Non-invasive Prefrontal/Frontal Brain Stimulation Is Not Effective in Modulating Food Reappraisal Abilities or Calorie Consumption in Obese Females

Background/Objectives: Previous studies suggest that non-invasive transcranial direct current stimulation (tDCS) applied to the prefrontal cortex modulates food choices and calorie intake in obese humans.Participants/Methods: In the present fully randomized, placebo-controlled, within-subject and double-blinded study, we applied single sessions of anodal, cathodal, and sham tDCS to the left dorsolateral prefrontal cortex (DLPFC) and contralateral frontal operculum in 25 hungry obese women and investigated possible influences on food reappraisal abilities as well as calorie intake. We hypothesized that tDCS, (i) improves the ability to regulate the desire for visually presented foods and, (ii) reduces their consumption.Results: We could not confirm an effect of anodal or cathodal tDCS, neither on the ability to modulate the desire for visually presented foods, nor on calorie consumption.Conclusions: The present findings do not support the notion of prefrontal/frontal tDCS as a promising treatment option for obesity

Zum gesellschaftlichen Nutzen pharmazeutischer Innovationen

Um den gesellschaftlichen Nutzen pharmazeutischer Innovationen abzuschätzen, bedarf es adäquater Beurteilungskriterien. Nach einem empirischen Abriß über die Entwicklung der Arzneimittelausgaben erörtern die Autoren die beurteilungsrelevanten gesamtwirtschaftlichen Ziele. Auf mikroökonomischer Basis bietet sich zur Ermittlung der Nützlichkeit von Arzneimitteln die Kosten-Nutzen-Analyse an. Im Prozeß gesundheitlicher Leistungserstellung nimmt das Arzneimittel den Rang eines Produktionsfaktors ein, der zumeist in Verbindung mit anderen Behandlungsarten eine Verbesserung des Gesundheitszustandes erzeugt. Dabei besitzen unter Effektivitäts- und Effizienzaspekten Innovations- und Imitationsprodukte unterschiedliche allokative Funktionen. Die beinahe weltweiten gesundheitspolitischen Regulierungen begünstigen die Imitationen und drohen damit die Forschungsanreize zu reduzieren

Simultaneous Acquisition of EEG and NIRS during Cognitive Tasks for an Open Access Dataset

We provide an open access multimodal brain-imaging dataset of simultaneous electroencephalography (EEG) and near-infrared spectroscopy (NIRS) recordings. Twenty-six healthy participants performed three cognitive tasks: 1) n-back (0-, 2- and 3-back), 2) discrimination/selection response task (DSR) and 3) word generation (WG) tasks. The data provided includes: 1) measured data, 2) demographic data, and 3) basic analysis results. For n-back (dataset A) and DSR tasks (dataset B), event-related potential (ERP) analysis was performed, and spatiotemporal characteristics and classification results for “target” vs. “non-target” (dataset A) and symbol “O” vs. symbol “X” (dataset B) are provided. Time-frequency analysis was performed to show the EEG spectral power to differentiate the task-relevant activations. Spatiotemporal characteristics of hemodynamic responses are also shown. For the WG task (dataset C), the EEG spectral power and spatiotemporal characteristics of hemodynamic responses are analyzed, and the potential merit of hybrid EEG-NIRS BCIs was validated with respect to classification accuracy. We expect that the dataset provided will facilitate performance evaluation and comparison of many neuroimaging analysis techniques

High-Resolution Optical Functional Mapping of the Human Somatosensory Cortex

Non-invasive optical imaging of brain function has been promoted in a number of fields in which functional magnetic resonance imaging (fMRI) is limited due to constraints induced by the scanning environment. Beyond physiological and psychological research, bedside monitoring and neurorehabilitation may be relevant clinical applications that are yet little explored. A major obstacle to advocate the tool in clinical research is insufficient spatial resolution. Based on a multi-distance high-density optical imaging setup, we here demonstrate a dramatic increase in sensitivity of the method. We show that optical imaging allows for the differentiation between activations of single finger representations in the primary somatosensory cortex (SI). Methodologically our findings confirm results in a pioneering study by Zeff et al. (2007) and extend them to the homuncular organization of SI. After performing a motor task, eight subjects underwent vibrotactile stimulation of the little finger and the thumb. We used a high-density diffuse-optical sensing array in conjunction with optical tomographic reconstruction. Optical imaging disclosed three discrete activation foci one for motor and two discrete foci for vibrotactile stimulation of the first and fifth finger, respectively. The results were co-registered to the individual anatomical brain anatomy (MRI) which confirmed the localization in the expected cortical gyri in four subjects. This advance in spatial resolution opens new perspectives to apply optical imaging in the research on plasticity notably in patients undergoing neurorehabilitation

A systematic review and activation likelihood estimation meta-Analysis of the central innervation of the lower urinary tract: Pelvic floor motor control and micturition

Purpose Functional neuroimaging is a powerful and versatile tool to investigate central lower urinary tract (LUT) control. Despite the increasing body of literature there is a lack of comprehensive overviews on LUT control. Thus, we aimed to execute a coordinate based meta-Analysis of all PET and fMRI evidence on descending central LUT control, i.e. pelvic floor muscle contraction (PFMC) and micturition. Materials and methods A systematic literature search of all relevant libraries was performed in August 2020. Coordinates of activity were extracted from eligible studies to perform an activation likelihood estimation (ALE) using a threshold of uncorrected p 0.001. Results 20 of 6858 identified studies, published between 1997 and 2020, were included. Twelve studies investigated PFMC (1xPET, 11xfMRI) and eight micturition (3xPET, 5xfMRI). The PFMC ALE analysis (n = 181, 133 foci) showed clusters in the primary motor cortex, supplementary motor cortex, cingulate gyrus, frontal gyrus, thalamus, supramarginal gyrus, and cerebellum. The micturition ALE analysis (n = 107, 98 foci) showed active clusters in the dorsal pons, including the pontine micturition center, the periaqueductal gray, cingulate gyrus, frontal gyrus, insula and ventral pons. Overlap of PFMC and micturition was found in the cingulate gyrus and thalamus. Conclusions For the first time the involved core brain areas of LUT motor control were determined using ALE. Furthermore, the involved brain areas for PFMC and micturition are partially distinct. Further neuroimaging studies are required to extend this ALE analysis and determine the differences between a healthy and a dysfunctional LUT. This requires standardization of protocols and task-execution

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Evaluierung und Optimierung von neuen analytischen Herangehensweisen für In-Vivo-Messungen mit Nah-Infrarot-Spektroskopie

Nah-Infrarot Spektroskopie (NIRS) ist eine nicht-invasive Technik im Bereich der neurowissenschaftlichen Bildgebung um hämodynamische Effekte zu messen, die neuronalen Ereignissen folgen. NIRS verwendet nah-infrarotes Licht um über den Farbunterschied von oxygeniertem und deoxygeniertem Hämoglobin die Konzentrationsdynamiken derselben zu ermitteln. Allerdings existieren Einschränkung für diese Technik: NIRS hat eine geringe Eindringtiefe in das Gewebe und eine hohe Empfindlichkeit gegenüber Artefakten, die von ruckartigen Bewegung oder kardio-vaskulären Oszillationen stammen. Des Weiteren ist die hämodynamische Antwort relativ langsam und überlappt sich bei schnellen Stimulationen. In der vorliegenden Arbeit stelle ich drei Analysekonzepte vor, die versuchen artefaktbedingte Defizite von NIRS überwinden. Diese sind: Allgemeines Lineare Model (GLM), maschinelles Lernen (ML) und funktionelle Konnektivität im Ruhezustand (RS-FC). Für die GLM-Analyse wird ein Model der erwarteten hämodynamischen Antwort generiert und mit gemessenen Daten verglichen. Ich zeige die Machbarkeit einer solchen Analyse für NIRS anhand einer Studie über Spiegelillusionen und einer Studie zur Entwicklung von Inhibition. In zuletzt genannter Studie werden schnelle Stimuluspräsentationen verwendet, weswegen die hämodynamischen Antworten überlappen. Diese können jedoch mit Hilfe des GLM entfaltet werden. Eine ähnliche Analyse wurde durchgeführt um den gemittelten und entfalteten Verlauf der hämodynamischen Antwort zu bestimmen. Beim maschinellen Lernen wird versucht die maximale Trennbarkeit zwischen zwei Konditionen mit Hilfe der gemessenen Daten zu bestimmen. ML-Analyse macht es zudem möglich Daten in Echtzeit zu interpretieren, wie hier am Beispiel von Gehirn-Computer Schnittstellen gezeigt wird. Eine weitere Anwendungsmöglichkeit von ML-Analysen ist die kontinuierliche Vorhersage des Aufmerksamkeitsstadiums eines Menschen. RS-FC hat das Ziel funktionelle, neuronale Netzwerke aus Ruhedaten zu extrahieren. Dies ist insbesondere sinnvoll um Menschen zu untersuchen die Konzentrationsschwierigkeiten haben, wie zum Beispiel Kinder oder Patienten mit schweren Schädigungen. Die RS-FC-Analyse korreliert langsame, spontane Oszillationen zwischen Bereichen des Gehirns die funktionelle Netzwerke bilden. In meiner Arbeit werden Netzwerke vorgestellt, die über weite Distanzen miteinander verbunden sind. Dies ist durch die Abdeckung des gesamten Kopfes mit Sensoren des NIRS möglich. Des Weiteren wird die Entwicklung von funktionellen Netzwerken von Kindern zu Erwachsenen dargestellt. Die RS-FC-Anaylse von NIRS-Daten wird zudem anhand von simultanen Messungen mit dem fMRT überprüft für welches RS-FC eigentlich entwickelt wurde. Alle drei vorgestellten Analysewerkzeuge können für NIRS verwendet werden. Jede Routine hat dabei spezifischen Anwendungsmöglichkeiten in den Neurowissenschaften. Daher öffnen sich viele neue Forschungsgebiete in denen es sinnvoll ist mit NIRS Daten zu erheben.Near-Infrared Spectroscopy (NIRS) is a non-invasive neuroimaging technology. It measures hemodynamic responses after local neuronal activity with near-infrared light. NIRS depends on the different colors of oxygenated and deoxygenated hemoglobin and measures their change of concentration over time. This technique suffers from several limitations as there is its low depth penetration depth and its sensibility to movement and cardio-vascular artifacts. Furthermore, the hemodynamic response is relative slow and tends to overlap in fast experimental design. In this work I present three recent analytical tools to overcome artifact related shortcomings of NIRS. The analyzing tools introduced here are General Linear Model analysis (GLM), Machine Learning tools (ML) and Resting-State Functional Connectivity analysis (RS-FC). For GLM analysis one creates a model of the expected hemodynamic response to stimuli and compares it to the actual measured data. I show the feasibility of GLM analysis for NIRS data in an experiment on mirror illusion and a developmental study on inhibition. The later contains overlapping hemodynamics which can be deconvolved by the GLM analysis. Further work on GLM-like approaches is done to yield a deconvolution of the average hemodynamic responses. ML tools maximize the separability between experimental conditions in the data and are capable of real-time data analysis. I show an example where we use ML techniques for Brain-Computer Interfaces. A second study shows the ability of ML routines to continuously predict the subject's attentional state. RS-FC aims to analyze functional networks between brain areas using resting-state data. This is useful for subjects that are not able to concentrate, like severely ill patients or young children. RS-FC analyzes slow, spontaneous oscillations which are correlated within areas forming a functional network. Here I present functional networks derived by RS-FC analysis in an experiment where the whole-head of the subjects was covered by NIRS sensors allowing also the estimation of long-distance connection between parietal and frontal brain areas. Further results are presented for developmental effects within resting-state networks. This approach is also validated against the \textit{golden standard} of fMRI where RS-FC analysis stems from. All three introduced analyzing techniques are feasible for NIRS data and have their specific applications in neuroscientific research which opens new fields of research for using NIRS

Investigation of Methods for data-driven functional performance testing using building automation data

Die mittels einer Gebäudeautomation verarbeiteten Daten werden heute noch fast ausschließlich für die Betriebsführung des Gebäudebetriebs genutzt. Bislang erfolgt kaum eine Wissens- und Informationsgenerierung aus diesen Daten, die einen enormen Informationsgehalt beinhalten. Dabei könnte dies helfen, um Funktionsprüfungen für einen fehlerfreien Gebäudebetrieb zu automatisieren und den energie- und kosteneffizienten Betrieb bei Einhaltung der Komfortansprüche sicherzustellen. Allerdings besteht eine hohe Einstiegshürde in der Entwicklung informationstechnologischer Methoden für IngenieurInnen, solche Lösungen skalierbar in Werkzeuge der Informations- und Kommunikationstechnologie zu verankern. Um den Informationsgehalt der Betriebsdaten nutzbar zu machen, werden bislang unterschiedliche Analysemethoden, teilweise mit Erfolg, erprobt. Häufig kommen modellbasierte Methoden zum Einsatz, welche einen hohen Grad an Expertenwissen voraussetzen, weswegen häufig Defizite hinsichtlich der Skalierung auftreten. Der derzeitige Forschungsstand zeigt, dass für individuelle Gebäude eine Fehlererkennung- und Diagnose möglich ist. Die Herausforderung liegt darin Methoden zu entwickeln, die generisch sind, um auf eine Vielzahl von unterschiedlichsten gebäudetechnischen Systemen anwendbar zu sein. Dafür bedarf es weiterer automatisierbarer Methoden, wobei die Skalierbarkeit als ein Schlüssel in dieser Arbeit angesehen wird. So könnten Fehler im Betrieb von Nicht-Wohngebäuden behoben werden, die in etwa 2 % des gesamten nationalen Energiebedarfs ausmachen. Das Ziel dieser Arbeit besteht in der Erprobung und Anwendung algorithmischer Lösungen, die die händischen Arbeitsschritte einer Funktionsprüfung automatisierbar gestalten und ExpertInnen unterstützen. Algorithmen, die ausschließlich auf datenbasierten Methoden beruhen, erscheinen hierbei besonders sinnvoll. Dabei wird die funktionsbasierte Sicht, hergeleitet aus der Systemtheorie, bis hin zur Informationsableitung mit Algorithmen aus dem Bereich der Künstlichen Intelligenz berücksichtigt. Diese Methoden werden anhand realer Betriebsdaten erprobt. Die Entwicklung dieser Methoden ist mit einem hohen Grad an Expertenwissen über den angewandten Algorithmus und der Interpretation von fehlerhaften Zuständen des Gebäudebetriebes verbunden. Unter der Zuhilfenahme von systemtheoretischen Ansätzen werden Abläufe entwickelt, mit denen mit geringem Expertenwissen Systeme automatisiert überprüft werden können. Die prototypische Umsetzung zeigt, dass Elemente der Funktionsprüfung automatisierbar sind. Durch eine modulare Gestaltung können diese auf eine Vielzahl von Systemen übertragen werden.To date, data processed by building automation systems is almost solely used for the operation of the building. So far, hardly any knowledge and information is derived from this data, although it contains an enormous amount of information. This would allow to automate functional tests and to ensure energy- and cost-efficient operation while maintaining comfort requirements. However, there is a barrier for engineers to develop information technology methods. In this way, scalable solutions could be implemented in ICT Tools. In order to make the information content of the operational data usable, so far different methods have been tested with mixed success. Often Model-based methods are used, which require a high degree of expert knowledge which led to deficits regarding scalability. The current state of research shows that fault detection and diagnosis is possible for individual buildings. The challenge is to develop methods that are generic so that they can be applied to a large number of different systems in buildings. Further automated methods are required where scalability is seen as a key aspect in this work. This would address operating errors in non-residential buildings which account for 2% of the total national energy demand. The goal of this work is to test and apply algorithmic solutions that automate and support functional performance tests. Algorithms that are based on data-driven methods seem to be particularly useful. Therefore, the functional view, derived from system theory, up to generation of information with algorithms from the field of artificial intelligence are considered. These methods are developed using operational time series data from existing buildings. The development and application of these methods require a high degree of expert knowledge in the application of these algorithms and the interpretation of faulty states of the building operation. With the help of system theory approaches, processes are developed that can be used to automatically validate a large number of systems with little expert knowledge. The prototypical realization shows that tasks of functional tests can be automated. With the developed modular design, these findings can be transferred to a large number of systems

Altered muscle activity during rest and during mental or physical activity is not a trait symptom of migraine - a neck muscle EMG study

Abstract Background Migraineurs have a high prevalence of neck pain prior to or during headache attacks. Whether neck pain is a symptom of migraine or an indicator for a constant neck muscle dysfunction potentially triggering migraine attacks is a topic of scientific debate. The presence of myofascial trigger points in neck muscles including the trapezius muscle, points towards muscle alterations associated with migraine. We measured electromyography (EMG) of the neck muscles in a large cohort to identify whether neck pain and neckmuscle tension reported by migraine patients can be attributed to increased neck muscle activation during rest, mental stress or physical activity. Methods Surface EMG responses of the trapezius muscle were recorded during a paradigm including rest periods, mental stress and physical activity of 102 participants (31 chronic migraine, 43 episodic migraine, 28 healthy participants). Results All groups showed increased trapezius activity during mental stress and physical activity compared to rest. There was no statistically significant difference between migraine patients and healthy controls for any of the 3 conditions except for the initial mental stress situation (F (2,56.022) = 8.302, p = 0.001), where controls increased tension by only 4.75%, episodic migraineurs by 17.39% and chronic migraineurs by 28.61%. Both migraine groups returned to resting EMG levels within the same timeframe as healthy controls. Conclusions Neck pain associated with migraine can therefore not be attributed to increased trapezius activity during rest, mental stress and physical activity or prolonged muscle activity and should not be seen as a constantly underlying trigger but rather as an accompanying symptom of migraine