PURPOSE: To examine relationship between ulam consumption and the working memory and cognitive flexibility among aging adults from low-income households who are more susceptible to cognitive decline.
STUDY TYPE: Cross-sectional.
POPULATION/SUBJECTS: Thirty-two aging adults (45-75 years old).
FIELD STRENGTH/SEQUENCE: Task-based fMRI, 3.0T, T1 -weighted anatomical images, T2 *-weighted imaging data.
ASSESSMENT: The dietary and ulam consumption were assessed using the respective validated Dietary History and semiquantitative Food Frequency questionnaires. Working memory and cognitive flexibility were evaluated by using neuropsychological batteries (ie, mini-mental state examination [MMSE], Digit Span, and Rey auditory verbal learning test [RAVLT]) and task-based fMRI (N-back and Stroop Color Word Test [SCWT]). Brodmann's areas 9 and 46 were the regions of interest (ROIs) of DLPFC activation.
STATISTICAL TESTS: Multiple linear regression used to understand the relationship between ulam consumption and the working memory and cognitive flexibility, while analysis of covariance (ANCOVA) was used to compare the difference of working memory and cognitive flexibility among four percentiles of ulam consumption, after age, gender, and education years adjustments. Significance was decided by two-sided, P
METHODS: Knee image is first oversegmented to produce homogeneous superpixels. Then, a ranking model is developed to rank the superpixels according to their affinities to standard priors, wherein background superpixels would have lower ranking values. Finally, seed labels are generated on the background superpixel using Fuzzy C-Means method.
RESULTS: SAGE has achieved better interobserver DSCs of 0.94 ± 0.029 and 0.93 ± 0.035 in healthy and OA knee segmentation, respectively. Good segmentation performance has been reported in femoral (Healthy: 0.94 ± 0.036 and OA: 0.93 ± 0.034), tibial (Healthy: 0.91 ± 0.079 and OA: 0.88 ± 0.095) and patellar (Healthy: 0.88 ± 0.10 and OA: 0.84 ± 0.094) cartilage segmentation. Besides, SAGE has demonstrated greater mean readers' time of 80 ± 19 s and 80 ± 27 s in healthy and OA knee segmentation, respectively.
CONCLUSIONS: SAGE enhances the efficiency of segmentation process and attains satisfactory segmentation performance compared to manual and random walks segmentation. Future works should validate SAGE on progressive image data cohort using OA biomarkers.