Impact of Zumba on Cognition and Quality of Life is Independent of APOE4 Carrier Status in Cognitively Unimpaired Older Women: A 6-Month Randomized Controlled Pilot Study

Objective: To investigate the association of a 6-month Zumba intervention with cognition and quality of life among older cognitively unimpaired apolipoprotein SMALL ELEMENT OF4 (APOE4) carrier and noncarrier women. Methods: Fifty-three women were randomly assigned to either twice-weekly Zumba group classes or maintenance of habitual exercise (control group) for 6 months. At baseline, 3, and 6 months, all participants underwent neuropsychological, physical activity, and quality-of-life assessments. Results: Overall, neuropsychological test scores and level of physical activity did not differ between intervention and control groups at any time. However, compared to the control group, quality of life was higher at 3 months, and visuospatial working memory and response inhibition improved more in the intervention group by 6 months. Apolipoprotein SMALL ELEMENT OF4 status did not affect the results. Discussion: Zumba may strengthen performance on visuospatial working memory among cognitively unimpaired older women but this needs to be tested in a larger clinical trial

Accuracy of dementia diagnosis—a direct comparison between radiologists and a computerized method

There has been recent interest in the application of machine learning techniques to neuroimaging-based diagnosis. These methods promise fully automated, standard PC-based clinical decisions, unbiased by variable radiological expertise. We recently used support vector machines (SVMs) to separate sporadic Alzheimer's disease from normal ageing and from fronto-temporal lobar degeneration (FTLD). In this study, we compare the results to those obtained by radiologists. A binary diagnostic classification was made by six radiologists with different levels of experience on the same scans and information that had been previously analysed with SVM. SVMs correctly classified 95% (sensitivity/specificity: 95/95) of sporadic Alzheimer's disease and controls into their respective groups. Radiologists correctly classified 65–95% (median 89%; sensitivity/specificity: 88/90) of scans. SVM correctly classified another set of sporadic Alzheimer's disease in 93% (sensitivity/specificity: 100/86) of cases, whereas radiologists ranged between 80% and 90% (median 83%; sensitivity/specificity: 80/85). SVMs were better at separating patients with sporadic Alzheimer's disease from those with FTLD (SVM 89%; sensitivity/specificity: 83/95; compared to radiological range from 63% to 83%; median 71%; sensitivity/specificity: 64/76). Radiologists were always accurate when they reported a high degree of diagnostic confidence. The results show that well-trained neuroradiologists classify typical Alzheimer's disease-associated scans comparable to SVMs. However, SVMs require no expert knowledge and trained SVMs can readily be exchanged between centres for use in diagnostic classification. These results are encouraging and indicate a role for computerized diagnostic methods in clinical practice

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guessing. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as patient-specific biomarker trends. The submission system remains open via the website https://tadpole.grand-challenge.org, while code for submissions is being collated by TADPOLE SHARE: https://tadpole-share.github.io/. Our work suggests that current prediction algorithms are accurate for biomarkers related to clinical diagnosis and ventricle volume, opening up the possibility of cohort refinement in clinical trials for Alzheimer's disease

Identifying AD-Sensitive and Cognition-Relevant Imaging Biomarkers via Joint Classification and Regression

Abstract. Traditional neuroimaging studies in Alzheimer’s disease (AD) typically employ independent and pairwise analyses between multimodal data, which treat imaging biomarkers, cognitive measures, and disease status as isolated units. To enhance mechanistic understanding of AD, in this paper, we conduct a new study for identifying imaging biomarkers that are associated with both cognitive measures and AD. To achieve this goal, we propose a new sparse joint classification and regression method. The imaging biomarkers identified by our method are AD-sensitive and cognition-relevant and can help reveal complex relationships among brain structure, cognition and disease status. Using the imaging and cognition data from Alzheimer’s Disease Neuroimaging Initiative database, the ef-fectiveness of the proposed method is demonstrated by clearly improved performance on predicting both cognitive scores and disease status.

Fostering resilience among mothers under stress: "Authentic\ua0Connections Groups" for medical professionals

We report on effects of an intervention to foster resilience among professional women at high risk for stress and burnout: health care providers (physicians, PhD clinicians, physician assistants, and nurse practitioners) who are mothers.Between February and November 2015, 40 mothers on staff at the Mayo Clinic, Arizona, were assigned randomly to either 1) 12 weekly 1-hour sessions of a structured, relational supportive intervention, the Authentic Connections Groups (n\ua0=\ua021) with protected time to attend sessions or to 2) 12 weekly hours of protected time to be used as desired (controls; n\ua0=\ua019). Participants were assessed at baseline, after the intervention, and 3 months follow-up on multiple psychological measures plus plasma cortisol.Across the 12 weeks of the intervention groups, there were zero dropouts. After the intervention, analyses of covariance showed significantly greater improvements (p\ua