The Utility of the CVLT-C as an Embedded Validity Indicator After Pediatric Mild Traumatic Brain Injury.

Results of neuropsychological examinations depend on valid data. Whereas clinicians previously believed that clinical skill was sufficient to identify non-credible performance by examinees on standard tests, research demonstrates otherwise. Consequently, studies on measures to detect suspect effort in adults have received tremendous attention in the previous twenty years, and incorporation of validity indicators into neuropsychological examinations is now seen as integral. Few studies exist that validate methods appropriate for the measurement of effort in pediatric populations. Of extant studies, most evaluate standalone measures originally developed for use with adults. The present study examined the utility of indices from the California Verbal Learning Test – Children's Version (CVLT-C) as embedded validity indicators in a pediatric sample. Participants were 225 outpatients aged 8 to 16 years old referred for clinical assessment after mild traumatic brain injury (mTBI). Non-credible performance (n = 39) was defined as failure of the Medical Symptom Validity Test (MSVT). Logistic regression demonstrated that only the Recognition Discriminability index was predictive of MSVT failure (OR = 2.88, p < .001). A cutoff of z ≤ -1.0 was associated with sensitivity of 51% and specificity of 91%. In the current study, CVLT-C Recognition Discriminability was useful in the identification of non-credible performance in a sample of relatively high-functioning pediatric outpatients with mTBI. Thus, this index can be added to the short list of embedded validity indicators appropriate for pediatric neuropsychological assessment.

Development of the DORI-TBI: An Investigation to Develop a New Screening Measure to Determine Consultation with a School-Based Brain Injury Resource Team

Brain injury is the leading cause of disability and death in children in the United States. Student re-entry into the school setting following a traumatic brain injury is crucial to student success. Multidisciplinary teams within the school district comprised of individuals with expertise in brain injury are ideal in implementing student specific treatment plans given their specialized training and wide range of expertise addressing student needs. Therefore, the purpose of this study is to develop and initially validate a quantitative instrument that school personnel can use to determine if a student, identified as having a traumatic brain injury, will benefit from district-level consultation from a brain injury team. Three studies were designed to investigate the research questions. In study one, the planning and construction of the DORI-TBI was completed. Study two addressed the content validity of the DORI-TBI through a comparison analysis with other referral forms, content review with experts in the field of TBI, and cognitive interviews with professionals to test the usability of the new screening tool. In study three, a field administration was conducted using vignettes to measure construct validity. Results produced a valid and reliable new screening instrument that can aid school-based teams to more efficiently utilize district level consultation with a brain injury support team.

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.