arXiv subjects

Christoforos Papasavvas

Publications and source records attributed to Christoforos Papasavvas.

4 recordsLinked to original sources

arXiv2023

A library of quantitative markers of seizure severity

Purpose: Understanding fluctuations of seizure severity within individuals is important for defining treatment outcomes and response to therapy, as well as developing novel treatments for epilepsy. Current methods for grading seizure severity rely on qualitative interpretations from patients and clinicians. Quantitative measures of seizure severity would complement existing approaches, for EEG monitoring, outcome monitoring, and seizure prediction. Therefore, we developed a library of quantitative electroencephalographic (EEG) markers that assess the spread and intensity of abnormal electrical activity during and after seizures. Methods: We analysed intracranial EEG (iEEG) recordings of 1056 seizures from 63 patients. For each seizure, we computed 16 markers of seizure severity that capture the signal magnitude, spread, duration, and post-ictal suppression of seizures. Results: Quantitative EEG markers of seizure severity distinguished focal vs. subclinical and focal vs. FTBTC seizures across patients. In individual patients, 71% had a moderate to large difference (ranksum r > 0.3) between focal and subclinical seizures in three or more markers. Circadian and longer-term changes in severity were found for 67% and 53% of patients, respectively. Conclusion: We demonstrate the feasibility of using quantitative iEEG markers to measure seizure severity. Our quantitative markers distinguish between seizure types and are therefore sensitive to established qualitative differences in seizure severity. Our results also suggest that seizure severity is modulated over different timescales. We envisage that our proposed seizure severity library will be expanded and updated in collaboration with the epilepsy research community to include more measures and modalities.

q-bio.NC↗

arXiv2021

Long-term changes in functional connectivity predict responses to intracranial stimulation of the human brain

Targeted electrical stimulation of the brain perturbs neural networks and modulates their rhythmic activity both at the site of stimulation and at remote brain regions. Understanding, or even predicting, this neuromodulatory effect is crucial for any therapeutic use of brain stimulation. To this end, we analyzed the stimulation responses in 131 stimulation sessions across 66 patients with focal epilepsy recorded through intracranial EEG (iEEG). We considered functional and structural connectivity features as predictors of the response at every iEEG contact. Taking advantage of multiple recordings over days, we also investigated how slow changes in interictal functional connectivity (FC) ahead of the stimulation relate to stimulation responses. The results reveal that, indeed, this long-term variability of FC exhibits strong association with the stimulation-induced increases in delta and theta band power. Furthermore, we show through cross-validation that long-term variability of FC improves prediction of responses above the performance of spatial predictors alone. These findings can enhance the patient-specific design of effective neuromodulatory protocols for therapeutic interventions.

q-bio.NC↗

arXiv2021

Gain modulation unlocks phase plasticity, thus allowing for malleable nonlinear dynamics

Nonlinear dynamics emerge through either nonlinear interactions between the variables or through nonlinearities imposed on their linear interactions. Their interactions can be conceptualized as modulations of input-output (I/O) functions, where one variable modulates another variable's I/O function. Within this framework, nonlinear interactions are manifested as gain modulations, where the gain (i.e. slope) of a variable's I/O function is modulated by another variable. By analyzing oscillatory dynamics in modulation phase space, I show that gain modulation qualitatively enhances phase plasticity by loosening the relationship between oscillation phase and instantaneous gain. This finding reveals how gain modulation renders nonlinear dynamics more adaptable. I discuss the direct implications of this finding on the ability of any nonlinear system to get entrained to rhythmic input and synchronize with other systems and how it relates to neural dynamics.

math.DS↗

arXiv2021

Local Morphological Measures Confirm that Folding within Small Partitions of the Human Cortex Follows Universal Scaling Law

The universal scaling law of cortical morphology describes cortical folding as the covariance of average grey matter thickness, pial surface area, and exposed surface area. It applies for mammalian species, humans, and across lobes, however it remains to be shown that local cortical folding obeys the same rules. Here, we develop a method to obtain morphological measures for small regions across the cortex and correct surface areas by curvature to account for differences in patch size, resulting in a map of local morphology. It enables a near-pointwise analysis of morphological variables and their regional changes due to processes such as healthy ageing. We confirm empirically that the theorised covariance of morphological measures still holds at this level of local partition sizes as predicted, justifying the use of independent variables derived from the scaling law to identify regional differences in folding, subject-specific abnormalities, and local effects of ageing.

q-bio.NC↗