Biology subjects

Karoly, P. J.

Publications and source records attributed to Karoly, P. J..

5 recordsLinked to original sources

bioRxiv2026

A Neural Mass Modelling Framework for Evaluating EEG Source Localisation of Seizure Activity

Electroencephalography and magnetoencephalography (EEG/MEG) provide non-invasive measurements of large-scale neural activity but do not directly reveal the underlying cortical sources, motivating the use of source localisation algorithms. However, objective evaluation of these methods remains challenging due to the absence of an experimentally verifiable ground truth. This study presents a simulation framework for generating biologically plausible ictal dynamics and their corresponding EEG signals to enable systematic benchmarking of source imaging approaches. Cortical seizure initiation and propagation were simulated using network-coupled neural mass (Epileptor) models, and combined with realistic forward models of the human head to produce macroscopic, electrophysiological data with known ground truth under varying conditions. Using this dataset, we evaluated established source localisation methods across idealised and realistic scenarios. Existing approaches achieved reasonable spatial accuracy under high-density, noise-free conditions; however, performance degraded substantially with reduced sensor coverage and added noise. This degradation was driven primarily by failures to recover source polarity, even when spatial localisation remained relatively accurate. These results suggest that current methods may be sufficient for identifying epileptogenic regions or tracking regional recruitment, but highlight polarity reconstruction as a key limitation for studies of seizure dynamics and network organisation. The proposed framework provides a reproducible and biologically grounded testbed for the development and evaluation of electrophysiological source localisation techniques.

neuroscience↗

bioRxiv2026

Multiday rhythms modulate human heart rate: an observational study in healthy adults

BackgroundChronobiology research has historically focused on circadian rhythms; however, longer infradian rhythms are prevalent in human physiology and may have important implications for health and wellbeing. Previous studies have identified widespread infradian rhythms across human physiology, often in the context of hormonal regulation and disease. Despite growing evidence of their ubiquity, the mechanisms, significance, and clinical relevance of these rhythms remain poorly understood, largely due to lack of longitudinal datasets and robust detection methods. The emergence of new wearable technologies enables rich, continuous data capture within individuals, allowing physiological rhythms to be studied at scale. MethodsThis study analyzed a cohort of healthy, young adults (N=623), with up to four years of wearable and questionnaire data collected through the University of Notre Dames (USA) NetHealth project. Participants who recorded at least three months of continuous (>80% adherence) heart rate data were included and significant infradian rhythms were identified using wavelet analysis. Unsupervised non-negative matrix factorization was performed to cluster similar wavelet power spectrum distributions. Individuals heart rate rhythms were compared to known environmental cycles (day-of-week, lunar, seasonal) and considering demographics and social networks. A second, smaller cohort (N=70) with heart rate and menstrual timing were included to analyze the interplay of hormonal regulation on monthly cycles. Multinomial logistic regression, and statistical tests (i.e., one-way ANOVA) were applied to quantify the effects of environmental, behavioral and demographic factors on heart rate rhythms. FindingsSignificant infradian rhythms of heart rate were detected in 69.7% (365/523) of the cohort and 35.9% (188/523) had two or more rhythms. Annual, biannual and 10-week rhythms were the most common. Within the 4-45-day band, individuals clustered into four multiday chronotypes based on dominant periodicities in their wavelet power spectra: weekly ([~]7 days), shorter-monthly ([~]25 days), longer-monthly ([~]35 days), and multi-month (>35 days). Heart rate rhythms were influenced by environmental cycles (day-of-week and seasonality) but were not tightly correlated to external cues. Additionally, heart rate rhythms were synchronized to the menstrual cycle in most menstruating females, although monthly rhythms were also observed in males and menopausal women. InterpretationThe prevalence of infradian, or multiday heart rate rhythms in healthy young people motivates further scientific investigation to understand the mechanisms of these rhythms and their potential association with autonomic function, and risk of disease or disease-specific symptoms. Characterizing physiological rhythms can drive new insights into how multiscale fluctuations modulate disease symptoms across neurological, psychiatric, and broader health conditions.

physiology↗

bioRxiv2025

Structural Eigenmodes of the Brain to Improve the Source Localisation of EEG: Application to Epileptiform Activity

A fundamental view of neuroscience is that, in addition to neuronal activity, the structure of the brain constrains and explains brain function. An alluring formalism in computational neuroscience has been the generation of structural eigenmodes of neural activity from a matrix representing the anatomy of the brain. Traditionally, brain connectomics has been the gold standard for the coupling between structure and function. However, it has recently been suggested that simpler brain geometry can provide more explanatory power in fMRI. An adjacent modality is the source localisation problem of EEG, which aims to identify the underlying generators of EEG recordings. The underdetermined nature of the problem requires sufficient constraints to produce realistic and unique solutions of source activity. In this work, we presented a simple framework for incorporating different forms of structural brain eigenmodes to constrain the source localisation problem in epilepsy. We found that geometric eigenmodes were able to reconstruct the spread of a seizure through the brain slightly better than connectome eigenmodes, and both types of structural modes significantly outperformed commonly used approaches.

neuroscience↗

bioRxiv2020

Spatiotemporal patterns of high-frequency activity (80-170 Hz) in long-term intracranial EEG

ObjectiveTo assess the variability in the rates and locations of high-frequency activity (HFA) and epileptiform spikes after electrode implantation, and to examine the long-term patterns of HFA using ambulatory intracranial EEG (iEEG) recordings. MethodsContinuous iEEG recordings obtained over an average of 1.4 years from 15 patients with drug-resistant focal epilepsy were used in this study. HFA was defined as high-frequency events with amplitudes clearly larger than the background, which was automatically detected using a custom algorithm. High-frequency oscillations (HFOs) were also visually annotated by three neurologists in randomly sampled segments of the total data. The automatically detected HFA was compared with the visually marked HFOs. The variations of HFA rates were compared with spikes and seizures on patient-specific and electrode-specific bases. ResultsHFA was a more general event that encompassed HFOs manually annotated by different reviewers. HFA and spike rates had high amounts of intra- and inter-patient variability. The rates and locations of HFA and spikes took up to weeks to stabilize after electrode implantation in some patients. Both HFA and spike rates showed strong circadian rhythms in all patients and some also showed multiday cycles. Furthermore, the circadian patterns of HFA and spike rates had patient-specific correlations with seizures, which tended to vary across electrodes. ConclusionsAnalysis of HFA and epileptiform spikes should account for post-implantation variability. Like seizures, HFA and epileptiform spikes show circadian rhythms. However, the circadian profiles can vary spatially within patients and their correlations to seizures are patient-specific.

neuroscience↗

bioRxiv2019

Critical slowing as a biomarker for seizure susceptibility

The human brain has the capacity to rapidly change state, and in epilepsy these state changes can be catastrophic, resulting in loss of consciousness, injury and even death. Theoretical interpretations considering the brain as a dynamical system would suggest that prior to a seizure recorded brain signals may exhibit critical slowing, a warning signal preceding many critical transitions in dynamical systems. Using long-term intracranial electroencephalography (iEEG) recordings from fourteen patients with focal epilepsy, we found key signatures of critical slowing prior to seizures. Signals related to a critically slowing process fluctuated over temporally long scales (hours to days), longer than would be detectable in standard clinical evaluation settings. Seizure risk was associated with a combination of these signals together with epileptiform discharges. These results provide strong validation of theoretical models and demonstrate that critical slowing is a reliable indicator that could be used in seizure forecasting algorithms.

neuroscience↗