35 results found
Qian Y, Expert P, Rieu T, et al., 2022, Quantifying the alignment of graph and features in deep learning, IEEE Transactions on Neural Networks and Learning Systems, Vol: 33, Pages: 1663-1672, ISSN: 1045-9227
We show that the classification performance of graph convolutional networks (GCNs) is related to the alignment between features, graph, and ground truth, which we quantify using a subspace alignment measure (SAM) corresponding to the Frobenius norm of the matrix of pairwise chordal distances between three subspaces associated with features, graph, and ground truth. The proposed measure is based on the principal angles between subspaces and has both spectral and geometrical interpretations. We showcase the relationship between the SAM and the classification performance through the study of limiting cases of GCNs and systematic randomizations of both features and graph structure applied to a constructive example and several examples of citation networks of different origins. The analysis also reveals the relative importance of the graph and features for classification purposes.
You J, Expert P, Costelloe C, 2021, Using text mining to track outbreak trends in global surveillance of emerging diseases: ProMED-mail, Journal of the Royal Statistical Society Series A: Statistics in Society, Vol: 184, Pages: 1245-1259, ISSN: 0964-1998
ProMED-mail (Program for Monitoring Emerging Disease) is an international disease outbreak monitoring and early warning system. Every year, users contribute thousands of reports that include reference to infectious diseases and toxins. However, due to the uneven distribution of the reports for each disease, traditional statistics-based text mining techniques, represented by term frequency-related algorithm, are not suitable. Thus, we conducted a study in three steps (i) report filtering, (ii) keyword extraction from reports and finally (iii) word co-occurrence network analysis to fill the gap between ProMED and its utilization. The keyword extraction was performed with the TextRank algorithm, keywords co-occurrence networks were then produced using the top keywords from each document and multiple network centrality measures were computed to analyse the co-occurrence networks. We used two major outbreaks in recent years, Ebola, 2014 and Zika 2015, as cases to illustrate and validate the process. We found that the extracted information structures are consistent with World Health Organisation description of the timeline and phases of the epidemics. Our research presents a pipeline that can extract and organize the information to characterize the evolution of epidemic outbreaks. It also highlights the potential for ProMED to be utilized in monitoring, evaluating and improving responses to outbreaks.
Boncea E, Expert P, Mitchell C, et al., 2021, Association between intrahospital transfer and hospital-acquired infection in the elderly: A retrospective case-control study in a UK hospital network, BMJ Quality & Safety, Vol: 30, Pages: 457-466, ISSN: 2044-5415
Background Intrahospital transfers have become more common as hospital staff balance patient needs with bed availability. However, this may leave patients more vulnerable to potential pathogen transmission routes via increased exposure to contaminated surfaces and contacts with individuals.Objective This study aimed to quantify the association between the number of intrahospital transfers undergone during a hospital spell and the development of a hospital-acquired infection (HAI).Methods A retrospective case–control study was conducted using data extracted from electronic health records and microbiology cultures of non-elective, medical admissions to a large urban hospital network which consists of three hospital sites between 2015 and 2018 (n=24 240). As elderly patients comprise a large proportion of hospital users and are a high-risk population for HAIs, the analysis focused on those aged 65 years or over. Logistic regression was conducted to obtain the OR for developing an HAI as a function of intrahospital transfers until onset of HAI for cases, or hospital discharge for controls, while controlling for age, gender, time at risk, Elixhauser comorbidities, hospital site of admission, specialty of the dominant healthcare professional providing care, intensive care admission, total number of procedures and discharge destination.Results Of the 24 240 spells, 2877 cases were included in the analysis. 72.2% of spells contained at least one intrahospital transfer. On multivariable analysis, each additional intrahospital transfer increased the odds of acquiring an HAI by 9% (OR=1.09; 95% CI 1.05 to 1.13).Conclusion Intrahospital transfers are associated with increased odds of developing an HAI. Strategies for minimising intrahospital transfers should be considered, and further research is needed to identify unnecessary transfers. Their reduction may diminish spread of contagious pathogens in the hospital environment.
Honeyford K, Coughlan C, Nijman R, et al., 2021, Changes in emergency department activity and the first COVID-19 lockdown; a cross sectional study, Western Journal of Emergency Medicine : Integrating Emergency Care with Population Health, Vol: 22, Pages: 603-607, ISSN: 1936-900X
BackgroundEmergency Department (ED) attendances fell across the UK after the ‘lockdown’ introduced on 23rd March 2020 to limit the spread of coronavirus disease 2019 (COVID-19). We hypothesised that reductions would vary by patient age and disease type. We examined pre- and in-lockdown ED attendances for two COVID-19 unrelated diagnoses; one likely to be affected by lockdown measures (gastroenteritis) and one likely to be unaffected (appendicitis). MethodsRetrospective cross-sectional study conducted across two EDs in one London hospital Trust. We compared all adult and paediatric ED attendances, before (January 2020) and during lockdown (March/April 2020). Key patient demographics, method of arrival and discharge location were compared. We used SNOMED codes to define attendances for gastroenteritis and appendicitis. ResultsED attendances fell from 1129 per day before lockdown to 584 in-lockdown; 51.7% of pre-lockdown rates. In-lockdown attendances were lowest for under-18s (16.0% of pre-lockdown). The proportion of patients admitted to hospital increased from 17.3% to 24.0% and the proportion admitted to intensive care increased four-fold. Attendances for gastroenteritis fell from 511 to 103; 20.2% of pre-lockdown rates. Attendances for appendicitis also decreased, from 144 to 41; 28.5% of pre-lockdown rates.ConclusionED attendances fell substantially following lockdown implementation. The biggest reduction was for under-18s. We observed reductions in attendances for gastroenteritis and appendicitis. This may reflect lower rates of infectious disease transmission, though the fall in appendicitis-related attendances suggests that behavioural factors are also important. Larger studies are urgently needed to understand changing patterns of ED use and access to emergency care during the COVID-19 pandemic.
Razak FA, Expert P, 2021, Modelling the spread of COVID-19 on Malaysian contact networks for practical reopening strategies in an institutional setting, Sains Malaysiana, Vol: 50, Pages: 1497-1509, ISSN: 0126-6039
Reopening strategies are crucial to balance efforts of economic revitalization and bringing back a sense of normalcywhile mitigating outbreaks and effectively flattening the infection curve. This paper proposes practical reopening,monitoring and testing strategies for institutions to reintroduce physical meetings based on SIR simulations run on astudent friendship network collected pre-COVID-19. These serve as benchmarks to assess several testing strategies thatcan be applied in physical classes. Our simulations show that the best outbreak mitigation results are obtained with fullknowledge of contact, but are also robust to non-compliance of students to new social interaction guidelines, simulatedby partial knowledge of the interactions. These results are not only applicable to institutions but also for any organizationor company wanting to navigate the COVID-19 ravaged world.
Qian Y, Expert P, Panzarasa P, et al., 2021, Geometric graphs from data to aid classification tasks with Graph Convolutional Networks, Patterns, Vol: 2, ISSN: 2666-3899
Traditional classification tasks learn to assign samples to given classes based solely on sample features. This paradigm is evolving to include other sources of information, such as known relations between samples. Here, we show that, even if additional relational information is not available in the dataset, one can improve classification by constructing geometric graphs from the features themselves, and using them within a Graph Convolutional Network. The improvement in classification accuracy is maximized by graphs that capture sample similarity with relatively low edge density. We show that such feature-derived graphs increase the alignment of the data to the ground truth while improving class separation. We also demonstrate that the graphs can be made more efficient using spectral sparsification, which reduces the number of edges while still improving classification performance. We illustrate our findings using synthetic and real-world datasets from various scientific domains.
Pi L, Expert P, Clarke JM, et al., 2021, Electronic health record enabled track and trace in an urban hospital network: implications for infection prevention and control
<jats:title>ABSTRACT</jats:title><jats:p>Healthcare-associated infections represent one of the most significant challenges for modern medicine as they can significantly impact patients’lives. Carbapenemase-producing Enterobacteriaceae (CPE) pose the greatest clinical threat, given the high levels of resistance to carbapenems, which are considered as agents of ‘last resort’ against life-threatening infections. Understanding patterns of CPE infection spreading in hospitals is paramount to design effective infection control protocols to mitigate the presence of CPE in hospitals. We used patient electronic health records from three urban hospitals to: i) track microbiologically confirmed carbapenemase producing <jats:italic>Escherichia coli</jats:italic> (CP-Ec) carriers and ii) trace the patients they shared place and time with until their identification. We show that yearly contact networks in each hospital consistently exhibit a core-periphery structure, highlighting the presence of a core set of wards where most carrier-contact interactions occured before being distributed to peripheral wards. We also identified functional communities of wards from the general patient movement network. The contact networks projected onto the general patient movement community structure showed a comprehensive coverage of the hospital. Our findings highlight that infections such as CP-Ec infections can reach virtually all parts of hospitals through first-level contacts.</jats:p>
Honeyford C, Costelloe C, Expert P, et al., 2021, Changes in Emergency Department attendances before and after COVID-19 lockdown implementation: a cross sectional study of one urban NHS Hospital Trust, Western Journal of Emergency Medicine : Integrating Emergency Care with Population Health, ISSN: 1936-900X
Turkheimer FE, Rosas FE, Dipasquale O, et al., 2021, A complex systems perspective on neuroimaging studies of behavior and its disorders, The Neuroscientist: reviews at the interface of basic and clinical neurosciences, Pages: 1-18, ISSN: 1073-8584
The study of complex systems deals with emergent behavior that arises as a result of nonlinear spatiotemporal interactions between a large number of components both within the system, as well as between the system and its environment. There is a strong case to be made that neural systems as well as their emergent behavior and disorders can be studied within the framework of complexity science. In particular, the field of neuroimaging has begun to apply both theoretical and experimental procedures originating in complexity science—usually in parallel with traditional methodologies. Here, we illustrate the basic properties that characterize complex systems and evaluate how they relate to what we have learned about brain structure and function from neuroimaging experiments. We then argue in favor of adopting a complex systems-based methodology in the study of neuroimaging, alongside appropriate experimental paradigms, and with minimal influences from noncomplex system approaches. Our exposition includes a review of the fundamental mathematical concepts, combined with practical examples and a compilation of results from the literature.
Lord L-D, Expert P, Atasoy S, et al., 2019, Dynamical exploration of the repertoire of brain networks at rest is modulated by psilocybin, NeuroImage, Vol: 199, Pages: 127-142, ISSN: 1053-8119
Growing evidence from the dynamical analysis of functional neuroimaging data suggests that brain function can be understood as the exploration of a repertoire of metastable connectivity patterns ('functional brain networks'), which potentially underlie different mental processes. The present study characterizes how the brain's dynamical exploration of resting-state networks is rapidly modulated by intravenous infusion of psilocybin, a tryptamine psychedelic found in "magic mushrooms". We employed a data-driven approach to characterize recurrent functional connectivity patterns by focusing on the leading eigenvector of BOLD phase coherence at single-TR resolution. Recurrent BOLD phase-locking patterns (PL states) were assessed and statistically compared pre- and post-infusion of psilocybin in terms of their probability of occurrence and transition profiles. Results were validated using a placebo session. Recurrent BOLD PL states revealed high spatial overlap with canonical resting-state networks. Notably, a PL state forming a frontoparietal subsystem was strongly destabilized after psilocybin injection, with a concomitant increase in the probability of occurrence of another PL state characterized by global BOLD phase coherence. These findings provide evidence of network-specific neuromodulation by psilocybin and represent one of the first attempts at bridging molecular pharmacodynamics and whole-brain network dynamics.
Amad A, Expert P, Lord L-D, et al., 2019, Plastic adaptation to pathology in psychiatry: are patients with psychiatric disorders pathological experts?, Neuroscientist, Vol: 26, Pages: 208-223, ISSN: 1073-8584
Psychiatric disorders share the same pattern of longitudinal evolution and have courses that tend to be chronic and recurrent. These aspects of chronicity and longitudinal evolution are currently studied under the deficit-oriented neuroprogression framework. Interestingly, considering the plasticity of the brain, it is also necessary to emphasize the bidirectional nature of neuroprogression. We review evidence highlighting alterations of the brain associated with the longitudinal evolution of psychiatric disorders from the framework of neuroplastic adaptation to pathology. This new framework highlights that substantial plasticity and remodeling may occur beyond the classic deficit-oriented neuroprogressive framework, which has been associated with progressive loss of gray matter thickness, decreased brain connectivity, and chronic inflammation. We also integrate the brain economy concept in the neuroplastic adaptation to pathology framework, emphasizing that to preserve its economy, i.e. function, the brain learns how to cope with the disease by adapting its architecture. Neuroplastic adaptation to pathology is a proposition for a paradigm shift to overcome the shortcomings of traditional psychiatric diagnostic boundaries; this approach can disentangle both the specific pathophysiology of psychiatric symptoms and the adaptation to pathology, thus offering a new framework for both diagnosis and treatment.
Patania A, Selvaggi P, Veronese M, et al., 2019, Topological gene-expression networks recapitulate brain anatomy and function, Network Neuroscience, Vol: 3, Pages: 744-762, ISSN: 2472-1751
Understanding how gene expression translates to and affects human behaviour is one of the ultimate goals of neuroscience. In this paper, we present a pipeline based on Mapper, a topological simplification tool, to analyze genes co-expression data. We first validate the method by reproducing key results from the literature on the Allen Human Brain Atlas and the correlations between resting-state fMRI and gene co-expression maps. We then analyze a dopamine-related gene-set and find that co-expression networks produced by Mapper returns a structure that matches the well-known anatomy of the dopaminergic pathway. Our results suggest that network based descriptions can be a powerful tool to explore the relationships between genetic pathways and their association with brain function and its perturbation due to illness and/or pharmacological challenges.
Figueroa CA, Cabral J, Mocking RJT, et al., 2019, Altered ability to access a clinically relevant control network in patients remitted from major depressive disorder, Human Brain Mapping, Vol: 40, Pages: 2771-2786, ISSN: 1065-9471
Neurobiological models to explain vulnerability of major depressive disorder (MDD) are scarce and previous functional magnetic resonance imaging studies mostly examined "static" functional connectivity (FC). Knowing that FC constantly evolves over time, it becomes important to assess how FC dynamically differs in remitted-MDD patients vulnerable for new depressive episodes. Using a recently developed method to examine dynamic FC, we characterized re-emerging FC states during rest in 51 antidepressant-free MDD patients at high risk of recurrence (≥2 previous episodes), and 35 healthy controls. We examined differences in occurrence, duration, and switching profiles of FC states after neutral and sad mood induction. Remitted MDD patients showed a decreased probability of an FC state (p < 0.005) consisting of an extensive network connecting frontal areas-important for cognitive control-with default mode network, striatum, and salience areas, involved in emotional and self-referential processing. Even when this FC state was observed in patients, it lasted shorter (p < 0.005) and was less likely to switch to a smaller prefrontal-striatum network (p < 0.005). Differences between patients and controls decreased after sad mood induction. Further, the duration of this FC state increased in remitted patients after sad mood induction but not in controls (p < 0.05). Our findings suggest reduced ability of remitted-MDD patients, in neutral mood, to access a clinically relevant control network involved in the interplay between externally and internally oriented attention. When recovering from sad mood, remitted recurrent MDD appears to employ a compensatory mechanism to access this FC state. This study provides a novel neurobiological profile of MDD vulnerability.
Turkheimer FE, Hellyer P, Kehagia AA, et al., 2019, Conflicting emergences. Weak vs. strong emergence for the modelling of brain function, NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS, Vol: 99, Pages: 3-10, ISSN: 0149-7634
McCutcheon RA, Nour MM, Dahoun T, et al., 2019, Mesolimbic dopamine function is related to salience network connectivity: an integrative PET and MR study, Biological Psychiatry, Vol: 85, Pages: 368-378, ISSN: 0006-3223
BackgroundA wide range of neuropsychiatric disorders, from schizophrenia to drug addiction, involve abnormalities in both the mesolimbic dopamine system and the cortical salience network. Both systems play a key role in the detection of behaviorally relevant environmental stimuli. Although anatomical overlap exists, the functional relationship between these systems remains unknown. Preclinical research has suggested that the firing of mesolimbic dopamine neurons may activate nodes of the salience network, but in vivo human research is required given the species-specific nature of this network.MethodsWe employed positron emission tomography to measure both dopamine release capacity (using the D2/3 receptor ligand 11C-PHNO, n = 23) and dopamine synthesis capacity (using 18F-DOPA, n = 21) within the ventral striatum. Resting-state functional magnetic resonance imaging was also undertaken in the same individuals to investigate salience network functional connectivity. A graph theoretical approach was used to characterize the relationship between dopamine measures and network connectivity.ResultsDopamine synthesis capacity was associated with greater salience network connectivity, and this relationship was particularly apparent for brain regions that act as information-processing hubs. In contrast, dopamine release capacity was associated with weaker salience network connectivity. There was no relationship between dopamine measures and visual and sensorimotor networks, indicating specificity of the findings.ConclusionsOur findings demonstrate a close relationship between the salience network and mesolimbic dopamine system, and they are relevant to neuropsychiatric illnesses in which aberrant functioning of both systems has been observed.
Veronese M, Moro L, Arcolin M, et al., 2019, Covariance statistics and network analysis of brain PET imaging studies, Scientific Reports, Vol: 9, ISSN: 2045-2322
The analysis of structural and functional neuroimaging data using graph theory has increasingly become a popular approach for visualising and understanding anatomical and functional relationships between different cerebral areas. In this work we applied a network-based approach for brain PET studies using population-based covariance matrices, with the aim to explore topological tracer kinetic differences in cross-sectional investigations. Simulations, test-retest studies and applications to cross-sectional datasets from three different tracers ([18F]FDG, [18F]FDOPA and [11C]SB217045) and more than 400 PET scans were investigated to assess the applicability of the methodology in healthy controls and patients. A validation of statistics, including the assessment of false positive differences in parametric versus permutation testing, was also performed. Results showed good reproducibility and general applicability of the method within the range of experimental settings typical of PET neuroimaging studies, with permutation being the method of choice for the statistical analysis. The use of graph theory for the quantification of [18F]FDG brain PET covariance, including the definition of an entropy metric, proved to be particularly relevant for Alzheimer’s disease, showing an association with the progression of the pathology. This study shows that covariance statistics can be applied to PET neuroimaging data to investigate the topological characteristics of the tracer kinetics and its related targets, although sensitivity to experimental variables, group inhomogeneities and image resolution need to be considered when the method is applied to cross-sectional studies.
McCutcheon R, Nour M, Dahoun T, et al., 2019, Mesolimbic dopamine function and salience net-work connectivity: An integrative PET and MR study, 31st Congress of the European-College-of-Neuropsychopharmacology (ECNP), Publisher: ELSEVIER, Pages: S596-S597, ISSN: 0924-977X
Asllani M, Expert P, Carletti T, 2018, A minimally invasive neurostimulation method for controlling abnormal synchronisation in the neuronal activity, PLoS Computational Biology, Vol: 14, ISSN: 1553-734X
Many collective phenomena in Nature emerge from the -partial- synchronisation of the units comprising a system. In the case of the brain, this self-organised process allows groups of neurons to fire in highly intricate partially synchronised patterns and eventually lead to high level cognitive outputs and control over the human body. However, when the synchronisation patterns are altered and hypersynchronisation occurs, undesirable effects can occur. This is particularly striking and well documented in the case of epileptic seizures and tremors in neurodegenerative diseases such as Parkinson’s disease. In this paper, we propose an innovative, minimally invasive, control method that can effectively desynchronise misfiring brain regions and thus mitigate and even eliminate the symptoms of the diseases. The control strategy, grounded in the Hamiltonian control theory, is applied to ensembles of neurons modelled via the Kuramoto or the Stuart-Landau models and allows for heterogeneous coupling among the interacting unities. The theory has been complemented with dedicated numerical simulations performed using the small-world Newman-Watts network and the random Erdős-Rényi network. Finally the method has been compared with the gold-standard Proportional-Differential Feedback control technique. Our method is shown to achieve equivalent levels of desynchronisation using lesser control strength and/or fewer controllers, being thus minimally invasive.
Expert P, de Nigris S, Takaguchi T, et al., 2017, Graph spectral characterisation of the XY model on complex networks, Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, Vol: 96, ISSN: 1063-651X
There is recent evidence that the XY spin model on complex networks can display three different macroscopic states in response to the topology of the network underpinning the interactions of the spins. In this work we present a way to characterize the macroscopic states of the XY spin model based on the spectral decomposition of time series using topological information about the underlying networks. We use three different classes of networks to generate time series of the spins for the three possible macroscopic states. We then use the temporal Graph Signal Transform technique to decompose the time series of the spins on the eigenbasis of the Laplacian. From this decomposition, we produce spatial power spectra, which summarize the activation of structural modes by the nonlinear dynamics, and thus coherent patterns of activity of the spins. These signatures of the macroscopic states are independent of the underlying network class and can thus be used as robust signatures for the macroscopic states. This work opens avenues to analyze and characterize dynamics on complex networks using temporal Graph Signal Analysis.
Lord L-D, Expert P, Fernandes HM, et al., 2016, Insights into Brain Architectures from the Homological Scaffolds of Functional Connectivity Networks, FRONTIERS IN SYSTEMS NEUROSCIENCE, Vol: 10, ISSN: 1662-5137
In recent years, the application of network analysis to neuroimaging data has provided useful insights about the brain's functional and structural organization in both health and disease. This has proven a significant paradigm shift from the study of individual brain regions in isolation. Graph-based models of the brain consist of vertices, which represent distinct brain areas, and edges which encode the presence (or absence) of a structural or functional relationship between each pair of vertices. By definition, any graph metric will be defined upon this dyadic representation of the brain activity. It is however unclear to what extent these dyadic relationships can capture the brain's complex functional architecture and the encoding of information in distributed networks. Moreover, because network representations of global brain activity are derived from measures that have a continuous response (i.e., interregional BOLD signals), it is methodologically complex to characterize the architecture of functional networks using traditional graph-based approaches. In the present study, we investigate the relationship between standard network metrics computed from dyadic interactions in a functional network, and a metric defined on the persistence homological scaffold of the network, which is a summary of the persistent homology structure of resting-state fMRI data. The persistence homological scaffold is a summary network that differs in important ways from the standard network representations of functional neuroimaging data: (i) it is constructed using the information from all edge weights comprised in the original network without applying an ad hoc threshold and (ii) as a summary of persistent homology, it considers the contributions of simplicial structures to the network organization rather than dyadic edge-vertices interactions. We investigated the information domain captured by the persistence homological scaffold by computing the strength of each node in the scaffol
Dhar D, Pruessner G, Expert P, et al., 2016, Directed Abelian sandpile with multiple downward neighbors, Physical Review E, Vol: 042107, ISSN: 1539-3755
We study the directed Abelian sandpile model on a square lattice, with K downward neighborsper site, K > 2. The K = 3 case is solved exactly, which extends the earlier known solution forthe K = 2 case. For K > 2, the avalanche clusters can have holes and side-branches and are thusqualitatively different from the K = 2 case where avalanche clusters are compact. However, we findthat the critical exponents for K > 2 are identical with those for the K = 2 case, and the largescale structure of the avalanches for K > 2 tends to the K = 2 case.
Rizzo G, Veronese M, Expert P, et al., 2016, MENGA: a new comprehensive tool for the integration of neuroimaging data and the Allen human brain transcriptome atlas, PLOS One, Vol: 11, ISSN: 1932-6203
INTRODUCTION: Brain-wide mRNA mappings offer a great potential for neuroscience research as they can provide information about system proteomics. In a previous work we have correlated mRNA maps with the binding patterns of radioligands targeting specific molecular systems and imaged with positron emission tomography (PET) in unrelated control groups. This approach is potentially applicable to any imaging modality as long as an efficient procedure of imaging-genomic matching is provided. In the original work we considered mRNA brain maps of the whole human genome derived from the Allen human brain database (ABA) and we performed the analysis with a specific region-based segmentation with a resolution that was limited by the PET data parcellation. There we identified the need for a platform for imaging-genomic integration that should be usable with any imaging modalities and fully exploit the high resolution mapping of ABA dataset. AIM: In this work we present MENGA (Multimodal Environment for Neuroimaging and Genomic Analysis), a software platform that allows the investigation of the correlation patterns between neuroimaging data of any sort (both functional and structural) with mRNA gene expression profiles derived from the ABA database at high resolution. RESULTS: We applied MENGA to six different imaging datasets from three modalities (PET, single photon emission tomography and magnetic resonance imaging) targeting the dopamine and serotonin receptor systems and the myelin molecular structure. We further investigated imaging-genomic correlations in the case of mismatch between selected proteins and imaging targets.
Zachariou N, Expert P, Takayasu M, et al., 2015, Generalised Sandpile Dynamics on Artificial and Real-World Directed Networks, PLOS One, Vol: 10, ISSN: 1932-6203
The main finding of this paper is a novel avalanche-size exponent τ 1.87 when the generalisedsandpile dynamics evolves on the real-world Japanese inter-firm network. The topologyof this network is non-layered and directed, displaying the typical bow tie structurefound in real-world directed networks, with cycles and triangles. We show that one canmove from a strictly layered regular lattice to a more fluid structure of the inter-firm networkin a few simple steps. Relaxing the regular lattice structure by introducing an interlayer distributionfor the interactions, forces the scaling exponent of the avalanche-size probabilitydensity function τ out of the two-dimensional directed sandpile universality class τ = 4/3,into the mean field universality class τ = 3/2. Numerical investigation shows that these twoclasses are the only that exist on the directed sandpile, regardless of the underlying topology,as long as it is strictly layered. Randomly adding a small proportion of links connectingnon adjacent layers in an otherwise layered network takes the system out of the mean fieldregime to produce non-trivial avalanche-size probability density function. Although these donot display proper scaling, they closely reproduce the behaviour observed on the Japaneseinter-firm network.
Turkheimer FE, Leech R, Expert P, et al., 2015, The brain's code and its canonical computational motifs. From sensory cortex to the default mode network: A multi-scale model of brain function in health and disease, NEUROSCIENCE AND BIOBEHAVIORAL REVIEWS, Vol: 55, Pages: 211-222, ISSN: 0149-7634
Petri G, Expert P, Turkheimer F, et al., 2014, Homological scaffolds of brain functional networks, JOURNAL OF THE ROYAL SOCIETY INTERFACE, Vol: 11, Pages: 901-901, ISSN: 1742-5689
Petri G, Expert P, 2014, Temporal stability of network partitions, Physical Review E, Vol: 90, ISSN: 1539-3755
Lord L-D, Expert P, Huckins JF, et al., 2013, Cerebral energy metabolism and the brain's functional network architecture: an integrative review, JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM, Vol: 33, Pages: 1347-1354, ISSN: 0271-678X
Pandit AS, Expert P, Lambiotte R, et al., 2013, Traumatic brain injury impairs small-world topology, NEUROLOGY, Vol: 80, Pages: 1826-1833, ISSN: 0028-3878
Objective: We test the hypothesis that brain networks associated with cognitive function shift away from a “small-world” organization following traumatic brain injury (TBI).Methods: We investigated 20 TBI patients and 21 age-matched controls. Resting-state functional MRI was used to study functional connectivity. Graph theoretical analysis was then applied to partial correlation matrices derived from these data. The presence of white matter damage was quantified using diffusion tensor imaging.Results: Patients showed characteristic cognitive impairments as well as evidence of damage to white matter tracts. Compared to controls, the graph analysis showed reduced overall connectivity, longer average path lengths, and reduced network efficiency. A particular impact of TBI is seen on a major network hub, the posterior cingulate cortex. Taken together, these results confirm that a network critical to cognitive function shows a shift away from small-world characteristics.Conclusions: We provide evidence that key brain networks involved in supporting cognitive function become less small-world in their organization after TBI. This is likely to be the result of diffuse white matter damage, and may be an important factor in producing cognitive impairment after TBI.
Petri G, Expert P, Jensen HJ, et al., 2013, Entangled communities and spatial synchronization lead to criticality in urban traffic, SCIENTIFIC REPORTS, Vol: 3, ISSN: 2045-2322
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