The effect of normalization of Partial Directed Coherence on the statistical assessment of connectivity patterns: a simulation study

Synergistic dynamics of heart rate variability and systolic blood pressure revealed by dual Poincaré plot analysis

Cardiac arrest (CA) survivors often suffer secondary brain injury due to impaired autonomic nervous system (reflected by heart rate HR) and hemodynamic function (reflected by blood pressure BP and baroreflex regulation). This study proposes a Dual Poincaré Plot, a novel method analyzing coupled variability in HR - BP dynamics to assess these impairments. Ten rats were categorized into good and poor neurological outcome groups in a rat model of CA. Dual Poincaré plot analysis, squared partial directed coherence, and sequence method were used to characterize the outcome. Results revealed differences in variability of HR, BP, and baroreflex, as well as coupling strength, between groups. Good outcome subjects exhibited 1) increased variability in both BP and HR, 2) enhanced baroreflex regulation, and 3) weakened cardiovascular coupling. Conversely, subjects with poor outcomes displayed 1) decreased HR-BP variability, 2) impaired baroreflex, and 3) stronger coupling. The Baroreflex Index from the Dual Poincaré plot showed a high correlation with the traditional sequence method (R2 = 0.91). These results imply that Dual Poincaré offers a real-time assessment of autonomic-hemodynamic interaction, effectively stratifying post-CA neurological recovery and potentially enhancing prognostic accuracy for timely interventions.

SEED-G: Simulated EEG Data Generator for Testing Connectivity Algorithms

EEG signals are widely used to estimate brain circuits associated with specific tasks and cognitive processes. The testing of connectivity estimators is still an open issue because of the lack of a ground-truth in real data. Existing solutions such as the generation of simulated data based on a manually imposed connectivity pattern or mass oscillators can model only a few real cases with limited number of signals and spectral properties that do not reflect those of real brain activity. Furthermore, the generation of time series reproducing non-ideal and non-stationary ground-truth models is still missing. In this work, we present the SEED-G toolbox for the generation of pseudo-EEG data with imposed connectivity patterns, overcoming the existing limitations and enabling control of several parameters for data simulation according to the user's needs. We first described the toolbox including guidelines for its correct use and then we tested its performances showing how, in a wide range of conditions, datasets composed by up to 60 time series were successfully generated in less than 5 s and with spectral features similar to real data. Then, SEED-G is employed for studying the effect of inter-trial variability Partial Directed Coherence (PDC) estimates, confirming its robustness.