Cortical and autonomic modulation of attentional control /

Whereas the role of the anterior cingulate cortex (ACC) in cognitive control has received considerable attention, much less work has been done on the role of the ACC in autonomic regulation. Its connections through the vagus nerve to the sinoatrial node of the heart are thought to exert modulatory control over cardiovascular arousal. Therefore, ACC is not only responsible for the implementation of cognitive control, but also for the dynamic regulation of cardiovascular activity that characterizes healthy heart rate and adaptive behaviour. However, cognitive control and autonomic regulation are rarely examined together. Moreover, those studies that have examined the role of phasic vagal cardiac control in conjunction with cognitive performance have produced mixed results, finding relations for specific age groups and types of tasks but not consistently. So, while autonomic regulatory control appears to support effective cognitive performance under some conditions, it is not presently clear just what factors contribute to these relations. The goal of the present study was, therefore, to examine the relations between autonomic arousal, neural responsivity, and cognitive performance in the context of a task that required ACC support. Participants completed a primary inhibitory control task with a working memory load embedded. Pre-test cardiovascular measures were obtained, and ontask ERPs associated with response control (N2/P3) and error-related processes (ERN/Pe) were analyzed. Results indicated that response inhibition was unrelated to phasic vagal cardiac control, as indexed by respiratory sinus arrhythmia (RSA). However, higher resting RSA was associated with larger ERN ampUtude for the highest working memory load condition. This finding suggests that those individuals with greater autonomic regulatory control exhibited more robust ACC error-related responses on the most challenging task condition. On the other hand, exploratory analyses with rate pressure product (RPP), a measure of sympathetic arousal, indicated that higher pre-test RPP (i.e., more sympathetic influence) was associated with more errors on "catch" NoGo trials, i.e., NoGo trials that simultaneously followed other NoGo trials, and consequently, reqviired enhanced response control. Higher pre-test RPP was also associated with smaller amplitude ERNs for all three working memory loads and smaller ampUtude P3s for the low and medium working memory load conditions. Thus, higher pretest sympathetic arousal was associated with poorer performance on more demanding "catch" NoGo trials and less robust ACC-related electrocortical responses. The findings firom the present study highlight tiie interdependence of electrocortical and cardiovascular processes. While higher pre-test parasympathetic control seemed to relate to more robust ACC error-related responses, higher pre-test sympathetic arousal resulted in poorer inhibitory control performance and smaller ACC-generated electrocortical responses. Furthermore, these results provide a base from which to explore the relation between ACC and neuro/cardiac responses in older adults who may display greater variance due to the vulnerabihty of these systems to the normal aging process.

The role of molecular, clinical and socioeconomic factors in the long-term survival of axillary node negative breast cancer patients

The overall objective of this study was to investigate factors associated with long-term survival in axillary node negative (ANN) breast cancer patients. Clinical and biological factors included stage, histopathologic grade, p53 mutation, Her-2/neu amplification, estrogen receptor status (ER), progesterone receptor status (PR) and vascular invasion. Census derived socioeconomic (SES) indicators included median individual and household income, proportions of university educated individuals, housing type, "incidence" of low income and an indicator of living in an affluent neighbourhood. The effects of these measures on breast cancer-specific survival and competing cause survival were investigated. A cohort study examining survival among axillary node negative (ANN) breast cancer patients in the greater Toronto area commenced in 1 989. Patients were followed up until death, lost-to-follow up or study termination in 2004. Data were collected from several sources measuring patient demographics, clinical factors, treatment, recurrence of disease and survival. Census level SES data were collected using census geo-coding of patient addresses' at the time of diagnosis. Additional survival data were acquired from the Ontario Cancer Registry to enhance and extend the observation period of the study. Survival patterns were examined using KaplanMeier and life table procedures. Associations were examined using log-rank and Wilcoxon tests of univariate significance. Multivariate survival analyses were perfonned using Cox proportional hazards models. Analyses were stratified into less than and greater than 5 year survival periods to observe whether known markers of short-tenn survival were also associated with reductions in long-tenn survival among breast cancer patients. The 15 year survival probabilities in this cohort were: for breast cancerspecific survival 0.88, competing causes survival 0.89 and for overall survival 0.78. Estrogen receptor (ER) and progesterone receptor (PR) status (Hazard Ratio (HR) ERIPR- versus ER+/PR+, 8.15,95% CI, 4.74, 14.00), p53 mutation (HR, 3.88, 95% CI, 2.00, 7.53) and Her-2 amplification (HR, 2.66, 95% CI, 1.36, 5.19) were associated with significant reductions in short-tenn breast cancer-specific survival «5 years following diagnosis), however, not with long-term survival in univariate analyses. Stage, histopathologic grade and ERiPR status were the clinicallbiologieal factors that were associated with short-term breast cancer specific survival in multivariate results. Living in an affluent neighbourhood (top quintile of median household income compared to the rest of the population) was associated with the largest significant increase in long-tenn breast cancer-specific survival after adjustment for stage, histopathologic grade and treatment (HR, 0.36, 95% CI, 0.12, 0.89).

Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.