Diversity-induced trivialization and resilience of neural dynamics.

Heterogeneity is omnipresent across all living systems. Diversity enriches the dynamical repertoire of these systems but remains challenging to reconcile with their manifest robustness and dynamical persistence over time, a fundamental feature called resilience. To better understand the mechanism underlying resilience in neural circuits, we considered a nonlinear network model, extracting the relationship between excitability heterogeneity and resilience. To measure resilience, we quantified the number of stationary states of this network, and how they are affected by various control parameters. We analyzed both analytically and numerically gradient and non-gradient systems modeled as non-linear sparse neural networks evolving over long time scales. Our analysis shows that neuronal heterogeneity quenches the number of stationary states while decreasing the susceptibility to bifurcations: a phenomenon known as trivialization. Heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in network size and connection probability by quenching the system's dynamic volatility.

Distinctive biophysical features of human cell-types: insights from studies of neurosurgically resected brain tissue.

Electrophysiological characterization of live human tissue from epilepsy patients has been performed for many decades. Although initially these studies sought to understand the biophysical and synaptic changes associated with human epilepsy, recently, it has become the mainstay for exploring the distinctive biophysical and synaptic features of human cell-types. Both epochs of these human cellular electrophysiological explorations have faced criticism. Early studies revealed that cortical pyramidal neurons obtained from individuals with epilepsy appeared to function "normally" in comparison to neurons from non-epilepsy controls or neurons from other species and thus there was little to gain from the study of human neurons from epilepsy patients. On the other hand, contemporary studies are often questioned for the "normalcy" of the recorded neurons since they are derived from epilepsy patients. In this review, we discuss our current understanding of the distinct biophysical features of human cortical neurons and glia obtained from tissue removed from patients with epilepsy and tumors. We then explore the concept of within cell-type diversity and its loss (i.e., "neural homogenization"). We introduce neural homogenization to help reconcile the epileptogenicity of seemingly "normal" human cortical cells and circuits. We propose that there should be continued efforts to study cortical tissue from epilepsy patients in the quest to understand what makes human cell-types "human".

Intrinsic neural diversity quenches the dynamic volatility of neural networks.

Heterogeneity is the norm in biology. The brain is no different: Neuronal cell types are myriad, reflected through their cellular morphology, type, excitability, connectivity motifs, and ion channel distributions. While this biophysical diversity enriches neural systems' dynamical repertoire, it remains challenging to reconcile with the robustness and persistence of brain function over time (resilience). To better understand the relationship between excitability heterogeneity (variability in excitability within a population of neurons) and resilience, we analyzed both analytically and numerically a nonlinear sparse neural network with balanced excitatory and inhibitory connections evolving over long time scales. Homogeneous networks demonstrated increases in excitability, and strong firing rate correlations-signs of instability-in response to a slowly varying modulatory fluctuation. Excitability heterogeneity tuned network stability in a context-dependent way by restraining responses to modulatory challenges and limiting firing rate correlations, while enriching dynamics during states of low modulatory drive. Excitability heterogeneity was found to implement a homeostatic control mechanism enhancing network resilience to changes in population size, connection probability, strength and variability of synaptic weights, by quenching the volatility (i.e., its susceptibility to critical transitions) of its dynamics. Together, these results highlight the fundamental role played by cell-to-cell heterogeneity in the robustness of brain function in the face of change.

Selective control of synaptic plasticity in heterogeneous networks through transcranial alternating current stimulation (tACS).

Transcranial alternating current stimulation (tACS) represents a promising non-invasive treatment for an increasingly wide range of neurological and neuropsychiatric disorders. The ability to use periodically oscillating electric fields to non-invasively engage neural dynamics opens up the possibility of recruiting synaptic plasticity and to modulate brain function. However, despite consistent reports about tACS clinical effectiveness, strong state-dependence combined with the ubiquitous heterogeneity of cortical networks collectively results in high outcome variability. Introducing variations in intrinsic neuronal timescales, we explored how such heterogeneity influences stimulation-induced change in synaptic connectivity. We examined how spike timing dependent plasticity, at the level of cells, intra- and inter-laminar cortical networks, can be selectively and preferentially engaged by periodic stimulation. Using leaky integrate-and-fire neuron models, we analyzed cortical circuits comprised of multiple cell-types, alongside superficial multi-layered networks expressing distinct layer-specific timescales. Our results show that mismatch in neuronal timescales within and/or between cells-and the resulting variability in excitability, temporal integration properties and frequency tuning-enables selective and directional control on synaptic connectivity by tACS. Our work provides new vistas on how to recruit neural heterogeneity to guide brain plasticity using non-invasive stimulation paradigms.

Loss of neuronal heterogeneity in epileptogenic human tissue impairs network resilience to sudden changes in synchrony.

A myriad of pathological changes associated with epilepsy can be recast as decreases in cell and circuit heterogeneity. We thus propose recontextualizing epileptogenesis as a process where reduction in cellular heterogeneity, in part, renders neural circuits less resilient to seizure. By comparing patch clamp recordings from human layer 5 (L5) cortical pyramidal neurons from epileptogenic and non-epileptogenic tissue, we demonstrate significantly decreased biophysical heterogeneity in seizure-generating areas. Implemented computationally, this renders model neural circuits prone to sudden transitions into synchronous states with increased firing activity, paralleling ictogenesis. This computational work also explains the surprising finding of significantly decreased excitability in the population-activation functions of neurons from epileptogenic tissue. Finally, mathematical analyses reveal a bifurcation structure arising only with low heterogeneity and associated with seizure-like dynamics. Taken together, this work provides experimental, computational, and mathematical support for the theory that ictogenic dynamics accompany a reduction in biophysical heterogeneity.

Arousal Fluctuations Govern Oscillatory Transitions Between Dominant [Formula: see text] and [Formula: see text] Occipital Activity During Eyes Open/Closed Conditions.

Arousal results in widespread activation of brain areas to increase their response in task and behavior relevant ways. Mediated by the Ascending Reticular Arousal System (ARAS), arousal-dependent inputs interact with neural circuitry to shape their dynamics. In the occipital cortex, such inputs may trigger shifts between dominant oscillations, where [Formula: see text] activity is replaced by [Formula: see text] activity, or vice versa. A salient example of this are spectral power alternations observed while eyes are opened and/or closed. These transitions closely follow fluctuations in arousal, suggesting a common origin. To better understand the mechanisms at play, we developed and analyzed a computational model composed of two modules: a thalamocortical feedback circuit coupled with a superficial cortical network. Upon activation by noise-like inputs originating from the ARAS, our model is able to demonstrate that noise-driven non-linear interactions mediate transitions in dominant peak frequency, resulting in the simultaneous suppression of [Formula: see text] limit cycle activity and the emergence of [Formula: see text] oscillations through coherence resonance. Reduction in input provoked the reverse effect - leading to anticorrelated transitions between [Formula: see text] and [Formula: see text] power. Taken together, these results shed a new light on how arousal shapes oscillatory brain activity.

Homeostatic coordination and up-regulation of neural activity by activity-dependent myelination.

Activity-dependent myelination (ADM) is a fundamental dimension of brain plasticity through which myelin changes as a function of neural activity. Mediated by structural changes in glia, ADM notably regulates axonal conduction velocity. Yet, it remains unclear how neural activity impacts myelination to orchestrate the timing of neural signalling, and how ADM shapes neural activity. We developed a model of spiking neurons enhanced with neuron-oligodendrocyte feedback and examined the relationship between ADM and neural activity. We found that ADM implements a homeostatic gain control mechanism that enhances neural firing rates and correlations through the temporal coordination of action potentials as axon lengths increase. Stimuli engage ADM plasticity to trigger bidirectional and reversible changes in conduction delays, as may occur during learning. Furthermore, ADM was found to enhance information transmission under various types of time-varying stimuli. These results highlight the role of ADM in shaping neural activity and communication.

A Hypothesis for Theta Rhythm Frequency Control in CA1 Microcircuits.

Computational models of neural circuits with varying levels of biophysical detail have been generated in pursuit of an underlying mechanism explaining the ubiquitous hippocampal theta rhythm. However, within the theta rhythm are at least two types with distinct frequencies associated with different behavioral states, an aspect that must be considered in pursuit of these mechanistic explanations. Here, using our previously developed excitatory-inhibitory network models that generate theta rhythms, we investigate the robustness of theta generation to intrinsic neuronal variability by building a database of heterogeneous excitatory cells and implementing them in our microcircuit model. We specifically investigate the impact of three key "building block" features of the excitatory cell model that underlie our model design: these cells' rheobase, their capacity for post-inhibitory rebound, and their spike-frequency adaptation. We show that theta rhythms at various frequencies can arise dependent upon the combination of these building block features, and we find that the speed of these oscillations are dependent upon the excitatory cells' response to inhibitory drive, as encapsulated by their phase response curves. Taken together, these findings support a hypothesis for theta frequency control that includes two aspects: (i) an internal mechanism that stems from the building block features of excitatory cell dynamics; (ii) an external mechanism that we describe as "inhibition-based tuning" of excitatory cell firing. We propose that these mechanisms control theta rhythm frequencies and underlie their robustness.

Synchronization and resilience in the Kuramoto white matter network model with adaptive state-dependent delays.

White matter pathways form a complex network of myelinated axons that regulate signal transmission in the nervous system and play a key role in behaviour and cognition. Recent evidence reveals that white matter networks are adaptive and that myelin remodels itself in an activity-dependent way, during both developmental stages and later on through behaviour and learning. As a result, axonal conduction delays continuously adjust in order to regulate the timing of neural signals propagating between different brain areas. This delay plasticity mechanism has yet to be integrated in computational neural models, where conduction delays are oftentimes constant or simply ignored. As a first approach to adaptive white matter remodeling, we modified the canonical Kuramoto model by enabling all connections with adaptive, phase-dependent delays. We analyzed the equilibria and stability of this system, and applied our results to two-oscillator and large-dimensional networks. Our joint mathematical and numerical analysis demonstrates that plastic delays act as a stabilizing mechanism promoting the network's ability to maintain synchronous activity. Our work also shows that global synchronization is more resilient to perturbations and injury towards network architecture. Our results provide key insights about the analysis and potential significance of activity-dependent myelination in large-scale brain synchrony.

Neurostimulation stabilizes spiking neural networks by disrupting seizure-like oscillatory transitions.

An improved understanding of the mechanisms underlying neuromodulatory approaches to mitigate seizure onset is needed to identify clinical targets for the treatment of epilepsy. Using a Wilson-Cowan-motivated network of inhibitory and excitatory populations, we examined the role played by intrinsic and extrinsic stimuli on the network's predisposition to sudden transitions into oscillatory dynamics, similar to the transition to the seizure state. Our joint computational and mathematical analyses revealed that such stimuli, be they noisy or periodic in nature, exert a stabilizing influence on network responses, disrupting the development of such oscillations. Based on a combination of numerical simulations and mean-field analyses, our results suggest that high variance and/or high frequency stimulation waveforms can prevent multi-stability, a mathematical harbinger of sudden changes in network dynamics. By tuning the neurons' responses to input, stimuli stabilize network dynamics away from these transitions. Furthermore, our research shows that such stabilization of neural activity occurs through a selective recruitment of inhibitory cells, providing a theoretical undergird for the known key role these cells play in both the healthy and diseased brain. Taken together, these findings provide new vistas on neuromodulatory approaches to stabilize neural microcircuit activity.

Activity-dependent myelination: A glial mechanism of oscillatory self-organization in large-scale brain networks.

Communication and oscillatory synchrony between distributed neural populations are believed to play a key role in multiple cognitive and neural functions. These interactions are mediated by long-range myelinated axonal fiber bundles, collectively termed as white matter. While traditionally considered to be static after development, white matter properties have been shown to change in an activity-dependent way through learning and behavior-a phenomenon known as white matter plasticity. In the central nervous system, this plasticity stems from oligodendroglia, which form myelin sheaths to regulate the conduction of nerve impulses across the brain, hence critically impacting neural communication. We here shift the focus from neural to glial contribution to brain synchronization and examine the impact of adaptive, activity-dependent changes in conduction velocity on the large-scale phase synchronization of neural oscillators. Using a network model based on primate large-scale white matter neuroanatomy, our computational and mathematical results show that such plasticity endows white matter with self-organizing properties, where conduction delay statistics are autonomously adjusted to ensure efficient neural communication. Our analysis shows that this mechanism stabilizes oscillatory neural activity across a wide range of connectivity gain and frequency bands, making phase-locked states more resilient to damage as reflected by diffuse decreases in connectivity. Critically, our work suggests that adaptive myelination may be a mechanism that enables brain networks with a means of temporal self-organization, resilience, and homeostasis.

A Connectome-Based, Corticothalamic Model of State- and Stimulation-Dependent Modulation of Rhythmic Neural Activity and Connectivity.

Rhythmic activity in the brain fluctuates with behaviour and cognitive state, through a combination of coexisting and interacting frequencies. At large spatial scales such as those studied in human M/EEG, measured oscillatory dynamics are believed to arise primarily from a combination of cortical (intracolumnar) and corticothalamic rhythmogenic mechanisms. Whilst considerable progress has been made in characterizing these two types of neural circuit separately, relatively little work has been done that attempts to unify them into a single consistent picture. This is the aim of the present paper. We present and examine a whole-brain, connectome-based neural mass model with detailed long-range cortico-cortical connectivity and strong, recurrent corticothalamic circuitry. This system reproduces a variety of known features of human M/EEG recordings, including spectral peaks at canonical frequencies, and functional connectivity structure that is shaped by the underlying anatomical connectivity. Importantly, our model is able to capture state- (e.g., idling/active) dependent fluctuations in oscillatory activity and the coexistence of multiple oscillatory phenomena, as well as frequency-specific modulation of functional connectivity. We find that increasing the level of sensory drive to the thalamus triggers a suppression of the dominant low frequency rhythms generated by corticothalamic loops, and subsequent disinhibition of higher frequency endogenous rhythmic behaviour of intracolumnar microcircuits. These combine to yield simultaneous decreases in lower frequency and increases in higher frequency components of the M/EEG power spectrum during states of high sensory or cognitive drive. Building on this, we also explored the effect of pulsatile brain stimulation on ongoing oscillatory activity, and evaluated the impact of coexistent frequencies and state-dependent fluctuations on the response of cortical networks. Our results provide new insight into the role played by cortical and corticothalamic circuits in shaping intrinsic brain rhythms, and suggest new directions for brain stimulation therapies aimed at state-and frequency-specific control of oscillatory brain activity.

White matter plasticity and maturation in human cognition.

White matter plasticity likely plays a critical role in supporting cognitive development. However, few studies have used the imaging methods specific to white matter tissue structure or experimental designs sensitive to change in white matter necessary to elucidate these relations. Here we briefly review novel imaging approaches that provide more specific information regarding white matter microstructure. Furthermore, we highlight recent studies that provide greater clarity regarding the relations between changes in white matter and cognition maturation in both healthy children and adolescents and those with white matter insult. Finally, we examine the hypothesis that white matter is linked to cognitive function via its impact on neural synchronization. We test this hypothesis in a population of children and adolescents with recurrent demyelinating syndromes. Specifically, we evaluate group differences in white matter microstructure within the optic radiation; and neural phase synchrony in visual cortex during a visual task between 25 patients and 28 typically developing age-matched controls. Children and adolescents with demyelinating syndromes show evidence of myelin and axonal compromise and this compromise predicts reduced phase synchrony during a visual task compared to typically developing controls. We investigate one plausible mechanism at play in this relationship using a computational model of gamma generation in early visual cortical areas. Overall, our findings show a fundamental connection between white matter microstructure and neural synchronization that may be critical for cognitive processing. In the future, longitudinal or interventional studies can build upon our knowledge of these exciting relations between white matter, neural communication, and cognition.

Disrupted network connectivity in pediatric brain tumor survivors is a signature of injury.

Cognition is compromised in pediatric brain tumor survivors but the neurophysiological basis of this compromise remains unclear. We hypothesized that reduced neural synchronization across brain networks is involved. To test this, we evaluated group differences using a retrospective cohort comparison design between 24 pediatric brain tumor survivors [11.81 ± 3.27)] and 24 age matched healthy children [12.04 ± 3.28)] in functional connectivity within a cerebellar network to examine local effects of the tumor, a whole brain network to examine diffuse effects of treatment (i.e., chemotherapy and radiation), and across multiple intrinsic connectivity networks. Neural activity was recorded during magnetoencephalography scanning while participants were at rest and functional connectivity within networks was measured using the phase lag index. We corroborated our findings using a computational model representing the local tumor effects on neural synchrony. Compared to healthy children, pediatric brain tumor survivors show increased functional connectivity for theta and beta frequency bands within the cerebellar network and increased functional connectivity for the theta band within the whole brain network that again localized to the cerebellum. Computational modeling showed that increased synchrony in the theta bad is observed following local clustering as well as sparse interarea brain connectivity. We also observed increased functional connectivity for the alpha frequency band in the ventral attention network and decreased functional connectivity within the gamma frequency band in the motor network within paedatric brain tumor survivors versus healthy children. Notably, increased gamma functional connectivity within the motor network predicted decreased reaction time on behavioral tasks in pediatric brain tumor survivors. Disrupted network synchrony may be a signature of neurological injury and disease.

Effect of Stimulation Waveform on the Non-linear Entrainment of Cortical Alpha Oscillations.

In the past decade, there has been a surge of interest in using patterned brain stimulation to manipulate cortical oscillations, in both experimental and clinical settings. But the relationship between stimulation waveform and its impact on ongoing oscillations remains poorly understood and severely restrains the development of new paradigms. To address some aspects of this intricate problem, we combine computational and mathematical approaches, providing new insights into the influence of waveform of both low and high-frequency stimuli on synchronous neural activity. Using a cellular-based cortical microcircuit network model, we performed numerical simulations to test the influence of different waveforms on ongoing alpha oscillations, and derived a mean-field description of stimulation-driven dynamics to better understand the observed responses. Our analysis shows that high-frequency periodic stimulation translates into an effective transformation of the neurons' response function, leading to waveform-dependent changes in oscillatory dynamics and resting state activity. Moreover, we found that randomly fluctuating stimulation linearizes the neuron response function while constant input moves its activation threshold. Taken together, our findings establish a new theoretical framework in which stimulation waveforms impact neural systems at the population-scale through non-linear interactions.

Suppression of underlying neuronal fluctuations mediates EEG slowing during general anaesthesia.

The physiological mechanisms by which anaesthetic drugs modulate oscillatory brain activity remain poorly understood. Combining human data, mathematical and computational analysis of both spiking and mean-field models, we investigated the spectral dynamics of encephalographic (EEG) beta-alpha oscillations, observed in human patients undergoing general anaesthesia. The effect of anaesthetics can be modelled as a reduction of neural fluctuation intensity, and/or an increase in inhibitory synaptic gain in the thalamo-cortical circuit. Unlike previous work, which suggested the primary importance of gamma-amino-butryic-acid (GABA) augmentation in causing a shift to low EEG frequencies, our analysis demonstrates that a non-linear transition, triggered by a simple decrease in neural fluctuation intensity, is sufficient to explain the clinically-observed appearance - and subsequent slowing - of the beta-alpha narrowband EEG peak. In our model, increased synaptic inhibition alone, did not correlate with the clinically-observed encephalographic spectral changes, but did cause the anaesthetic-induced decrease in neuronal firing rate. Taken together, our results show that such a non-linear transition results in functional fragmentation of cortical and thalamic populations; highly correlated intra-population dynamics triggered by anaesthesia decouple and isolate neural populations. Our results are able to parsimoniously unify and replicate the observed anaesthetic effects on both the EEG spectra and inter-regional connectivity, and further highlight the importance of neural activity fluctuations in the genesis of altered brain states.

Stochastic resonance mediates the state-dependent effect of periodic stimulation on cortical alpha oscillations.

Brain stimulation can be used to engage and modulate rhythmic activity in brain networks. However, the outcomes of brain stimulation are shaped by behavioral states and endogenous fluctuations in brain activity. To better understand how this intrinsic oscillatory activity controls the susceptibility of the brain to stimulation, we analyzed a computational model of the thalamo-cortical system in two distinct states (rest and task-engaged) to identify the mechanisms by which endogenous alpha oscillations (8Hz-12Hz) are modulated by periodic stimulation. Our analysis shows that the different responses to stimulation observed experimentally in these brain states can be explained by a passage through a bifurcation combined with stochastic resonance - a mechanism by which irregular fluctuations amplify the response of a nonlinear system to weak periodic signals. Indeed, our findings suggest that modulation of brain oscillations is best achieved in states of low endogenous rhythmic activity, and that irregular state-dependent fluctuations in thalamic inputs shape the susceptibility of cortical population to periodic stimulation.

Changes in White Matter Microstructure Impact Cognition by Disrupting the Ability of Neural Assemblies to Synchronize.

Cognition is compromised by white matter (WM) injury but the neurophysiological alterations linking them remain unclear. We hypothesized that reduced neural synchronization caused by disruption of neural signal propagation is involved. To test this, we evaluated group differences in: diffusion tensor WM microstructure measures within the optic radiations, primary visual area (V1), and cuneus; neural phase synchrony to a visual attention cue during visual-motor task; and reaction time to a response cue during the same task between 26 pediatric patients (17/9: male/female) treated with cranial radiation treatment for a brain tumor (12.67 ± 2.76 years), and 26 healthy children (16/10: male/female; 12.01 ± 3.9 years). We corroborated our findings using a corticocortical computational model representing perturbed signal conduction from myelin. Patients show delayed reaction time, WM compromise, and reduced phase synchrony during visual attention compared with healthy children. Notably, using partial least-squares-path modeling we found that WM insult within the optic radiations, V1, and cuneus is a strong predictor of the slower reaction times via disruption of neural synchrony in visual cortex. Observed changes in synchronization were reproduced in a computational model of WM injury. These findings provide new evidence linking cognition with WM via the reliance of neural synchronization on propagation of neural signals.SIGNIFICANCE STATEMENT By comparing brain tumor patients to healthy children, we establish that changes in the microstructure of the optic radiations and neural synchrony during visual attention predict reaction time. Furthermore, by testing the directionality of these links through statistical modeling and verifying our findings with computational modeling, we infer a causal relationship, namely that changes in white matter microstructure impact cognition in part by disturbing the ability of neural assemblies to synchronize. Together, our human imaging data and computer simulations show a fundamental connection between WM microstructure and neural synchronization that is critical for cognitive processing.