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  • Neuroinformatics
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Grüning, André;

    Introduction to Workshop "From Neuroscience to Machine Learning". See https://www.humanbrainproject.eu/en/follow-hbp/events/from-neuroscience-to-machine-learning/

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Other literature type . 2018
    License: CC BY NC ND
    Data sources: ZENODO
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Presentation . 2018
    License: CC BY NC ND
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Other literature type . 2018
      License: CC BY NC ND
      Data sources: ZENODO
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Presentation . 2018
      License: CC BY NC ND
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Senk, Johanna; Carde, Corto; Hagen, Espen; Kuhlen, Torsten W.; +2 Authors

    Neuronal network models and corresponding computer simulations are invaluable tools to aid the interpretation of the relationship between neuron properties, connectivity and measured activity in cortical tissue. Spatiotemporal patterns of activity propagating across the cortical surface as observed experimentally can for example be described by neuronal network models with layered geometry and distance-dependent connectivity. The interpretation of the resulting stream of multi-modal and multi-dimensional simulation data calls for integrating interactive visualization steps into existing simulation-analysis workflows. Here, we present a set of interactive visualization concepts called views for the visual analysis of activity data in topological network models, and a corresponding reference implementation VIOLA (VIsualization Of Layer Activity). The software is a lightweight, open-source, web-based and platform-independent application combining and adapting modern interactive visualization paradigms, such as coordinated multiple views, for massively parallel neurophysiological data. For a use-case demonstration we consider spiking activity data of a two-population, layered point-neuron network model subject to a spatially confined excitation originating from an external population. With the multiple coordinated views, an explorative and qualitative assessment of the spatiotemporal features of neuronal activity can be performed upfront of a detailed quantitative data analysis of specific aspects of the data. Furthermore, ongoing efforts including the European Human Brain Project aim at providing online user portals for integrated model development, simulation, analysis and provenance tracking, wherein interactive visual analysis tools are one component. Browser-compatible, web-technology based solutions are therefore required. Within this scope, with VIOLA we provide a first prototype. Comment: 38 pages, 10 figures, 3 tables

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Frontiers in Neuroin...arrow_drop_down
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    Frontiers in Neuroinformatics
    Article
    License: CC BY
    Data sources: Sygma
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Europe PubMed Central
    Article . 2018
    Data sources: PubMed Central
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    https://doi.org/10.48550/arxiv...
    Article . 2018
    License: arXiv Non-Exclusive Distribution
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Frontiers in Neuroin...arrow_drop_down
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      Frontiers in Neuroinformatics
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      License: CC BY
      Data sources: Sygma
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Europe PubMed Central
      Article . 2018
      Data sources: PubMed Central
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      https://doi.org/10.48550/arxiv...
      Article . 2018
      License: arXiv Non-Exclusive Distribution
      Data sources: Datacite
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Koen Nelissen; Prosper Agbesi Fiave; Wim Vanduffel;

    Prehension movements typically include a reaching phase, guiding the hand toward the object, and a grip phase, shaping the hand around it. The dominant view posits that these components rely upon largely independent parieto-frontal circuits: a dorso-medial circuit involved in reaching and a dorso-lateral circuit involved in grasping. However, mounting evidence suggests a more complex arrangement, with dorso-medial areas contributing to both reaching and grasping. To investigate the role of the dorso-medial reaching circuit in grasping, we trained monkeys to reach-and-grasp different objects in the dark and determined if hand configurations could be decoded from functional magnetic resonance imaging (MRI) responses obtained from the reaching and grasping circuits. Indicative of their established role in grasping, object-specific grasp decoding was found in anterior intraparietal (AIP) area, inferior parietal lobule area PFG and ventral premotor region F5 of the lateral grasping circuit, and primary motor cortex. Importantly, the medial reaching circuit also conveyed robust grasp-specific information, as evidenced by significant decoding in parietal reach regions (particular V6A) and dorsal premotor region F2. These data support the proposed role of dorso-medial "reach" regions in controlling aspects of grasping and demonstrate the value of complementing univariate with more sensitive multivariate analyses of functional MRI (fMRI) data in uncovering information coding in the brain.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Cerebral Cortexarrow_drop_down
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    Cerebral Cortex
    Other literature type . Article . 2017 . Peer-reviewed
    License: OUP Terms of Use and Content Access Policy
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Cerebral Cortex
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    Cerebral Cortex
    Article . 2016
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Cerebral Cortexarrow_drop_down
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      Cerebral Cortex
      Other literature type . Article . 2017 . Peer-reviewed
      License: OUP Terms of Use and Content Access Policy
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      Cerebral Cortex
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      Cerebral Cortex
      Article . 2016
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Gianluca Quaglio; Maurizio Corbetta; Theodoros Karapiperis; Katrin Amunts; +3 Authors

    These are exciting times in neuroscience research. Over the past decade, owing in large part to substantial technological innovation, the field of neuroscience has expanded massively, tackling complex topics with profound policy and social implications. These new scientific advances promise fundamental insight into the organisation and function of the brain, and potentially the treatment of mental and neurological disorders, while capturing the imagination of the general public. On Nov 29, 2016, opportunities and challenges in brain research were discussed in a workshop in the European Parliament, in which three of the world's leading brain research initiatives, from the European Union (EU), the USA, and Japan, were presented.The Human Brain Project (HBP), launched in 2013, is a 10-year multinational European initiative in the framework of the EU's Horizon 2020 research programme. The project is one of the first two Future and Emerging Technologies Flagship projects, developed by the EU to underwrite long-term research and innovation efforts. The primary objective of the HBP is to build a European infrastructure for brain research, medicine, and information technology. The underlying idea of the HBP infrastructure is to harness a comprehensive toolbox of the most advanced information technologies, including cloud-based collaboration and development platforms, with databases for metadata and provenance tracking, data analytics, robotics, simulation, medical and neuroinformatics, and computing infrastructure and services, including leading-edge supercomputers and neuromorphic systems.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ The Lancet Neurologyarrow_drop_down
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    The Lancet Neurology
    Article . 2017 . Peer-reviewed
    License: Elsevier TDM
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    NARCIS
    Article . 2017
    Data sources: NARCIS
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ The Lancet Neurologyarrow_drop_down
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      The Lancet Neurology
      Article . 2017 . Peer-reviewed
      License: Elsevier TDM
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      NARCIS
      Article . 2017
      Data sources: NARCIS
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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    Authors: Victor M. Saenger; Joshua Kahan; Thomas Foltynie; Karl J. Friston; +16 Authors

    Deep brain stimulation (DBS) for Parkinson's disease is a highly effective treatment in controlling otherwise debilitating symptoms. Yet the underlying brain mechanisms are currently not well understood. Whole-brain computational modeling was used to disclose the effects of DBS during resting-state functional Magnetic Resonance Imaging in ten patients with Parkinson's disease. Specifically, we explored the local and global impact that DBS has in creating asynchronous, stable or critical oscillatory conditions using a supercritical bifurcation model. We found that DBS shifts global brain dynamics of patients towards a Healthy regime. This effect was more pronounced in very specific brain areas such as the thalamus, globus pallidus and orbitofrontal regions of the right hemisphere (with the left hemisphere not analyzed given artifacts arising from the electrode lead). Global aspects of integration and synchronization were also rebalanced. Empirically, we found higher communicability and coherence brain measures during DBS-ON compared to DBS-OFF. Finally, using our model as a framework, artificial in silico DBS was applied to find potential alternative target areas for stimulation and whole-brain rebalancing. These results offer important insights into the underlying large-scale effects of DBS as well as in finding novel stimulation targets, which may offer a route to more efficacious treatments In this work, Gustavo Deco is supported by the ERC Advanced Grant: DYSTRUCTURE (n. 295129), by the Spanish Research Project PSI2016-75688-P and by the the European Union's Horizon 2020 research and innovation programme under grant agreement n. 720270 (HBP SGA1). Morten Kringelbach is supported by the ERC Consolidator Grant CAREGIVING (n. 615539) and the Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117). Victor M Saenger is supported by the Research Personnel Training program PSI2013-42091-P funded by the Spanish Ministry of Economy and Competitiveness. info:eu-repo/semantics/publishedVersion

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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    Europe PubMed Central
    Article . 2017
    Data sources: PubMed Central
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    Scientific Reports
    Article . 2017
    Data sources: DOAJ-Articles
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    Neuronal networks can synchronize their activity through excitatory and inhibitory connections, which is conducive to synaptic plasticity. This synchronization is reflected in rhythmic fluctuations of the extracellular field. In the hippocampus, theta and gamma band LFP oscillations are a hallmark of the processing of spatial information and memory. Fragile X syndrome (FXS) is an intellectual disability and the most common genetic cause of autism spectrum disorder (Belmonte and Bourgeron, 2006).Here, we investigated how neuronal network synchronization in the mouse hippocampus is compromised by the Fmr1 mutation that causes FXS (Santos et al., 2014), relating recently observed single-cell level impairments (Arbab et al., 2017) to neuronal network aberrations. We implanted tetrodes in hippocampus of freely moving Fmr1-KO and littermate wildtype (WT) mice (Mientjes et al., 2006), to record spike trains from multiple, isolated neurons as well as LFPs in a spatial exploration paradigm.Compared to wild type mice, Fmr1-KO mice displayed greater power of hippocampal theta oscillations, and higher coherence in the slow gamma band. Additionally, spike trains of Fmr1-KO interneurons show decreased spike-count correlations and they are hypersynchronized with theta and slow gamma oscillations. The hypersynchronization of Fmr1-KO oscillations and spike timing reflects functional deficits in local networks. This network hypersynchronization pathologically decreases the heterogeneity of spike-LFP phase coupling, compromising information processing within the hippocampal circuit. These findings may reflect a pathophysiological mechanism explaining cognitive impairments in FXS and autism, in which there is anomalous processing of social and environmental cues and associated deficits in memory and cognition.

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    NARCIS
    Article . 2018
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    Neurobiology of Disease
    Article . 2018
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    Radboud Repository
    Article . 2018
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    Neurobiology of Disease
    Article . 2018
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    Neurobiology of Disease
    Article . 2018 . Peer-reviewed
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      Neurobiology of Disease
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    Authors: Wu, Jianxiao; Eickhoff, Simon B; Hoffstaedter, Felix; Patil, Kaustubh R; +3 Authors

    Abstract The recent availability of population-based studies with standard neuroimaging measurements and extensive psychometric characterization opens promising perspectives to investigate the relationships between interindividual variability in brain regions’ connectivity and behavioral phenotypes. However, the multivariate nature of the prediction model based on connectivity within a network of brain regions severely limits the interpretation of the brain-behavior patterns from a cognitive neuroscience perspective. To address this issue, we here propose a connectivity-based psychometric prediction (CBPP) framework based on individual region’s connectivity profile. Preliminary to the development of this region-wise machine learning approach, we performed an extensive assessment of the general CBPP framework based on whole-brain connectivity information. Because a systematic evaluation of different parameters was lacking from previous literature, we evaluated several approaches pertaining to the different steps of a CBPP study. We hence tested 72 different approach combinations in a cohort of over 900 healthy adults across 98 psychometric variables. Overall, our extensive evaluation combined to an innovative region-wise machine learning approach, offering a framework that optimizes both prediction performance and neurobiological validity (and hence interpretability) to study brain-behavior relationships.

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    bioRxiv
    Preprint . 2020
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    Cerebral Cortex
    Article . 2021 . Peer-reviewed
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    Europe PubMed Central
    Other literature type . 2021
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    Cerebral Cortex
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      Preprint . 2020
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      Cerebral Cortex
      Article . 2021 . Peer-reviewed
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      Europe PubMed Central
      Other literature type . 2021
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      Cerebral Cortex
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    Authors: Paulina Anna Dąbrowska; Nicole Voges; Michael von Papen; Junji Ito; +4 Authors

    International audience; Resting state has been established as a classical paradigm of brain activity studies, mostly based on large-scale measurements such as functional magnetic resonance imaging or magneto- and electroencephalography. This term typically refers to a behavioral state characterized by the absence of any task or stimuli. The corresponding neuronal activity is often called idle or ongoing. Numerous modeling studies on spiking neural networks claim to mimic such idle states, but compare their results with task- or stimulus-driven experiments, or to results from experiments with anesthetized subjects. Both approaches might lead to misleading conclusions. To provide a proper basis for comparing physiological and simulated network dynamics, we characterize simultaneously recorded single neurons’ spiking activity in monkey motor cortex at rest and show the differences from spontaneous and task- or stimulus-induced movement conditions. We also distinguish between rest with open eyes and sleepy rest with eyes closed. The resting state with open eyes shows a significantly higher dimensionality, reduced firing rates, and less balance between population level excitation and inhibition than behavior-related states.

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    Cerebral Cortex Communications
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    Europe PubMed Central
    Article . 2021
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      Cerebral Cortex Communications
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    Authors: Robin Gutzen; Robin Gutzen; Michael von Papen; Guido Trensch; +5 Authors

    Computational neuroscience relies on simulations of neural network models to bridge the gap between the theory of neural networks and the experimentally observed activity dynamics in the brain. The rigorous validation of simulation results against reference data is thus an indispensable part of any simulation workflow. Moreover, the availability of different simulation environments and levels of model description require also validation of model implementations against each other to evaluate their equivalence. Despite rapid advances in the formalized description of models, data, and analysis workflows, there is no accepted consensus regarding the terminology and practical implementation of validation workflows in the context of neural simulations. This situation prevents the generic, unbiased comparison between published models, which is a key element of enhancing reproducibility of computational research in neuroscience. In this study, we argue for the establishment of standardized statistical test metrics that enable the quantitative validation of network models on the level of the population dynamics. Despite the importance of validating the elementary components of a simulation, such as single cell dynamics, building networks from validated building blocks does not entail the validity of the simulation on the network scale. Therefore, we introduce a corresponding set of validation tests and present an example workflow that practically demonstrates the iterative model validation of a spiking neural network model against its reproduction on the SpiNNaker neuromorphic hardware system. We formally implement the workflow using a generic Python library that we introduce for validation tests on neural network activity data. Together with the companion study (Trensch et al., 2018), the work presents a consistent definition, formalization, and implementation of the verification and validation process for neural network simulations.

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    Authors: Ji Chen; Ya Yan; Fengjuan Yuan; Jianbo Cao; +3 Authors

    While there have been multiple fMRI studies into the brain functional changes after acutely stimulated peripheral infection, knowledge for the effect of chronic peripheral infection on whole brain morphology is still quite limited. The present study was designed to investigate the brain structural and emotional changes after peripheral local infection initiated chronic systemic inflammation and the relationship between circulating inflammatory markers and brain grey matter. Specifically, in-vivo T2-weighted MRI was performed on rats with lipopolysaccharide (LPS)-induced chronic pulmonary inflammation (CPI) for 4 months and those without. Grey matter volume was quantified using diffeomorphic anatomical registration through exponentiated lie (DARTEL) enhanced voxel-based morphometry followed by between-group comparison. Open field experiment was conducted to test the potential anxiety-like behaviors after CPI, along with the ELISA estimated inflammatory markers were correlated to grey matter volume. Guided by image findings, we undertook a focused histological investigation with immunefluorescence and Nissl staining. A widespread decrease of grey matter volume in CPI-model rats was revealed. 8 of the 12 measured inflammatory markers presented differential neuroanatomical correlation patterns with three of the pro-inflammatory cytokines (IL-1β, IL-6 and TNF-α) and CRP being the most notable. Lower grey matter volumes in some of the inflammatory markers related regions (amygdala, CA2 and cingulate cortex) were associated with more-severe anxiety-like behaviors. Furthermore, grey matter volumes in amygdala and CA3 were correlated negatively with the expressions of glial proteins (S100β and Nogo-A), while the grey matter volume in hypo-thalamus was changing positively with neural cell area. Overall, the neuroanatomical association patterns and the histopathology underpinning the MRI observations we demonstrated here would probably serve as one explanation for the cerebral and emotional deficits presented in the patients with CPI, which would furthermore yield new insights into the adverse effects the many other systemic inflammation and inflammatory autoimmune diseases would pose on brain morphology.

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    Brain Behavior and Immunity
    Article . 2019 . Peer-reviewed
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      Brain Behavior and Immunity
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    Authors: Grüning, André;

    Introduction to Workshop "From Neuroscience to Machine Learning". See https://www.humanbrainproject.eu/en/follow-hbp/events/from-neuroscience-to-machine-learning/

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    Presentation . 2018
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