neuroVIISAS: Approaching Multiscale Simulation of the Rat Connectome
Abstract
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.
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The tracer-based connectome covers all cortical atlas regions.
“This tracer-based connectome covers all cortical atlas regions.”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Schmitt and Eipert 2012
The rat connectome database contains more than 450,237 ipsi- and 175,654 contralateral connections from more than 6183 peer reviewed publications describing connectivity observations based on anterograde and retrograde neuronal tracer injections in normal juvenile or adult rats.
“This database contains more than 450,237 ipsi- and 175,654 contralateral connections from more than 6183 peer reviewed publications which describe connectivity observations based on anterograde and retrograde neuronal tracer injections in normal juvenile or adult rats.”
From Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics (2018) · cites this paper as Schmitt and Eipert 2012
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Image Registration of Sectioned Brains
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Self-citationOrexinergic innervation of the extended amygdala and basal ganglia in the rat
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Self-citationNeurofitter: A parameter tuning package for a wide range of electrophysiological neuron models
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A Digital Atlas to Characterize the Mouse Brain Transcriptome
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Localization in the brain: new solutions emerging
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Morphological multiscale decomposition of connected regions with emphasis on cell clusters
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Self-citationRadial symmetries based decomposition of cell clusters in binary and gray level images
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Why Nonlinear Biomedical Physics?
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BAMS neuroanatomical ontology: design and implementation
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Modeling Structure-Function Relationships in Synthetic DNA Sequences using Attribute Grammars
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Online workbenches for neural network connections
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An evaluation of the use of multidimensional scaling for understanding brain connectivity
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Graph Signatures for Visual Analytics
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Causal localization of neural function: the Shapley value method
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EXPERIENCE IN REASONING WITH THE FOUNDATIONAL MODEL OF ANATOMY IN OWL DL
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Shapley ratings in brain networks
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Creating Neuroscience Ontologies
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Intelligent Approaches to Mining the Primary Research Literature: Techniques, Systems, and Examples
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Automated recognition of brain region mentions in neuroscience literature
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On the Decomposition of Cell Clusters
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Self-citationToward a workbench for rodent brain image data systems architecture and design
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Correlating thalamocortical connectivity and activity
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Computational neuroanatomy: ontology-based representation of neural components and connectivity
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Extracting Data Records from Unstructured Biomedical Full Text
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NeuroTerrain – a client-server system for browsing 3D biomedical image data sets
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Computational Intelligence in Medical Informatics
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LONI Visualization Environment
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Neuroanatomical Affiliation Visualization-Interface System
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Automatic Network Fingerprinting through Single-Node Motifs
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Bio-Algorithms and Med-Systems
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Functional Topology Classification of Biological Computing Networks
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BacGrid: simulations of bacteria using the grid
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BrainView: a computer program for reconstruction and interactive visualization of 3D data sets
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Volumes of the cytoarchitectonic areas in the rat cerebral cortex.
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Unobtrusive integration of data management with fMRI analysis
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Self-citationOptimal sparse coding of song in a size-constrained auditory system?
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Parallelized segmentation of a serially sectioned whole human brain
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Self-citationA first principles approach to differential expression in microarray data analysis
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A neural network-based system for high throughput fluorescence micrograph evaluation
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Complex-Network-Based Methodology For Analysis Of Biomedical Data
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Neural connectivity in the rat: theory, methods and applications
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Spike distributions of a population based hierarchical network of the rat amygdaloid complex
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Self-citationModelling the connectivity between terms in the neuroscience literature
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A hybrid approach to quantify lamination of the cerebral cortex
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Self-citation
Flagged first, then by citations
4 of the 194 references couldn’t be matched to a known paper.
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The Scalable Brain Atlas: Instant Web-Based Access to Public Brain Atlases and Related Content
Bakker, R., Tiesinga, P. H. E., & Kötter, R. (2015) · Neuroinformatics · doi:10.1007/s12021-014-9258-x · Cited by 298 · see citing works
“Dubach and Bowden 2009), the Mouse BIRN Atlasing toolkit (Lee et al. 2010), the Three-Dimensional Rodent Atlas System (Hjornevik et al. 2007), and the neuroVIISAS integration and simulation platform (Schmitt and Eipert 2012). In principle, each of these products can retrieve and display spatial brain data. What is lac…”
MethodCoCoMac 2.0 and the future of tract-tracing databases
Bakker, R., Wächtler, T., & Diesmann, M. (2012) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2012.00030 · Cited by 176 · see citing works
Straathof, M., Sinke, M. R. T., Dijkhuizen, R. M., & Otte, W. M. (2018) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x18809547 · Cited by 109 · see citing works
Sinke, M. R. T., Otte, W. M., Christiaens, D., Schmitt, O., Leemans, A., van der Toorn, A., Sarabdjitsingh, R. A., Joëls, M., & Dijkhuizen, R. M. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1628-y · Cited by 70 · see citing works
“2014), rat (Schmitt and Eipert 2012), macaque (Stephan et al.”
MethodSelf-citationA multiscale cerebral neurochemical connectome of the rat brain
Noori, H. R., Schöttler, J., Ercsey-Ravasz, M., Cosa‐Linan, A., Varga, M., Toroczkai, Z., & Spanagel, R. (2017) · PLoS Biology · doi:10.1371/journal.pbio.2002612 · Cited by 43 · see citing works
Simulation Neurotechnologies for Advancing Brain Research: Parallelizing Large Networks in NEURON
Lytton, W. W., Seidenstein, A. H., Durá-Bernal, S., McDougal, R. A., Schürmann, F., & Hines, M. L. (2016) · Neural Computation · doi:10.1162/neco_a_00876 · Cited by 43 · see citing works
“The use of Python as a common language is encouraging further development of common file formats not only for simulation output and subsequent data analysis but also for network design (Djurfeldt et al., 2010; Gardner et al., 2003; Schmitt & Eipert, 2012).”
MethodThe connectome of the basal ganglia
Schmitt, O., Eipert, P., Kettlitz, R., Leßmann, F., & Wree, A. (2014) · Brain Structure and Function · doi:10.1007/s00429-014-0936-0 · Cited by 37 · see citing works
“Regions of the nervous system of this particular project of the rat connectome are organized hierarchically (Schmitt and Eipert 2012), in graph theory such a hierarchy is called Value reliability weight of connection strength, t variable of reliability weight for transport directions of tracers, w…”
BackgroundMethodInfluentialSelf-citationVisNEST — Interactive analysis of neural activity data
Nowke, C., Schmidt, M., van Albada, S. J., Eppler, J. M., Bakker, R., Diesrnann, M., Hentschel, B., & Kuhlen, T. (2013) · IEEE Symposium on Biological Data Visualization (BioVis) · doi:10.1109/biovis.2013.6664348 · Cited by 34 · see citing works
“A system that targets multi-scale visualization is neuroVIISAS [18], a generic platform integrating different data modalities for the analysis of biologically realistic neuronal networks.”
Network models to enhance the translational impact of cross-species studies
Brynildsen, J. K., Rajan, K., Henderson, M. X., & Bassett, D. S. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00720-x · Cited by 29 · see citing works
The Intrinsic Connectome of the Rat Amygdala
Schmitt, O., Eipert, P., Philipp, K., Kettlitz, R., Fuellen, G., & Wree, A. (2012) · Frontiers in Neural Circuits · doi:10.3389/fncir.2012.00081 · Cited by 31 · see citing works
“…cortex (Sugar et al., 2011), nucleus of the solitary tract (Palombi et al., 2006), reticular formation (Humphries et al., 2006), hippocampus (Burns and Young, 2000), and the visual system (Burns, 1997), for an overview of connectome studies we refer to Sporns (2011), Schmitt and Eipert (2012).”
BackgroundMethodInfluentialSelf-citationNorepinephrine is a negative regulator of the adult periventricular neural stem cell niche
Weselek, G., Keiner, S., Fauser, M., Wagenführ, L., Müller, J., Kaltschmidt, B., Brandt, M., Gerlach, M., Redecker, C., Hermann, A., et al. (2020) · Stem Cells · doi:10.1002/stem.3232 · Cited by 28 · see citing works
“The only major region that does not receive input from the LC are the basal ganglia including the striatum.(33,43,44) By performing histological and neurochemical analyses of”
BackgroundScalable Interactive Visualization for Connectomics
Haehn, D., Hoffer, J., Matejek, B., Suissa‐Peleg, A., Al-Awami, A. K., Kamentsky, L. D., Gonda, F., Meng, E., Zhang, W., Schalek, R. L., et al. (2017) · Informatics · doi:10.3390/informatics4030029 · Cited by 26 · see citing works
“The neuroVIISAS system provides similar visualizations and pairs them with slice-based renderings [40].”
MethodFauser, M., Ricken, M. A., Markert, F., Weis, N., Schmitt, O., Gimsa, J., Winter, C., Badstübner-Meeske, K., & Storch, A. (2021) · Neurobiology of Disease · doi:10.1016/j.nbd.2021.105404 · Cited by 25 · see citing works
“bilateral STN-DBS for its neurorestorative actions within the nigrostriatal system in conjunction with the fact that there are no relevant projections from contralateral STN into the SN (Schmitt and Eipert, 2012; Schmitt et al., 2016) additionally points to more indirect or”
BackgroundInfluentialSelf-citationStraathof, M., Sinke, M. R. T., Roelofs, T. J. M., Blezer, E. L. A., Sarabdjitsingh, R. A., van der Toorn, A., Schmitt, O., Otte, W. M., & Dijkhuizen, R. M. (2020) · Scientific Reports · doi:10.1038/s41598-019-56834-9 · Cited by 23 · see citing works
Self-citationPapp, E. A., Leergaard, T. B., Csucs, G., & Bjaalie, J. G. (2016) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2016.00011 · Cited by 21 · see citing works
“Building on commonly available elements for the NIH ImageJ software (Schneider et al., 2012), we have implemented an image processing module for automatic detection of biotinylated dextran amine (BDA) and Phaseolus vulgaris leucoagglutinin ( Pha -”
MethodSukhinin, D. I., Engel, A. K., Manger, P. R., & Hilgetag, C. C. (2016) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2016.00016 · Cited by 21 · see citing works
“The assembly of the data from published tract-tracing report faces similar problems as previously addressed by the CoCoMac database (Bakker et al., 2012) or projects such as BAMS (Bota et al., 2005) and neuroVIISAS (Schmitt and Eipert, 2012), see details below.”
MethodPathological changes of brain oscillations following ischemic stroke
Satō, Y., Schmitt, O., Ip, Z., Rabiller, G., Omodaka, S., Tominaga, T., Yazdan-Shahmorad, A., & Liu, J. (2022) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x221105677 · Cited by 22 · see citing works
Self-citationBrainTrawler: A visual analytics framework for iterative exploration of heterogeneous big brain data
Ganglberger, F., Swoboda, N., Frauenstein, L., Kaczanowska, J., Haubensak, W. E., & Bühler, K. (2019) · Computers & Graphics · doi:10.1016/j.cag.2019.05.032 · Cited by 18 · see citing works
26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2
Rubchinsky, L. L., Ahn, S., Klijn, W., Cumming, B., Yates, S., Karakasis, V., Peyser, A., Woodman, M., Diaz-Pier, S., Deraeve, J., et al. (2017) · BMC Neuroscience · doi:10.1186/s12868-017-0371-2 · Cited by 17 · see citing works
Mapping the community structure of the rat cerebral cortex with weighted stochastic block modeling
Faskowitz, J., & Sporns, O. (2019) · Brain Structure and Function · doi:10.1007/s00429-019-01984-9 · Cited by 15 · see citing works
“2017) or using alternative node definitions or informatically collated data (Schmitt and Eipert 2012).”
BackgroundBota, M., Talpalaru, Ş., Hintiryan, H., Dong, H., & Swanson, L. W. (2014) · The Journal of Comparative Neurology · doi:10.1002/cne.23592 · Cited by 12 · see citing works
“…nervous system networks are available only in generic public software platforms such as Cytoscape (http://www.cytoscape.org, RRID: nif-0000–30404) or are embedded in downloadable programs (neuroVIISAS; Schmitt and Eipert, 2012) that cannot be used directly on the web by the neuro-science community.”
Prediction of regional functional impairment following experimental stroke via connectome analysis
Schmitt, O., Badurek, S., Liu, W., Wang, Y.-M., Rabiller, G., Kanoke, A., Eipert, P., & Liu, J. (2017) · Scientific Reports · doi:10.1038/srep46316 · Cited by 12 · see citing works
Self-citationHelf, C., Kober, M., Markert, F., Lanto, J., Overhoff, L., Badstübner-Meeske, K., Storch, A., & Fauser, M. (2023) · Neuroreport · doi:10.1097/wnr.0000000000001917 · Cited by 11 · see citing works
Integrating Visualizations into Modeling NEST Simulations
Nowke, C., Zielasko, D., Weyers, B., Peyser, A., Hentschel, B., & Kuhlen, T. (2015) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2015.00029 · Cited by 10 · see citing works
“Schmitt and Eipert (2012) presents neuroVIISAS, a generic platform for the integration of data modalities required for the analysis and simulation of biologically realistic neural systems.”
BackgroundTowards Differential Connectomics with NeuroVIISAS
Schwanke, S., Jenssen, J., Eipert, P., & Schmitt, O. (2018) · Neuroinformatics · doi:10.1007/s12021-018-9389-6 · Cited by 10 · see citing works
Self-citationConnectomes: from a sparsity of networks to large-scale databases
Kaiser, M. (2023) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2023.1170337 · Cited by 9 · see citing works
“NeuroVIISAS (Schmitt and Eipert, 2012) provides network analysis routines and the ability to interact with simulation tools like NEST (Gewaltig and Diesmann, 2007) to study network dynamics.”
MethodŠanda, P., Hlinka, J., van den Berg, M., Škoch, A., Bazhenov, M., Keliris, G. A., & Krishnan, G. P. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012099 · Cited by 9 · see citing works
“The results from the experiments were then used to constrain the computational model of DMLNwith the connectivity based onNeuroVIISAS atlas for rodent brain [Schmitt and Eipert, 2012].”
InfluentialAlberts, T., Antipova, V., Holzmann, C., Hawlitschka, A., Schmitt, O., Kurth, J., Stenzel, J., Lindner, T., Krause, B. J., Wree, A., et al. (2022) · Toxins · doi:10.3390/toxins14020094 · Cited by 8 · see citing works
Self-citationKaiser, M. (2020) · The MIT Press eBooks · doi:10.7551/mitpress/11980.001.0001 · Cited by 25 · see citing works
The Mind-Brain Relationship as a Mathematical Problem
Ascoli, G. A. (2013) · ISRN Neuroscience · doi:10.1155/2013/261364 · Cited by 7 · see citing works
Golgi: Interactive Online Brain Mapping
Brown, R. A., & Swanson, L. W. (2015) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2015.00026 · Cited by 7 · see citing works
Boorman, D. C., Crawford, L., Henderson, L. A., & Keay, K. A. (2025) · Communications Biology · doi:10.1038/s42003-025-07993-1 · Cited by 7 · see citing works
“Thedirectionand strengthofeach of these connections was then applied based on known anatomical projections (Rat Connectome Project), or pruned if there was no evidence of a projection, and force directed graphs were created using Gephi.”
InfluentialModeling and simulation for prediction of multiple sclerosis progression
Prathapan, V., Eipert, P., Wigger, N., Kipp, M., Appali, R., & Schmitt, O. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108416 · Cited by 6 · see citing works
Self-citationSchmitt, O., Eipert, P., Schwanke, S., Leßmann, F., Jennifer, M., Beier, J., Kadir, K., Karnitzki, A., Sellner, L., Klünker, A.-C., et al. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby048 · Cited by 6 · see citing works
Self-citationReaction-diffusion models in weighted and directed connectomes
Schmitt, O., Nitzsche, C., Eipert, P., Prathapan, V., Hütt, M. ‐., & Hilgetag, C. C. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010507 · Cited by 6 · see citing works
“bib file (included in [88]) provides the links of connections with original research literature and need to be configured in neuroVIISAS [52].”
Self-citationSchümann, F., Schmitt, O., Wree, A., & Hawlitschka, A. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24021685 · Cited by 5 · see citing works
“NeuroVIISAS allows to perform extensive network simulations using information from publications that have collected tract-tracing data, and to retrieve and graphically display information from different rat brain areas about their connections and connection strengths with other brain areas [65,66].”
BackgroundSelf-citationPhotonics meets connectomics: case of diffuse, long-range horizontal projections in rat cortex
Johnson, B. A., & Frostig, R. D. (2015) · Neurophotonics · doi:10.1117/1.nph.2.4.041403 · Cited by 5 · see citing works
“4 These electronic databases are associated with software allowing users to access the multiple publications from which the connectivity data were derived and to compare the results of these different studies.”
BackgroundMethodResultInfluentialRelationships and representations of brain structures, connectivity, dynamics and functions
Schmitt, O. (2025) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2025.111332 · Cited by 4 · see citing works
Self-citationConnectome-based prediction of functional impairment in experimental stroke models
Schmitt, O., Eipert, P., Wang, Y., Kanoke, A., Rabiller, G., & Liu, J. (2024) · PLoS ONE · doi:10.1371/journal.pone.0310743 · Cited by 4 · see citing works
Self-citationStatz, M., Schleuter, F., Weber, H., Kober, M., Plocksties, F. V., Timmermann, D., Storch, A., & Fauser, M. (2023) · Neuroscience Letters · doi:10.1016/j.neulet.2023.137459 · Cited by 4 · see citing works
“thalamic nucleus, the actual site of stimulation [35,36].”
The brainstem connectome database
Schmitt, O., Eipert, P., Ruß, F., Beier, J., Kadir, K., & Horn, A. K. E. (2022) · Scientific Data · doi:10.1038/s41597-022-01219-3 · Cited by 4 · see citing works
Self-citationWree, A., & Schmitt, O. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00215-3 · Cited by 3 · see citing works
Self-citationneuroVIISAS-based construction of a stereotactic rhesus monkey brain atlas for connectome research
Kohnen, K., Eipert, P., Budde, L., & Schmitt, O. (2025) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2025.110656 · Cited by 1 · see citing works
Self-citationMRI-based Measurement of Structural and Functional Connectivity in Health and Disease
Sinke, M. R. T. (2020) · doi:10.33540/77
Statz, M., Weber, H., Weis, F., Kober, M., Bathel, H., Plocksties, F. V., van Rienen, U., Timmermann, D., Storch, A., & Fauser, M. (2024) · Brain Research · doi:10.1016/j.brainres.2024.149128
“On the structural level, significant NEergic projections from the LC to the STN have been described in rodents and non-human primates (Belujon et al., 2007, Schmitt and Eipert, 2012, Schmitt et al., 2016); however, clinical studies are not available to date (reviewed in (Emmi et al., 2023)).”
Critical Regions and Connections Form Pathways and Clusters in the Mouse Brain
Hotama, C. F., Kralik, J. D., & Jeong, J. (2025) · European Journal of Neuroscience · doi:10.1111/ejn.16673
“Currently, there is substantial development on the mesoscale and macroscale connectomes in mammalian species such as with mice, rats, monkeys and even humans (Bakker, Wachtler, and Diesmann 2012; Grandjean et al. 2017; Oh et al. 2014; Schmitt and Eipert 2012; van Essen 2011).”
Proteomic Characterization of Spinal Cord Myelin in the Mouse
Schmitt, O., Kaddatz, H., Mikkat, S., Kipp, M., Schümann, A., & Joost, S. (2025) · ASN NEURO · doi:10.1080/17590914.2025.2595945
“A possible use of the data would be the simulation (Schmitt et al., 2011; 2012; 2016; Schmitt & Eipert, 2012) of the connec-tional spread of demyelination (Balk et al., 2014; 2015; Bermel & Villoslada, 2014; Puthenparampil et al., 2017) and the resulting predictions of disease progression (Schmitt…”
Self-citationSchmitt, O., Morawska, P., Prathapan, V., & Eipert, P. (2026) · The Cerebellum · doi:10.1007/s12311-026-02042-x
Self-citationDeciphering nervous systems wiring: Tract-tracing and complementary methods
Schmitt, O., Eipert, P., Maestro, R. T., Bellmann, M., Haase, S., Morawska, P., & Prathapan, V. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106943
Self-citationFAIRsharing record for: Brainstem Connectome
FAIRsharing Team, & Schmitt, O. (2018) · FAIRsharing.org · doi:10.25504/fairsharing.hd7sxq
Self-citation
- Authors
- Schmitt, O., & Eipert, P.
- Affiliations
- Universitätsmedizin Rostock (DE)
- University of Rostock (DE)
- Corresponding author
- Schmitt, Oliver
- ORCID
- Schmitt, Oliver
- Venue
- Neuroinformatics
- Published
- 2012-02-15
- Volume
- 10
- Issue
- 3
- Pages
- 243-267
- ISSN
- 1539-2791 (ISSN-L)
- 1559-0089
- OpenAlex
- W2109181987
- Cited by
- 51
- References
- 194
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 4 · 2023: 5 · 2022: 4 · 2021: 2 · 2020: 4 · 2019: 2 · 2018: 4 · 2017: 4 · 2016: 3 · 2015: 5 · 2014: 2 · 2013: 2 · 2012: 3
- FWCI
- 1.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Neuroinformatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Universität Greifswald
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Undergraduate Neuroscience Education and Research
- Keywords
- NEST simulator, small-world network properties, cerebral hemispheres, neural oscillations, network dynamics
- MeSH terms
- Animals, Brain, Brain Mapping, Computer Simulation, Models, Neurological, Neural Pathways, Rats
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