Steve Rapp

Positive and negative affect, depression, and cognitive processes in the Cognition in the Study of Tamoxifen and Raloxifene (Co-STAR) Trial

ABSTRACT Objectives: This study examined the relationship between positive and negative affect, depressive symptoms, and cognitive performance. Methods: The sample consisted of 1479 non-demented, postmenopausal women (mean age = 67 years) at increased risk of breast cancer enrolled in the National Surgical Adjuvant Breast and Bowel Project’s Study of Tamoxifen and Raloxifene. At each annual visit, women completed a standardized neuropsychological battery and self-report measures of affect and depression. Data from three visits were used in linear mixed models for repeated measures using likelihood ratio tests. Separate analyses were performed to relate positive/negative affect and depression to each cognitive measure. Results: Higher positive affect was associated with better letter fluency (p = .006) and category fluency (p < .0001). Higher negative affect was associated with worse global cognitive function (p < .0001), verbal memory (CVLT List B; p = .002), and spatial ability (p < .0001). Depressive symptoms were negatively associated with verbal knowledge (p = .004), figural memory (p < .0001), and verbal memory (p’s ≤ .0001). Discussion: Findings are consistent with some prior research demonstrating a link between positive affect and increased verbal fluency and between depressive symptoms and decreased memory. The most novel finding shows that negative affect is related to decreased global cognition and visuospatial ability. Overall, this research in a large, longitudinal sample supports the notion that positive affect is related to increases and negative affect to decreases in performance on distinct cognitive measures.

Hippocampal dose volume histogram predicts Hopkins Verbal Learning Test scores after brain irradiation

Purpose Radiation-induced cognitive decline is relatively common after treatment for primary and metastatic brain tumors; however, identifying dosimetric parameters that are predictive of radiation-induced cognitive decline is difficult due to the heterogeneity of patient characteristics. The memory function is especially susceptible to radiation effects after treatment. The objective of this study is to correlate volumetric radiation doses received by critical neuroanatomic structures to post–radiation therapy (RT) memory impairment. Methods and materials Between 2008 and 2011, 53 patients with primary brain malignancies were treated with conventionally fractionated RT in prospectively accrued clinical trials performed at our institution. Dose-volume histogram analysis was performed for the hippocampus, parahippocampus, amygdala, and fusiform gyrus. Hopkins Verbal Learning Test-Revised scores were obtained at least 6 months after RT. Impairment was defined as an immediate recall score ≤15. For each anatomic region, serial regression was performed to correlate volume receiving a given dose (VD(Gy)) with memory impairment. Results Hippocampal V53.4Gy to V60.9Gy significantly predicted post-RT memory impairment (P < .05). Within this range, the hippocampal V55Gy was the most significant predictor (P = .004). Hippocampal V55Gy of 0%, 25%, and 50% was associated with tumor-induced impairment rates of 14.9% (95% confidence interval [CI], 7.2%-28.7%), 45.9% (95% CI, 24.7%-68.6%), and 80.6% (95% CI, 39.2%-96.4%), respectively. Conclusions The hippocampal V55Gy is a significant predictor for impairment, and a limiting dose below 55 Gy may minimize radiation-induced cognitive impairment.

Impact of Intensive Lifestyle Intervention for Weight Management on Self-Reported Cognitive Function—The Action for Health in Diabetes (Look AHEAD) Randomized Controlled Trial

Background: Intensive lifestyle interventions (ILI) to reduce weight and increase physical activity may preserve higher-order cognition in overweight and obese adults with type 2 diabetes (T2D). Methods: Adults with T2D who enrolled in a randomized clinical trial of a 10-year ILI compared with diabetes support and education (DSE; N=5,084; mean age=58.7 years; mean BMI=35.9 kg/m2; 36.8% racial/ethnic minority) provided self-assessments of difficulty with memory, problem-solving, and decision-making abilities at baseline and over ≥10 years of follow-up. The Health Utilities Index assessed memory and problem-solving; the Beck Depression Inventory-II assessed decision-making. Analyses included the full sample and sub-groups based on baseline weight status and history of cardiovascular disease (CVD). Results: At baseline, 12%, 16%, and 23% of all participants reported some difficulty with problem-solving, decision-making, and memory, respectively. For those without baseline self-identified cognitive difficulties in a specific domain, ILI was associated with lower odds of decision-making difficulties at follow-up compared to DSE (odds ratio [OR]=0.85, [95% CI 0.75,0.97]). Among those who were not obese, ILI was associated with lower odds of problem-solving difficulties at follow-up (OR=0.69 [0.51,0.95]). For participants with self-identified cognitive difficulties at baseline who had a history of CVD, ILI may have worsened difficulties in problem-solving at follow-up visits (OR=2.95 [1.38,6.31]). Conclusions: A long-term ILI targeting weight loss and physical activity may protect self-reported higher-order cognitive abilities in adults with T2D without preexisting memory, problem-solving, or decision-making problems. However, among those with preexisting problems, ILI was not protective. Disclosure G.R. Dutton: None. M. Espeland: Consultant; Self; Boehringer Ingelheim GmbH, Janssen Pharmaceuticals, Inc.. Research Support; Self; National Institute of Diabetes and Digestive and Kidney Diseases, National Institute on Aging, National Heart, Lung, and Blood Institute. R.H. Neiberg: None. O. Carmichael: None. K.M. Hayden: None. K.C. Johnson: None. R.W. Jeffery: None. L.D. Baker: None. D. Cook: None. D. Kitzman: Advisory Panel; Self; AbbVie Inc.. Research Support; Self; AstraZeneca. S.R. Rapp: None.

RELATIVE IMPACT OF SAMPLE SIZE AND DIMENSIONALITY OF PREDICTORS ON TWO MACHINE LEARNING METHODS FOR DETECTION OF ALZHEIMER'S DISEASE

Background: Spatial gradients in cortical thinning are a hallmark of dementias, and are shown to follow stereotypical patterns specific to each dementia. The signature of the disease is muchmore clearly visible in cortical thickness gradients taken between different brain regions, for example anterior-posterior gradients in AD as AD is known to affect the posterior cortices such as the medial temporal lobes, the precuneus etc. preferentially and early in the course of the disease. Keeping this idea of preferential gradient is mind, We reformulate our previously developed novel imaging cortical biomarker based on graph-theoretic analysis of inter-regional covariance of cortical thickness, called ThickNet features [1] to develop novel covariance features based on dissimilarity in thickness. These features capture the spatial thickness gradients within each subject. We call them Dissimilarity based Extraction of Covariance LInked NEtwork (DECLINE) features.We show that they outperform ThickNet features for the early detection of Alzheimer’s disease. DECLINE features are first of its kind and show promise in detecting the cognitive decline predictive of AD.Methods: Cortical thickness is extracted from the MRI scan using the method [2] for each patient and the features are registered to a common atlas surface to establish vertex-wise correspondence. Then the cortex of each patient is partitioned into large number (e.g. K1⁄4300 vertices per patch) of small areas by k-means clustering of vertices spatially on the atlas surface (Figure 1). A graph is then constructed by establishing a link between two such patches if dissimilarity (abs. difference inmean-thickness) in thickness is above a given threshold (e.g. 0.5mm). From this binary undirected graph, we compute several graph-theoretic properties called DECLINE features to represent each patient (See Figure 2), namely nodal degree, betweenness centrality and clustering coefficient. Using the same ADNI dataset on which we demonstrated the diagnostic utility of ThickNet features, we show that DECLINE features outperform ThickNet features, and show potential for the early detection of Alzheimer’s disease. Results: Using our Repeated Holdout, Stratified Training set (RHsT) cross-validation method proposed in [1] (See Figure 3), DECLINE features produced an area under ROC (AUC) of 0.93 in discriminating AD from healthy controls (CN), and an AUC of 0.87 in discriminating MCI converters (MCIc) from CN. The results presented in Figure 4 show DECLINE features significantly outperform ThickNet features in all the experiments. Conclusions: We present novel DECLINE measures based on inter-regional covariance of cortical thickness from structural MRI and demonstrate their performance on a benchmark ADNI dataset for early detection of Alzheimer’s disease.