Criticality in probabilistic models of spreading dynamics in brain networks: Epileptic seizures

Downmodulation of Potassium Conductances Induces Epileptic Seizures in Cortical Network Models Via Multiple Synergistic Factors

Voltage-gated potassium conductances g K play a critical role not only in normal neural function, but also in many neurological disorders and related therapeutic interventions. In particular, in an important animal model of epileptic seizures, 4-aminopyridine (4-AP) administration is thought to induce seizures by reducing g K in cortex and other brain areas. Interestingly, 4-AP has also been useful in the treatment of neurological disorders such as multiple sclerosis and spinal cord injury, where it is thought to improve action potential propagation in axonal fibers. Here, we examined g K downmodulation in biophysical models of cortical networks that included different neuron types organized in layers, potassium diffusion in interstitial and larger extracellular spaces, and glial buffering. Our findings are fourfold. First, g K downmodulation in pyramidal and fast-spiking inhibitory interneurons led to differential effects, making the latter much more likely to enter depolarization block. Second, both neuron types showed an increase in the duration and amplitude of action potentials, with more pronounced effects in pyramidal neurons. Third, a sufficiently strong g K reduction dramatically increased network synchrony, resulting in seizure-like dynamics. Fourth, we hypothesized that broader action potentials were likely to not only improve their propagation, as in 4-AP therapeutic uses, but also to increase synaptic coupling. Notably, graded-synapses incorporating this effect further amplified network synchronization and seizure-like dynamics. Overall, our findings elucidate different effects that g K downmodulation may have in cortical networks, explaining its potential role in both pathological neural dynamics and therapeutic applications.

Real-Time Epileptic Seizure Prediction Method With Spatio-Temporal Information Transfer Learning

Despite numerous studies aimed at improving accuracy, the accurate prediction of epileptic seizures remains a challenge in clinical practice due to the high computational cost, poor real-time performance, and over-reliance on labelled data. To address these issues, a real-time seizure prediction method with spatio-temporal information transfer learning (RTSPM-STITL) has been proposed in this study. In the RTSPM-STITL method, the human brain is regarded as a time-varying high-dimensional neurodynamic system, in which epileptic seizures are viewed as state transitions caused by time-varying system parameters. Specifically, the spatio-temporal information transfer (STIT) model is firstly constructed by the recurrent neural network (RNN) and trained by the Force Learning (a real-time learning mechanism). Then the STIT model is utilized to transform the high-dimensional neurodynamic data into low-dimensional time series to capture the dynamic features of epileptic seizures. Also, the critical slowing down effect (CSD) of seizure dynamics is used to detect warning signals. The experimental results demonstrate that the proposed method can achieve higher accuracy and sensitivity without labeled data on both the CHB-MIT and Siena scalp EEG databases. Especially, the parameters of the STIT model can be updated in real-time based on patient data, without iterative training. More importantly, the STIT model can maintain high sensitivity and accuracy with only 48400 parameters, which is reduced by more than 91% compared with contrast models in this experiment. Therefore, the proposed method can significantly reduce the computational cost and accurately predict epileptic seizures, as well as with high real-time, practicality, applicability, and interpretability.

Controllability of nonlinear epileptic-seizure spreading dynamics in large-scale subject-specific brain networks

Closed-loop electrical stimulation has become an important alternative to resective surgery for control of pharmacologically-resistant focal epileptic seizures. Seizure spread across large-scale brain networks, rather than its focal onset, is what commonly leads to major disruptions in sensorimotor and cognitive processing, as well as loss-of-consciousness, one of the main impairing aspects of the disorder. Electrical stimulation, triggered by early detection of seizure onset in epileptogenic zones (EZs), has been applied to prevent spread and its subsequent effects. Here, we show how linear feedback seizure-spread controllability in subject-specific (white-matter tractography) Epileptor network models is affected by variations in brain excitability, network coupling strength, control latency and gain, and actuation targets. Feedback control can qualitatively change the nonlinear seizure dynamics, and the paths to seizure termination and spread prevention. Notably, control onset latency is a critical parameter leading to a phase transition in spread controllability. Consequently, the efficacy of EZ-only actuation is limited depending on network excitability, coupling strength, and practical latencies for detection and actuation. Additional feedback-stabilization control of theoretically-derived optimal node subsets in the network are necessary for spread prevention. Finally, we contrast our linear-feedback controllability assessment with other measures based on minimum-energy (Gramian) controllability and nonlinear pulse-perturbation approaches.

Dynamic reconfiguration of aperiodic brain activity supports cognitive functioning in epilepsy: A neural fingerprint identification

Temporal lobe epilepsy (TLE) is characterized by alterations of brain dynamic on a large-scale associated with altered cognitive functioning. Here, we aimed at analyzing dynamic reconfiguration of brain activity, using the neural fingerprint approach, to delineate subject-specific characteristics and their cognitive correlates in TLE. We collected 10 min of resting-state scalp-electroencephalography (EEG, 128 channels), free from epileptiform activity, from 68 TLE patients and 34 controls. The functional network was defined by the spatiotemporal spreading, across cortical regions, of aperiodic bursts of signals' amplitude (neuronal avalanches), encapsulated into the avalanche transition matrix (ATM). The fingerprint analysis of the ATMs revealed more stereotyped patterns in patients with respect to controls, with the greatest stereotypy in bilateral TLE. Finally, indices extracted from individual patterns of brain dynamics correlated with the memory impairment in unilateral TLE. This study helped understand how dynamic brain activity in TLE is shaped and provided patient-specific indices useful for personalized medicine.

Neuronal avalanches in temporal lobe epilepsy as a noninvasive diagnostic tool investigating large scale brain dynamics

The epilepsy diagnosis still represents a complex process, with misdiagnosis reaching 40%. We aimed at building an automatable workflow, helping the clinicians in the diagnosis of temporal lobe epilepsy (TLE). We hypothesized that neuronal avalanches (NA) represent a feature better encapsulating the rich brain dynamics compared to classically used functional connectivity measures (Imaginary Coherence; ImCoh). We analyzed large-scale activation bursts (NA) from source estimation of resting-state electroencephalography. Using a support vector machine, we reached a classification accuracy of TLE versus controls of 0.86 ± 0.08 (SD) and an area under the curve of 0.93 ± 0.07. The use of NA features increase by around 16% the accuracy of diagnosis prediction compared to ImCoh. Classification accuracy increased with larger signal duration, reaching a plateau at 5 min of recording. To summarize, NA represents an interpretable feature for an automated epilepsy identification, being related with intrinsic neuronal timescales of pathology-relevant regions.

A synergetic turn in cognitive neuroscience of brain diseases

Despite significant improvements in our understanding of brain diseases, many barriers remain. Cognitive neuroscience faces four major challenges: complex structure-function associations; disease phenotype heterogeneity; the lack of transdiagnostic models; and oversimplified cognitive approaches restricted to the laboratory. Here, we propose a synergetics framework that can help to perform the necessary dimensionality reduction of complex interactions between the brain, body, and environment. The key solutions include low-dimensional spatiotemporal hierarchies for brain-structure associations, whole-brain modeling to handle phenotype diversity, model integration of shared transdiagnostic pathophysiological pathways, and naturalistic frameworks balancing experimental control and ecological validity. Creating whole-brain models with reduced manifolds combined with ecological measures can improve our understanding of brain disease and help identify novel interventions. Synergetics provides an integrated framework for future progress in clinical and cognitive neuroscience, pushing the boundaries of brain health and disease toward more mature, naturalistic approaches.

Altered spread of waves of activities at large scale is influenced by cortical thickness organization in temporal lobe epilepsy: a magnetic resonance imaging-high-density electroencephalography study

Temporal lobe epilepsy is a brain network disorder characterized by alterations at both the structural and the functional levels. It remains unclear how structure and function are related and whether this has any clinical relevance. In the present work, we adopted a novel methodological approach investigating how network structural features influence the large-scale dynamics. The functional network was defined by the spatio-temporal spreading of aperiodic bursts of activations (neuronal avalanches), as observed utilizing high-density electroencephalography in patients with temporal lobe epilepsy. The structural network was modelled as the region-based thickness covariance. Loosely speaking, we quantified the similarity of the cortical thickness of any two brain regions, both across groups and at the individual level, the latter utilizing a novel approach to define the subject-wise structural covariance network. In order to compare the structural and functional networks (at the nodal level), we studied the correlation between the probability that a wave of activity would propagate from a source to a target region and the similarity of the source region thickness as compared with other target brain regions. Building on the recent evidence that large-waves of activities pathologically spread through the epileptogenic network in temporal lobe epilepsy, also during resting state, we hypothesize that the structural cortical organization might influence such altered spatio-temporal dynamics. We observed a stable cluster of structure-function correlation in the bilateral limbic areas across subjects, highlighting group-specific features for left, right and bilateral temporal epilepsy. The involvement of contralateral areas was observed in unilateral temporal lobe epilepsy. We showed that in temporal lobe epilepsy, alterations of structural and functional networks pair in the regions where seizures propagate and are linked to disease severity. In this study, we leveraged on a well-defined model of neurological disease and pushed forward personalization approaches potentially useful in clinical practice. Finally, the methods developed here could be exploited to investigate the relationship between structure-function networks at subject level in other neurological conditions.