtends the emergence of quasi-critical brain states that act to flexibly transition the brain between unique modes of information processing.