Biology subjects

Liley, D.

Publications and source records attributed to Liley, D..

5 recordsLinked to original sources

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

Inferring a simple mechanism for alpha-blocking by fitting a neural population model to EEG spectra

Alpha blocking, a phenomenon where the alpha rhythm is reduced by attention to a visual, auditory, tactile or cognitive stimulus, is one of the most prominent features of human electroencephalography (EEG) signals. Here we identify a simple physiological mechanism by which opening of the eyes causes attenuation of the alpha rhythm. We fit a neural population model to EEG spectra from 82 subjects, each showing different degrees of alpha blocking upon opening of their eyes. Although it is notoriously difficult to estimate parameters from fitting such models, we show that, by regularizing the differences in parameter estimates between eyes-closed and eyes-open states, we can reduce the uncertainties in these differences without significantly compromising fit quality. From this emerges a parsimonious explanation for the spectral changes between states: Just a single parameter, pei, corresponding to the strength of a tonic, excitatory input to the inhibitory population, is sufficient to explain the reduction in alpha rhythm upon opening of the eyes. When comparing parameter estimates across different subjects we find that the inferred differential change in pei for each subject increases monotonically with the degree of alpha blocking observed. In contrast, other parameters show weak or negligible differential changes that do not scale with the degree of alpha attenuation in each subject. Thus most of the variation in alpha blocking across subjects can be attributed to the strength of a tonic afferent signal to the inhibitory cortical population. Author summaryOne of the most striking features of the human electroencephalogram (EEG) is the presence of neural oscillations in the range of 8-13 Hz. It is well known that attenuation of these alpha oscillations, a process known as alpha blocking, arises from opening of the eyes, though the cause has remained obscure. In this study we infer the mechanism underlying alpha blocking by fitting a neural population model to EEG spectra from 82 different individuals. Although such models have long held the promise of being able to relate macroscopic recordings of brain activity to microscopic neural parameters, their utility has been limited by the difficulty of inferring these parameters from fits to data. Our approach is to fit both eyes-open and eyes-closed EEG spectra together, minimizing the number of parameter changes required to transition from one spectrum to the other. Surprisingly, we find that there is just one parameter, the external input to the inhibitory neurons in cortex, that is responsible for attenuating the alpha oscillations. We demonstrate how the strength of this inhibitory input scales monotonically with the degree of alpha blocking observed over all 82 subjects.

neuroscience↗

bioRxiv2019

In search of Universal Cortical Power Changes Linked to NMDA-Antagonist based Anesthetic Induced Reductions in Consciousness.

B. AbstractO_ST_ABSBackground.C_ST_ABSDespite their intriguing nature, investigations of the neurophysiology of N-methyl-D-aspartate (NMDA)-antagonists Xenon (Xe) and nitrous oxide (N2O) are limited and have revealed inconsistent frequency-dependent alterations, in spectral power and functional connectivity. Discrepancies are likely due to using low resolution electroencephalography restricted to sensor level changes, concomitant anesthetic agent administration and dosage. Our intention was to describe the effects of equivalent stepwise levels of Xe and N2O administration on oscillatory source power using a crossover design, to explore universal mechanisms of NMDA-based anesthesia.\n\nMethods.22 healthy males participated in a study of simultaneous magnetoencephalography and electroencephalography recordings. In separate sessions, equivalent subanesthetic doses of gaseous anesthetic agents N2O and Xe (0.25, 0.50, 0.75 equi MAC-awake) and 1.30 MAC-awake Xe (for Loss of Responsiveness) were administered. Source power in various frequency bands was computed and statistically assessed relative to a conscious baseline.\n\nResults.Delta (l-4Hz) and theta (4-8Hz) band power was significantly increased at the highest Xe concentration (42%, 1.30 MAC-awake) relative to baseline for both magnetoencephalography and electroencephalography source power (p<0.005). A reduction in frontal alpha (8-13 Hz) power was observed upon N2O administration, and shown to be stronger than equivalent Xe dosage reductions (p=0.005). Higher frequency activity increases were observed in magnetoencephalographic but not encephalographic signals for N2O alone with occipital low gamma (30-49Hz) and widespread high gamma (51-99Hz) rise in source power.\n\nConclusions.Magnetoencephalography source imaging revealed unequivocal and widespread power changes in dissociative anesthesia, which were divergent to source electroencephalography. Loss of Responsiveness anesthesia at 42% Xe (1.30 MAC-awake) demonstrated, similar to inductive agents, low frequency power increases in frontal delta and global theta. N2O sedation yielded a rise in high frequency power in the gamma range which was primarily occipital for lower gamma bandwidth (3049 Hz) and substantially decreased alpha power, particularly in frontal regions.\n\nClinical trial number and Registry URLNot applicable.\n\nPrior PresentationsPelentritou Andria, Kuhlmann Levin; Lee Heonsoo; Cormack John; Mcguigan Steven; Woods Will; Sleigh Jamie; Lee UnCheol; Muthukumaraswamy Suresh; Liley David. Searching For Universal Cortical Power Changes Linked To Anesthetic Induced Reductions In Consciousness. The Science of Consciousness April 4th 2018. Tucson, Arizona, USA.\n\nSummary StatementNot applicable.

neuroscience↗

bioRxiv2018

Parameter estimation and identifiability in a neural population model for electro-cortical activity

Electroencephalography (EEG) provides a non-invasive measure of brain electrical activity. Neural population models, where large numbers of interacting neurons are considered collectively as a macroscopic system, have long been used to understand features in EEG signals. By tuning dozens of input parameters describing the excitatory and inhibitory neuron populations, these models can reproduce prominent features of the EEG such as the alpha-rhythm. However, the inverse problem, of directly estimating the parameters from fits to EEG data, remains unsolved. Solving this multi-parameter non-linear fitting problem will potentially provide a real-time method for characterizing average neuronal properties in human subjects. Here we perform unbiased fits of a 22-parameter neural population model to EEG data from 82 individuals, using both particle swarm optimization and Markov chain Monte Carlo sampling. We estimate how much is learned about individual parameters by computing Kullback-Leibler divergences between posterior and prior distributions for each parameter. Results indicate that only a single parameter, that determining the dynamics of inhibition, is directly identifiable, while other parameters have large, though correlated, uncertainties. We show that the eigenvalues of the Fisher information matrix are roughly uniformly spaced over a log scale, indicating that the model is sloppy, like many of the regulatory network models in systems biology. These eigenvalues indicate that the system can be modeled with a low effective dimensionality, with inhibition being prominent in driving system behavior.\n\nAuthor summaryElectroencephalography (EEG), where electrodes are used to measure electric potential on the outside of the scalp, provides a simple, non-invasive way to study brain activity. Physiological interpretation of features in EEG signals has often involved use of collective models of neural populations. These neural population models have dozens of input parameters to describe the properties of inhibitory and excitatory neurons. Being able to estimate these parameters by direct fits to EEG data holds the promise of providing a real-time non-invasive method of inferring neuronal properties in different individuals. However, it has long been impossible to fit these nonlinear, multi-parameter models effectively. Here we describe fits of a 22-parameter neural population model to EEG spectra from 82 different subjects, all exhibiting alpha-oscillations. We show how only one parameter, that describing inhibitory dynamics, is constrained by the data, although all parameters are correlated. These results indicate that inhibition plays a central role in the generation and modulation of the alpha-rhythm in humans.

neuroscience↗