Cross-Disciplinary Collaboration Behavior of Practitioners in the Treatment of Anxiety Disorders

Current research on the collaborative behaviors of conventional and alternative health care providers for the treatment of anxiety is lacking. While there are multiple studies looking at alternative health care integration into primary care, none of them look at anxiety specifically. The purpose of this paper is to provide a preliminary exploration of possible barriers to collaboration between conventional and alternative health care providers for the treatment of anxiety. Quantitative data on collaboration behavior patterns were obtained with an anonymous survey. Data from the surveys were analyzed using a chi-square analysis. Along with these numerical data narrative data from the survey and interviews were collected in order to assess beliefs about the barriers to collaboration from different health care providers. The results indicate that conventional providers collaborate the least with alternative providers and alternative providers collaborate the least with conventional providers. The descriptive results regarding the barriers to collaboration from the study illustrated a common theme, specifically, the lack of education of conventional providers on alternative health care practices on anxiety. This is an exploratory study: therefore it would be beneficial for future researchers to look deeper into the beliefs of health care providers on the barriers to collaboration, possibly identifying the specific barriers to collaboration for each type of healthcare provider.

Self-Injurious Behavior And Its Relationship To Eating Disorder Symptom Severity

Widely held clinical assumptions about self-harming eating disorder patients were tested in this project. Specifically, the present study had two aims: (1) to confirm research that suggests patients with self-injurious behavior exhibit greater severity in eating disorder symptomology; and (2) to document the treatment course for these patients (e.g. reported change in eating disorder attitudes, beliefs, and behaviors) from admission to discharge. Data from 43 participants who received treatment at a Partial Hospitalization Program (PHP) for Eating Disorders were used in the current study. The length of treatment required for study inclusion reflected mean lengths of stay (Williamson, Thaw, & Varnardo-Sullivan, 2001) and meaningful treatment lengths in prior research (McFarlane et al., 2013; McFarlane, Olmsted, & Trottier, 2008): five to eight weeks. Scores on the Eating Disorder Inventory-III (Garner, 2004) at the time of admission and discharge were compared. These results suggest that there are no significant differences between eating disordered patients who engage in self-injury and those who do not in terms of symptom severity or pathology at admission. The results further suggest that patients in both groups see equivalent reductions in symptoms from admission to discharge across domains and also share non-significant changes in emotional dysregulation over the course of treatment. Importantly, these results also suggest that general psychological maladjustment is higher at discharge for eating disordered patients who engage in self-injury.

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.