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Software for Managing Parametric Studies. The Grid denotes a worldwide network of supercomputers used for scientific and engineering computations involving data sets too large to fit on desktop computers.

Heretofore, parametric studies on the Grid have been impeded by the need to create control language scripts and edit input data files painstaking tasks that are necessary for managing multiple jobs on multiple computers. ILab reflects an object-oriented approach to automation of these tasks: All data and operations are organized into packages in order to accelerate development and debugging.

For convenience and to enable reuse, this object is serialized to and from disk storage.

At run time, the current ILab experiment is used to generate required input files and shell scripts, create directories, copy data files, and then both basde and monitor the execution of all computational processes. Keith, Lauren; Ross, Brian D.

Management of glioblastoma multiforme remains a challenging problem despite recent advances in targeted therapies. Timely assessment of therapeutic agents is hindered by the lack of standard quantitative imaging protocols for determining targeted response. Clinical response assessment for brain tumors is determined by volumetric changes assessed at 10 weeks post-treatment initiation. Further, current clinical criteria fail to use advanced quantitative imaging approaches, such as diffusion and perfusion magnetic resonance imaging.

Development of the parametric response mapping PRM applied to diffusion-weighted magnetic resonance imaging has provided a sensitive and early biomarker of successful cytotoxic therapy in brain tumors while maintaining a spatial context within the tumor.

Although PRM provides an earlier readout than volumetry and sometimes greater sensitivity compared with traditional whole-tumor diffusion statistics, it is not routinely used for baser management; an automated and standardized software for performing the analysis and for the generation of a clinical report document is required for this. We present a semiautomated and seamless workflow for image coregistration, segmentation, and PRM classification of glioblastoma multiforme diffusion-weighted magnetic resonance imaging scans.

The software solution can be integrated using local hardware or performed remotely in the cloud while providing connectivity to existing picture archive and communication systems. This is an important step toward implementing PRM analysis of solid tumors in routine clinical practice.

Machine learning-based dual-energy CT parametric mapping. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid HCrandom forest RFand artificial neural networks ANN were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes.

After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that bxsed the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method.

Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. Machine learning methods outperformed the reference method in terms of accuracy and noise basedd when generating parametric mapsencouraging further exploration of the techniques.

Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency. Robust biological parametric mapping: Mapping the quantitative relationship between structure and function in the human brain is an important and challenging problem. Numerous volumetric, surface, region of interest and voxelwise image processing techniques have been developed to statistically assess potential correlations between imaging and non-imaging metrics.

Recently, biological paproach mapping has extended the widely popular statistical parametric approach to enable application of the general linear model to multiple image modalities both for regressors and regressands along with scalar valued observations. This approach offers great promise for direct, voxelwise assessment of structural and functional relationships with multiple imaging modalities.


However, as presented, the biological parametric mapping approach is not robust to outliers and may lead to invalid inferences e. To enable widespread application of this approach, we introduce robust regression and robust inference in the neuroimaging context of application approacj the general linear model.

Through simulation and empirical studies, we demonstrate that our robust approach reduces sensitivity to outliers without substantial degradation in power.

The robust approach and associated software package provides a reliable way to quantitatively assess voxelwise correlations between structural and functional neuroimaging modalities.

based modeling approach: Topics by

These powerful tools allows us the super-resolution localization of the brain activation which is not possible using the conventional NIRS analysis tools. One-dimensional statistical parametric mapping in Python. Statistical parametric mapping SPM is a topological methodology for detecting field changes in smooth n-dimensional continua.

Many classes of biomechanical data are smooth and contained within discrete bounds and as such are well suited to SPM analyses. Three example applications are presented: Source code and documentation are available t01 This software package processes Atmospheric Infrared Sounder AIRS Level 2 swath standard product geophysical parameters, and generates global, colorized, annotated maps. It automatically generates daily and multi-day averaged colorized and annotated maps of various AIRS Level 2 swath geophysical parameters.

The software scales and colorizes global grids utilizing AIRS-specific color tables, and annotates images with title and color bar. This software can be tailored for use with other swath data products for the purposes of visualization.

Can color-coded parametric maps improve dynamic enhancement pattern analysis in MR mammography? Thus, the TSIC of the whole lesion can be assessed. MRM was performed according to a standard protocol 1. Both methods were rated by 2 observers in consensus on an ordinal scale.

Receiver operating characteristics ROC analysis was used to compare both methods. The sensitivity was Therefore, the CCPM method is a feasible approach to assessing dynamic data in MRM and condenses several imaging series into one parametric map. In recent years multiple brain MR imaging modalities have emerged; however, analysis methodologies have mainly remained modality specific.

In addition, when comparing across imaging modalities, most researchers have been forced to rely on simple region-of-interest type analyses, which do not allow the voxel-by-voxel comparisons necessary to answer more sophisticated neuroscience questions. To overcome these limitations, we developed a toolbox for multimodal image analysis called biological parametric autmated BPMbased on a voxel-wise use of the general linear model.

The BPM toolbox incorporates information obtained from other modalities as regressors in a voxel-wise analysis, thereby permitting investigation of more sophisticated hypotheses. It has a high degree of integration with the SPM statistical parametric mapping software relying on it for visualization and statistical inference. Furthermore, statistical inference for a correlation field, approsch than a widely-used T-field, has been implemented in the correlation analysis for more accurate results.

An example with in-vivo data is presented demonstrating the potential of the BPM methodology as a tool for multimodal image analysis. The analysis of large and complex parameterized software systems, e. Thus, such systems are generally validated only in regions local to anticipated operating points rather than through characterization of the entire feasible operational envelope of the system.

We have addressed the factors deterring such an analysis with a tool to support envelope assessment: Additional test-cases, automatically generated from models e. The distributed test runs of the software system produce vast amounts of data, making manual analysis impossible. Our tool automatically analyzes the generated data through a combination of unsupervised Bayesian clustering techniques AutoBayes and supervised learning of critical parameter ranges using eauipment treatment learner TAR3.

The tool has been developed around the Trick simulation environment, which is widely used within NASA. One of the major challenges of neurostimulation is actually to address the back pain component in patients suffering from refractory chronic back qpproach leg pain.

Evolution of extrema features reveals optimal stimuli for biological state transitions

Facing a tremendous expansion of neurostimulation techniques and available devices, implanters and patients can still remain confused as they need to select the right tool for the right indication.

To be rissk to evaluate and compare objectively patient outcomes, depending on therapeutical strategies, it appears essential to develop a rational and quantitative approach to pain assessment for those who undergo neurostimulation implantation.


We developed a touch equlpment interface, in Poitiers University Hospital and N 3 Lab, called the “Neuro-Pain’T”, to detect, record and quantify the painful area surface and intensity changes in an implanted patient within time. The second aim of this software is to analyse the link between a paraesthesia coverage generated by a type of neurostimulation and a potential analgesic rsk, measured by pain surface reduction, pain intensity reduction within the painful surface and local change in pain characteristics distribution.

The third aim of Neuro-Pain’T is to correlate these clinical parameters to global patient data and functional outcome analysis, via a network database Neuro-Databaseto be able to provide a concise but objective approach of the neurostimulation efficacy, summarized by an index called “RFG Index”. This software has been used in more than patients sinceleading us to define three clinical aufomated grouped as a clinical component of the RFG Index, which might be helpful to assess neurostimulation efficacy and compare implanted devices.

The Neuro-Pain’T is an original software designed to objectively and quantitatively characterize reduction of a painful area in a given individual, in terms of intensity, surface and pain typology, in response to a treatment strategy or implantation of an analgesic device. Because pain is a physical sensation. Free software helps map and display data. This process can be tedious and is often done manually, since available commercial or in-house software usually can do only part of the job. To expedite this process, we introduce the Generic Mapping Tools GMTwhich is a free, public domain software package that can be used to manipulate columns of tabular data, time series, and gridded data sets and to display these data in a variety of forms ranging from simple x-y plots to maps and color, perspective, and shaded-relief illustrations.

parametric mapping software: Topics by

GMT uses the PostScript page description language, which can create arbitrarily complex images in gray tones or bit true color by superimposing multiple plot files. Line drawings, bitmapped images, and text can be easily combined in one illustration. PostScript plot files are device-independent, meaning the same file can be printed at dots per inch dpi on an ordinary laserwriter or at dpi on a phototypesetter when ultimate quality is needed.

The system apprkach offered free of charge to federal agencies and nonprofit educational organizations worldwide and is distributed over the computer network Internet. Recent advances in parametric neuroreceptor mapping with dynamic PET: Tracer kinetic modeling in dynamic positron emission tomography PET has been widely used to investigate the characteristic distribution patterns or dysfunctions of neuroreceptors in brain diseases.

Its practical goal has progressed from regional data quantification to parametric mapping that produces images of kinetic-model parameters by fully exploiting approoach spatiotemporal information ti dynamic PET data. Graphical analysis GA is a major parametric mapping technique that is independent on any compartmental model configuration, robust to noise, and computationally efficient.

In this paper, we provide an overview of jeth advances in the parametric mapping of neuroreceptor binding based on GA methods. The associated basic concepts in tracer kinetic modeling are presented, including commonly-used compartment models and major parameters of interest.

Technical details of GA approaches for reversible and irreversible equipmrnt are described, considering both plasma input and reference tissue input models.

27th Annual Computational Neuroscience Meeting (CNS*2018): Part One

Their statistical properties are discussed in view of parametric imaging. Spatiotemporal analysis of plantar pressure measurements using statistical parametric mapping.

Pedobarography produces large sets of plantar pressure samples that are routinely subsampled e. We hypothesize that these data reductions discard gait information that can be used to differentiate between groups or conditions. To test the hypothesis of null information loss, we created an implementation of statistical parametric mapping SPM for dynamic plantar pressure datasets i. Our SPM software framework brings all plantar pressure videos into anatomical and temporal correspondence, then performs statistical tests at each sampling location in space and time.

Novelly, we introduce non-linear temporal registration into the framework in order to normalize for timing differences within the stance phase. Using STAPP, we tested our hypothesis on plantar pressure videos from 33 healthy subjects walking at different speeds.