GABAergic synaptic scaling is triggered by changes in spiking activity rather than AMPA receptor activation.

Homeostatic plasticity represents a set of mechanisms that are thought to recover some aspect of neural function. One such mechanism called AMPAergic scaling was thought to be a likely candidate to homeostatically control spiking activity. However, recent findings have forced us to reconsider this idea as several studies suggest AMPAergic scaling is not directly triggered by changes in spiking. Moreover, studies examining homeostatic perturbations in vivo have suggested that GABAergic synapses may be more critical in terms of spiking homeostasis. Here, we show results that GABAergic scaling can act to homeostatically control spiking levels. We found that perturbations which increased or decreased spiking in cortical cultures triggered multiplicative GABAergic upscaling and downscaling, respectively. In contrast, we found that changes in AMPA receptor (AMPAR) or GABAR transmission only influence GABAergic scaling through their indirect effect on spiking. We propose that GABAergic scaling represents a stronger candidate for spike rate homeostat than AMPAergic scaling.

Modulation of Spectral Representation and Connectivity Patterns in Response to Visual Narrative in the Human Brain.

The regional brain networks and the underlying neurophysiological mechanisms subserving the cognition of visual narrative in humans have largely been studied with non-invasive brain recording. In this study, we specifically investigated how regional and cross-regional cortical activities support visual narrative interpretation using intracranial stereotactic electroencephalograms recordings from thirteen human subjects (6 females, and 7 males). Widely distributed recording sites across the brain were sampled while subjects were explicitly instructed to observe images from fables presented in "sequential" order, and a set of images drawn from multiple fables presented in "scrambled" order. Broadband activity mainly within the frontal and temporal lobes were found to encode if a presented image is part of a visual narrative (sequential) or random image set (scrambled). Moreover, the temporal lobe exhibits strong activation in response to visual narratives while the frontal lobe is more engaged when contextually novel stimuli are presented. We also investigated the dynamics of interregional interactions between visual narratives and contextually novel series of images. Interestingly, the interregional connectivity is also altered between sequential and scrambled sequences. Together, these results suggest that both changes in regional neuronal activity and cross-regional interactions subserve visual narrative and contextual novelty processing.

Spectral Encoding of Seen and Attended Object Categories in the Human Brain.

Local field potentials (LFPs) encode visual information via variations in power at many frequencies. These variations are complex and depend on stimulus and cognitive state in ways that have yet to be fully characterized. Specifically, the frequencies (or combinations of frequencies) that most robustly encode specific types of visual information are not fully known. To address this knowledge gap, we used intracranial EEG to record LFPs at 858 widely distributed recording sites as human subjects (six males, five females) indicated whether briefly presented natural scenes depicted one of three attended object categories. Principal component analysis applied to power spectra of the LFPs near stimulus onset revealed a broadband component (1-100 Hz) and two narrowband components (1-8 and 8-30 Hz, respectively) that encoded information about both seen and attended categories. Interestingly, we found that seen and attended categories were not encoded with the same fidelity by these distinct spectral components. Model-based tuning and decoding analyses revealed that power variations along the broadband component were most sharply tuned and offered more accurate decoding for seen than for attended categories. Power along the narrowband delta-theta (1-8 Hz) component robustly decoded information about both seen and attended categories, while the alpha-beta (8-30 Hz) component was specialized for attention. We conclude that, when viewing natural scenes, information about the seen category is encoded via broadband and sub-gamma (<30 Hz) power variations, while the attended category is most robustly encoded in the sub-gamma range. More generally, these results suggest that power variation along different spectral components can encode qualitatively different kinds of visual information.SIGNIFICANCE STATEMENT In this article, we characterize how changes in visual stimuli depicting specific objects (cars, faces, and buildings) and changes in attention to those objects affect the frequency content of local field potentials in the human brain. In contrast to many previous studies that have investigated encoding by variations in power at high (>30 Hz) frequencies, we find that the most important variation patterns are broadband (i.e., distributed across multiple frequencies) and narrowband, but in lower frequencies (<30 Hz). Interestingly, we find that seen and attended categories are not encoded with the same fidelity by these distinct spectral encoding patterns, suggesting that power at different frequencies can encode qualitatively different kinds of information.

Assessing the impact of the Lebanese National Polio Immunization Campaign using a population-based computational model.

BACKGROUND: After the re-introduction of poliovirus to Syria in 2013, Lebanon was considered at high transmission risk due to its proximity to Syria and the high number of Syrian refugees. However, after a large-scale national immunization initiative, Lebanon was able to prevent a potential outbreak of polio among nationals and refugees. In this work, we used a computational individual-simulation model to assess the risk of poliovirus threat to Lebanon prior and after the immunization campaign and to quantitatively assess the healthcare impact of the campaign and the required standards that need to be maintained nationally to prevent a future outbreak. METHODS: Acute poliomyelitis surveillance in Lebanon was along with the design and coverage rate of the recent national polio immunization campaign were reviewed from the records of the Lebanese Ministry of Public Health. Lebanese population demographics including Syrian and Palestinian refugees were reviewed to design individual-based models that predicts the consequences of polio spread to Lebanon and evaluate the outcome of immunization campaigns. The model takes into account geographic, demographic and health-related features. RESULTS: Our simulations confirmed the high risk of polio outbreaks in Lebanon within 10 days of case introduction prior to the immunization campaign, and showed that the current immunization campaign significantly reduced the speed of the infection in the event poliomyelitis cases enter the country. A minimum of 90% national immunization coverage was found to be required to prevent exponential propagation of potential transmission. CONCLUSIONS: Both surveillance and immunization efforts should be maintained at high standards in Lebanon and other countries in the area to detect and limit any potential outbreak. The use of computational population simulation models can provide a quantitative approach to assess the impact of immunization campaigns and the burden of infectious diseases even in the context of population migration.

A computational model to monitor and predict trends in bacterial resistance.

Current concern over the emergence of multidrug-resistant superbugs has renewed interest in approaches that can monitor existing trends in bacterial resistance and make predictions of future trends. Recent advances in bacterial surveillance and the development of online repositories of susceptibility tests across wide geographical areas provide an important new resource, yet there are only limited computational tools for its exploitation. Here we propose a hybrid computational model called BARDmaps for automated analysis of antibacterial susceptibility tests from surveillance records and for performing future predictions. BARDmaps was designed to include a structural computational model that can detect patterns among bacterial resistance changes as well as a behavioural computational model that can use the detected patterns to predict future changes in bacterial resistance. Data from the European Antimicrobial Resistance Surveillance Network (EARS-Net) were used to validate and apply the model. BARDmaps was compared with standard curve-fitting approaches used in epidemiological research. Here we show that BARDmaps can reliably predict future trends in bacterial resistance across Europe. BARDmaps performed better than other curve-fitting approaches for predicting future resistance levels. In addition, BARDmaps was also able to detect abrupt changes in bacterial resistance in response to outbreaks and interventions as well as to compare bacterial behaviour across countries and drugs. In conclusion, BARDmaps is a reliable tool to automatically predict and analyse changes in bacterial resistance across Europe. We anticipate that BARDmaps will become an invaluable tool both for clinical providers and governmental agencies to help combat the threat posed by antibiotic-resistant bacteria.

A Rich-Club Organization in Brain Ischemia Protein Interaction Network.

Ischemic stroke involves multiple pathophysiological mechanisms with complex interactions. Efforts to decipher those mechanisms and understand the evolution of cerebral injury is key for developing successful interventions. In an innovative approach, we use literature mining, natural language processing and systems biology tools to construct, annotate and curate a brain ischemia interactome. The curated interactome includes proteins that are deregulated after cerebral ischemia in human and experimental stroke. Network analysis of the interactome revealed a rich-club organization indicating the presence of a densely interconnected hub structure of prominent contributors to disease pathogenesis. Functional annotation of the interactome uncovered prominent pathways and highlighted the critical role of the complement and coagulation cascade in the initiation and amplification of injury starting by activation of the rich-club. We performed an in-silico screen for putative interventions that have pleiotropic effects on rich-club components and we identified estrogen as a prominent candidate. Our findings show that complex network analysis of disease related interactomes may lead to a better understanding of pathogenic mechanisms and provide cost-effective and mechanism-based discovery of candidate therapeutics.

Molecular Architecture of Spinal Cord Injury Protein Interaction Network.

Spinal cord injury (SCI) is associated with complex pathophysiological processes that follow the primary traumatic event and determine the extent of secondary damage and functional recovery. Numerous reports have used global and hypothesis-driven approaches to identify protein changes that contribute to the overall pathology of SCI in an effort to identify potential therapeutic interventions. In this study, we use a semi-automatic annotation approach to detect terms referring to genes or proteins dysregulated in the SCI literature and develop a curated SCI interactome. Network analysis of the SCI interactome revealed the presence of a rich-club organization corresponding to a "powerhouse" of highly interacting hub-proteins. Studying the modular organization of the network have shown that rich-club proteins cluster into modules that are specifically enriched for biological processes that fall under the categories of cell death, inflammation, injury recognition and systems development. Pathway analysis of the interactome and the rich-club revealed high similarity indicating the role of the rich-club proteins as hubs of the most prominent pathways in disease pathophysiology and illustrating the centrality of pro-and anti-survival signal competition in the pathology of SCI. In addition, evaluation of centrality measures of single nodes within the rich-club have revealed that neuronal growth factor (NGF), caspase 3, and H-Ras are the most central nodes and potentially an interesting targets for therapy. Our integrative approach uncovers the molecular architecture of SCI interactome, and provide an essential resource for evaluating significant therapeutic candidates.

Bioinformatics approach to understanding interacting pathways in neuropsychiatric disorders.

Bioinformatics-based applications have been incorporated into several medical disciplines, including cancer, neuroscience, and recently psychiatry. Both the increasing interest in the molecular aspect of neuropsychiatry and the availability of high-throughput discovery and analysis tools have encouraged the incorporation of bioinformatics and neurosystems biology techniques into psychiatry and neuroscience research. As applied to neuropsychiatry, systems biology involves the acquisition and processing of high-throughput datasets to infer new information. A major component in bioinformatics output is pathway analysis that provides an insight into and prediction of possible underlying pathogenic processes which may help understand disease pathogenesis. In addition, this analysis serves as a tool to identify potential biomarkers implicated in these disorders. In this chapter, we summarize the different tools and algorithms used in pathway analysis along with their applications to the different layers of molecular investigations, from genomics to proteomics.