Improving the accuracy of brain activation maps in the group-level analysis of fMRI data utilizing spatiotemporal Gaussian process model

OBJECTIVE: Accuracy and precision of the statistical analysis methods used for brain activation maps are essential. Adjusting models to consider spatiotemporal correlation embedded in fMRI data may increase their accuracy, but it also introduces a high computational cost. The present study aimed to apply and assess the spatiotemporal Gaussian process (STGP) model to improve accuracy and reduce cost. METHODS: We applied the spatiotemporal Gaussian process (STGP) model for both simulated and experimental memory tfMRI data and compared the findings with fast, fully Bayesian, and General Linear Models (GLM). To assess their accuracy and precision, the models were fitted to the simulated data (1000 voxels,100 times point for 50 people), and an average of accuracy indexes of 100 repetitions was computed. Functional and activation maps for all models were calculated in experimental data analysis. RESULTS: STGP model resulted in a higher Z-score in the whole brain, in the 1000 most activated voxels, and in the frontal lobe as the approved memory area. Based on the simulated data, the STGP model showed more accuracy and precision than the other two models. However, its computational time was more than the GLM, as the price of model correction, but much less than that of the fast, fully Bayesian model. CONCLUSION: Spatiotemporal correlation further improved the accuracy of the STGP compared to the GLM and fast, fully Bayesian model. This can result in more accurate activation maps. Moreover, the STGP model’s computational speed appears to be reasonable for model application

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a non-contact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms

In silico testing of the semi-closed loop infusion system with a new simulator

Goal directed fluid therapy (GDFT) implies the flow-related parameters guided infusion of fluids. It requires adherence to complex clinical algorithms and fluid protocols, as well as simultaneous monitoring of several parameters and evaluation of their fluid responsiveness or actual response to fluid challenges. Automated clinical decision support systems (ACDSS) are used to ease the task. However, they are based on the flow-related (hemodynamic) parameters – arterial blood pressure, cardiac output, etc. Meanwhile, infusions guided by hemodynamic endpoints may lead to edema. A mini Volume Loading Test (mVLT) may be helpful in detection of imminent edema from changes in hemodilution during stepwise infusion which is conventionally used for hemodynamic optimization. We developed an ACDSS which is based on evaluation of both hemodynamic and hemodilution parameters. It operates on the basis of our unique algorithm which implies interchangeable application of fluid loading, vasopressor injection and red cell transfusion. This ACDSS is used in our PC-based command centre of a prototype semi-closed loop (SCL) infusion system. We developed a simulator – ‘Virtual Patient’ – on the basis of our previous clinical records aiming to test a new controller, as well as train the research team before starting a clinical trial. In silico testing continued for 12 hours on five occasions. Primary endpoint was the compliance of a controller with our clinical algorithm and the stability of operation in a spectrum of arterial hypotension and bleeding scenarios. The prototype SCL infusion system was found ready for clinical validation