Biology subjects

Karoly, P.

Publications and source records attributed to Karoly, P..

5 recordsLinked to original sources

bioRxiv2026

Characterising cortical perturbation-recovery dynamics using TMS-EEG: The Brain Dynamic Toolbox

Objective: To adapt dynamical measures used in computational and experimental perturbation models to human transcranial magnetic stimulation and electroencephalography (TMS-EEG) and determine whether analysis of the extended post-stimulation trajectory reveals additional features of the cortical response to long-interval intracortical inhibition (LICI). Methods: Single pulse and paired pulse LICI TMS-EEG responses from the bilateral dorsolateral prefrontal cortex were re-analysed in 16 healthy adults. At the stimulation site, 75 single-pulse and 75 paired-pulse trials were delivered; paired pulses comprised conditioning and test stimuli separated by 100 ms. Responses from 20 frontocentral EEG channels were analysed from 50-950 ms following TMS using the open source Brain Dynamic Metric Toolbox. Perturbative Integration Latency Index (PILI) indexed cortical resilience via response energy and recovery, Autocorrelation Function Width (ACFW) captured temporal memory, and Lyapunov Exponent (LE) indicated the dynamical stability. Linear mixed-effects models examined temporal, spectral, and time-frequency effects from the measured metrics. Results: LICI altered the temporal evolution of the dynamical measures. PILI distinguished conditions in the earliest post-stimulation window before converging, whereas ACFW differences persisted across a broader early interval. LE showed weaker and less consistent discrimination. Spectral effects were most evident for ACFW, while time-frequency analyses revealed later, frequency-specific LICI effects for PILI and ACFW but not LE. Conclusions: The extended post-stimulation trajectory revealed LICI-related differences in cortical resilience and temporal memory with distinct temporal and spectral profiles, while effects on dynamical stability were less consistent. Significance: The Brain Dynamic Metric Toolbox provides a reproducible framework for quantifying perturbation-recovery dynamics in TMS-EEG and demonstrates that the extended perturbational response contains information about LICI not captured by discrete early response measures.

neuroscience↗

bioRxiv2024

Cortical connectivity, local dynamics and stability correlates of global conscious states

Consciousness emerges from complex neural processes, yet its precise neurobiological correlates remain uncertain. Here a space-time-resolved inference-based framework is applied to estimate the neurophysiological variables of a wholecortex model and analyze the neural mechanism correlates of global consciousness by way of a correlation analysis between behavioural and neural variable time-series. Using magnetoencephalography (MEG) data from 15 participants under Xenon-induced anesthesia, interconnected neural mass models (NMMs) were developed and time-evolving regional neurophysiological variables and inter-regional connectivity strengths were inferred from the data. Analyses revealed significant correlations between consciousness levels and inter-regional connectivity, particularly in posterior parietal, occipital, and prefrontal regions. Moreover, results support a parietal, rather than frontal, network backbone to facilitate global consciousness. Regional-level analyses further identified correlates of consciousness within the posterior parietal and occipital regions. Lastly, reductions in consciousness were linked to stabilized cortical dynamics, reflected by changes in the eigenmodes of the system. This framework provides a novel, inference-based approach to investigating consciousness, offering a time-resolved perspective on neural mechanism correlates during altered states.

neuroscience↗

bioRxiv2022

Space-time resolved inference-based whole-brain neurophysiological mechanism imaging: application to resting-state alpha rhythm

Neural mechanisms are complex and difficult to image. This paper presents a new space-time resolved whole-brain imaging framework, called Neurophysiological Mechanism Imaging (NMI), that identifies neurophysiological mechanisms within cerebral cortex at the macroscopic scale. By fitting neural mass models to electromagnetic source imaging data using a novel nonlinear inference method, population averaged membrane potentials and synaptic connection strengths are efficiently and accurately imaged across the whole brain at a resolution afforded by source imaging. The efficiency of the framework enables return of the augmented source imaging results overnight using high performance computing. This suggests it can be used as a practical and novel imaging tool. To demonstrate the framework, it has been applied to resting-state magnetoencephalographic source estimates. The results suggest that endogenous inputs to cingulate, occipital, and inferior frontal cortex are essential modulators of resting-state alpha power. Moreover, endogenous input and inhibitory and excitatory neural populations play varied roles in mediating alpha power in different resting-state sub-networks. The framework can be applied to arbitrary neural mass models and has broad applicability to image neural mechanisms in different brain states. HighlightsO_LIThe whole-brain imaging framework can disclose the neurophysiological substrates of complicated brain functions in a spatiotemporal manner. C_LIO_LIDeveloped a semi-analytical Kalman filter to estimate neurophysiological variables in the nonlinear neural mass model efficiently and accurately from large-scale electromagnetic time-series. C_LIO_LIThe semi-analytical Kalman filter is 7.5 times faster and 5% more accurate in estimating model parameters than the unscented Kalman filter. C_LIO_LIProvided several group-level statistical observations based on neurophysiological variables and visualised them in a whole-brain manner to show different perspectives of neurophysiological mechanisms. C_LIO_LIApplied the framework to study resting-state alpha oscillation and found novel relationships between local neurophysiological variables in specific brain regions and alpha power. C_LI

neuroscience↗

bioRxiv2020

Epidynamics characterize and navigate the map of seizure dynamics

Seizures are a disruption of normal brain activity present across a vast range of species, diseases, and conditions. Here we introduce an organizing principle that leads to the first objective Taxonomy of Seizure Dynamics (TSD) based on bifurcation theory, and applied it to the analysis of EEG data. The "dynamotype" of a seizure is the part of its dynamic composition that defines its observable characteristics, including how it starts, evolves and terminates. Analyzing over 2000 focal-onset seizures recorded from 7 epilepsy centers on five continents, we find evidence of all 16 dynamotypes predicted in TSD. We demonstrate that patients dynamotypes evolve during their lifetime and display complex but systematic variations including hierarchy (certain dynamotypes are more common), non-bijectivity (a patient may display multiple dynamotypes) and pairing preference (multiple dynamotypes may occur during one seizure). TSD not only provides a way to stratify patients in complement to present practical classifications but also guides biophysically based mechanistic approaches and provides a language to describe the most critical features of seizure dynamics. Impact statementTaxonomy of Seizure Dynamics (TSD) provides a rigorous method for classifying and quantifying seizures and a principled framework for understanding seizure initiation and propagation.

bioinformatics↗

bioRxiv2019

Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy

Epilepsy diagnosis can be costly, time-consuming and not uncommonly inaccurate. The reference standard diagnostic monitoring is continuous video-EEG monitoring, ideally capturing all events or concordant interictal discharges. Automating EEG data review would save time and resources, thus enabling more people to receive reference standard monitoring and also potentially herald a more quantitative approach to therapeutic outcomes. There is substantial research into automated detection of seizures and epileptic activity from EEG. However, automated detection software is not widely used in the clinic; and, despite numerous published algorithms, few methods have regulatory approval for detecting epileptic activity from EEG.\n\nThis study reports on a deep learning algorithm for computer-assisted EEG review. Deep, convolutional neural networks were trained to detect epileptic discharges using a pre-existing dataset of over 6000 labelled events in a cohort of 103 patients with idiopathic generalized epilepsy (IGE). Patients underwent 24-hour ambulatory outpatient EEG, and all data was curated and confirmed independently by two epilepsy specialists (Seneviratne et al, 2016). The resulting automated detection algorithm was then used to review diagnostic scalp-EEG for seven patients (four with IGE and three with events mimicking seizures) to validate performance in a clinical setting.\n\nThe automated detection algorithm showed state-of-the-art performance for detecting epileptic activity from clinical EEG, with mean sensitivity of >95% and corresponding mean false positive rate of 1 detection per minute. Importantly, diagnostic case studies showed that the automated detection algorithm reduced human review time by 80%-99%, without compromising event detection or diagnostic accuracy. The presented results demonstrate that computer-assisted review can increase the speed and accuracy of EEG assessment and has the potential to greatly improve therapeutic outcomes.

neuroscience↗