Miedema, M. | Search bioRxiv

bioRxiv2026

Denoising the central autonomic network: characterizing processes jointly associated with arousal and non-neuronal physiological artefacts in dynamic fMRI analyses

Using a multimodal dataset including fMRI, EEG-fMRI and concurrent physiological recordings, we investigated the effect of denoising systemic low frequency oscillations (sLFOs) on the characterization of dynamic signatures of central autonomic regulation and their relation to ongoing physiological states. We demonstrated that the time-frequency profiles of couplings between BOLD time series and cardiac and respiratory processes were statistically comparable between regions of the brain associated with autonomic function and non-autonomic motor regions, suggesting that these couplings largely do not reflect neuronal activation related to autonomic activity. We further showed that model-based (via physiological response functions) and data-driven (via CompCor nuisance regressors extracted from cerebrospinal fluid) methods of sLFO denoising had a statistically similar effect on these frequency profiles. We novelly applied co-activation pattern analysis to assess state dynamics of autonomic-associated regions of the brain, finding that such brain states interrelate decreases in vigilance and increases in heart rate, respiratory flow, and head motion, thus providing evidence for global arousal processes affecting BOLD signal in autonomic-associated regions. Lastly, we modelled sliding-window dynamic functional connectivity within the central autonomic network (CAN) as modulated by heart rate variability, showing significant differences in model outcomes linked to each denoising pipeline. With these findings, we provide a comprehensive discussion of the implications for the application of denoising techniques to the CAN and comment on the origins of dynamic components of the BOLD signal linked to autonomic regulation, highlighting the global role played by arousal.

neuroscience↗

bioRxiv2018

Optimized breath detection algorithm in Electrical Impedance Tomography

ObjectiveThis paper define a method for optimizing the breath delineation algorithms used in Electrical Impedance Tomography (EIT). In lung EIT the identification of the breath phases is central for generating tidal impedance variation images, subsequent data analysis and clinical evaluation. The optimisation of these algorithms is particularly important in neonatal care since the existing breath detectors developed for adults may give insufficient reliability in neonates due to their very irregular breathing pattern.\n\nApproachOur approach is generic in the sense that it relies on the definition of a gold standard and the associated definition of detector sensitivity and specificity, an optimisation criterion and a set of detector parameters to be investigated. The gold standard has been defined by 11 clinicians with previous experience with EIT and the performance of our approach is described and validated using a neonatal EIT dataset acquired within the EU-funded CRADL project.\n\nMain resultsThree different algorithms are proposed that are improving the breath detector performance by adding conditions on 1) maximum tidal breath rate obtained from zero-crossings of the EIT breathing signal, 2) minimum tidal impedance amplitude and 3) minimum tidal breath rate obtained from Time-Frequency (TF) analysis.\n\nSignificanceBased on the gold standard, the most crucial parameters of the proposed algorithms are optimised by using a simple exhaustive search and a weighted metric defined in connection with the Receiver Operating Characterics (ROC). This provides a practical way to achieve any desirable trade-off between the sensitivity and the specificity of the detectors.

bioengineering↗