Innovations In Clinical Neuroscience

CNS Summit 2016

A peer-reviewed, evidence-based journal for clinicians in the field of neuroscience

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Innovations in CLINICAL NEUROSCIENCE [ V O L U M E 1 4 , N U M B E R 1 – 2 , J A N U A R Y – F E B R U A R Y 2 0 1 7 , S U P P L E M E N T ] S14 required for the United Kingdom, including the street scene and bus appearance. Conclusion: Cross-cultural adaptation o f the VRFCAT revealed significant variations across English-speaking cultures and highlighted the importance of appropriate adaptation of functional assessments for use in multinational trials. D isclosures/funding: AS Atkins, BK Saxby, SE Kelley, M Hamby, P Roux, M Gonzalez, J Madden, and M Stankovic are full-time employees of NeuroCog Trials. RSE Keefe is owner and CEO of NeuroCog Trials, the company that developed the VRFCAT as a proprietary instrument and provides commercial distribution and support services. Support provided by National Institute of Mental Health under award R44MH084240. Development of neurophysiological- based biomarkers for neurodegenerative disease and psychiatric disorders using EEG Presenters: Waninger S 1 , Berka C 1 , Stikic M 1 , Korszen S 1 , Salat D 2 , Verma A 3 Affiliations: 1 Advanced Brain Monitoring, Inc., Carlsbad, California; 2 MGH/MIT/HMS Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts ; 3 Biogen, Cambridge, Massachusetts Background: Successful drug development for neurodegenerative diseases and psychiatric disorders requires objective, reliable, and accurate measures to evaluate disease progression and therapeutic efficacy. The potential of neurophysiological biomarkers using electroencephalography (EEG) has been highlighted in ongoing studies at Advanced Brain Monitoring on Parkinson's disease (PD), Alzheimer's disease (AD), mild cognitive impairment (MCI), mood disorders, and posttraumatic stress disorder (PTSD). Design: Using our wireless B-Alert X24 system with a standard 10–20 montage, we acquired and analyzed EEG data both during resting state and neurocognitive tasks designed to activate the neural circuits involved in attention, memory, and emotion and elicit event-related potentials (ERPs). For resting state, data were acquired for five minutes with eyes closed (EC) and five minutes with eyes open (EO). These data were decontaminated and converted from the time domain to the f requency domain using Fast Fourier Transform (FFT) to calculate power spectral densities (PSD) grouped into the standard EEG bandwidths (delta, theta, alpha, beta, and gamma). Comparison of P SD from AD and MCI patients to healthy, age-matched controls indicated distinguishing features, particularly the "slowing" of EEG exemplified by an increase in slow wave bands and a decrease in fast power that is typically observed in patients with cognitive decline. The source of the abnormal EEG in MCI patients was localized to the middle and superior temporal gyrus and fusiform gyrus using low resolution electromagnetic tomography (LORETA). Variables extracted from the resting state data (i.e., absolute PSDs, relative PSDs, and wavelets) were grouped together into a feature vector, and the most discriminative variables were selected to construct linear discriminate function analysis (lDFA) models. Results: Application of lDFA to the MCI dataset resulted in high accuracy and specificity using auto-validation or leave- one-out cross-validation. Although a great deal of information was derived from resting state data, we captured engagement of neural circuits during neurocognitive tasks that stimulate and elicit neural patterns associated with attention, memory, and emotion. One such task, three-choice vigilance (3CVT), is a visual choice reaction time task designed to measure sustained attention and target detection. EEG data acquired during the 3-CVT task indicated significantly longer peak latency in the parietal N2 component of the ERP in a PTSD cohort compared to healthy control cohort. Conclusion: These data provided further evidence that PTSD patients have increased difficulty with attentional resources, and identified a potential biomarker for PTSD disease progression. Both resting state and event-related EEG data have potential for use as cost- efficient, noninvasive, pharmacodynamic endpoints in neurodegenerative disease and psychiatric disorder clinical trials of experimental therapeutics. Disclosures/funding: None reported. Discrepancies between CGI-S score and PANSS item level scores—an exploratory analysis P resenters: Kott A, Daniel D, Affiliations: Bracket Global, Wayne, Pennsylvania Objectives: The objective of this study was to identify and characterize factors a ssociated with discrepancies in the rating of the Clinical Global Impression- Severity (CGI-S) scale versus the Positive and Negative Syndrome Scale (PANSS) in a large database of schizophrenia clinical trials rating data. Design: We retrospectively analyzed 67,698 subject visits from 15 multicenter, double-blind, placebo- controlled schizophrenia trials. CGI-S versus PANSS item level discrepancies were operationally defined as ratings where CGI-S score was at least three points below the highest individual PANSS item score. Using univariate logistic regression models, we assessed the effect of overall and individual symptom severity, PANSS total versus CGI-S discrepancy, region, study type, visit type, and changes in raters on the presence of the discrepancies. Results: In this database, the prevalence of CGI-S versus PANSS item level discrepancies was 1.33 percent (904/67,698). Visit type, study type, and region had significant effects on the presence of item discrepancies. Increased item severity and different raters significantly increased the odds of PANSS item CGI-S discrepancies, while the odds significantly decreased with increasing overall severity. Conclusion: We found a prevalence of more than one percent of PANSS item level versus CGI-S discrepancies. In their majority, these discrepancies were distinctly different from PANSS total score versus CGI-S discrepancies. We identified multiple factors significantly impacting on the presence of these item level discrepancies. We conclude that algorithms assessing possible PANSS- CGI discrepancies should consider individual item severity in addition to total scores. Further research is necessary to replicate and understand better the findings. Disclosures/funding: Both authors are full time employees of Bracket.

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