The cerebellum, long known for its role in motor control, has increasingly been implicated in cognitive and affective functions. Despite this broadened perspective, its connectivity remains undercharacterized relative to the cerebral cortex. Here, we present the first comprehensive cerebellar connectome analysis derived from a large-scale, meta-analytic database of over 7,800 high-resolution tract-tracing studies in the rat brain. Leveraging the neuroVIISAS framework, we constructed a directionally weighted, hierarchically organized cerebellar subnetwork integrating both intrinsic and extrinsic connections, including lateralization and interhemispheric projections. Our methodological pipeline involved region expansion, graph-theoretical filtering, and systematic edge weighting by anatomical significance. The resulting network, encompassing 862 regions and over 21,000 edges, was analyzed across multiple topological scales. Mesoscale analysis revealed hallmark properties of small-world and scale-free networks, while motif and modularity analyses identified non-random, functionally coherent microcircuits and subsystems. Local connectome metrics uncovered key integrative hubs–especially within brainstem-cerebellar loops–and exposed gradients of modularity, controllability, and vulnerability. A novel vulnerability analysis showed that the removal of high-significance edges leads to rapid and irregular degradation of clustering in the empirical network, in contrast to the robustness of rewired surrogate models. This indicates the presence of structurally privileged bottlenecks essential for cerebellar integration. Our results collectively highlight the cerebellum’s dual design: functionally specialized yet structurally efficient, with both local modularity and long-range integration. This study establishes a robust foundation for future multimodal, dynamic, and cross-species connectomic research. Integrating empirical data on neuronal dynamics, synaptic plasticity, and gene expression will be essential to fully realize the translational potential of cerebellar network models in both health and disease.

Background: Digital brain atlases are indispensable for primate connectomics, providing precise stereotactic references that enable reproducible mapping of structural and functional data. New method: We provide a fully digitized, bilaterally complete 3D reconstruction of the Paxinos et al. rhesus macaque atlas, implemented within the existing neuroVIISAS platform. The contribution of this work is the creation of a reusable, stereotactically embedded resource, rather than the introduction of new computational methods. Using polygon-based segmentation, we systematically digitized 1722 anatomical contours from the Paxinos et al. (2009) stereotactic atlas of the rhesus monkey, including cortical, subcortical, and non-neuronal regions, and embedded them into a stereotactic coordinate system. Mirroring procedures ensured full bilateral representation, while volumetric and surface calculations yielded quantitative benchmarks spanning nuclei of less than 0.1 mm3 to cortical regions exceeding 2000 mm3. Results: The atlas supports advanced visualization in 2D and 3D, including interactive rotation, transparency, and connectivity overlays, facilitating structural exploration and connectome simulations. Integration with neuroVIISAS enables hierarchical ontologies, quantitative analyses, and direct interfacing with simulation environments. Comparison with existing methods Validation against stereological data and comparison with independent resources (SARM, ONPRC18) confirmed the reliability of delineations while highlighting methodological differences across atlases. Beyond structural applications, functional connectivity studies, such as gradient analyses in macaques (Xu et al., 2020), demonstrate how atlas-based frameworks bridge species by systematically linking macaque organization to human cortical architecture. Conclusion: Together, these methodological advances establish a reproducible, bilaterally complete, and volumetrically validated stereotactic reference for the rhesus monkey brain, enhancing both experimental design and translational connectomics.

In light of extensive work that has created a wide range of techniques for predicting the course of multiple sclerosis (MS) disease, this paper attempts to provide an overview of these approaches and put forth an alternative way to predict the disease progression. For this purpose, the existing methods for estimating and predicting the course of the disease have been categorized into clinical, radiological, biological, and computational or artificial intelligence-based markers. Weighing the weaknesses and strengths of these prognostic groups is a profound method that is yet in need and works directly at the level of diseased connectivity. Therefore, we propose using the computational models in combination with established connectomes as a predictive tool for MS disease trajectories. The fundamental conduction-based Hodgkin-Huxley model emerged as promising from examining these studies. The advantage of the Hodgkin-Huxley model is that certain properties of connectomes, such as neuronal connection weights, spatial distances, and adjustments of signal transmission rates, can be taken into account. It is precisely these properties that are particularly altered in MS and that have strong implications for processing, transmission, and interactions of neuronal signaling patterns.The Hodgkin-Huxley (HH) equations as a point-neuron model are used for signal propagation inside a small network. The objective is to change the conduction parameter of the neuron model, replicate the changes in myelin properties in MS and observe the dynamics of the signal propagation across the network. The model is initially validated for different lengths, conduction values, and connection weights through three nodal connections. Later, these individual factors are incorporated into a small network and simulated to mimic the condition of MS. The signal propagation pattern is observed after inducing changes in conduction parameters at certain nodes in the network and compared against a control model pattern obtained before the changes are applied to the network. The signal propagation pattern varies as expected by adapting to the input conditions. Similarly, when the model is applied to a connectome, the pattern changes could give an insight into disease progression. This approach has opened up a new path to explore the progression of the disease in MS. The work is in its preliminary state, but with a future vision to apply this method in a connectome, providing a better clinical tool.

Experimental rat models of stroke and hemorrhage are important tools to investigate cerebrovascular disease pathophysiology mechanisms, yet how significant patterns of functional impairment induced in various models of stroke are related to changes in connectivity at the level of neuronal populations and mesoscopic parcellations of rat brains remain unresolved. To address this gap in knowledge, we employed two middle cerebral artery occlusion models and one intracerebral hemorrhage model with variant extent and location of neuronal dysfunction. Motor and spatial memory function was assessed and the level of hippocampal activation via Fos immunohistochemistry. Contribution of connectivity change to functional impairment was analyzed for connection similarities, graph distances and spatial distances as well as the importance of regions in terms of network architecture based on the neuroVIISAS rat connectome. We found that functional impairment correlated with not only the extent but also the locations of the injury among the models. In addition, via coactivation analysis in dynamic rat brain models, we found that lesioned regions led to stronger coactivations with motor function and spatial learning regions than with other unaffected regions of the connectome. Dynamic modeling with the weighted bilateral connectome detected changes in signal propagation in the remote hippocampus in all 3 stroke types, predicting the extent of hippocampal hypoactivation and impairment in spatial learning and memory function. Our study provides a comprehensive analytical framework in predictive identification of remote regions not directly altered by stroke events and their functional implication.

Connectomes represent comprehensive descriptions of neural connections in a nervous system to better understand and model central brain function and peripheral processing of afferent and efferent neural signals. Connectomes can be considered as a distinctive and necessary structural component alongside glial, vascular, neurochemical, and metabolic networks of the nervous systems of higher organisms that are required for the control of body functions and interaction with the environment. They are carriers of functional phenomena such as planning behavior and cognition, which are based on the processing of highly dynamic neural signaling patterns. In this study, we examine more detailed connectomes with edge weighting and orientation properties, in which reciprocal neuronal connections are also considered. Diffusion processes are a further necessary condition for generating dynamic bioelectric patterns in connectomes. Based on our precise connectome data, we investigate different diffusion-reaction models to study the propagation of dynamic concentration patterns in control and lesioned connectomes. Therefore, differential equations for modeling diffusion were combined with well-known reaction terms to allow the use of connection weights, connectivity orientation and spatial distances. Three reaction-diffusion systems Gray-Scott, Gierer-Meinhardt and Mimura-Murray were investigated. For this purpose, implicit solvers were implemented in a numerically stable reaction-diffusion system within the framework of neuroVIISAS. The implemented reaction-diffusion systems were applied to a subconnectome which shapes the mechanosensitive pathway that is strongly affected in the multiple sclerosis demyelination disease. It was found that demyelination modeling by connectivity weight modulation changes the oscillations of the target region, i.e. the primary somatosensory cortex, of the mechanosensitive pathway. In conclusion, a new application of reaction-diffusion systems to weighted and directed connectomes has been realized. Because the implementation was realized in the neuroVIISAS framework many possibilities for the study of dynamic reaction-diffusion processes in empirical connectomes as well as specific randomized network models are available now.

Connectivity data of the nervous system and subdivisions, such as the brainstem, cerebral cortex and subcortical nuclei, are necessary to understand connectional structures, predict effects of connectional disorders and simulate network dynamics. For that purpose, a database was built and analyzed which comprises all known directed and weighted connections within the rat brainstem. A longterm metastudy of original research publications describing tract tracing results form the foundation of the brainstem connectome (BC) database which can be analyzed directly in the framework neuroVIISAS. The BC database can be accessed directly by connectivity tables, a web-based tool and the framework. Analysis of global and local network properties, a motif analysis, and a community analysis of the brainstem connectome provides insight into its network organization. For example, we found that BC is a scale-free network with a small-world connectivity. The Louvain modularity and weighted stochastic block matching resulted in partially matching of functions and connectivity. BC modeling was performed to demonstrate signal propagation through the somatosensory pathway which is affected in Multiple sclerosis.

The comparison of connectomes is an essential step to identify changes in structural and functional neuronal networks. However, the connectomes themselves as well as the comparisons of connectomes could be manifold. In most applications, comparisons of connectomes are applied to specific sets of data. In many studies collections of scripts are applied optimized for certain species (non-generic approaches) or diseases (control versus disease group connectomes). These collections of scripts have a limited functionality which do not support functional and topographic mappings of connectomes (hemispherical asymmetries, peripheral nervous system). The platform-independent and generic neuroVIISAS framework is built to circumvent limitations that come with variants of nomenclatures, connectivity lists and connectional hierarchies as well as restrictions to structural connectome analyses. A new analytical module is introduced into the framework to compare different types of connectomes and different representations of the same connectome within a unique software environment. As an example a differential analysis of the partial connectome of the laboratory rat that is based on virus tract tracing with the same regions of non-virus tract tracing has been performed. A relatively large connectional coherence between the two different techniques was found. However, some detected connections are described by virus tract-tracing only.

Motivation Structural connectomics supports understanding aspects of neuronal dynamics and brain functions. Conducting metastudies of tract-tracing publications is one option to generate connectome databases by collating neuronal connectivity data. Meanwhile, it is a common practice that the neuronal connections and their attributes of such retrospective data collations are extracted from tract-tracing publications manually by experts. As the description of tract-tracing results is often not clear-cut and the documentation of interregional connections is not standardized, the extraction of connectivity data from tract-tracing publications could be complex. This might entail that different experts interpret such non-standardized descriptions of neuronal connections from the same publication in variable ways. Hitherto, no investigation is available that determines the variability of extracted connectivity information from original tract-tracing publications. A relatively large variability of connectivity information could produce significant misconstructions of adjacency matrices with faults in network and graph analyzes. The objective of this study is to investigate the inter-rater and inter-observation variability of tract-tracing-based documentations of neuronal connections. To demonstrate the variability of neuronal connections, data of 16 publications which describe neuronal connections of subregions of the hypothalamus have been assessed by way of example. Results A workflow is proposed that allows detecting variability of connectivity at different steps of data processing in connectome metastudies. Variability between three blinded experts was found by comparing the connection information in a sample of 16 publications that describe tract-tracing-based neuronal connections in the hypothalamus. Furthermore, observation scores, matrix visualizations of discrepant connections and weight variations in adjacency matrices are analyzed. Availability The resulting data and software are available at http://neuroviisas.med.uni-rostock.de/neuroviisas.shtml

Recent advances in functional connectivity suggest that shared neuronal activation patterns define brain networks linking anatomically separate brain regions. We sought to investigate how cortical stroke disrupts multiple brain regions in processing spatial information. We conducted a connectome investigation at the mesoscale-level using the neuroVIISAS-framework, enabling the analysis of directed and weighted connectivity in bilateral hemispheres of cortical and subcortical brain regions. We found that spatial-exploration induced brain activation mapped by Fos, a proxy of neuronal activity, was differentially affected by stroke in a region-specific manner. The extent of hypoactivation following spatial exploration is inversely correlated with the spatial distance between the region of interest and region damaged by stroke, in particular within the parietal association and the primary somatosensory cortex, suggesting that the closer a region is to a stroke lesion, the more it would be affected during functional activation. Connectome modelling with 43 network parameters failed to reliably predict regions of hypoactivation in stroke rats exploring a novel environment, despite a modest correlation found for the centrality and hubness parameters in the home-caged animals. Further investigation in the inhibitory versus excitatory neuronal networks and microcircuit connectivity is warranted to improve the accuracy of predictability in post-stroke functional impairment.

The basal ganglia of the laboratory rat consist of a few core regions that are specifically interconnected by efferents and afferents of the central nervous system. In nearly 800 reports of tract-tracing investigations the connectivity of the basal ganglia is documented. The readout of connectivity data and the collation of all the connections of these reports in a database allows to generate a connectome. The collation, curation and analysis of such a huge amount of connectivity data is a great challenge and has not been performed before (Bohland et al. PloS One 4:e7200, 2009) in large connectomics projects based on meta-analysis of tract-tracing studies. Here, the basal ganglia connectome of the rat has been generated and analyzed using the consistent cross-platform and generic framework neuroVIISAS. Several advances of this connectome meta-study have been made: the collation of laterality data, the network-analysis of connectivity strengths and the assignment of regions to a hierarchically organized terminology. The basal ganglia connectome offers differences in contralateral connectivity of motoric regions in contrast to other regions. A modularity analysis of the weighted and directed connectome produced a specific grouping of regions. This result indicates a correlation of structural and functional subsystems. As a new finding, significant reciprocal connections of specific network motifs in this connectome were detected. All three principal basal ganglia pathways (direct, indirect, hyperdirect) could be determined in the connectome. By identifying these pathways it was found that there exist many further equivalent pathways possessing the same length and mean connectivity weight as the principal pathways. Based on the connectome data it is unknown why an excitation pattern may prefer principal rather than other equivalent pathways. In addition to these new findings the local graph-theoretical features of regions of the connectome have been determined. By performing graph theoretical analyses it turns out that beside the caudate putamen further regions like the mesencephalic reticular formation, amygdaloid complex and ventral tegmental area are important nodes in the basal ganglia connectome. The connectome data of this meta-study of tract-tracing reports of the basal ganglia are available for further network studies, the integration into neocortical connectomes and further extensive investigations of the basal ganglia dynamics in population simulations.

Event Abstract Back to Event Central and peripheral monosynaptic, polysynaptic and collaterals connectivity in the rat Oliver Schmitt1*, Peter Eipert1, Rene Hoffmann2, Paulinne Morawska1, Ann-Christin Klünker1, Jennifer Meinhardt1, Felix Lessmann1, Julia Beier1, Kanar Kadir1, Adrian Karnitzki1, Jörg Jenssen1, Lena Kuch1, Linda Sellner1 and Andreas Wree1 1 University of Rostock, Anatomy, Germany 2 University of Rostock, Mathematics, Germany Most stereotaxic tract-tracing studies were performed in the laboratory rat. Therefore, the most comprehensive knowledge of central and peripheral nervous system connectivity is available for this tetrapode vertebrate. The rat connectome project is a long term metastudy that aims to collate all connections described in peer reviewed articles documenting neuronal connections detected by stereotaxic tract-tracing techniques in juvenile and adult normal rats (non-genetically and non-experimentally modified). So far, connections of 4300 reports have been collated and currated by experts in neuroanatomy. These data, have been imported in the generic framework neuroVIISAS (http://neuroviisas.med.uni-rostock.de/) for advanced connectome analysis and simulation. To combine the different granularities of the collated connections, an extensive hierarchy of parts of the nervous system of the rat was created, containing all the regions participating in the imported connections and distiguishing the different hemispheric parts. This hierarchical approach and the extend of collated data is unique and allows the most precise connectome analysis on different levels of granularity with regard to ipsi-, contra-, bi- and unilateral specifications of connections. The hierarchical terminology is directly related to brain regions defined in different stereoteaxic atlases of the rat central nervous system. 2D- and 3D-atlas data are directly available and are used to visualize connectivity spatially. In addition 223 single neuron parameters of the Senselab database (http://neuroelectro.org) have been related to types of neurons used in neuron models implemented in NEST. In neuroVIISAS an interface to NEST (http://www.nest-initiative.org) is available that allows to use all NEST neuron models and moduls of the simulation engine in combination with real world connectivity and neuron parameters. To complete the number of critical parameters of realistic simulations we will present first results of a high-throughput-high-resolution identification of single cells of a terabyte virtual-slide dataset. For the first time, the rat connectome project also includes collateral connections from multi-tracer reports as well as pathways from transneuronal tract-tracing publications. This different type of connectivity data can be efficiently seperated from the conventional monosynaptic non-collateral one and integrated in population simulations. Currently the connectome consists of 232688 ipsi- and contralateral weighted (connection strength) and directed connections, completed by 2253 transneuronal pathways and 605 collateral sources. In conclusion, a nearly complete collation of consistent multiscale connectivity data of a whole nervous system of the rat is available (http://neuroviisas.med.uni-rostock.de). The laboratory rat is a well known vertebrate of which a huge amount of neuroscientific data exist. Such an outstanding source of connectivity, neuroanatomical, neurophysiological and behavioral data could be a promising starting point for multimodal large scale simulations in order to understand cognition and behavior of a complex vertebrate nervous systems. Keywords: connectomics, digital atlasing, rat nervous system, virtual slides, computational neuroscience, cell detection Conference: Neuroinformatics 2014, Leiden, Netherlands, 25 Aug - 27 Aug, 2014. Presentation Type: Demo, to be considered for oral presentation Topic: Digital atlasing Citation: Schmitt O, Eipert P, Hoffmann R, Morawska P, Klünker A, Meinhardt J, Lessmann F, Beier J, Kadir K, Karnitzki A, Jenssen J, Kuch L, Sellner L and Wree A (2014). Central and peripheral monosynaptic, polysynaptic and collaterals connectivity in the rat. Front. Neuroinform. Conference Abstract: Neuroinformatics 2014. doi: 10.3389/conf.fninf.2014.18.00058 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 23 Apr 2014; Published Online: 04 Jun 2014. * Correspondence: Prof. Oliver Schmitt, University of Rostock, Anatomy, Rostock, 18057, Germany, schmitt@med.uni-rostock.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Oliver Schmitt Peter Eipert Rene Hoffmann Paulinne Morawska Ann-Christin Klünker Jennifer Meinhardt Felix Lessmann Julia Beier Kanar Kadir Adrian Karnitzki Jörg Jenssen Lena Kuch Linda Sellner Andreas Wree Google Oliver Schmitt Peter Eipert Rene Hoffmann Paulinne Morawska Ann-Christin Klünker Jennifer Meinhardt Felix Lessmann Julia Beier Kanar Kadir Adrian Karnitzki Jörg Jenssen Lena Kuch Linda Sellner Andreas Wree Google Scholar Oliver Schmitt Peter Eipert Rene Hoffmann Paulinne Morawska Ann-Christin Klünker Jennifer Meinhardt Felix Lessmann Julia Beier Kanar Kadir Adrian Karnitzki Jörg Jenssen Lena Kuch Linda Sellner Andreas Wree PubMed Oliver Schmitt Peter Eipert Rene Hoffmann Paulinne Morawska Ann-Christin Klünker Jennifer Meinhardt Felix Lessmann Julia Beier Kanar Kadir Adrian Karnitzki Jörg Jenssen Lena Kuch Linda Sellner Andreas Wree Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. 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Event Abstract Back to Event neuroVIISAS: The Integration of Atlases and Connectomes for Modeling Peter Eipert1 and Oliver Schmitt1* 1 University of Rostock, Anatomy, Germany Modeling supports the understanding of complex biological systems like nervous systems and whole organisms. The building blocks to generate models of organisms are heterogeneous and involve multiple structures (organs and their subdivisions, regions of the CNS and their connections) and functions (motor action, perception, dynamics of the stimulus-response behavior). neuroVIISAS (neuro Visualization, Imagemapping, Information System for Analysis and Simulation) is build to allow the integration of structures in terms of 3D-reconstructions, connections and modeling of neuron populations (Schmitt and Eipert, 2012). It is an open environment with regard to import and export of data and a generic tool that can be used for the development of digital atlases, brain mapping, connectome and simulation projects of all types of nervous systems. For the modeling of populations of neurons the NEural Simulation Tool (NEST v 2.2.1) is used (Gewaltig and Diesmann, 2007). This approach enables the modeler to use more realistic connectome data for an accurate definition of connections between populations of neurons in NEST. How this is achieved will be demonstrated with examples of basal ganglia networks (Fig. 1). Furthermore, the interactions of visualization, simulation and analysis of complex neuronal networks will be shown. In addition to high level analysis and visualization functions of neuroVIISAS the import of connectivity data will be explained. The software can be downloaded from http://neuroviisas.med.uni-rostock.de/index-Dateien/Page455.htm. Figure 1: The bilateral basal ganglia network. The hierarchical triangle visualization shows the subdivision of regions. The adjacency matrix shows color coded weights of connections. The smart organic graph layout with orthogonal bus router and symmetry option displays the color coded bilateral connections. Figure 1 References Gewaltig M-O, Diesmann M (2007) NEST (Neural Simulation Tool) Scholarpedia 2(4):1430. Schmitt O, Eipert P (2012) neuroVIISAS: approaching multi-scale simulation of the rat connectome. Neuroinformatics 10: 243-67. Keywords: connectome, atlasing, Simulations, neuroontology, visualization Conference: Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013. Presentation Type: Demo Topic: Computational neuroscience Citation: Eipert P and Schmitt O (2013). neuroVIISAS: The Integration of Atlases and Connectomes for Modeling. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00016 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 04 Mar 2013; Published Online: 11 Jul 2013. * Correspondence: Prof. Oliver Schmitt, University of Rostock, Anatomy, Rostock, 18057, Germany, schmitt@med.uni-rostock.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Peter Eipert Oliver Schmitt Google Peter Eipert Oliver Schmitt Google Scholar Peter Eipert Oliver Schmitt PubMed Peter Eipert Oliver Schmitt Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Event Abstract Back to Event The intrinsic and extrinsic connectome of subregions of the basal ganglia Oliver Schmitt1*, Peter Eipert1, Konstanze Philipp1, Richard Kettlitz1 and Andreas Wree1 1 Department of Anatomy, Uni Rostock, Germany The motoric part of the basal ganglia (BG) network in the rat receives input from the primary motor cortex and consists of the caudate putamen complex, lateral and medial globus pallidus, substantia nigra, subthalamic nucleus and some thalamic nuclei (parafascicular, ventromedial, mediodorsal, ventrolateral, lateral habenula). Most of these classical components are directly (monosynaptically) interconnected. In a metastudy of 2200 tract-tracing publications of the rat central nervous system much more regions were found that are directly connected to functionally important regions of the motoric BG. In this contribution extrinsic and intrinsic connectivity of the BG has been analyzed. Using conventional global and local graph evaluation methods, new approaches of vulnerability and pathway analysis as well as techniques of visual analytics revealed new patterns of reciprocal connections. The unilateral intrinsic BG consists of 14 nodes which are connected by 122 edges resulting in a line density of 67.033% and an average cluster coefficient of 0.735. The average path length is 1.335. It was found that the accumbens nucleus has most ipsilateral and contralateral inputs while the lateral agranular prefrontal cortex has most ipsi- and contralateral outputs. The caudate putamen complex has the largest eigenvector centrality and the lowest Shapley rate. This indicates its importance for the intrinsic network structure of the BG. The substantia nigra pars compacta has a relative high rank with regard to vulnerability, however, the substantia nigra reticular part and the medial globus pallidus are more important to preserve network structure following removal of these nuclei. Keywords: computational neuroscience Conference: 5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012. Presentation Type: Poster Topic: Neuroinformatics Citation: Schmitt O, Eipert P, Philipp K, Kettlitz R and Wree A (2013). The intrinsic and extrinsic connectome of subregions of the basal ganglia. Front. Neuroinform. Conference Abstract: 5th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2013.08.00016 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Mar 2013; Published Online: 27 Nov 2013. * Correspondence: Dr. Oliver Schmitt, Department of Anatomy, Uni Rostock, Rostock, Germany, schmitt@med.uni-rostock.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Oliver Schmitt Peter Eipert Konstanze Philipp Richard Kettlitz Andreas Wree Google Oliver Schmitt Peter Eipert Konstanze Philipp Richard Kettlitz Andreas Wree Google Scholar Oliver Schmitt Peter Eipert Konstanze Philipp Richard Kettlitz Andreas Wree PubMed Oliver Schmitt Peter Eipert Konstanze Philipp Richard Kettlitz Andreas Wree Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Event Abstract Back to Event Poperties of the Intrinsic and Extrinsic Uni- and Bilateral Connectome of the Spinal Cord of the Rat Oliver Schmitt1*, Peter Eipert1, Ann-Kristin Klünker1, Richard Kettlitz1, Paulinne Morawska1, Jennifer Meinhardt1 and Andreas Wree1 1 University of Rostock, Anatomy, Germany The spinal cord (SC) of the rat consists of 34 segments from the cervical to the coccygeal level which contain 10 principal layers (Rexed I-X) on the left and the right side surrounded by white matter. Here, efferents to and afferents from peripheral organs as well as intrinsic connections (C) of SC have been investigated in about 800 peer-reviewed tract tracing publications. The connectivity data of these publications have been transfered into the rat connectome dataset in neuroVIISAS (Schmitt and Eipert 2012, Schmitt and et al. 2012). SC regions were organized in a hierarchy to allow multiresolution analysis (Fig. 1). In the intrinsic bilateral connectome of the SC (891 regions) 26222 C were documented at the level of layers (ipsi: 11297, contra: 805). 5213 C are reciprocal which appears to be significant because in Erdös-Rényi (ER) simulations an average of 437.1 reciprocal connections was found. The small-worldness coefficient (SWC) is 3.598 (ER: 1). Furthermore, the connections of the network have a scale-free distribution. Local connectivity analysis revealed that cervical, thoracic and lumbal layers II-VIII possess most intrinsic connections. The layers III-IX of thoracal segment 3 have the largest eigenvector centrality of 0.709 as well as the largest Katz-index. The extrinsic contralateral (146 regions, C=7405) and ipsilateral connectome (171 regions, C=4178) have been analyzed at the level of segments. In contrast to the intrinsic connectome the SWS increases to 5.207 in the ipsilateral and decrese to 2.19 in the contralateral extrinsic SC connectome. In conclusion, we build the first bilateral connectome of the SC of a mamalian for which the most detailed tract tracing investigations exist and integrated it into the complete rat nervous system connectome. Figure 1: The regions at the level of segments of the left and right side of the SC are arranged symmetrically. Connections are directed and colors of connections indicate the density of connections, respectively, the weights. CS: cervical, T: thoracic, L: lumbal, Sa: sacral. Figure 1 References Schmitt O, Eipert P (2012) Neuroinformatics 10:243-267. Schmitt O, Eipert P, Philipp K, Kettlitz R, Fuellen G, Wree A (2012) Front Neural Circuits 6:81. Keywords: connectome, Spinal Cord, rat, Modeling and simulations, visualization Conference: Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013. Presentation Type: Poster Topic: General neuroinformatics Citation: Schmitt O, Eipert P, Klünker A, Kettlitz R, Morawska P, Meinhardt J and Wree A (2013). Poperties of the Intrinsic and Extrinsic Uni- and Bilateral Connectome of the Spinal Cord of the Rat. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00080 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 04 Mar 2013; Published Online: 11 Jul 2013. * Correspondence: Prof. Oliver Schmitt, University of Rostock, Anatomy, Rostock, 18057, Germany, schmitt@med.uni-rostock.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Oliver Schmitt Peter Eipert Ann-Kristin Klünker Richard Kettlitz Paulinne Morawska Jennifer Meinhardt Andreas Wree Google Oliver Schmitt Peter Eipert Ann-Kristin Klünker Richard Kettlitz Paulinne Morawska Jennifer Meinhardt Andreas Wree Google Scholar Oliver Schmitt Peter Eipert Ann-Kristin Klünker Richard Kettlitz Paulinne Morawska Jennifer Meinhardt Andreas Wree PubMed Oliver Schmitt Peter Eipert Ann-Kristin Klünker Richard Kettlitz Paulinne Morawska Jennifer Meinhardt Andreas Wree Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Event Abstract Back to Event The rat BST-amygdala macroconnectome: a case study of functional-structural modules revealed by network analysis Mihail Bota1*, Oliver Schmitt2 and Peter Eipert2 1 University of Southern California, Dept. of Neurobiology, United States 2 University Rostock, Dept. of Anatomy, Germany The amygdalar (AMY) nuclei and the bed nuclei of stria terminalis (BST) are heavily interconnected (Dong et al., 1996), and crucial for the behavior of mammals. In rats, the functions of different AMY regions include agonistic behavior and fear conditions, while the anterior part BST is involved in chronic stress, and its posterior part in addiction. Moreover, different roles of individual rat BST nuclei have been postulated (Dong et al., 2004). Thus, the network analysis of the structural connections between BST and AMY may help understanding the functionality of these regions. We statistically analyzed the neuroanatomical connections reports stored in BAMS, and associated with the AMY and BST regions. The analyzed adjacency-matrix is made of 33 nodes connected by 506 edges (line-density 51%), and it was populated from more than 5000 individual reports of neuroanatomical connections, manually collated from the original research references, and mapped onto the rat nomenclature Swanson 2004 (Swanson, 2004). These data allows graph theoretical analyses of a directed and weighted local BST-AMY-connectome (Figure 1). The results of a weighted modularity analysis yielded functional-topographical groupings of the rat BST and AMY. Thus, the amygdalar nuclei involved in agonistic behavior and those involved in fear and pain are grouped in separate clusters. The majority of BST nuclei grouped in a single cluster, but a second cluster was identified. The members of the latter were also identified as a separate group in a recent statistical analysis of the gene expression patterns of 52 receptors and neurotransmitters in the rat (Bota et al., 2012). Furthermore, motif-analysis revealed a significant amount of motifs containing reciprocal connections (165 reciprocal connections) in the BST-AMY-network. These findings may indicate a regulatory role through positive or negative feedback mechanisms of particular regions (BSTal, BSTtr, BSTov, CEAl). We will present the results of our analysis over the connectivity data manually collated from all published and original research literature pertinent to the rat BST and AMY, and discuss the functional relevance of our findings. We also propose new experiments, for a more precise identification of functionality of individual rat BST and AMY regions. Figure 1 Keywords: Network analysis, connectome, Amygdala, stress, Addiction, Neuroanatomy, Data Mining Conference: Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013. Presentation Type: Poster Topic: General neuroinformatics Citation: Bota M, Schmitt O and Eipert P (2013). The rat BST-amygdala macroconnectome: a case study of functional-structural modules revealed by network analysis. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00029 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 29 Apr 2013; Published Online: 11 Jul 2013. * Correspondence: Dr. Mihail Bota, University of Southern California, Dept. of Neurobiology, Los Angeles, CA, California, 90089, United States, mbota08@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Mihail Bota Oliver Schmitt Peter Eipert Google Mihail Bota Oliver Schmitt Peter Eipert Google Scholar Mihail Bota Oliver Schmitt Peter Eipert PubMed Mihail Bota Oliver Schmitt Peter Eipert Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

neuroVIISAS is a generic platform which allows the integration of neuroontologies, mapping functions for brain atlas development, and connectivity data administration; all of which are required for the analysis of structurally and neurobiologically realistic simulations of networks. What makes neuroVIISAS unique is the ability to integrate neuroontologies, image stacks, mappings, visualizations, analyzes and simulations to use them for modelling and simulations. Based on the analysis of over 2020 tracing studies, atlas terminologies and registered histological stacks of images, neuroVIISAS permits the definition of neurobiologically realistic networks that are transferred to the simulation engine NEST. The analysis on a local and global level, the visualization of connectivity data and the results of simulations offer new possibilities to study structural and functional relationships of neural networks. This paper describes the major components and techniques of how to analyse, visualize and simulate with neuroVIISAS shown on a model network at a coarse CNS level (106 regions, 1566 connections) out of 13681 regions and 134043 connections of the left and right part of the CNS. This network of major components of the left and right hemisphere has small-world properties of the Watts-Strogatz model. Furthermore, synchronized subpopulations, oscillations of rate distributions and a time shift of population activities of the left and right hemisphere were observed in the neurocomputational simulations. In summary, a generic platform has been developed that realizes data-analysis-visualization integration for the exploration of network dynamics on multiple levels.

The connectomes of nervous systems or parts there of are becoming important subjects of study as the amount of connectivity data increases. Because most tract-tracing studies are performed on the rat, we conducted a comprehensive analysis of the amygdala connectome of this species resulting in a meta-study. The data were imported into the neuroVIISAS system, where regions of the connectome are organized in a controlled ontology and network analysis can be performed. A weighted digraph represents the bilateral intrinsic (connections of regions of the amygdala) and extrinsic (connections of regions of the amygdala to non-amygdaloid regions) connectome of the amygdala. Its structure as well as its local and global network parameters depend on the arrangement of neuronal entities in the ontology. The intrinsic amygdala connectome is a small-world and scale-free network. The anterior cortical nucleus (72 in- and out-going edges), the posterior nucleus (45), and the anterior basomedial nucleus (44) are the nuclear regions that posses most in- and outdegrees. The posterior nucleus turns out to be the most important nucleus of the intrinsic amygdala network since its Shapley rate is minimal. Within the intrinsic amygdala, regions were determined that are essential for network integrity. These regions are important for behavioral (processing of emotions and motivation) and functional (memory) performances of the amygdala as reported in other studies.

The orexinergic system interacts with several functional states of emotions, stress, hunger, wakefulness and behavioral arousal through four pathways originating in the lateral hypothalamus (LH). Hundreds of orexinergic efferents have been described by tracing studies and direct immunohistochemistry of orexin in the forebrain, olfactory regions, hippocampus, amygdala, septum, basal ganglia, thalamus, hypothalamus, brain stem and spinal cord. Most of these tracing studies investigated the whole orexinergic projection to all regions of the intracranial part of the CNS. To identify the orexinergic efferents at the subnuclear level of resolution, we focussed on the orexinergic target in the amygdala, which is substantially involved in the LH output and contributes mostly to the functional outcome of the orexinergic system and the basal ganglia. Immunohistochemical identification of axonal orexin A and orexin B in male adult rats has been performed on serial sections. In the extended amygdala many new orexinergic targets were found in the anterior amygdaloid area (dense), anterior cortical nucleus (moderate), amygdalostriatal transition region (moderate), basolateral regions (moderate), basomedial nucleus (moderate), several bed nucleus of the stria terminals regions (few to dense), central amygdaloid subdivisions (dense), posteromedial cortical nucleus (moderate) and medial amygdaloid subnuclei (dense). Furthermore, the entopeduncular nucleus has been newly identified as another target for orexinergic fibers with a high density. These results suggest that subdivisions and subnuclei of the extended amygdala are specific targets of the orexinergic system.