Expertise and training in fine motor skills has been associated with changes in brain structure, function, and connectivity. Fewer studies have explored the neural effects of athletic activities that do not seem to rely on precise fine motor control (e.g., distance running). Here, we compared resting-state functional connectivity in a sample of adult male collegiate distance runners (n = 11; age = 21.3 +/- 2.5) and a group of healthy age matched non-athlete male controls (n = 11; age = 20.6 +/- 1.1), to test the hypothesis that expertise in sustained aerobic motor behaviors affects resting state functional connectivity in young adults. Although generally considered an automated repetitive task, locomotion, especially at an elite level, likely engages multiple cognitive actions including planning, inhibition, monitoring, attentional switching and multi-tasking, and motor control. Here, we examined connectivity in three resting-state networks that link such executive functions with motor control: the default mode network (DMN), the frontoparietal network (FPN), and the motor network (MN). We found two key patterns of significant between-group differences in connectivity that are consistent with the hypothesized cognitive demands of elite endurance running. First, enhanced connectivity between the FPN and brain regions often associated with aspects of working memory and other executive functions (frontal cortex), suggest endurance running may stress executive cognitive functions in ways that increase connectivity in associated networks. Second, we found significant anti-correlations between the DMN and regions associated with motor control (paracentral area), somatosensory functions (post-central region), and visual association abilities (occipital cortex). DMN deactivation with task-positive regions has been shown to be generally beneficial for cognitive performance, suggesting anti-correlated regions observed here are engaged during running. For all between-group differences, there were significant associations between connectivity, self-reported physical activity, and estimates of maximum aerobic capacity, suggesting a dose-response relationship between engagement in endurance running and connectivity strength. Together these results suggest that differences in experience with endurance running are associated with differences in functional brain connectivity. High intensity aerobic activity that requires sustained, repetitive locomotor and navigational skills may stress cognitive domains in ways that lead to altered brain connectivity, which in turn has implications for understanding the beneficial role of exercise for brain and cognitive function over the lifespan.

Background. Impaired insight, or unawareness of illness, is a common symptom of schizophrenia. Clinical insight is awareness of having a mental disorder; cognitive insight is ability to self-reflect (self­reflectiveness) and certainty in cognitions (self­certainty). In schizophrenia insight is associated with brain function and improving insight is a potential early intervention point. This study investigated whether insight is impaired in youth at ultra high-risk (UHR) for psychosis, and if it is related to major brain networks. Methods. Data from a larger UHR study was used, including 55 UHR adolescents and 55 controls assessed with the Structured Interview of Prodromal Symptoms, MATRICS Consensus Cognitive Battery, Scale to Assess Unawareness of Mental Disorder, and Beck Cognitive Insight Scale, as well as resting state functional MRI scans. UHR and control groups were tested for differences in self-reflectiveness and self-certainty, and correlations between insight dimensions and clinical and cognitive measures. Functional connectivity was calculated for the default mode, the cingulo-opercular, and central executive networks and regressed on participants’ reported clinical and cognitive insight, while covarying for head motion. Results. Self-reflectiveness was higher in the UHR group (d = 1.28), but the groups did not differ in self-certainty (d = 0.28). Among UHR, poorer clinical insight was related to greater symptom severity. Default mode connectivity was negatively correlated with self-reflectiveness (R2 = .091) and clinical insight (R2 = .399) in UHR, but no such correlations were found in controls. Cerebello-prefrontal cortex connectivity was negatively associated with self-certainty in the UHR group (R2 = .089 - .138). Conclusions. Default mode connectivity appears to be associated with the facets of insight concerning self-awareness, whereas cerebello-prefrontal connectivity appears to be associated specifically with self-certainty. This is the first study to relate major brain networks to insight before the onset of psychosis, and is consistent with models proposing that different facets of insight are related to self-awareness and executive functioning through networks associated with these processes.

Given a set P of n points in R^d, we consider the k-connected interference minimization problem, in which the objective is to assign a transmission radius to each node in P such that the resulting network is k-connected and the maximum interference is minimized. We show for any n and any 1 <= k < n, Omega(sqrt(kn)) and Omega(k log n) are lower bounds on the worst-case minimum maximum interference in the symmetric and asymmetric models, respectively. In the symmetric case, we present polynomial-time algorithms that build a k-connected network on any given set of n nodes with interference O(sqrt(kn)) in one dimension and O(min{k sqrt(n), k log lambda}) in two dimensions, where lambda denotes the ratio of the longest to shortest distances between any pair of nodes. In the asymmetric case, we present a polynomial-time algorithm that builds a strongly k-connected network with maximum interference O(k log lambda) in two dimensions. / October 2016

To study potential hyperactivity and hyperconnectivity based on the latent resource hypothesis, this study assessed functional connectivity in survivors of childhood brain tumors compared to their healthy peers during an attention task using psychophysiological interaction (PPI) analyses and evaluated for a relationship with performance. Twenty-three survivors and 23 healthy controls completed a letter n-back task in the scanner. An empirically-based seed was placed in the parietal lobe, a theoretical seed was placed in the hippocampus, and a control seed was placed in the occipital lobe. Differences in both performance and functional connectivity networks from each seed emerged between groups, with some findings supporting the latent resource hypothesis and other networks showing compensatory function in survivors. Attention networks, phonological networks, and executive function networks were all found to differ between controls and survivors.

Women have greater incidence and prevalence of Major Depressive Disorder (MDD) than men during the reproductive life phase when ovarian hormones fluctuate, suggesting that ovarian hormones have a significant role in MDD etiology in women. As the core symptoms of MDD are indicative of alterations in stress responding, emotional processing, and mood regulation, examining the effects of the estrogen on these processes in women may provide a better understanding of the role of estrogen in the sex difference in MDD rates. The general aim of this dissertation was to examine neural, emotional, and attentional processes related to stress response alterations and cognitive bias in MDD in women. To examine menstrual phase and estradiol level effects on the neural and mood response to psychosocial stress, healthy, normally cycling women were examined at either the high or low estradiol phase of the menstrual cycle. Participants were exposed to the Montreal Imaging Stress Task (MIST), with brain activity measured through functional magnetic resonance imaging (fMRI), and behavioral response assessed with subjective mood and stress measures. We found that women during the high estradiol phase showed significantly less hippocampal deactivation during psychosocial stress compared to women during the low estradiol phase. Additionally, women with higher estradiol levels also had less subjective distress in response to the MIST than women with lower estradiol levels. These results suggest that high estradiol may be protective against the shifts in brain system activity and negative mood responses associated with psychosocial stress. Periods of low estradiol may enhance the negative impact of psychosocial stress on neural activity and mood and thus contribute to MDD risk in vulnerable women. The relation of cognitive bias to depression history in women was examined in postmenopausal women with and without a history of major depression using an emotion dot probe task during fMRI. Women with remitted MDD showed greater attentional facilitation for negative images than women with no history of MDD that was directly correlated with amygdala activity for negative images and amygdala-hippocampal connectivity in a resting scan. These findings provide evidence that differences in activity and functional connectivity in emotional processing networks may provide a neurobiological basis for continued cognitive bias in remitted MDD. Preliminary data indicate that estradiol treatment reduces amygdala-hippocampal connectivity specifically in women with a history of MDD and has interactive effects with MDD history on the mood response to psychosocial stress following the MIST such that women with a history of MDD appear to benefit from estradiol treatment while women without such history do not. Women with a history of or vulnerability to MDD may be particularly sensitive to the positive effects of estradiol on brain systems important to regulating emotional responses to psychosocial stress. The findings presented in this dissertation suggest that estrogen fluctuations across the menstrual cycle and at other reproductive events may contribute to depression risk through effects on brain systems integral to emotional evaluation and response with potential cognitive consequences.

We summarize and present recent results in the field of infinite matroid theory. We define and prove basic properties of infinite matroids and we discuss known classes of examples of these structures. We focus on the topic of connectivity of infinite matroids and we link some matroid properties to connectivity. The main result of this work is the proof of existence of infinite matroids with arbitrary finite connectivity, but without finite circuits or cocircuits. Powered by TCPDF (www.tcpdf.org)

La compréhension du fonctionnement cérébral est en constante évolution depuis l’essor des neurosciences.Les nouvelles modalités d’imagerie ont permis de mettre en évidence une architecture de notre cerveau en réseaux complexes. Mon travail a pour but de développer une méthode mettant en évidence les réseaux les plus représentatifs d’un groupe de sujet en IRM fonctionnelle.Dans un premier temps, j’ai développé une méthode de réduction des données basées sur le clustering.J’ai introduit une nouvelle caractérisation de l’information fonctionnelle par le profil de connectivité.Celui-ci permet de réduire le biais induit par le bruit présent au sein des données d’IRM fonctionnelle.De plus ce profil ne nécessite pas d’a priori sur les données contrairement aux méthodesi nférentielles classiques.Dans un deuxième temps, j’ai développé une méthode qui permet l’identification de réseaux communs sur un groupe de sujets tout en prenant en compte les variabilités spatiales et fonctionnelles inter-sujets. Les réseaux obtenus peuvent ensuite être caractérisés par leur distribution spatiale mais également par les liens de connectivités se manisfestant en leur sein.Cette méthode permet également la comparaison des réseaux de différents groupes de sujets et la mise en évidence de l’implication de réseaux différents en fonction de stimulations différentes ou d’un état pathologique. / The comprehension of cerebral operations is in constant evolution since the rise of the neurosciences.New methods of imagery made it possible to highlight an architecture of our brain in complex networks.The purpose of my work is to develop a method to find the most representative networks of a group of subjects in Functional MRI.In the first step, I developed a method to reduce the fMRI data size based on clustering. I introduced a new characterization of functional information by the profile of connectivity. This one makes it possible to reduce the variance induced by the noise present within the data of Functional MRI.Moreover this profile does not require a priori information on the data contrary to the traditional inferential methods.In the second step, I developed a method to identify common networks on a group of subjects while taking into account of spatial and functional inter-subjects variability. The networks obtained can then be characterized by their spatial organization but also by their inner connectivity links.This method also allows the comparison of the networks of various groups of subjects, making it possible to highlight the implications of different networks according to different stimulations or pathological states.

The connectivity of fluvial processes or hydrological connectivity are terms often used to describe internal linkages in landscape that intensify/reduce water and sediment fluxes as well as the substances they contain. It is based on the concept of landscape connectivity defined, in particular, by fragmentation of landscape and barriers that obstruct the movement of material. While researches focusing on water erosion or runoff focus on the volume of different fluxes, connectivity reveals new information on catchment functioning as well as reactions to various types of stimuli, and thus represents a more complex phenomenon. This master thesis informs about the topic as a whole, it describes the main forms of its modelling and specific influences that have major impact on presented results. Three approaches of modelling were tested. The main concept lies in evaluation of the index of connectivity, firstly, with approach proposed by Borselli et al. (2008) and implemented in software ArcGIS and secondly, in software SedInConnect 2.0. The third approach is modelling connectivity in software LAPSUS 5.0 that belongs to landscape evolution models. Modelling in LAPSUS proved that using even this indirect method for evaluation of hydrological connectivity is a suitable approach. Selected area for modelling...

The appearance of Voice over IP (VoIP) revolutionized the telecommunications word, this technology delivers voice communications over the internet protocol (IP) networks instead of the public switched telephone network (PSTN), calls can be made between two VoIP phones as well as between a VoIP phone and an analog phone connected to a VoIP adapter [1]. The use of this technology gives access to more communication options compared to the conventional telephony but the users face different problems, mostly connectivity and performance issues related to different factors such as latency and jitter [2], these factors affect directly the call quality and can result in choppy voice, echoes, or even in a call failure. The main objective of this work was to create a tool for automatic analysis and evaluation from packet traces, identify connectivity and performance issues, reconstruct the audio streams and estimate the call quality. The results of this work showed that the objectives sated above are met, where a tool that automatically analyzes VoIP calls is created, this tool takes non encrypted pcap files as input and returns a list of calls with different parameters related to connectivity and performance such as delay and jitter, it does as well reconstruct the audio of every VoIP stream and plots the waveform and spectrum of the reconstructed audio for evaluation purposes.

Connectivity is an important component of coral reef studies for its role in the enhancement of ecosystem resilience. Previous genetic structure and physical circulation studies in the Red Sea reveal a homogeneity within the coral reef complexes in the central and northern parts of the basin. Yet, genetic isolation and relatively low connectivity has been observed in the southern Red Sea. Raitsos et al. (2017) recently hypothesized that coral reefs in the southern Red Sea are more connected with regions outside the basin, rather than with the central and northern Red Sea. Using a physical circulation approach based on a 3-D backward particle tracking simulation, we further investigate this hypothesis. A long-term (> 10 years), very high resolution (1km) MITgcm simulation is used to provide detailed information on velocity in the complex coastal regions of the Red Sea and the adjacent narrow Bab-El-Mandeb Strait. The particle tracking simulation results support the initial hypothesis that the coastal regions in the southern Red Sea exhibit a consistently higher connectivity with the regions outside the Bab-El-Mandeb Strait, than with the central and northern Red Sea. Substantially high levels of connectivity, facilitated by the circulation and eddies, is observed with the coastal regions in the Gulf of Aden. A strong seasonality in connectivity, related to the monsoon-driven circulation, is also evident with the regions outside of the Red Sea. The winter surface intrusion plays a leading role in transporting the particles from the Gulf of Aden and the Indian Ocean into the Red Sea, while the summer subsurface intrusion also supports the transport of particles into the Red Sea in the intermediate layer. In addition, the connectivity with the central and northern Red Sea is more affected by the intensity of the eddies. Evidence also suggests that potential connectivity exists between the coastal southern Red Sea and the coasts of Oman, Socotra, Somalia, Kenya, Tanzania and the north coast of the Madagascar.