Innovations In Clinical Neuroscience

CNS Summit 2016

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

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[ 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 ] Innovations in CLINICAL NEUROSCIENCE S9 employee and shareholder of Teva Pharmaceuticals, Malvern, Pennsylvania. DK is an officer and shareholder of Click T herapeutics. TS is an officer and shareholder of Click Therapeutics. AB is an employee and shareholder of Click Therapeutics. SL is a consultant of Click Therapeutics. NS is an officer and s hareholder of Click Therapeutics. 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 frequency domain using Fast Fourier Transform (FFT) to calculate power spectral densities (PSD) grouped into the standard EEG bandwidths (delta, theta, alpha, beta, and gamma). Results: Comparison of PSD 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 b ands 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 t emporal 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) are grouped together into a feature vector and the most discriminative variables are selected to construct linear discriminate function analysis (lDFA) models. Application of lDFA to the MCI dataset results in high accuracy and specificity using auto-validation or leave- one-out cross-validation. Although a great deal of information is derived from resting state data, we also have the capability to capture 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 indicates significantly longer peak latency in the parietal N2 component of the ERP in a PTSD cohort compared to healthy control cohort. Conclusion: These data provide further evidence that PTSD patients have increased difficulty with attentional resources and identifies 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. Evaluating the use of an artificial intelligence platform on mobile devices to measure adherence in subjects with an acute exacerbation of schizophrenia Presenters: Shafner L 1 , McCue M 2 , Rubin A 2 , Dong X 2 , Hanson E 2 , Mahableshwarkar A 2 , Hanina A 1 , Macek T 2 Affiliations: 1 AiCure, New York, New York; 2 Takeda Development Center Americas, Inc., Deerfield, Illinois Objective: The need to minimize medication nonadherence is particularly i mportant in central nervous system (CNS) clinical trials. An artificial intelligence (AI) platform was assessed in measuring and increasing medication adherence in subjects with schizophrenia i n a Phase 2, randomized, double-blind study. Design: Subjects in the TAK-063 study who were stable after three or more weeks of inpatient treatment and were discharged were followed up as outpatients for the remainder of the six- week period. Subjects were given devices with the AI application downloaded and asked to use the application for dosing administrations. The primary adherence measure for the study was based on scheduled pill counts; AI platform adherence data were tested for exploratory purposes. Results: The AI platform was used by 26 subjects for 372 subject days; 744 adherence parameters were collected. Five subjects discontinued early (19.2%). For subjects who completed the trial, mean (standard deviation [SD]) cumulative adherence rates based on visual confirmation of drug ingestion (AI application) and on pill count were 82.5 percent and 99.7 percent, respectively. The mean time to use the AI platform was 86.8 seconds per pill. Conclusion: Subjects with acutely exacerbated schizophrenia who were eligible for discharge from the inpatient setting and who completed the study demonstrated high rates of adherence using the mobile AI application. Subjects were able to easily use the technology. Use of the platform did not appear to increase the dropout rate. This study demonstrates the feasibility of using AI platforms to ensure high adherence, provide reliable adherence data, and rapidly detect nonadherence in CNS trials. Disclosures/funding: Adam Hanina and Laura Shafner are employees of AiCure, New York, New York, and consultants to Takeda. Xinxin Dong, Elizabeth Hanson, Thomas A. Macek, Atul Mahableshwarkar, and Maggie McCue are employees of Takeda Development Center Americas, Inc., Deerfield, Illinois. Anne Rubin is a former employee of Takeda.

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