Preliminary findings of cerebral responses on transcutaneous vagal nerve stimulation on experimental heat pain.

Transcutaneous vagal nerve stimulation (TVNS) is a promising complementary method of pain relief. However, the neural networks associated with its analgesic effects are still to be elucidated. Therefore, we conducted two functional magnetic resonance imaging (fMRI) sessions, in a randomized order, with twenty healthy subjects who were exposed to experimental heat pain stimulation applied to the right forearm using a Contact Heat-Evoked Potential Stimulator. While in one session TVNS was administered bilaterally to the concha auriculae with maximal, non-painful intensity, the stimulation device was switched off in the other session (placebo condition). Pain thresholds were measured before and after each session. Heat stimulation elicited fMRI activation in cerebral pain processing regions. Activation magnitude in the secondary somatosensory cortex, posterior insula, anterior cingulate and caudate nucleus was associated with heat stimulation without TVNS. During TVNS, this association was only seen for the right anterior insula. TVNS decreased fMRI signals in the anterior cingulate cortex in comparison with the placebo condition; however, there was no relevant pain reducing effect over the group as a whole. In contrast, TVNS compared to the placebo condition showed an increased activation in the primary motor cortex, contralateral to the site of heat stimulation, and in the right amygdala. In conclusion, in the protocol used here, TVNS specifically modulated the cerebral response to heat pain, without having a direct effect on pain thresholds

Untersuchungen zum Einfluss der transkutanen aurikulären elektrischen Ohrstimulation auf experimentell induzierten Schmerz

Die transkutane Vagusnervstimulation (tVNS) findet als Methode zur Schmerzlinderung zunehmend mehr Beachtung, allerdings ist der zugrunde liegende Wirkmechanismus noch weitgehend unklar. Das Ziel dieser Dissertation war es daher, die potentiellen hypoalgetischen Effekte der tVNS in einer Kohorte aus gesunden Probanden zu untersuchen und anschließend mittels funktioneller Bildgebung den zerebralen Wirkmechanismus zu erforschen. Für die Studien wurden junge, gesunde, rechtshändige Probanden ausgewählt. Experimentelle elektrische oder thermische Schmerzen wurden mittels eines zertifizierten Schmerzgenerators am rechten Mittelfinger oder am Unterarm zugefügt. Für die tVNS fanden TENS-Geräte Verwendung. In einem Cross-Over-Design wurde die Reaktion der Probanden auf tVNS- und Placebo-Anwendung untersucht. Die Analyse der erhobenen Daten zeigte ein unterschiedliches Verhalten der Probanden auf tVNS. Während einige Probanden mit der erwarteten Hypoalgesie reagierten (Responder), zeigten Andere eine Hyperalgesie (Non-Responder). Unter Placebo- Bedingungen unterschieden sich die Probanden nicht. Die Vitalparameter, wie Blutdruck und Herzfrequenz, änderten sich während der gesamten Untersuchung nicht signifikant. Die Gruppenanalyse der zerebralen Antwort ergab keine signifikanten Ergebnisse. Erst nach Selektion der Responder (8/20 Probanden) zeigte sich unter tVNS eine reduzierte Aktivität in Arealen der affektiven Schmerzverarbeitung. Die Existenz von Respondern und Non-Respondern auf tVNS ist eine plausible Erklärung für die widersprüchlichen Ergebnisse in den vorausgegangenen experimentellen und klinischen Studien. Aufgrund von anatomischen Voraussetzungen der Methode ist eine reduzierte Wirkung, z.B. bei abweichender Innervation des Ohres oder unterschiedlicher Ausgangsaktivierung des Vagusnerven, möglich. Das nächste Ziel sollte daher sein, die Responder der Methode bereits vor der Behandlung zu ermitteln.Transcutaneous vagal nerve stimulation (tVNS) is gaining increasing attention as a method of pain relief, but the underlying mechanism of action is still largely unclear. The aim of this dissertation was therefore to investigate the potential hypoalgetic effects of tVNS in a cohort of healthy volunteers and then to investigate the cerebral mechanism of action by means of functional imaging. For the studies, young, healthy, right-handed subjects were selected. Experimental electrical or thermal pain was added to the right middle finger or the forearm using a certified pain generator. TENS devices were used for the tVNS. In a cross-over design, the response of subjects to tVNS and to placebo was investigated. The analysis of the collected data showed a different behavior of the subjects on tVNS. While some subjects responded with the expected hypoalgesia (responders), others showed hyperalgesia (non-responders). The subjects did not differ from placebo. The vital parameters, such as blood pressure and heart rate, did not significantly change during the whole study. The group analysis of the cerebral response revealed no significant results. Only after the selection of the responder (8/20 subjects) we found a reduced activity under tVNS in areas of affective pain processing. The existence of responders and non-responders on tVNS is a plausible explanation for the contradictory results in the previous experimental and clinical studies. On account of the anatomical presuppositions of the method, a reduced effect, e.g. with deviating innervation of the ear or different output activation of the vagus nerve is possible. The aim should therefore be to determine the responders of the method before treatment

A level set based framework for quantitative evaluation of breast tissue density from MRI data.

Breast density is a risk factor associated with the development of breast cancer. Usually, breast density is assessed on two dimensional (2D) mammograms using the American College of Radiology (ACR) classification. Magnetic resonance imaging (MRI) is a non-radiation based examination method, which offers a three dimensional (3D) alternative to classical 2D mammograms. We propose a new framework for automated breast density calculation on MRI data. Our framework consists of three steps. First, a recently developed method for simultaneous intensity inhomogeneity correction and breast tissue and parenchyma segmentation is applied. Second, the obtained breast component is extracted, and the breast-air and breast-body boundaries are refined. Finally, the fibroglandular/parenchymal tissue volume is extracted from the breast volume. The framework was tested on 37 randomly selected MR mammographies. All images were acquired on a 1.5T MR scanner using an axial, T1-weighted time-resolved angiography with stochastic trajectories sequence. The results were compared to manually obtained groundtruth. Dice's Similarity Coefficient (DSC) as well as Bland-Altman plots were used as the main tools for evaluation of similarity between automatic and manual segmentations. The average Dice's Similarity Coefficient values were 0.96±0.0172 and 0.83±0.0636 for breast and parenchymal volumes, respectively. Bland-Altman plots showed the mean bias (%) ± standard deviation equal 5.36±3.9 for breast volumes and -6.9±13.14 for parenchyma volumes. The automated framework produced sufficient results and has the potential to be applied for the analysis of breast volume and breast density of numerous data in clinical and research settings

Final steps for breast tissue extraction.

<p>Left: the concavities of the fibroglandular tissue are closed with morphological operations. Right: the approximate location of the sternum bone is computed and the cut is done along its lower boundary.</p

Bland-Altman with the regression line of differences on average (left) and linear regression (right) plots for the Parenchyma Volume (PV) values of 37 datasets.

<p>No exact influence of the breast density on the agreement results is observed.</p

An overview of the automated breast density evaluation framework.

<p>The approach consists of three main steps: segmentation and bias field correction, breast tissue delineation, and fibroglandular tissue extraction. The results of the bias field correction (Step 1) are used both for breast tissue (Step 2) and parenchyma (Step 3) extraction. In Step 3, the results of Steps 1 and 2 are utilized for parenchyma extraction. The data flow is schematically explained by the lines, connecting the pipeline steps.</p

Coefficients of the regression lines of the differences.

<p>Coefficients of the regression lines of the difference between the two methods on the average of the two methods for BV and PV.</p><p>Coefficients of the regression lines of the differences.</p

Another intensity inhomogeneity example.

<p>The intensities of the breast tissue close to the breast air boundaries (marked by the red rectangle) have higher values than the intensities of the breast tissue located in the middle of the breast.</p

Example slices from 4 different datasets (correspondent to 4 subjects).

<p>Each dataset belongs to a different ACR group.</p

Results for slices, shown in <b>Figure 1</b>.

<p>Left column: segmented images; middle column: corrected images; right column: histograms of the corrected images. The breast tissue becomes homogeneous after the correction. The histograms show the clearly separated intensity classes.</p