Electroencephalogram based Causality Graph Analysis in Behavior Tasks of Parkinson’s Disease Patients

Electroencephalographic (EEG) signals of the human brains represent electrical activities for a number of channels recorded over a the scalp. The main purpose of this thesis is to investigate the interactions and causality of different parts of a brain using EEG signals recorded during a performance subjects of verbal fluency tasks. Subjects who have Parkinson's Disease (PD) have difficulties with mental tasks, such as switching between one behavior task and another. The behavior tasks include phonemic fluency, semantic fluency, category semantic fluency and reading fluency. This method uses verbal generation skills, activating different Broca's areas of the Brodmann's areas (BA44 and BA45). Advanced signal processing techniques are used in order to determine the activated frequency bands in the granger causality for verbal fluency tasks. The graph learning technique for channel strength is used to characterize the complex graph of Granger causality. Also, the support vector machine (SVM) method is used for training a classifier between two subjects with PD and two healthy controls. Neural data from the study was recorded at the Colorado Neurological Institute (CNI). The study reveals significant difference between PD subjects and healthy controls in terms of brain connectivities in the Broca's Area BA44 and BA45 corresponding to EEG electrodes. The results in this thesis also demonstrate the possibility to classify based on the flow of information and causality in the brain of verbal fluency tasks. These methods have the potential to be applied in the future to identify pathological information flow and causality of neurological diseases.

The Perceived Effects of Dance for Individuals with Parkinson’s Disease and Healthy Amateurs

The objective of the present study was to compare the effects of dance participation on physical and psychological functioning as perceived by two distinct groups of dancers: dancers with Parkinson's disease (PD) and healthy amateur (HA) dancers. Dancers in the Parkinson's sample group were gathered from participants in the Dance for PD® program, while healthy amateur dancers were recruited from university dance departments and through social media. Both groups were administered measures related to affect, self-efficacy, quality of life, and which aspects of dance classes were most helpful and/or challenging. Several open-ended questions for both groups were included, along with questions specific to each group. Results of the study indicated that there was no difference between the two groups on positive affect experienced while dancing, but that HA dancers experienced higher levels of negative affect than PD dancers. HA dancers exhibited higher levels of self-efficacy, but there was no difference between the groups on perceived quality of life. Additionally, both groups identified the same two components of dance classes as the most helpful: "moving and getting some exercise" and "doing something fun." Thematic analysis of responses to open-ended questions found that, in general, HA and PD dancers identified similar factors which made dance unique from other forms of exercise. The primary differences were that HA dancers more strongly emphasized artistic and spiritual components of dance, whereas PD dancers focused on the importance of the dance instructors and tailoring movements to individuals with PD. More differences were found between the two groups with respect to the negative aspects of dance classes. Notably, PD dancers identified almost no negative aspects, while HA dancers described internal and external pressure, criticism, and competition as problematic. Future research could benefit from ensuring that both groups are administered the same standardized measures to allow for additional comparisons between groups and with normative samples.

Towards Closed-loop Deep Brain Stimulation: Behavior Recognition from Human STN

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson’s disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient’s behavior. Thus behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. This thesis proposes novel detection and classification techniques for behavior recognition based on deep brain LFP. Behavior detection from such signals is the vital step in developing the next generation of closed-loop DBS devices. LFP recordings from 13 subjects are utilized in this study to design and evaluate our method. Recordings were performed during the surgery and the subjects were asked to perform various behavioral tasks. Various techniques are used understand how the behaviors modulate the STN. One method studies the time-frequency patterns in the STN LFP during the tasks. Another method measures the temporal inter-hemispheric connectivity of the STN as well as the connectivity between STN and Pre-frontal Cortex (PFC). Experimental results demonstrate that different behaviors create different m odulation patterns in STN and it’s connectivity. We use these patterns as features to classify behaviors. A method for single trial recognition of the patient’s current task is proposed. This method uses wavelet coefficients as features and support vector machine (SVM) as the classifier for recognition of a selection of behaviors: speech, motor, and random. The proposed method is 82.4% accurate for the binary classification and 73.2% for classifying three tasks. As the next step, a practical behavior detection method which asynchronously detects behaviors is proposed. This method does not use any priori knowledge of behavior onsets and is capable of asynchronously detect the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity and to detect the finger movements. Our experimental results using STN LFP recorded from eight patients with PD demonstrate this is a promising approach for behavior detection and developing novel closed-loop DBS systems.