USING TECHNICAL ANALYSIS TO PREDICT FUTURE PERFORMANCE OF INTEREST RATE FUTURES

During the recent years financial markets have been going through one of the most challenging periods in history as first USA and now Europe is struggling with a damaging credit crisis. As crisis is not calming the market is very volatile and technical analysis has gained more ground. This study analyses the performance of technical trading rules in the short term interest rate futures market. The purpose of this study is to examine if these trading rules can generate excess returns in relation to Buy-and-hold rule and if efficient market hypothesis can be criticized due to market inefficiencies. The weak-form of the efficient market hypothesis states that all historical trading data is reflected in the asset prices. This is challenged by Relative Strength Index, its new modifications and Moving Average rules as they use historical trading data to try to yield higher returns than what is attained by buying and holding. As Relative Strength Index in this study is based on the original rule of 14-day periods a new modification of it, DRSI, applies two different length indices with influences from Moving Average rule. There are three versions of DRSI rule used in this study with some differences in an optimization process. In this study 1-50 method of Moving Average rule is used. Data of this study consists of 132 short term interest rate futures denominated in seven currencies: USD, EUR, GBP, JPY, CHF, NZD and MYR. The futures data gathered from January 1st 2000 to July 31st 2009 contains total amount of 149,212 observations after filtration process. As previous studies have shown there is a difference in profitability between developed and developing market while technical trading rules are used and therefore also in this study data is divided into two periods, illiquid and liquid. The findings of this study show that trading rules often generate higher risk adjusted returns but they cannot consistently generate excess returns versus Buy-and-hold method. However, it is shown that the MYR denominated market is not efficient and Moving Average and DRSI common optimization rules yield significant excess returns. In addition it was found that optimization has a huge impact on returns of trading rules.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Quantification of anisotropy and orientation in 3D electron microscopy and diffusion tensor imaging in injured rat brain

Diffusion tensor imaging (DTI) reveals microstructural features of grey and white matter non-invasively. The contrast produced by DTI, however, is not fully understood and requires further validation. We used serial block-face scanning electron microscopy (SBEM) to acquire tissue metrics, i.e., anisotropy and orientation, using three-dimensional Fourier transform-based (3D-FT) analysis, to correlate with fractional anisotropy and orientation in DTI. SBEM produces high-resolution 3D data at the mesoscopic scale with good contrast of cellular membranes. We analysed selected samples from cingulum, corpus callosum, and perilesional cortex of sham-operated and traumatic brain injury (TBI) rats. Principal orientations produced by DTI and 3D-FT in all samples were in good agreement. Anisotropy values showed similar patterns of change in corresponding DTI and 3D-FT parameters in sham-operated and TBI rats. While DTI and 3D-FT anisotropy values were similar in grey matter, 3D-FT anisotropy values were consistently lower than fractional anisotropy values from DTI in white matter. We also evaluated the effect of resolution in 3D-FT analysis. Despite small angular differences in grey matter samples, lower resolution datasets provided reliable results, allowing for analysis of larger fields of view. Overall, 3D SBEM allows for more sophisticated validation studies of diffusion imaging contrast from a tissue microstructural perspective.Peer reviewe

RatLesNetv2: a fully convolutional network for rodent brain lesion segmentation

We present a fully convolutional neural network (ConvNet), named RatLesNetv2, for segmenting lesions in rodent magnetic resonance (MR) brain images. RatLesNetv2 architecture resembles an autoencoder and it incorporates residual blocks that facilitate its optimization. RatLesNetv2 is trained end to end on three-dimensional images and it requires no preprocessing. We evaluated RatLesNetv2 on an exceptionally large dataset composed of 916 T2-weighted rat brain MRI scans of 671 rats at nine different lesion stages that were used to study focal cerebral ischemia for drug development. In addition, we compared its performance with three other ConvNets specifically designed for medical image segmentation. RatLesNetv2 obtained similar to higher Dice coefficient values than the other ConvNets and it produced much more realistic and compact segmentations with notably fewer holes and lower Hausdorff distance. The Dice scores of RatLesNetv2 segmentations also exceeded inter-rater agreement of manual segmentations. In conclusion, RatLesNetv2 could be used for automated lesion segmentation, reducing human workload and improving reproducibility. RatLesNetv2 is publicly available at https://github.com/jmlipman/RatLesNetv2

Spatial signatures of anesthesia-induced burst-suppression differ between primates and rodents

During deep anesthesia, the electroencephalographic (EEG) signal of the brain alternates between bursts of activity and periods of relative silence (suppressions). The origin of burst-suppression and its distribution across the brain remain matters of debate. In this work, we used functional magnetic resonance imaging (fMRI) to map the brain areas involved in anesthesia-induced burst-suppression across four mammalian species: humans, long-tailed macaques, common marmosets, and rats. At first, we determined the fMRI signatures of burst-suppression in human EEG-fMRI data. Applying this method to animal fMRI datasets, we found distinct burst-suppression signatures in all species. The burst-suppression maps revealed a marked inter-species difference: in rats, the entire neocortex engaged in burst-suppression, while in primates most sensory areas were excluded-predominantly the primary visual cortex. We anticipate that the identified species-specific fMRI signatures and whole-brain maps will guide future targeted studies investigating the cellular and molecular mechanisms of burst-suppression in unconscious states

Histopathological modeling of status epilepticus-induced brain damage based on in vivo diffusion tensor imaging in rats

Non-invasive magnetic resonance imaging (MRI) methods have proved useful in the diagnosis and prognosis of neurodegenerative diseases. However, the interpretation of imaging outcomes in terms of tissue pathology is still challenging. This study goes beyond the current interpretation of in vivo diffusion tensor imaging (DTI) by constructing multivariate models of quantitative tissue microstructure in status epilepticus (SE)-induced brain damage. We performed in vivo DTI and histology in rats at 79 days after SE and control animals. The analyses focused on the corpus callosum, hippocampal subfield CA3b, and layers V and VI of the parietal cortex. Comparison between control and SE rats indicated that a combination of microstructural tissue changes occurring after SE, such as cellularity, organization of myelinated axons, and/or morphology of astrocytes, affect DTI parameters. Subsequently, we constructed a multivariate regression model for explaining and predicting histological parameters based on DTI. The model revealed that DTI predicted well the organization of myelinated axons (cross-validated R = 0.876) and astrocyte processes (cross-validated R = 0.909) and possessed a predictive value for cell density (CD) (cross-validated R = 0.489). However, the morphology of astrocytes (cross-validated R > 0.05) was not well predicted. The inclusion of parameters from CA3b was necessary for modeling histopathology. Moreover, the multivariate DTI model explained better histological parameters than any univariate model. In conclusion, we demonstrate that combining several analytical and statistical tools can help interpret imaging outcomes to microstructural tissue changes, opening new avenues to improve the non-invasive diagnosis and prognosis of brain tissue damage

Hippocampal position and orientation as prognostic biomarkers for posttraumatic epileptogenesis: an experimental study in a rat lateral fluid percussion model

Objective This study was undertaken to identify prognostic biomarkers for posttraumatic epileptogenesis derived from parameters related to the hippocampal position and orientation. Methods Data were derived from two preclinical magnetic resonance imaging (MRI) follow-up studies: EPITARGET (156 rats) and Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx; University of Eastern Finland cohort, 43 rats). Epileptogenesis was induced with lateral fluid percussion-induced traumatic brain injury (TBI) in adult male Sprague Dawley rats. In the EPITARGET cohort, T2*-weighted MRI was performed at 2, 7, and 21 days and in the EpiBioS4Rx cohort at 2, 9, and 30 days and 5 months post-TBI. Both hippocampi were segmented using convolutional neural networks. The extracted segmentation mask was used for a geometric construction, extracting 39 parameters that described the position and orientation of the left and right hippocampus. In each cohort, we assessed the parameters as prognostic biomarkers for posttraumatic epilepsy (PTE) both individually, using repeated measures analysis of variance, and in combination, using random forest classifiers. Results The extracted parameters were highly effective in discriminating between sham-operated and TBI rats in both the EPITARGET and EpiBioS4Rx cohorts at all timepoints (t; balanced accuracy > .9). The most discriminating parameter was the inclination of the hippocampus ipsilateral to the lesion at t = 2 days and the volumes at t >= 7 days after TBI. Furthermore, in the EpiBioS4Rx cohort, we could effectively discriminate epileptogenic from nonepileptogenic animals with a longer MRI follow-up, at t = 150 days (area under the curve = .78, balanced accuracy = .80, p = .0050), based on the orientation of both hippocampi. We found that the ipsilateral hippocampus rotated outward on the horizontal plane, whereas the contralateral hippocampus rotated away from the vertical direction. Significance We demonstrate that assessment of TBI-induced hippocampal deformation by clinically translatable MRI methodologies detects subjects with prior TBI as well as those at high risk of PTE, paving the way toward subject stratification for antiepileptogenesis studies

Sleep-State Dependent Alterations in Brain Functional Connectivity under Urethane Anesthesia in a Rat Model of Early-Stage Parkinson's Disease

Parkinson's disease (PD) is characterized by the gradual degeneration of dopaminergic neurons in the substantia nigra, leading to striatal dopamine depletion. A partial unilateral striatal 6-hydroxydopamine (6-OHDA) lesion causes 40-60% dopamine depletion in the lesioned rat striatum, modeling the early stage of PD. In this study, we explored the connectivity between the brain regions in partially 6-OHDA lesioned male Wistar rats under urethane anesthesia using functional magnetic resonance imaging (fMRI) at 5 weeks after the 6-OHDA infusion. Under urethane anesthesia, the brain fluctuates between the two states, resembling rapid eye movement (REM) and non-REM sleep states. We observed clear urethane-induced sleep-like states in 8/19 lesioned animals and 8/18 control animals. 6-OHDA lesioned animals exhibited significantly lower functional connectivity between the brain regions. However, we observed these differences only during the REM-like sleep state, suggesting the involvement of the nigrostriatal dopaminergic pathway in REM sleep regulation. Corticocortical and corticostriatal connections were decreased in both hemispheres, reflecting the global effect of the lesion. Overall, this study describes a promising model to study PD-related sleep disorders in rats using fMRI.Peer reviewe