The Use of the Rey 15-Item Test and Recognition Trial to Evaluate Suboptimal Effort in Neuropsychological Assessment with a Pediatric Mild TBI Sample

The utilization of symptom validity tests (SVTs) in pediatric assessment is receiving increasing empirical support. The Rey 15-Item Test (FIT) is an SVT commonly used in adult assessment, with limited research in pediatric populations. Given that FIT classification statistics across studies to date have been quite variable, Boone, Salazar, Lu, Warner-Chacon, and Razani (2002) developed a recognition trial to use with the original measure to enhance accuracy. The current study aims to assess the utility of the FIT and recognition trial in a pediatric mild traumatic brain injury (TBI) sample (N = 112; M = 14.6 years), in which a suboptimal effort base rate of 17% has been previously established (Kirkwood & Kirk, 2010). All participants were administered the FIT as part of an abbreviated neuropsychological evaluation; failure on the Medical Symptom Validity Test (MSVT) was used as the criterion for suspect effort. The traditional adult cut-off score of(99%), but poor sensitivity (6%). When the recognition trial was also utilized, a combination score of(sensitivity = 64%, specificity = 93%). Results indicate that the FIT with recognition trial may be useful in the assessment of pediatric suboptimal effort, at least among relatively high functioning children following mild TBI.

E-Book Usage on a Global Scale: Patterns, Trends, and Opportunities

This study examines worldwide usage of over 600,000 e-books from Ebook Library (EBL) and ebrary. Using multiple modes of analysis, the study shows that there are variations in usage by geographic region as well as by subject. The study examines usage in relation to availability of titles, different types of usage per session, usage of the top ten percent of titles, and intensive and extensive use. These patterns can be used for benchmarking and as a model for local e-book studies.

The Creation of a Legal Ordinance and Program Manual for the Purpose of Illicit Discharge Detection and Elimination

Illegal dumping and improper disposal of pollutants in urban areas can contribute significant pollutant loads to the municipal separate storm sewer system (MS4) and natural environments. Illicit discharges to the MS4 can pose a significant risk to human and environmental health. The Clean Water Act requires that municipalities implement a legal mechanism and plan to detect and eliminate illicit discharges to the MS4. The methodology for program creation included the analysis of other municipal illicit discharge programs, review of state and federal guidance publications, and the review of illicit discharge case-studies. This paper describes a systematic approach applied to the creation and implementation of a legal ordinance and program manual designed for the purpose of illicit discharge detection and elimination (IDDE).

Developing a Citizen Volunteer Lake Monitoring Program for the Flathead Basin in Northwestern Montana for the early Detection and Prevention of the Introduction of Zebra Mussels and other Aquatic Invasive Species (AIS)

Preventing the introduction of aquatic invasive species (AIS) like zebra and quagga mussels in the U.S. is a high priority. This Capstone demonstrates zebra and quagga mussels are of concern as aquatic invasive species and a volunteer monitoring and intervention program is an effective means for early detection of AIS. This Capstone developed an AIS citizen volunteer lake monitoring program consistent with other programs concerned about AIS prevention and early detection. This Capstone concludes implementing such a voluntary program will help reduce the spread of zebra and quagga mussels and will provide early detection information to appropriate agencies empowered with response actions if species are found.

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.