Pupillary response to percutaneous auricular vagus nerve stimulation in alcohol withdrawal syndrome: A pilot trial.

BACKGROUND: Autonomic symptoms in alcohol withdrawal syndrome (AWS) are associated with a sympathetic-driven imbalance of the autonomic nervous system. To restore autonomic balance in AWS, novel neuromodulatory approaches could be beneficial. We conducted a pilot trial with percutaneous auricular vagus nerve stimulation (pVNS) in AWS and hypothesized that pVNS will enhance the parasympathetic tone represented by a reduction of pupillary dilation in a parasympatholytic pharmacological challenge. METHODS: Thirty patients suffering from alcohol use disorder, undergoing AWS, and stable on medication, were recruited in this open-label, single-arm pilot trial with repeated-measure design. Peripheral VNS (monophasic volt impulses of 1 msec, alternating polarity, frequency 1 Hz, amplitude 4 mV) was administered at the left cymba conchae for 72 h, followed by pupillometry under a tropicamide challenge. We assessed craving with a visual analog scale. We used pupillary mean as the dependent variable in a repeated-measures ANOVA (rmANOVA). RESULTS: A repeated-measures ANOVA resulted in a significant difference for pupillary diameter across time and condition (F(2,116) = 27.97, p < .001, ηp2 > .14). Tukey-adjusted post hoc analysis revealed a significant reduction of pupillary diameter after pVNS. Alcohol craving was significantly reduced after pVNS (p < .05, Cohen's d = 1.27). CONCLUSION: Our study suggests that pVNS activates the parasympathetic nervous system in patients with acute AWS, and that this activation is measurable by pupillometry. To this end, pVNS could be beneficial as a supportive therapy for AWS. Potential confounding effects of anti-craving treatment should be kept in mind.

Influence of reconstruction settings in electrical impedance tomography on figures of merit and physiological parameters.

OBJECTIVE: Electrical impedance tomography (EIT) is a non-invasive and relatively cheap imaging technique allowing continuous monitoring of lung function at the bedside. However, image reconstruction and processing are not yet standardized for clinical use, limiting comparability and reproducibility between studies. In addition, optimal reconstruction settings still have to be identified for different clinical applications. In this work (i) a systematic way to select 'good' EIT algorithm parameters is developed and (ii) an evaluation of these parameters in terms of correct functional imaging and consistency is performed. APPROACH: First, 19 200 reconstruction models are generated by full factorial design of experiment in 5D space. Then, in order to quantify the quality of reconstruction, known conductivity changes are introduced and figures of merit (FoM) are calculated from the response image. These measures are further used to select a subset of reconstruction models, matching certain FoM thresholds, and are then used for in vivo evaluation. For this purpose, EIT images of one piglet are reconstructed to assess changes in tidal impedance and end-expiratory lung impedance, at positive end expiratory pressure of 0 and 15 cmH2O. From ground truth spirometry measurements, physiological criteria are formulated and the subset of models is further reduced. Finally, the remaining reconstruction models are evaluated on physiological data gathered from published data in the literature to assess the generalization possibilities. MAIN RESULTS: Parametrization of EIT image reconstruction has a strong influence on the resulting FoM and the derived physiological parameter. While numerous reconstruction models showed reasonable values for a single parameter, in total only 12 matched all simulation and physiological criteria. After validation on further physiological data, only a single reconstruction model remained with a noise figure of 0.3, target size of 0.08, weight radius of 0.3, normalized voltage and strong weighting of lung and heart regions. Furthermore, the relationship between the reconstruction settings and some FoM could be partly explained by using a linear statistical model. SIGNIFICANCE: The quest for standard reconstruction settings is highly relevant for future clinical applications. Simulation measures might help to assess the quality of the reconstruction models, but further evaluation of more data and different experimental settings is required.

Detection of thoracic vascular structures by electrical impedance tomography: a systematic assessment of prominence peak analysis of impedance changes.

OBJECTIVE: Electrical impedance tomography (EIT) is a non-invasive and radiation-free bedside monitoring technology, primarily used to monitor lung function. First experimental data shows that the descending aorta can be detected at different thoracic heights and might allow the assessment of central hemodynamics, i.e. stroke volume and pulse transit time. APPROACH: First, the feasibility of localizing small non-conductive objects within a saline phantom model was evaluated. Second, this result was utilized for the detection of the aorta by EIT in ten anesthetized pigs with comparison to thoracic computer tomography (CT). Two EIT belts were placed at different thoracic positions and a bolus of hypertonic saline (10 ml, 20%) was administered into the ascending aorta while EIT data were recorded. EIT images were reconstructed using the GREIT model, based on the individual's thoracic contours. The resulting EIT images were analyzed pixel by pixel to identify the aortic pixel, in which the bolus caused the highest transient impedance peak in time. MAIN RESULTS: In the phantom, small objects could be located at each position with a maximal deviation of 0.71 cm. In vivo, no significant differences between the aorta position measured by EIT and the anatomical aorta location were obtained for both measurement planes if the search was restricted to the dorsal thoracic region of interest (ROIs). SIGNIFICANCE: It is possible to detect the descending aorta at different thoracic levels by EIT using an intra-aortic bolus of hypertonic saline. No significant differences in the position of the descending aorta on EIT images compared to CT images were obtained for both EIT belts.

Automatic multimodal detection for long-term seizure documentation in epilepsy.

OBJECTIVE: This study investigated sensitivity and false detection rate of a multimodal automatic seizure detection algorithm and the applicability to reduced electrode montages for long-term seizure documentation in epilepsy patients. METHODS: An automatic seizure detection algorithm based on EEG, EMG, and ECG signals was developed. EEG/ECG recordings of 92 patients from two epilepsy monitoring units including 494 seizures were used to assess detection performance. EMG data were extracted by bandpass filtering of EEG signals. Sensitivity and false detection rate were evaluated for each signal modality and for reduced electrode montages. RESULTS: All focal seizures evolving to bilateral tonic-clonic (BTCS, n=50) and 89% of focal seizures (FS, n=139) were detected. Average sensitivity in temporal lobe epilepsy (TLE) patients was 94% and 74% in extratemporal lobe epilepsy (XTLE) patients. Overall detection sensitivity was 86%. Average false detection rate was 12.8 false detections in 24h (FD/24h) for TLE and 22 FD/24h in XTLE patients. Utilization of 8 frontal and temporal electrodes reduced average sensitivity from 86% to 81%. CONCLUSION: Our automatic multimodal seizure detection algorithm shows high sensitivity with full and reduced electrode montages. SIGNIFICANCE: Evaluation of different signal modalities and electrode montages paces the way for semi-automatic seizure documentation systems.