Divergent Influences of Cardiovascular Disease Risk Factor Domains on Cognition, Grey and White Matter Morphology

OBJECTIVE: Hypertension, diabetes, dyslipidemia, and obesity are associated with preclinical alterations in cognition and brain structure; however, this often comes from studies of comprehensive risk scores or single isolated factors. We examined associations of empirically derived cardiovascular disease risk factor domains with cognition and brain structure. METHODS: A total of 124 adults (age, 59.8 [13.1] years; 41% African American; 50% women) underwent neuropsychological and cardiovascular assessments and structural magnetic resonance imaging. Principal component analysis of nine cardiovascular disease risk factors resulted in a four-component solution representing 1, cholesterol; 2, glucose dysregulation; 3, metabolic dysregulation; and 4, blood pressure. Separate linear regression models for learning, memory, executive functioning, and attention/information processing were performed, with all components entered at once, adjusting for age, sex, and education. MRI analyses included whole-brain cortical thickness and tract-based fractional anisotropy adjusted for age and sex. RESULTS: Higher blood pressure was associated with poorer learning (B = -0.19; p = .019), memory (B = -0.22; p = .005), and executive functioning performance (B = -0.14; p = .031), and lower cortical thickness within the right lateral occipital lobe. Elevated glucose dysregulation was associated with poorer attention/information processing performance (B = -0.21; p = .006) and lower fractional anisotropy in the right inferior and bilateral superior longitudinal fasciculi. Cholesterol was associated with higher cortical thickness within left caudal middle frontal cortex. Metabolic dysfunction was positively associated with right superior parietal lobe, left inferior parietal lobe, and left precuneus cortical thickness. CONCLUSIONS: Cardiovascular domains were associated with distinct cognitive, gray, and white matter alterations and distinct age groups. Future longitudinal studies may assist in identifying vulnerability profiles that may be most important for individuals with multiple cardiovascular disease risk factors

Treatment Expenditure Pattern of Epileptic Patients: A Study from a Tertiary Care Hospital, Kolkata, India

Introduction. Neurological diseases are very important causes of prolonged morbidity and disability leading to profound financial loss. Epilepsy is one of the most important neurological disorders. It being a cost intensive disorder poses a significant economic burden to the country. Aims and Objectives. The study was conducted among the persons with epilepsy (PWE) to assess their expenditure pattern for epilepsy treatment and its rural urban difference. Materials and Methods. 315 PWE selected by systematic random sampling and their caregivers were interviewed with the predesigned, pretested semistructured proforma. Subsequently data were compiled and analyzed using SPSS 18.0 software. Results and Conclusion. Majority of the study population were in the age group of 16–30 years. Majority belonged to classes IV and V of Prasad socioeconomic status scale. Average total expenditure per month for treatment of epilepsy was 219 INR, mainly contributed by drugs, travel, investigations, and so forth. Rural population was having higher treatment expenditure for epilepsy specially for travel and food and lodging in order to get epilepsy treatment. Wage loss in the last three months was present in 42.86% study subjects which was both affected by seizure episodes and travel for visits. Better district care would have helped in this situation

Quantification of intrinsic subtype ambiguity in Luminal A breast cancer and its relationship to clinical outcomes

Abstract Background PAM50 gene profiling assigns each cancer to a single intrinsic subtype. However, individual cancers vary in their adherence to a prototype, and due to bulk tissue sampling, some may exhibit expression patterns that indicate intra-tumor admixture of multiple subtypes. Our objective was to develop admixture metrics from PAM50 gene expression profiles in order to stratify Luminal A (LumA) cases according to their degree of subtype admixture, and then relate such admixture to clinical and molecular variables. Methods We re-constructed scaled, normalized PAM50 profiles for 1980 cases (674 LumA) in the METABRIC cohort and for each case computed its Mahalanobis (M-) distance from its assigned centroid and M-distance from all other centroids. We used t-SNE plots to visualize overlaps in subtype clustering. With Normal-like cases excluded, we developed two metrics: Median Distance Criteria (MDC) classified pure cases as those located within the 50th percentile of the LumA centroid and > =50th percentile from any other centroid. Distance Ratio Criteria (DRC) was computed as the ratio of M-distances from the LumA centroid to the nearest non-assigned centroid. Pure and admixed LumA cases were compared on clinical/molecular traits. TCGA LumA cases (n = 509) provided independent validation. Results Compared to pure cases in METABRIC, admixed ones had older age at diagnosis, larger tumor size, and higher grade and stage. These associations were stronger for the DRC metric compared to MDC. Admixed cases were associated with HER2 gain, high proliferation, higher PAM50 recurrence scores, more frequent TP53 mutation, and less frequent PIK3CA mutation. Similar results were observed in the TCGA validation cohort, which also showed a positive association between admixture and number of clonal populations estimated by PyClone. LumA-LumB confusion predominated, but other combinations were also present. Degree of admixture was associated with overall survival in both cohorts, as was disease-free survival in TCGA, independent of age, grade and stage (HR = 2.85, Tertile 3 vs.1). Conclusions Luminal A breast cancers subgrouped based on PAM50 subtype purity support the hypothesis that admixed cases have worse clinical features and survival. Future analyses will explore more extensive genomic metrics for admixture and their spatial significance within a single tumor

Multivariate pattern analysis strategies in detection of remitted major depressive disorder using resting state functional connectivity

Understanding abnormal resting-state functional connectivity of distributed brain networks may aid in probing and targeting mechanisms involved in major depressive disorder (MDD). To date, few studies have used resting state functional magnetic resonance imaging (rs-fMRI) to attempt to discriminate individuals with MDD from individuals without MDD, and to our knowledge no investigations have examined a remitted (r) population. In this study, we examined the efficiency of support vector machine (SVM) classifier to successfully discriminate rMDD individuals from healthy controls (HCs) in a narrow early-adult age range. We empirically evaluated four feature selection methods including multivariate Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net feature selection algorithms. Our results showed that SVM classification with Elastic Net feature selection achieved the highest classification accuracy of 76.1% (sensitivity of 81.5% and specificity of 68.9%) by leave-one-out cross-validation across subjects from a dataset consisting of 38 rMDD individuals and 29 healthy controls. The highest discriminating functional connections were between the left amygdala, left posterior cingulate cortex, bilateral dorso-lateral prefrontal cortex, and right ventral striatum. These appear to be key nodes in the etiopathophysiology of MDD, within and between default mode, salience and cognitive control networks. This technique demonstrates early promise for using rs-fMRI connectivity as a putative neurobiological marker capable of distinguishing between individuals with and without rMDD. These methods may be extended to periods of risk prior to illness onset, thereby allowing for earlier diagnosis, prevention, and intervention. Keywords: Resting state fMRI, Major depressive disorder, Machine learning, MVP