P4‐570: Repeated Baseline Eeg Measures Are Effective For Discrimination Of Amnestic From Non‐Amnestic Mci Patients

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153097/1/alzjjalz201908117.pd

P3‐455: Eeg Topology Combined With Computer Based Cognitive Assessment As Screening Tool For Cognitive Decline

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152558/1/alzjjalz2019063490.pd

P4‐554: An Ehr‐Based Cohort Discovery Tool For Identifying Probable Ad

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153029/1/alzjjalz201908101.pd

Preliminary effects of pagoclone, a partial GABAA agonist, on neuropsychological performance

Pagoclone is a novel cyclopyrrolone that acts as a partial GABAA receptor agonist. Preclinical studies suggest that pagoclone may have clinical utility as an anxiolytic agent, as well as a reduced incidence of side-effects. The present study was conducted to determine whether pagoclone would affect healthy individuals’ performances on neuropsychological measures as a function of dose within the projected therapeutic range. Twelve healthy adult subjects were randomly assigned to dosage groups in a 3-way crossover study. Participants were administered neuropsychological measures six hours following dosing on Day 1 and Day 6 of administration of the drug. Dose effects were noted on measures of alertness, learning, and memory and movement time. Significant effects were also noted on measures of alertness, learning and memory, information processing and psychomotor speed. Overall, the results of this small, preliminary study do not support a finding of behavioral toxicity for these doses of pagoclone. Rather, a pattern was found of transient and mild negative effects on learning and memory scores at the highest dose administered, though these changes were small and no longer evident by the sixth day of use

Cohort discovery and risk stratification for Alzheimer’s disease: an electronic health record‐based approach

BackgroundWe sought to leverage data routinely collected in electronic health records (EHRs), with the goal of developing patient risk stratification tools for predicting risk of developing Alzheimer’s disease (AD).MethodUsing EHR data from the University of Michigan (UM) hospitals and consensus‐based diagnoses from the Michigan Alzheimer’s Disease Research Center, we developed and validated a cohort discovery tool for identifying patients with AD. Applied to all UM patients, these labels were used to train an EHR‐based machine learning model for predicting AD onset within 10 years.ResultsApplied to a test cohort of 1697 UM patients, the model achieved an area under the receiver operating characteristics curve of 0.70 (95% confidence interval = 0.63‐0.77). Important predictive factors included cardiovascular factors and laboratory blood testing.ConclusionRoutinely collected EHR data can be used to predict AD onset with modest accuracy. Mining routinely collected data could shed light on early indicators of AD appearance and progression.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155901/1/trc212035-sup-0001-SuppMat.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155901/2/trc212035_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155901/3/trc212035.pd

Self-report of Cognition and Objective Test Performance in Posttraumatic Headache

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74675/1/j.1526-4610.1996.3605300.x.pd

Design and Rationale of the Cognitive Intervention to Improve Memory in Heart Failure Patients Study

BACKGROUND: Memory loss is an independent predictor of mortality among heart failure patients. Twenty-three percent to 50% of heart failure patients have comorbid memory loss, but few interventions are available to treat the memory loss. The aims of this 3-arm randomized controlled trial were to (1) evaluate efficacy of computerized cognitive training intervention using BrainHQ to improve primary outcomes of memory and serum brain-derived neurotrophic factor levels and secondary outcomes of working memory, instrumental activities of daily living, and health-related quality of life among heart failure patients; (2) evaluate incremental cost-effectiveness of BrainHQ; and (3) examine depressive symptoms and genomic moderators of BrainHQ effect. METHODS: A sample of 264 heart failure patients within 4 equal-sized blocks (normal/low baseline cognitive function and gender) will be randomly assigned to (1) BrainHQ, (2) active control computer-based crossword puzzles, and (3) usual care control groups. BrainHQ is an 8-week, 40-hour program individualized to each patient's performance. Data collection will be completed at baseline and at 10 weeks and 4 and 8 months. Descriptive statistics, mixed model analyses, and cost-utility analysis using intent-to-treat approach will be computed. CONCLUSIONS: This research will provide new knowledge about the efficacy of BrainHQ to improve memory and increase serum brain-derived neurotrophic factor levels in heart failure. If efficacious, the intervention will provide a new therapeutic approach that is easy to disseminate to treat a serious comorbid condition of heart failure

Hyperparameter optimization for recommender systems through Bayesian optimization

AbstractRecommender systems represent one of the most successful applications of machine learning in B2C online services, to help the users in their choices in many web services. Recommender system aims to predict the user preferences from a huge amount of data, basically the past behaviour of the user, using an efficient prediction algorithm. One of the most used is the matrix-factorization algorithm. Like many machine learning algorithms, its effectiveness goes through the tuning of its hyper-parameters, and the associated optimization problem also called hyper-parameter optimization. This represents a noisy time-consuming black-box optimization problem. The related objective function maps any possible hyper-parameter configuration to a numeric score quantifying the algorithm performance. In this work, we show how Bayesian optimization can help the tuning of three hyper-parameters: the number of latent factors, the regularization parameter, and the learning rate. Numerical results are obtained on a benchmark problem and show that Bayesian optimization obtains a better result than the default setting of the hyper-parameters and the random search

A Randomized Controlled Trial to Evaluate if Computerized Cognitive Rehabilitation Improves Neurocognition in Ugandan Children with HIV

Objectives: Clinically stable children with HIV can have neuromotor, attention, memory, visual?spatial, and executive function impairments. We evaluated neuropsychological and behavioral benefits of computerized cognitive rehabilitation training (CCRT) in Ugandan HIV children. Design: One hundred fifty-nine rural Ugandan children with WHO Stage I or II HIV disease (6 to 12 years; 77 boys, 82 girls; M?=?8.9, SD?=?1.86 years) were randomized to one of three treatment arms over a 2-month period. Methods: The CCRT arm received 24 one-hour sessions over 2 months, using Captain's Log (BrainTrain Corporation) programmed for games targeting working memory, attention, and visual?spatial analysis. These games progressed in difficulty as the child's performance improved. The second arm was a ?limited CCRT? with the same games rotated randomly from simple to moderate levels of training. The third arm was a passive control group receiving no training. All children were assessed at enrollment, 2 months (immediately following CCRT), and 3 months after CCRT completion. Results: The CCRT group had significantly greater gains through 3 months of follow-up compared to passive controls on overall Kaufman Assessment Battery for Children?second edition (KABC-II) mental processing index (p?Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140132/1/aid.2016.0026.pd

Blood Pressure and Cognitive Decline Over 8 Years in Middle-Aged and Older Black and White Americans

Although the association between high blood pressure (BP), particularly in midlife, and late-life dementia is known, less is known about variations by race and sex. In a prospective national study of 22 164 blacks and whites ≥45 years without baseline cognitive impairment or stroke from the REGARDS cohort study (Reasons for Geographic and Racial Differences in Stroke), enrolled 2003 to 2007 and followed through September 2015, we measured changes in cognition associated with baseline systolic and diastolic BP (SBP and DBP), as well as pulse pressure (PP) and mean arterial pressure, and we tested whether age, race, and sex modified the effects. Outcomes were global cognition (Six-Item Screener; primary outcome), new learning (Word List Learning), verbal memory (Word List Delayed Recall), and executive function (Animal Fluency Test). Median follow-up was 8.1 years. Significantly faster declines in global cognition were associated with higher SBP, lower DBP, and higher PP with increasing age ( P<0.001 for age×SBP×follow-up-time, age×DBP×follow-up-time, and age×PP×follow-up-time interaction). Declines in global cognition were not associated with mean arterial pressure after adjusting for PP. Blacks, compared with whites, had faster declines in global cognition associated with SBP ( P=0.02) and mean arterial pressure ( P=0.04). Men, compared with women, had faster declines in new learning associated with SBP ( P=0.04). BP was not associated with decline of verbal memory and executive function, after controlling for the effect of age on cognitive trajectories. Significantly faster declines in global cognition over 8 years were associated with higher SBP, lower DBP, and higher PP with increasing age. SBP-related cognitive declines were greater in blacks and men