SynCoPa: visualizing connectivity paths and synapses over detailed morphologies

Galindo Ruedas, Sergio, Toharia Rabasco, Pablo ORCID: https://orcid.org/0000-0003-2429-1300, Robles Sánchez, Óscar David ORCID: https://orcid.org/0000-0002-3881-7273 and Pastor Pérez, Luis ORCID: https://orcid.org/0000-0002-7900-7509 (2021). SynCoPa: visualizing connectivity paths and synapses over detailed morphologies. "Frontiers in Neuroinformatics", v. 15 ; p. 753997. ISSN 1662-5196. https://doi.org/10.3389/fninf.2021.753997.

Descripción

Título: SynCoPa: visualizing connectivity paths and synapses over detailed morphologies
Autor/es:
Tipo de Documento: Artículo
Título de Revista/Publicación: Frontiers in Neuroinformatics
Fecha: 27 Diciembre 2021
ISSN: 1662-5196
Volumen: 15
Materias:
Palabras Clave Informales: Article, Artificial neural network, Bioinformatics, Bioinformatics visualization, Brain perfusion, Conductance, Cost, Data visualization, Dendritic cell, Dynamics, Electroencephalography, Functional magnetic resonance imaging, Geometry, Joint neuron morphology and connectivity visualization, Learning algortithm, Mathematical analysis, Microscopy, Morphology, Nerve cell, Network analysis, Neuron network connectivity visual analytics, Neuroscience, Publication, Scientific and data visualization in neuroscience, Simulation, Software, Spatial Analysis, Synapse, Syncopa, Visual analytics in neuroscience, Visual attention
Escuela: E.T.S. de Ingenieros Informáticos (UPM)
Departamento: Arquitectura y Tecnología de Sistemas Informáticos
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Brain complexity has traditionally fomented the division of neuroscience into somehow separated compartments; the coexistence of the anatomical, physiological, and connectomics points of view is just a paradigmatic example of this situation. However, there are times when it is important to combine some of these standpoints for getting a global picture, like for fully analyzing the morphological and topological features of a specific neuronal circuit. Within this framework, this article presents SynCoPa, a tool designed for bridging gaps among representations by providing techniques that allow combining detailed morphological neuron representations with the visualization of neuron interconnections at the synapse level. SynCoPa has been conceived for the interactive exploration and analysis of the connectivity elements and paths of simple to medium complexity neuronal circuits at the connectome level. This has been done by providing visual metaphors for synapses and interconnection paths, in combination with the representation of detailed neuron morphologies. SynCoPa could be helpful, for example, for establishing or confirming a hypothesis about the spatial distributions of synapses, or for answering questions about the way neurons establish connections or the relationships between connectivity and morphological features. Last, SynCoPa is easily extendable to include functional data provided, for example, by any of the morphologically-detailed simulators available nowadays, such as Neuron and Arbor, for providing a deep insight into the circuits features prior to simulating it, in particular any analysis where it is important to combine morphology, network topology, and physiology.

Proyectos asociados

Tipo
Código
Acrónimo
Responsable
Título
Gobierno de España
C080020-09
Sin especificar
Sin especificar
Cajal Blue Brain Project
Gobierno de España
TIN2017- 83132
Sin especificar
Sin especificar
Sin especificar
Gobierno de España
PID2020-113013RB-C21
Sin especificar
Sin especificar
Sin especificar
Gobierno de España
PID2020-113013RB-C22
Sin especificar
Sin especificar
Sin especificar
Horizonte 2020
785907
Sin especificar
Sin especificar
Human Brain Project SGA2
Horizonte 2020
945539
Sin especificar
Sin especificar
Human Brain Project SGA3

Más información

ID de Registro: 86727
Identificador DC: https://oa.upm.es/86727/
Identificador OAI: oai:oa.upm.es:86727
URL Portal Científico: https://portalcientifico.upm.es/es/ipublic/item/9759605
Identificador DOI: 10.3389/fninf.2021.753997
URL Oficial: https://www.frontiersin.org/journals/neuroinformat...
Depositado por: iMarina Portal Científico
Depositado el: 23 Ene 2025 12:26
Ultima Modificación: 03 Mar 2025 12:01