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Table of contents

Febrile Illness with Skin Rashes

As part of this latter line of research, he investigates fundamental aspects of first-passage processes. Sid has published more than articles in major peer-reviewed journals, as well as two books: the monograph A Guide to First-Passage Processes Cambridge Univ. Press, and the graduate text, jointly with P. Krapivsky and E. Press, Elementary introduction: the consensus time, the fixation probability 2. Solution on the complete graph and the complete bipartite graph 3.

The voter model on complex networks: fast consensus and the fixation probability Bio Sid Redner received an A.


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My expertise lies in investigating the topology and organisational principles of various kinds of brain networks, from C. I am currently working on applying and adapting techniques from network control theory to probe neuronal or near-neuronal level wiring diagrams from smaller organisms. I co-instruct Phys Complex Networks alongside Prof. In Vittoria joined the Inserm French National Institute for Health and Medical Research in Paris where she now leads the EPIcx lab working on the characterization and modeling of the spread of emerging infectious diseases, by integrating methods of complex systems with statistical physics approaches, computational sciences, geographic information systems, and mathematical epidemiology.

Fostering opportunities for the education, employment, and career advancement of women in Network Science Women in Network Science WiNS fosters opportunities for the education, employment, and career advancement of women in Network Science. By leveraging professional and social contacts among its members, WiNS promotes the presence and visibility of women as participants, speakers, and organizers of scholarly gatherings within network science, social networks, and complex systems.

A social and professional network, WiNS encourages its members to discuss issues concerning gender and representation in network science and related fields, and encourage the thoughtful development of strategies and solutions. Our goal is to promote and publicize the work and expertise of scholars in network science who identify as women.

We extend an open invitation to women to join us in building a more inclusive network science community. WINS Website. Using a unique data set that encompasses mobile, desktop, and television consumption across all categories of media content, we refute this conventional wisdom on three levels. First, news consumption of any sort is heavily outweighed by other forms of media consumption, comprising at most Second, to the extent that Americans do consume news, it is mostly from television, which accounts for roughly five times as much as news consumption as online, while a supermajority of Americans consume little or no news online at all.

To the extent that Americans are misinformed or uninformed about important political issues, our results suggest that a combination of ordinary news bias--especially on television--and avoidance of news altogether are more serious concerns for democracy than any form of overtly fake news. Prior to joining MSR in , he was from a professor of Sociology at Columbia University, and then a principal research scientist at Yahoo! Research, where he directed the Human Social Dynamics group.

Rogers Award. She is also on the faculty of Northeastern's Network Science Institute. Tina earned her Ph. Her research is rooted in data mining and machine learning; and spans theory, algorithms, and applications of massive data from networked representations of physical and social phenomena. Tina's work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, and cyber situational awareness.

Her algorithms have been incorporated into systems used by the government and industry e. Many network communication measures adopted in the systems neuroscience literature are intrinsically asymmetric. To date, this asymmetric behaviour of communication models has not been explored. We characterized cortical regions and subsystems as senders, receivers or neutral, based on the extent of asymmetry in the efficiency of information transfer in one direction relative to the other.

Several cortical regions and subsystems showed significant asymmetry in sending and receiving efficiency Fig. In particular, for the measure of navigation efficiency, primary sensory-motor regions tended to be senders higher efficiency of efferent paths and functionally heterogeneous multimodal regions were more likely to be receivers higher efficiency of afferent paths. This gives rise to a sending-receiving cortical hierarchy Fig. This association was stronger than those found for ensembles of geometrically randomized, topologically randomized and cost-preserving topologically randomized null networks.

These results provide cross-modal validation of the predominant directions of information flow that we infer from the geometry and topology of the structural connectomes. We conclude that asymmetric measures of neural communication provide meaningful insight into patterns of human cortical information processing, revealing a hierarchy of cortical senders and receivers.

Introduction

Evidence suggests that functional connectivity FC biomarkers, derived from resting state fMRI, may be able to fill this gap. However, low disease signal and lack of generalizability of predictive models derived from FC biomarkers hamper clinical utility. Here, we introduce a framework that combines connectome predictive modeling CPM and differential identifiability based on group level principal component analysis PCA. We demonstrate that this framework not only improves test-retest reliability but, importantly, also improves the stability, anatomical specificity, and generalizability of models developed to predict AD related cognitive changes from FC.

All included cognitively impaired individuals were Amyloid Positive, so as to not confound with other pathologies. Subjects received a comprehensive battery of cognitive and clinical evaluations. Following pre-processing of fMRI data, whole-brain FC matrices were estimated as Pearson correlation coefficients between node time-series using a region parcellation.

Test and Retest , therefore two whole-brain FCs were estimated for each subject. FCs were decomposed and subsequently reconstructed using group level PCA PCs to maximize test-retest correspondence as measured by Differential Identifiability. CPM was used to model the association between FC reconstructed using various ranges of PCs and z-scored neurocognitive scores, clinical scores, and age. The bootstrap mean at each edge was used as the representative correlation estimate. This process was repeated using the remaining FC data Mode2. The stability of this step across ranges of PCs was evaluated in two ways.

First, the divergence between Mode1 and Mode2 correlation matrices was quantified by their Frobenius norm Fig. Second, the stability between Mode1 and Mode2 masks was quantified by their overlap of significant edges Fig.

Introduction

Anatomical specificity was evaluated by comparing the similarity between the battery measures to the similarity between their corresponding edge masks Fig. We hypothesized that masks from highly similar measures would exhibit greater spatial overlap. FC strength within each mask was calculated for all subjects. A linear model was used to fit the relationship between FC strength, and each battery measure. A leave one out procedure was used, hence fitting 82 instances of this model.


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    • Results: Differential identifiability was optimized at 82 PCs Fig. At this level of FC reconstruction, the stability of correlations Fig. Additionally, overlap between masks generated from different battery measures reflected the correlation between the measures themselves Fig.

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    Conclusions: Maximizing differential identifiability of FC data not only improves test-retest reliability of whole-brain functional connectomes, but importantly, also improves the stability, anatomical specificity, accuracy, and generalizability of prediction of AD related outcomes from FC. Our framework integrating CPM and differential identifiability represents an important step in improving the clinical utility of FC biomarkers.

    Although several studies demonstrated that the fronto-parietal and default mode functional networks play a key role in conscious processes, it is still not clear which topological network measures that quantifies different features of whole-brain functional network organization are altered in patients with disorders of consciousness DOC. Herein, we investigate the functional connectivity of unresponsive wakefulness syndrome UWS and minimally conscious state MCS patients from a topological network perspective, by using resting state EEG recording.

    Network-based statistical analysis reveals a subnetwork of decreased functional connectivity in UWS compared to the MCS patients, mainly involving the interhemispheric fronto-parietal connectivity patterns. Network topological analysis reveals increased values of Local-Community-Paradigm correlation, as well as higher clustering coefficient and local efficiency in UWS patients compared to MCS patients.

    At the nodal level, the UWS patients showed altered functional topology in several limbic and temporo-parieto-occipital regions. However, the network tends towards a more community-oriented organization in patients with UWS higher values of LCP-corr , pointing out a gradual and cumulative enrichment of neural connections inside the same local community.

    A Study in Scarlet Audiobook by A. Conan Doyle - Full Audiobook - Subtitles - Sherlock Holmes

    It can be reasonable to assume that the higher tendency towards a community-oriented topological organization in UWS may represent an epiphenomenon of diffusely emergent possibly dysfunctional connections, resulting in aberrant self-reinforcing loops. Taken together, our results highlight i the involvement of the interhemispheric fronto-parietal functional connectivity in the pathophysiology of consciousness disorders and ii an aberrant connectome organization both at the network topology and at the nodal level in UWS compared to the MCS patients.

    Although identification accuracy is high using resting-state FCs, other tasks have moderate to low values[12]. In addition, individual fingerprinting is not task independent, i. FCs from one task cannot be used to identify individuals from a database of FCs of another task. Here we propose a multidimensional framework based on group-level Principal Component Analysis PCA decomposition of FCs, which not only increases the identification accuracy significantly, but also makes the fingerprinting process potentially task independent. By applying the PCA-space PCS framework on each task separately, we show that identification accuracy increases significantly for each task, including resting-state.

    Additionally, we found that FCs from two tasks for each individual are sufficient to create a database that enables the identification of an individual FC under any task with extremely high accuracy. Interestingly, for this to succeed, one of those two tasks must be resting-state, but any other task can be used as the second one, with relational task producing the best results within this dataset.


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    In other words, resting-state and a non-resting-state task seem to cover the entire cognitive space for individual FC fingerprinting. Matamalas, Epidemic containment driven by link importance. Matamalas, Epidemic containment driven by link importance Epidemic containment is a major concern when confronting large-scale infections in complex networks. Many works have been devoted to analytically understand how to restructure the network to minimize the impact of major outbreaks.

    In many cases, the strategies consist in the isolation of certain nodes, while less attention has been paid to the intervention on links. In epidemic spreading, links inform about the probability of carrying the contagion of the disease from infected to susceptible individuals. Here, we confront this challenge and propose a set of discrete-time governing equations that can be closed and analyzed, assessing the contribution of links to spreading processes in complex networks.