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

Predicting Future Clinical Changes of MCI Patients Using Longitudinal and Multimodal Biomarkers

Accurate prediction of clinical changes of mild cognitive impairment (MCI) patients, including both qualitative change (i.e., conversion to Alzheimer's disease (AD)) and quantitative change (i.e., cognitive scores) at future time points, is important for early diagnosis of AD and for monitoring the disease progression. In this paper, we propose to predict future clinical changes of MCI patients by using both baseline and longitudinal multimodality data. To do this, we first develop a longitudinal feature selection method to jointly select brain regions across multiple time points for each modality. Specifically, for each time point, we train a sparse linear regression model by using the imaging data and the corresponding clinical scores, with an extra ‘group regularization’ to group the weights corresponding to the same brain region across multiple time points together and to allow for selection of brain regions based on the strength of multiple time points jointly. Then, to further reflect the longitudinal changes on the selected brain regions, we extract a set of longitudinal features from the original baseline and longitudinal data. Finally, we combine all features on the selected brain regions, from different modalities, for prediction by using our previously proposed multi-kernel SVM. We validate our method on 88 ADNI MCI subjects, with both MRI and FDG-PET data and the corresponding clinical scores (i.e., MMSE and ADAS-Cog) at 5 different time points. We first predict the clinical scores (MMSE and ADAS-Cog) at 24-month by using the multimodality data at previous time points, and then predict the conversion of MCI to AD by using the multimodality data at time points which are at least 6-month ahead of the conversion. The results on both sets of experiments show that our proposed method can achieve better performance in predicting future clinical changes of MCI patients than the conventional methods

In what ways does the mandatory nature of Victoria's municipal public health planning framework impact on the planning process and outcomes?

BACKGROUND: Systems for planning are a critical component of the infrastructure for public health. Both in Australia and internationally there is growing interest in how planning processes might best be strengthened to improve health outcomes for communities. In Australia the delivery of public health varies across states, and mandated municipal public health planning is being introduced or considered in a number of jurisdictions. In 1988 the Victorian State government enacted legislation that made it mandatory for each local government to produce a Municipal Public Health Plan, offering us a 20-year experience to consider. RESULTS: In-depth interviews were undertaken with those involved in public health planning at the local government level, as part of a larger study on local public health infrastructure and capacity. From these interviews four significant themes emerge. Firstly, there is general agreement that the Victorian framework of mandatory public health planning has led to improvements in systems for planning. However, there is some debate about the degree of that improvement. Secondly, there is considerable variation in the way in which councils approach planning and the priority they attach to the process. Thirdly, there is concern that the focus is on producing a plan rather than on implementing the plan. Finally, some tension over priorities is evident. Those responsible for developing Municipal Public Health Plans express frustration over the difficulty of having issues they believe are important addressed through the MPHP process. CONCLUSION: There are criticisms of Victoria's system for public health planning at the local government level. Some of these issues may be specific to the arrangement in Victoria, others are problems encountered in public health planning generally. In Victoria where the delivery structure for public health is diverse, a system of mandatory planning has created a minimum standard. The implementation of the framework was slow and factors in the broader political environment had a significant impact. Work done in recent years to support the process appears to have led to improvements. There are lessons for other states as they embark upon mandated public health plans

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