HOW SLEEP DISTURBANCES AFFECT THOSE WITH BORDERLINE PERSONALITY DISORDER AND THE IMPLICATIONS FOR TREATMENT

This thesis argues that there is limited research on Borderline Personality Disorder’s comorbidity with sleep disorders, and by pointing out the gaps in knowledge this will encourage researchers and doctors to consider this topic as important in the health care field. Sleep disorders can be anything from reduced total sleep time, fragmented sleep, and changes in sleep architecture, and all of these can cause and be caused by disruption of the circadian clock. There are various ways in which circadian clock disruption can cause diseases, cancer, and mental disorders through genes, sleep, and the environment. Borderline Personality Disorder comorbid with sleep disorders can cause a vicious cycle with one disorder increasing the other’s intensity. These two disorders together can lead to higher rates of depressive, anxious, and suicidal symptoms. The current treatment options for BPD and sleep disturbances are limited and there is no standard way to treat these. With this being the case, we need to discover a way to treat both of these disorders in a patient without causing severe side effects and without ignoring one of the disorders altogether. Through a holistic view of a whole person through treatment plans that are specific to each individual, it is possible to alleviate some of the symptoms of BPD and sleep disorders, grant these people back a sense of control and self-autonomy over their lives, and strive towards the possibility of recovery

Emotional processing in Parkinson's disease and anxiety: an EEG study of visual affective word processing

A general problem in the design of an EEG-BCI system is the poor quality and low robustness of the extracted features, affecting overall performance. However, BCI systems that are applicable in real-time and outside clinical settings require high performance. Therefore, we have to improve the current methods for feature extraction. In this work, we investigated EEG source reconstruction techniques to enhance the extracted features based on a linearly constrained minimum variance (LCMV) beamformer. Beamformers allow for easy incorporation of anatomical data and are applicable in real-time. A 32-channel EEG-BCI system was designed for a two-class motor imagery (MI) paradigm. We optimized a synchronous system for two untrained subjects and investigated two aspects. First, we investigated the effect of using beamformers calculated on the basis of three different head models: a template 3-layered boundary element method (BEM) head model, a 3-layered personalized BEM head model and a personalized 5-layered finite difference method (FDM) head model including white and gray matter, CSF, scalp and skull tissue. Second, we investigated the influence of how the regions of interest, areas of expected MI activity, were constructed. On the one hand, they were chosen around electrodes C3 and C4, as hand MI activity theoretically is expected here. On the other hand, they were constructed based on the actual activated regions identified by an fMRI scan. Subsequently, an asynchronous system was derived for one of the subjects and an optimal balance between speed and accuracy was found. Lastly, a real-time application was made. These systems were evaluated by their accuracy, defined as the percentage of correct left and right classifications. From the real-time application, the information transfer rate (ITR) was also determined. An accuracy of 86.60 ± 4.40% was achieved for subject 1 and 78.71 ± 0.73% for subject 2. This gives an average accuracy of 82.66 ± 2.57%. We found that the use of a personalized FDM model improved the accuracy of the system, on average 24.22% with respect to the template BEM model and on average 5.15% with respect to the personalized BEM model. Including fMRI spatial priors did not improve accuracy. Personal fine- tuning largely resolved the robustness problems arising due to the differences in head geometry and neurophysiology between subjects. A real-time average accuracy of 64.26% was reached and the maximum ITR was 6.71 bits/min. We conclude that beamformers calculated with a personalized FDM model have great potential to ameliorate feature extraction and, as a consequence, to improve the performance of real-time BCI systems