Ictal time-irreversible intracranial EEG signals as markers of the epileptogenic zone.

OBJECTIVE: To show that time-irreversible EEG signals recorded with intracranial electrodes during seizures can serve as markers of the epileptogenic zone. METHODS: We use the recently developed method of mapping time series into directed horizontal graphs (dHVG). Each node of the dHVG represents a time point in the original intracranial EEG (iEEG) signal. Statistically significant differences between the distributions of the nodes' number of input and output connections are used to detect time-irreversible iEEG signals. RESULTS: In 31 of 32 seizure recordings we found time-irreversible iEEG signals. The maximally time-irreversible signals always occurred during seizures, with highest probability in the middle of the first seizure half. These signals spanned a large range of frequencies and amplitudes but were all characterized by saw-tooth like shaped components. Brain regions removed from patients who became post-surgically seizure-free generated significantly larger time-irreversibilities than regions removed from patients who still had seizures after surgery. CONCLUSIONS: Our results corroborate that ictal time-irreversible iEEG signals can indeed serve as markers of the epileptogenic zone and can be efficiently detected and quantified in a time-resolved manner by dHVG based methods. SIGNIFICANCE: Ictal time-irreversible EEG signals can help to improve pre-surgical evaluation in patients suffering from pharmaco-resistant epilepsies.K.S. gratefully acknowledges support by the Swiss National Science Foundation (SNF 32003B_155950). H.G. gratefully acknowledges support by a Research Grant of the Inselspital Bern. R.G.A. acknowledges funding from the Volkswagen foundation and was supported by the Spanish Ministry of Economy and Competitiveness (Grant FIS2014-54177- R). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642563 (R.G.A.). MG gratefully acknowledges the financial support of the EPSRC via grant EP/N014391/1, funding from Epilepsy Research UK via grant number A1007 and was generously supported by a Wellcome Trust Institutional Strategic Support Award (WT105618MA)

Efficient Variational Bayesian Structure Learning of Dynamic Graphical Models

Estimating time-varying graphical models are of paramount importance in various social, financial, biological, and engineering systems, since the evolution of such networks can be utilized for example to spot trends, detect anomalies, predict vulnerability, and evaluate the impact of interventions. Existing methods require extensive tuning of parameters that control the graph sparsity and temporal smoothness. Furthermore, these methods are computationally burdensome with time complexity O(NP^3) for P variables and N time points. As a remedy, we propose a low-complexity tuning-free Bayesian approach, named BADGE. Specifically, we impose temporally-dependent spike-and-slab priors on the graphs such that they are sparse and varying smoothly across time. A variational inference algorithm is then derived to learn the graph structures from the data automatically. Owning to the pseudo-likelihood and the mean-field approximation, the time complexity of BADGE is only O(NP^2). Additionally, by identifying the frequency-domain resemblance to the time-varying graphical models, we show that BADGE can be extended to learning frequency-varying inverse spectral density matrices, and yields graphical models for multivariate stationary time series. Numerical results on both synthetic and real data show that that BADGE can better recover the underlying true graphs, while being more efficient than the existing methods, especially for high-dimensional cases

Neurophysiology of prospective memory in typical and atypical ageing

The ability to delay an intention is known as ‘prospective memory’ (PM) and underpins many day-to-day activities. The ubiquity of PM makes it essential for independent living in older adults. Research suggests that PM function declines as we age and may be further exacerbated with the development of mild cognitive impairment (MCI). To date, there has been no research examining the neurophysiology of PM in older adults with MCI. This thesis addresses a series of questions to help understand the neurophysiology of PM and how it may be affected by ageing and MCI: 1) Are there neurophysiological differences between highly salient PM cues and less salient PM cues? 2) Can the neurophysiological reorientation of attention be identified in PM tasks? 3) Are there behavioural and neurophysiological differences between young adults, older adults and older adults with MCI during PM tasks? 4) Are there behavioural and neurophysiological differences when maintaining a PM intention between young adults, older adults and older adults with MCI? 5) Can machine learning be used to understand spatiotemporal patterns of brain activity in response to PM between young adults, older adults and older adults with MCI? To answer these questions behavioural and time-locked electroencephalographic (EEG) responses were examined during PM tasks and were modelled with a machine learning method known as Spiking Neural Networks (SNN). Results suggest that: there are behavioural and neurophysiological differences between the PM cues and the neurophysiological reorientation of attention can be detected in PM tasks; older adults are not impaired in PM tasks possibly due to compensatory neural mechanisms; older adults with MCI may be impaired in some PM tasks, which may be due to deficits in attention and feelings of knowing; modelling PM with SNNs may offer useful ways of understanding spatiotemporal connectivity in PM and MCI