Investigating two mobile just-in-time adaptive interventions to foster psychological resilience: research protocol of the DynaM-INT study

BACKGROUND

Stress-related disorders such as anxiety and depression are highly prevalent and cause a tremendous burden for affected individuals and society. In order to improve prevention strategies, knowledge regarding resilience mechanisms and ways to boost them is highly needed. In the Dynamic Modelling of Resilience - interventional multicenter study (DynaM-INT), we will conduct a large-scale feasibility and preliminary efficacy test for two mobile- and wearable-based just-in-time adaptive interventions (JITAIs), designed to target putative resilience mechanisms. Deep participant phenotyping at baseline serves to identify individual predictors for intervention success in terms of target engagement and stress resilience.

METHODS

DynaM-INT aims to recruit N = 250 healthy but vulnerable young adults in the transition phase between adolescence and adulthood (18-27 years) across five research sites (Berlin, Mainz, Nijmegen, Tel Aviv, and Warsaw). Participants are included if they report at least three negative burdensome past life events and show increased levels of internalizing symptoms while not being affected by any major mental disorder. Participants are characterized in a multimodal baseline phase, which includes neuropsychological tests, neuroimaging, bio-samples, sociodemographic and psychological questionnaires, a video-recorded interview, as well as ecological momentary assessments (EMA) and ecological physiological assessments (EPA). Subsequently, participants are randomly assigned to one of two ecological momentary interventions (EMIs), targeting either positive cognitive reappraisal or reward sensitivity. During the following intervention phase, participants' stress responses are tracked using EMA and EPA, and JITAIs are triggered if an individually calibrated stress threshold is crossed. In a three-month-long follow-up phase, parts of the baseline characterization phase are repeated. Throughout the entire study, stressor exposure and mental health are regularly monitored to calculate stressor reactivity as a proxy for outcome resilience. The online monitoring questionnaires and the repetition of the baseline questionnaires also serve to assess target engagement.

DISCUSSION

The DynaM-INT study intends to advance the field of resilience research by feasibility-testing two new mechanistically targeted JITAIs that aim at increasing individual stress resilience and identifying predictors for successful intervention response. Determining these predictors is an important step toward future randomized controlled trials to establish the efficacy of these interventions.

Data2Dynamics: a modeling environment tailored to parameter estimation in dynamical systems

Modeling of dynamical systems using ordinary differential equations is a popular approach in the field of systems biology. Two of the most critical steps in this approach are to construct dynamical models of biochemical reaction networks for large datasets and complex experimental conditions and to perform efficient and reliable parameter estimation for model fitting. We present a modeling environment for MATLAB that pioneers these challenges. The numerically expensive parts of the calculations such as the solving of the differential equations and of the associated sensitivity system are parallelized and automatically compiled into efficient C code. A variety of parameter estimation algorithms as well as frequentist and Bayesian methods for uncertainty analysis have been implemented and used on a range of applications that lead to publications.

AVAILABILITY AND IMPLEMENTATION

The Data2Dynamics modeling environment is MATLAB based, open source and freely available at http://www.data2dynamics.org.

CONTACT

andreas.raue@fdm.uni-freiburg.de

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

[Regadenoson as a new stress agent in myocardial perfusion imaging. Initial experience in The Netherlands]

OBJECTIVE

Regadenoson is a recently approved selective adenosine-2A receptor agonist to induce pharmacological stress in myocardial perfusion imaging (MPI) procedures using a single bolus injection.

MATERIAL AND METHODS

We included 123 patients referred for MPI because of suspected coronary arterial disease (CAD). Of these, 66 patients underwent a regadenoson stress test and 57 patients underwent an adenosine stress test preceding standard myocardial SPECT imaging. Technicians, physicians and patients were asked to report their experience using questionnaires.

RESULTS

As compared to adenosine, regadenoson did not produce any atrio-ventricular block (0 vs. 10% with adenosine), but did produce minor tachycardia and minimal blood pressure changes while all other side effects were milder and shorter. There were fewer patients with severe complaints after taking regadenoson than adenosine (17% vs. 32%, respectively, p<0.01). The most frequent complaint reported was dyspnea, followed by flushing and chest pain. However, when they did occur, they usually disappeared rapidly. The overall symptom score, including severity and duration of side effects, was significantly lower after regadenoson than after adenosine (6.7±6.3 vs. 10.0±7.9, respectively; p<0.01.) SPECT imaging results were similar. The regadenoson procedure was faster and more practical.

CONCLUSION

Regadenoson, the new selective adenosine-2A receptor agonist, is a stress agent for MPI with a patient- and department friendly profile.

[Differential diagnosis of myeloproliferative neoplasms. Quantitative NF-E2 immunohistochemistry for differentiating between essential thrombocythemia and primary myelofibrosis]

BACKGROUND

Besides essential thrombocythemia (ET), polycythemia vera (PV) and primary myelofibrosis (PMF) the myeloproliferative neoplasms (MPN) defined by the World Health Organization (WHO) comprise the entity of unclassifiable MPNs (MPN, U). The exact differential diagnosis of the specific MPN entities can be challenging particularly at early stages of the diseases. So far, pathologists have had to rely only on histomorphological evaluation of bone marrow biopsies in combination with laboratory data because helpful ancillary tests are not yet available. Even molecular tests, such as JAK2 mutation analysis are not helpful particularly in the differential diagnosis of ET and PMF because both entities are associated with the V617F mutation in 50 % of the cases. Recently overexpression of the transcription factor NF-E2 in MPN was described.

MATERIALS AND METHODS

A collective of samples consisting of 163 bone marrow biopsies including 139 MPN cases was stained immunohistochemically for NF-E2 and analyzed regarding the subcellular localization of NF-E2 in erythroid progenitor cells. The results were compared between the MPN entities as well as the controls and statistical analyses were conducted.

RESULTS AND DISCUSSION

This study showed that NF-E2 immunohistochemistry and analysis of the proportion of nuclear positive erythroblasts of all erythroid precursor cells can help to distinguish between ET and PMF even in early stages of the diseases. An MPN, U case showing a proportion of more than 20 % nuclear positive erythroblasts can be classified as a PMF with 92 % accuracy.

Comprehensive estimation of input signals and dynamics in biochemical reaction networks

Motivation: Cellular information processing can be described mathematically using differential equations. Often, external stimulation of cells by compounds such as drugs or hormones leading to activation has to be considered. Mathematically, the stimulus is represented by a time-dependent input function.

Parameters such as rate constants of the molecular interactions are often unknown and need to be estimated from experimental data, e.g. by maximum likelihood estimation. For this purpose, the input function has to be defined for all times of the integration interval. This is usually achieved by approximating the input by interpolation or smoothing of the measured data. This procedure is suboptimal since the input uncertainties are not considered in the estimation process which often leads to overoptimistic confidence intervals of the inferred parameters and the model dynamics.

Results: This article presents a new approach which includes the input estimation into the estimation process of the dynamical model parameters by minimizing an objective function containing all parameters simultaneously. We applied this comprehensive approach to an illustrative model with simulated data and compared it to alternative methods. Statistical analyses revealed that our method improves the prediction of the model dynamics and the confidence intervals leading to a proper coverage of the confidence intervals of the dynamic parameters. The method was applied to the JAK-STAT signaling pathway.

Availability: MATLAB code is available on the authors' website http://www.fdmold.uni-freiburg.de/~schelker/.

Contact:max.schelker@fdm.uni-freiburg.de

Supplementary Information: Additional information is available at Bioinformatics Online.

Influence of cell-to-cell variability on spatial pattern formation

Many spatial patterns in biology arise through differentiation of selected cells within a tissue, which is regulated by a genetic network. This is specified by its structure, parameterisation and the noise on its components and reactions. The latter, in particular, is not well examined because it is rather difficult to trace. The authors use suitable local mathematical measures based on the Voronoi diagram of experimentally determined positions of epidermal plant hairs (trichomes) to examine the variability or noise in pattern formation. Although trichome initiation is a highly regulated process, the authors show that the experimentally observed trichome pattern is substantially disturbed by cell-to-cell variations. Using computer simulations, they find that the rates concerning the availability of the protein complex that triggers trichome formation plays a significant role in noise-induced variations of the pattern. The focus on the effects of cell noise yields further insights into pattern formation of trichomes. The authors expect that similar strategies can contribute to the understanding of other differentiation processes by elucidating the role of naturally occurring fluctuations in the concentration of cellular components or their properties.

Predictive mathematical models of cancer signalling pathways

Complex intracellular signalling networks integrate extracellular signals and convert them into cellular responses. In cancer cells, the tightly regulated and fine-tuned dynamics of information processing in signalling networks is altered, leading to uncontrolled cell proliferation, survival and migration. Systems biology combines mathematical modelling with comprehensive, quantitative, time-resolved data and is most advanced in addressing dynamic properties of intracellular signalling networks. Here, we introduce different modelling approaches and their application to medical systems biology, focusing on the identifiability of parameters in ordinary differential equation models and their importance in network modelling to predict cellular decisions. Two related examples are given, which include processing of ligand-encoded information and dual feedback regulation in erythropoietin (Epo) receptor signalling. Finally, we review the current understanding of how systems biology could foster the development of new treatment strategies in the context of lung cancer and anaemia.

EPILAB: a software package for studies on the prediction of epileptic seizures

A Matlab®-based software package, EPILAB, was developed for supporting researchers in performing studies on the prediction of epileptic seizures. It provides an intuitive and convenient graphical user interface. Fundamental concepts that are crucial for epileptic seizure prediction studies were implemented. This includes, for example, the development and statistical validation of prediction methodologies in long-term continuous recordings. Seizure prediction is usually based on electroencephalography (EEG) and electrocardiography (ECG) signals. EPILAB is able to process both EEG and ECG data stored in different formats. More than 35 time and frequency domain measures (features) can be extracted based on univariate and multivariate data analysis. These features can be post-processed and used for prediction purposes. The predictions may be conducted based on optimized thresholds or by applying classifications methods such as artificial neural networks, cellular neuronal networks, and support vector machines. EPILAB proved to be an efficient tool for seizure prediction, and aims to be a way to communicate, evaluate, and compare results and data among the seizure prediction community.

Registration of 2D x-ray images to 3D MRI by generating pseudo-CT data

Spatial and soft tissue information provided by magnetic resonance imaging can be very valuable during image-guided procedures, where usually only real-time two-dimensional (2D) x-ray images are available. Registration of 2D x-ray images to three-dimensional (3D) magnetic resonance imaging (MRI) data, acquired prior to the procedure, can provide optimal information to guide the procedure. However, registering x-ray images to MRI data is not a trivial task because of their fundamental difference in tissue contrast. This paper presents a technique that generates pseudo-computed tomography (CT) data from multi-spectral MRI acquisitions which is sufficiently similar to real CT data to enable registration of x-ray to MRI with comparable accuracy as registration of x-ray to CT. The method is based on a k-nearest-neighbors (kNN)-regression strategy which labels voxels of MRI data with CT Hounsfield Units. The regression method uses multi-spectral MRI intensities and intensity gradients as features to discriminate between various tissue types. The efficacy of using pseudo-CT data for registration of x-ray to MRI was tested on ex vivo animal data. 2D-3D registration experiments using CT and pseudo-CT data of multiple subjects were performed with a commonly used 2D-3D registration algorithm. On average, the median target registration error for registration of two x-ray images to MRI data was approximately 1 mm larger than for x-ray to CT registration. The authors have shown that pseudo-CT data generated from multi-spectral MRI facilitate registration of MRI to x-ray images. From the experiments it could be concluded that the accuracy achieved was comparable to that of registering x-ray images to CT data.

Identifiability and observability analysis for experimental design in nonlinear dynamical models

Dynamical models of cellular processes promise to yield new insights into the underlying systems and their biological interpretation. The processes are usually nonlinear, high dimensional, and time-resolved experimental data of the processes are sparse. Therefore, parameter estimation faces the challenges of structural and practical nonidentifiability. Nonidentifiability of parameters induces nonobservability of trajectories, reducing the predictive power of the model. We will discuss a generic approach for nonlinear models that allows for identifiability and observability analysis by means of a realistic example from systems biology. The results will be utilized to design new experiments that enhance model predictiveness, illustrating the iterative cycle between modeling and experimentation in systems biology.

Robust initialization of 2D-3D image registration using the projection-slice theorem and phase correlation

PURPOSE

The image registration literature comprises many methods for 2D-3D registration for which accuracy has been established in a variety of applications. However, clinical application is limited by a small capture range. Initial offsets outside the capture range of a registration method will not converge to a successful registration. Previously reported capture ranges, defined as the 95% success range, are in the order of 4-11 mm mean target registration error. In this article, a relatively computationally inexpensive and robust estimation method is proposed with the objective to enlarge the capture range.

METHODS

The method uses the projection-slice theorem in combination with phase correlation in order to estimate the transform parameters, which provides an initialization of the subsequent registration procedure.

RESULTS

The feasibility of the method was evaluated by experiments using digitally reconstructed radiographs generated from in vivo 3D-RX data. With these experiments it was shown that the projection-slice theorem provides successful estimates of the rotational transform parameters for perspective projections and in case of translational offsets. The method was further tested on ex vivo ovine x-ray data. In 95% of the cases, the method yielded successful estimates for initial mean target registration errors up to 19.5 mm. Finally, the method was evaluated as an initialization method for an intensity-based 2D-3D registration method. The uninitialized and initialized registration experiments had success rates of 28.8% and 68.6%, respectively.

CONCLUSIONS

The authors have shown that the initialization method based on the projection-slice theorem and phase correlation yields adequate initializations for existing registration methods, thereby substantially enlarging the capture range of these methods.

Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood

MOTIVATION

Mathematical description of biological reaction networks by differential equations leads to large models whose parameters are calibrated in order to optimally explain experimental data. Often only parts of the model can be observed directly. Given a model that sufficiently describes the measured data, it is important to infer how well model parameters are determined by the amount and quality of experimental data. This knowledge is essential for further investigation of model predictions. For this reason a major topic in modeling is identifiability analysis.

RESULTS

We suggest an approach that exploits the profile likelihood. It enables to detect structural non-identifiabilities, which manifest in functionally related model parameters. Furthermore, practical non-identifiabilities are detected, that might arise due to limited amount and quality of experimental data. Last but not least confidence intervals can be derived. The results are easy to interpret and can be used for experimental planning and for model reduction.

AVAILABILITY

An implementation is freely available for MATLAB and the PottersWheel modeling toolbox at http://web.me.com/andreas.raue/profile/software.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A longitudinal study of tremor frequencies in Parkinson's disease and essential tremor

OBJECTIVE

There is evidence that the tremor frequency in essential tremor (ET) decreases with time. Longitudinal studies on the evolution of tremor frequencies in Parkinson's disease (PD) have so far not been published. Here, we present a longitudinal analysis of tremor frequencies in PD and ET.

METHODS

We analyzed the standardized accelerometric and electromyographic tremor recordings of 53 patients with PD and 38 patients with ET who underwent repeated routine tremor recordings between 1991 and 2002.

RESULTS

In an average follow-up period of 44.9 months in PD and 50.6 months in ET, the average number of tremor recordings was 3.3 in PD and 3.7 in ET. In both disorders, tremor frequencies tended to decrease with time. The average annual decrease of the tremor frequency was 0.09 Hz/year in Parkinsonian rest tremor, 0.08 Hz/year in Parkinsonian postural tremor and 0.12 Hz/year in ET.

CONCLUSIONS

The tremor frequency decreases with time in both PD and ET. The similarity of this decrease in PD and ET may point to a common underlying pathophysiological mechanism.

SIGNIFICANCE

Decreasing tremor frequencies with time may be functionally important by inducing larger tremor amplitudes due to the low-pass filtering properties of muscles and limbs.

An error model for protein quantification

MOTIVATION

Quantitative experimental data is the critical bottleneck in the modeling of dynamic cellular processes in systems biology. Here, we present statistical approaches improving reproducibility of protein quantification by immunoprecipitation and immunoblotting.

RESULTS

Based on a large data set with more than 3600 data points, we unravel that the main sources of biological variability and experimental noise are multiplicative and log-normally distributed. Therefore, we suggest a log-transformation of the data to obtain additive normally distributed noise. After this transformation, common statistical procedures can be applied to analyze the data. An error model is introduced to account for technical as well as biological variability. Elimination of these systematic errors decrease variability of measurements and allow for a more precise estimation of underlying dynamics of protein concentrations in cellular signaling. The proposed error model is relevant for simulation studies, parameter estimation and model selection, basic tools of systems biology.

AVAILABILITY

Matlab and R code is available from the authors on request. The data can be downloaded from our website www.fdm.uni-freiburg.de/~ckreutz/data.

Data-based identifiability analysis of non-linear dynamical models

MOTIVATION

Mathematical modelling of biological systems is becoming a standard approach to investigate complex dynamic, non-linear interaction mechanisms in cellular processes. However, models may comprise non-identifiable parameters which cannot be unambiguously determined. Non-identifiability manifests itself in functionally related parameters, which are difficult to detect.

RESULTS

We present the method of mean optimal transformations, a non-parametric bootstrap-based algorithm for identifiability testing, capable of identifying linear and non-linear relations of arbitrarily many parameters, regardless of model size or complexity. This is performed with use of optimal transformations, estimated using the alternating conditional expectation algorithm (ACE). An initial guess or prior knowledge concerning the underlying relation of the parameters is not required. Independent, and hence identifiable parameters are determined as well. The quality of data at disposal is included in our approach, i.e. the non-linear model is fitted to data and estimated parameter values are investigated with respect to functional relations. We exemplify our approach on a realistic dynamical model and demonstrate that the variability of estimated parameter values decreases from 81 to 1% after detection and fixation of structural non-identifiabilities.

Parameter estimation in ordinary differential equations for biochemical processes using the method of multiple shooting

In silico investigations by simulating dynamical models of biochemical processes play an important role in systems biology. If the parameters of a model are unknown, results from simulation studies can be misleading. Such a scenario can be avoided by estimating the parameters before analysing the system. Almost all approaches for estimating parameters in ordinary differential equations have either a small convergence region or suffer from an immense computational cost. The method of multiple shooting can be situated in between of these extremes. In spite of its good convergence and stability properties, the literature regarding the practical implementation and providing some theoretical background is rarely available. All necessary information for a successful implementation is supplied here and the basic facts of the involved numerics are discussed. To show the performance of the method, two illustrative examples are discussed.