This master´s thesis deals with processing and analysis of data, acquired from experimental examination performed with functional magnetic resonance imaging technique. It is an oddball type experimental task and its goal is an examination of cognitive functions of the subject. The principles of functional magnetic resonance imaging, possibilities of experimental design, processing of acquired data, modeling of a response of organism and statistical analysis are described in this work. Furthermore, particular parts of preprocessing and analysis are carried out using real data set from experiment. The main goal of this work is suggestion and realization of model, which enables advanced categorization of stimuli, considering the type of previous rare stimulus and the number of frequent stimuli within them. With its in-depth categorization, this model enables detail exploration of cerebral processes, associated mainly with attention, memory, expectancy or cognitive closure. The second point of that work is an evaluation of models of hemodynamic response, which are applied in statistical analysis of data from fMRI experiment. Comparison of basis functions, the models of hemodynamic response to experimental stimulation used for general linear model, is performed in this work. The result of this comparison is an evaluation of detection efficiency of activated voxels, false positivity rate and computational and user difficulty.

Gadolinium-based contrast agents (GBCAs) are in widespread use to enhance magnetic resonance angiography images for evaluating vascular pathology. However, there are safety concerns and limitations regarding the use of GBCAs. It has been shown that the magnetic resonance imaging (MRI) signal intensity (T1-weighted images) in some of the brain's tissues is higher for patients who had multiple exposures to GBCAs compared to patients who had never had exposure to GBCAs. This implies that GBCAs are not sufficiently removed from body such that GBCAs may potentially have long-term effects on the human body. These potential safety concerns have led to an increased interest in alternative contrast agents. Methemoglobin (metHb) and oxygen-free hemoglobin (HHb) are two forms of hemoglobin with paramagnetic properties. It has been shown that the T1-weighted signal intensity of blood is changed during MRI scans for metHb and HHb, leading to enhanced contrast of MRI images. The ability of metHb and HHb to change the signal intensity has led to the idea that they can be used as effective contrast agents. MetHb can be made by exposing oxyhemoglobin (oxyHb) to nitric oxide (NO) and HHb can be made by removing the oxygen from hemoglobin using nitrogen (N2). In this study, a new gas delivery system was developed to make metHb and HHb. The new gas delivery system was developed to have greater experimental control compared to the PermSelect hollow-fiber module that was used in preliminary studies to make metHb. The same system can be used to make HHb. Initial experiments showed significant amounts of undesired nitrite (NO2-) formation during metHb formation due to the presence of contaminants in the NO gas source. To minimize this problem, flow of NO from the gas source was bubbled in a sodium hydroxide solution in order to reduce the NO2- concentration. Following metHb formation, continuous delivery of NO also led to the formation of ferrous nitrosyl hemoglobin (HbIINO). MRI studies showed that HbIINO can also increase the signal intensity of an MRI image. It is unknown as to whether metHb, HHb, or HbIINO would be a stronger and more appropriate contrast agent and to what extent the T1-weighted signal is affected by the concentration. This study evaluated T1-weighted images of blood samples over a range of metHb and HHb concentrations, as well as HbIINO concentrations. Comparison of T1 values showed that metHb is the strongest contrast agent and that HHb is a relatively weak contrast agent. This study showed for the first time that HbIINO can provide a contrast effect, although not as strong as metHb but stronger than HHb. With metHb providing a viable contrast between 10-20%, metHb has the potential to be a safe and effective contrast agent since it can be naturally converted back to hemoglobin.

Opioid overdose is a growing epidemic plaguing the United States. Overdose related death has risen from 16,849 in 1999 to 69,029 in 2018. Almost 7 out of 10 of these deaths were due to opioids with 47% being caused by fentanyl or other synthetic opioids. There is a strong need to reduce the amount of overdose-related deaths. Indirect methods should be a first priority, and include counseling and care. For some individuals, this treatment option is unavailable because the drug user may not have the desire or economic means to pursue it. In this case, a more direct preventative approach is needed. This paper presents a novel method of detecting poor peripheral oxygenation, a biomarker linked to opioid overdose. A wristwatch near-infrared spectroscopy device (NIRS) was developed. SPICE simulations were conducted to confirm proper operation of electrical systems. The device was fabricated on a printed circuit board and mounted to a 3D printed enclosure. Absorbance of green, red and infrared (IR) light were measured. Additionally, peripheral capillary oxygen saturation (SpO2) modulation index and changes in concentration of oxyhemoglobin and deoxyhemoglobin were calculated from raw data. A brachial occlusion test was performed to mimic the effects of opioid overdose on peripheral oxygenation. A statistically significant difference (p < 0.05) was observed between pre-occlusion and during-occlusion groups in two subjects for measurement of peak-to-peak values of green raw data, red raw data, IR raw data, oxyhemoglobin concentration change, and deoxyhemoglobin concentration change. Peak-to-peak was observed as a consistent indicator of poor peripheral oxygenation and could serve as a useful metric in the detection of opioid overdose.

Electroencephalography and functional magnetic resonance are two different methods for measuring of neural activity. EEG signals have excellent time resolution, fMRI scans capture records of brain activity in excellent spatial resolution. It is assumed that the joint analysis can take advantage of both methods simultaneously. Statistical Parametric Mapping (SPM8) is freely available software which serves to automatic analysis of fMRI data estimated with general linear model. It is not possible to estimate automatic EEG–fMRI analysis with it. Therefore software EEG Regressor Builder was created during master thesis. It preprocesses EEG signals into EEG regressors which are loaded with program SPM8 where joint EEG–fMRI analysis is estimated in general linear model. EEG regressors consist of vectors of temporal changes in absolute or relative power values of EEG signal in the specified frequency bands from selected electrodes due to periods of fMRI acquisition of individual images. The software is tested on the simultaneous EEG-fMRI data of a visual oddball experiment. EEG regressors are calculated for temporal changes in absolute and relative EEG power values in three frequency bands of interest ( 8-12Hz, 12-20Hz a 20-30Hz) from the occipital electrodes (O1, O2 and Oz). Three types of test analyzes is performed. Data from three individuals is examined in the first. Accuracy of results is evaluated due to the possibilities of setting of calculation method of regressor. Group analysis of data from twenty-two healthy patients is performed in the second. Group EEG regressors analysis is realized in the third through the correlation matrix due to the specified type of power and frequency band outside of the general linear model.