Intraoperative magnetic resonance imaging-guided transsphenoidal surgery for giant pituitary adenomas

Giant pituitary adenomas (GPAs), defined as  ≥40mm in one extension, present a challenging subgroup of pituitary adenomas in terms of radical tumor removal and complication rates. The potential impact of intraoperative magnetic resonance imaging (iMRI) is investigated in a consecutive series and the results compared to the literature. From November 2004 until February 2005, six (five male) patients were operated for GPAs via an iMRI-guided transsphenoidal approach in the PoleStar™ N20. Clinical, endocrinological, and neuroradiological outcomes (at 3months and yearly postoperative over 4years) were assessed. Mean age was 46years (range, 34-60). All patients presented with preoperative visual field defects, five with pituitary failure. Five adenomas were clinically nonfunctioning, one was producing GH and TSH. Preoperative imaging showed invasion of the cavernous sinus in all and extension to the interventricular foramen in two patients (one with occlusive hydrocephalus). Resection was total in four and subtotal (small cavernous sinus remnants) in two patients, leading to transsphenoidal reoperation in one patient. Visual acuity and fields improved in all six patients. The patient with occlusive hydrocephalus developed a postoperative cerebrospinal fluid leak (subsequently revised), two patients developed temporary, one permanent central diabetes insipidus, and one of them transient hyponatremia. Compared to the preoperative situation, endocrine status in the long-term follow-up (mean, 25months) remained unchanged in four and worsened in two. Two patients were considered not to require hormone replacement therapy. IMRI supports transsphenoidal resections of GPAs because residual adenoma and related risk structures are easily detected and localized intraoperatively, extending the restricted visual access of the microscope beyond mere surface anatomy to a three-dimensional view. More radical removal of adenomas in a single surgical session combined with low complication rates are accomplished. This may add to a favorable clinical and endocrinological outcome in GPA

Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons : comparison and implementation

The spiking activity of single neurons can be well described by a nonlinear integrate-and-fire model that includes somatic adaptation. When exposed to fluctuating inputs sparsely coupled populations of these model neurons exhibit stochastic collective dynamics that can be effectively characterized using the Fokker-Planck equation. This approach, however, leads to a model with an infinite-dimensional state space and non-standard boundary conditions. Here we derive from that description four simple models for the spike rate dynamics in terms of low-dimensional ordinary differential equations using two different reduction techniques: one uses the spectral decomposition of the Fokker-Planck operator, the other is based on a cascade of two linear filters and a nonlinearity, which are determined from the Fokker-Planck equation and semi-analytically approximated. We evaluate the reduced models for a wide range of biologically plausible input statistics and find that both approximation approaches lead to spike rate models that accurately reproduce the spiking behavior of the underlying adaptive integrate-and-fire population. Particularly the cascade-based models are overall most accurate and robust, especially in the sensitive region of rapidly changing input. For the mean-driven regime, when input fluctuations are not too strong and fast, however, the best performing model is based on the spectral decomposition. The low-dimensional models also well reproduce stable oscillatory spike rate dynamics that are generated either by recurrent synaptic excitation and neuronal adaptation or through delayed inhibitory synaptic feedback. The computational demands of the reduced models are very low but the implementation complexity differs between the different model variants. Therefore we have made available implementations that allow to numerically integrate the low-dimensional spike rate models as well as the Fokker-Planck partial differential equation in efficient ways for arbitrary model parametrizations as open source software. The derived spike rate descriptions retain a direct link to the properties of single neurons, allow for convenient mathematical analyses of network states, and are well suited for application in neural mass/mean-field based brain network models.Characterizing the dynamics of biophysically modeled, large neuronal networks usually involves extensive numerical simulations. As an alternative to this expensive procedure we propose efficient models that describe the network activity in terms of a few ordinary differential equations. These systems are simple to solve and allow for convenient investigations of asynchronous, oscillatory or chaotic network states because linear stability analyses and powerful related methods are readily applicable. We build upon two research lines on which substantial efforts have been exerted in the last two decades: (i) the development of single neuron models of reduced complexity that can accurately reproduce a large repertoire of observed neuronal behavior, and (ii) different approaches to approximate the Fokker-Planck equation that represents the collective dynamics of large neuronal networks. We combine these advances and extend recent approximation methods of the latter kind to obtain spike rate models that surprisingly well reproduce the macroscopic dynamics of the underlying neuronal network. At the same time the microscopic properties are retained through the single neuron model parameters. To enable a fast adoption we have released an efficient Python implementation as open source software under a free license

The Price of Privacy - An Evaluation of the Economic Value of Collecting Clickstream Data

The analysis of clickstream data facilitates the understanding and prediction of customer behavior in e-commerce. Companies can leverage such data to increase revenue. For customers and website users, on the other hand, the collection of behavioral data entails privacy invasion. The objective of the paper is to shed light on the trade-off between privacy and the business value of cus- tomer information. To that end, the authors review approaches to convert clickstream data into behavioral traits, which we call clickstream features, and propose a categorization of these features according to the potential threat they pose to user privacy. The authors then examine the extent to which different categories of clickstream features facilitate predictions of online user shopping pat- terns and approximate the marginal utility of using more privacy adverse information in behavioral prediction models. Thus, the paper links the literature on user privacy to that on e-commerce analytics and takes a step toward an economic analysis of privacy costs and benefits. In par- ticular, the results of empirical experimentation with large real-world e-commerce data suggest that the inclusion of short-term customer behavior based on session-related information leads to large gains in predictive accuracy and business performance, while storing and aggregating usage behavior over longer horizons has comparably less value

The effects of social norms among peer groups on risk behavior: A multilevel approach to differentiate perceived and collective norms

Social norms have been found to be an important factor in individuals’ health and risk behaviors. Past research has typically addressed which social norms individuals perceive in their social environments (e.g., in their peer group). The present article explores normative social influences beyond such perceptions by applying a multilevel approach and differentiating between perceived norms at the individual level and collective norms at the group level. Data on norms and three road traffic risk behaviors (speeding, driving after drinking, and texting while driving) were obtained from a representative survey among young German car drivers (N = 311 anchor respondents) and their peer groups (overall N = 1,244). Multilevel modeling (MLM) revealed that beyond individual normative perceptions of peers’ behavior and approval, actual collective norms (peers’ actual risk behavior and attitudes) affect individuals’ risk behaviors. Findings are discussed with regard to theorizing normative influences on risk behavior and practical implications

Privacy Policies and Users’ Trust: Does Readability Matter?

Over the years, a drastic increase in online information disclosure spurs a wave of concerns from multiple stakeholders. Among others, users resent the “behind the closed doors” processing of their personal data by companies. Privacy policies are supposed to inform users how their personal information is handled by a website. However, several studies have shown that users rarely read privacy policies for various reasons, not least because limitedly readable policy texts are difficult to understand. Based on our online survey with over 440 responses, we examine the objective and subjective readability of privacy policies and investigate their impact on users’ trust in five big Internet services. Our findings show the stronger a user believes in having understood the privacy policy, the higher he or she trusts a web site across all companies we studied. Our results call for making readability of privacy policies more accessible to an average reader

Control-guided Communication: Efficient Resource Arbitration and Allocation in Multi-hop Wireless Control Systems

In future autonomous systems, wireless multi-hop communication is key to enable collaboration among distributed agents at low cost and high flexibility. When many agents need to transmit information over the same wireless network, communication becomes a shared and contested resource. Event-triggered and self-triggered control account for this by transmitting data only when needed, enabling significant energy savings. However, a solution that brings those benefits to multi-hop networks and can reallocate freed up bandwidth to additional agents or data sources is still missing. To fill this gap, we propose control-guided communication, a novel co-design approach for distributed self-triggered control over wireless multi-hop networks. The control system informs the communication system of its transmission demands ahead of time, and the communication system allocates resources accordingly. Experiments on a cyber-physical testbed show that multiple cart-poles can be synchronized over wireless, while serving other traffic when resources are available, or saving energy. These experiments are the first to demonstrate and evaluate distributed self-triggered control over low-power multi-hop wireless networks at update rates of tens of milliseconds.Comment: Accepted final version to appear in: IEEE Control Systems Letter