High Dimensional Classification of Structural MRI Alzheimer’s Disease Data Based on Large Scale Regularization

In this work we use a large scale regularization approach based on penalized logistic regression to automatically classify structural MRI images (sMRI) according to cognitive status. Its performance is illustrated using sMRI data from the Alzheimer Disease Neuroimaging Initiative (ADNI) clinical database. We downloaded sMRI data from 98 subjects (49 cognitive normal and 49 patients) matched by age and sex from the ADNI website. Images were segmented and normalized using SPM8 and ANTS software packages. Classification was performed using GLMNET library implementation of penalized logistic regression based on coordinate-wise descent optimization techniques. To avoid optimistic estimates classification accuracy, sensitivity, and specificity were determined based on a combination of three-way split of the data with nested 10-fold cross-validations. One of the main features of this approach is that classification is performed based on large scale regularization. The methodology presented here was highly accurate, sensitive, and specific when automatically classifying sMRI images of cognitive normal subjects and Alzheimer disease (AD) patients. Higher levels of accuracy, sensitivity, and specificity were achieved for gray matter (GM) volume maps (85.7, 82.9, and 90%, respectively) compared to white matter volume maps (81.1, 80.6, and 82.5%, respectively). We found that GM and white matter tissues carry useful information for discriminating patients from cognitive normal subjects using sMRI brain data. Although we have demonstrated the efficacy of this voxel-wise classification method in discriminating cognitive normal subjects from AD patients, in principle it could be applied to any clinical population

Social isolation and incident heart failure hospitalization in older women: Women\u27s health initiative study findings

Background The association of social isolation or lack of social network ties in older adults is unknown. This knowledge gap is important since the risk of heart failure (HF) and social isolation increase with age. The study examines whether social isolation is associated with incident HF in older women, and examines depressive symptoms as a potential mediator and age and race and ethnicity as effect modifiers. Methods and Results This study included 44 174 postmenopausal women of diverse race and ethnicity from the WHI (Women\u27s Health Initiative) study who underwent annual assessment for HF adjudication from baseline enrollment (1993-1998) through 2018. We conducted a mediation analysis to examine depressive symptoms as a potential mediator and further examined effect modification by age and race and ethnicity. Incident HF requiring hospitalization was the main outcome. Social isolation was a composite variable based on marital/partner status, religious ties, and community ties. Depressive symptoms were assessed using CES-D (Center for Epidemiology Studies-Depression). Over a median follow-up of 15.0 years, we analyzed data from 36 457 women, and 2364 (6.5%) incident HF cases occurred; 2510 (6.9%) participants were socially isolated. In multivariable analyses adjusted for sociodemographic, behavioral, clinical, and general health/functioning; socially isolated women had a higher risk of incident HF than nonisolated women (HR, 1.23; 95% CI, 1.08-1.41). Adding depressive symptoms in the model did not change this association (HR, 1.22; 95% CI, 1.07-1.40). Neither race and ethnicity nor age moderated the association between social isolation and incident HF. Conclusions Socially isolated older women are at increased risk for developing HF, independent of traditional HF risk factors. Registration URL: http://www.clinicaltrials.gov; Unique identifier: NCT00000611

Designing clinical trials for assessing the effects of cognitive training and physical activity interventions on cognitive outcomes: The Seniors Health and Activity Research Program Pilot (SHARP-P) Study, a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>The efficacy of non-pharmacological intervention approaches such as physical activity, strength, and cognitive training for improving brain health has not been established. Before definitive trials are mounted, important design questions on participation/adherence, training and interventions effects must be answered to more fully inform a full-scale trial.</p> <p>Methods</p> <p>SHARP-P was a single-blinded randomized controlled pilot trial of a 4-month physical activity training intervention (PA) and/or cognitive training intervention (CT) in a 2 × 2 factorial design with a health education control condition in 73 community-dwelling persons, aged 70-85 years, who were at risk for cognitive decline but did not have mild cognitive impairment.</p> <p>Results</p> <p>Intervention attendance rates were higher in the CT and PACT groups: CT: 96%, PA: 76%, PACT: 90% (p=0.004), the interventions produced marked changes in cognitive and physical performance measures (p≤0.05), and retention rates exceeded 90%. There were no statistically significant differences in 4-month changes in composite scores of cognitive, executive, and episodic memory function among arms. Four-month improvements in the composite measure increased with age among participants assigned to physical activity training but decreased with age for other participants (intervention*age interaction p = 0.01). Depending on the choice of outcome, two-armed full-scale trials may require fewer than 1,000 participants (continuous outcome) or 2,000 participants (categorical outcome).</p> <p>Conclusions</p> <p>Good levels of participation, adherence, and retention appear to be achievable for participants through age 85 years. Care should be taken to ensure that an attention control condition does not attenuate intervention effects. Depending on the choice of outcome measures, the necessary sample sizes to conduct four-year trials appear to be feasible.</p> <p>Trial Registration</p> <p>Clinicaltrials.gov Identifier: <a href="http://www.clinicaltrials.gov/ct2/show/NCT00688155">NCT00688155</a></p