Perspectivas bibliométricas sobre la conectómica: enfoques desde la química y la neurociencia

Introducción. La conectómica, una ciencia ómica emergente, ha contribuido al diagnóstico preventivo de enfermedades neurodegenerativas. Este campo integra información química y neurocientífica, analizando las funciones de los neurotransmisores y las alteraciones dentro de las conexiones cerebrales....

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Tipo de recurso:
Fecha de publicación:
2026
Institución:
Universidad de Caldas
Repositorio:
Repositorio Institucional U. Caldas
Idioma:
eng
OAI Identifier:
oai:repositorio.ucaldas.edu.co:ucaldas/27018
Acceso en línea:
https://repositorio.ucaldas.edu.co/handle/ucaldas/27018
https://doi.org/10.17151/biosa.2021.20.1.1
Palabra clave:
Análisis bibliométrico
Conexiones cerebrales
Resonancia magnética
Redes neuronales
Trastornos neurodegenerativos
Bibliometric analysis
Brain connections
Magnetic resonance imaging
Neurodegenerative disorders
Neural networks
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openAccess
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Biosalud - 2026
id REPOUCALDA_387dc6500d2a72d7b22604f263f1e819
oai_identifier_str oai:repositorio.ucaldas.edu.co:ucaldas/27018
network_acronym_str REPOUCALDA
network_name_str Repositorio Institucional U. Caldas
repository_id_str
dc.title.none.fl_str_mv Perspectivas bibliométricas sobre la conectómica: enfoques desde la química y la neurociencia
Bibliometric Insights into Connectomics: Chemistry and Neuroscience Perspectives
title Perspectivas bibliométricas sobre la conectómica: enfoques desde la química y la neurociencia
spellingShingle Perspectivas bibliométricas sobre la conectómica: enfoques desde la química y la neurociencia
Análisis bibliométrico
Conexiones cerebrales
Resonancia magnética
Redes neuronales
Trastornos neurodegenerativos
Bibliometric analysis
Brain connections
Magnetic resonance imaging
Neurodegenerative disorders
Neural networks
title_short Perspectivas bibliométricas sobre la conectómica: enfoques desde la química y la neurociencia
title_full Perspectivas bibliométricas sobre la conectómica: enfoques desde la química y la neurociencia
title_fullStr Perspectivas bibliométricas sobre la conectómica: enfoques desde la química y la neurociencia
title_full_unstemmed Perspectivas bibliométricas sobre la conectómica: enfoques desde la química y la neurociencia
title_sort Perspectivas bibliométricas sobre la conectómica: enfoques desde la química y la neurociencia
dc.subject.none.fl_str_mv Análisis bibliométrico
Conexiones cerebrales
Resonancia magnética
Redes neuronales
Trastornos neurodegenerativos
Bibliometric analysis
Brain connections
Magnetic resonance imaging
Neurodegenerative disorders
Neural networks
topic Análisis bibliométrico
Conexiones cerebrales
Resonancia magnética
Redes neuronales
Trastornos neurodegenerativos
Bibliometric analysis
Brain connections
Magnetic resonance imaging
Neurodegenerative disorders
Neural networks
description Introducción. La conectómica, una ciencia ómica emergente, ha contribuido al diagnóstico preventivo de enfermedades neurodegenerativas. Este campo integra información química y neurocientífica, analizando las funciones de los neurotransmisores y las alteraciones dentro de las conexiones cerebrales. Además, permite realizar simulaciones teóricas que vinculan la estructura biofísica del cerebro con las interacciones neuronales, proporcionando conocimientos sobre la función cerebral. Objetivo. Este estudio explora la conectómica como ciencia ómica y sus contribuciones a la química y las neurociencias. Metodología. Se llevó a cabo un análisis bibliométrico utilizando Scopus, RStudio y VOSviewer para identificar las tendencias globales de la investigación en conectómica. Resultados. La co-citación y la co-ocurrencia de palabras clave analizan aspectos clave de la investigación en conectómica. Los hallazgos resaltan la necesidad de promover estudios sobre esta ciencia ómica emergente en Colombia. Conclusiones. La conectómica juega un papel crucial en la comprensión de las redes neuronales y en el avance de tratamientos para trastornos neurodegenerativos. Este campo se beneficia de técnicas instrumentales como la resonancia magnética y los modelos de aprendizaje automático para el procesamiento de datos. Estas herramientas enfatizan la importancia de la investigación conectómica en la neurociencia y las ciencias cognitivas.
publishDate 2026
dc.date.none.fl_str_mv 2026-05-22T14:41:27Z
2026-05-23T06:45:15Z
2026-05-22T14:41:27Z
2026-05-23T06:45:15Z
2026-05-22
dc.type.none.fl_str_mv Artículo de revista
http://purl.org/coar/resource_type/c_6501
http://purl.org/coar/resource_type/c_2df8fbb1
Text
info:eu-repo/semantics/article
Journal article
http://purl.org/redcol/resource_type/ART
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/version/c_970fb48d4fbd8a85
status_str publishedVersion
dc.identifier.none.fl_str_mv 1657-9550
https://repositorio.ucaldas.edu.co/handle/ucaldas/27018
10.17151/biosa.2021.20.1.1
2462-960X
https://doi.org/10.17151/biosa.2021.20.1.1
identifier_str_mv 1657-9550
10.17151/biosa.2021.20.1.1
2462-960X
url https://repositorio.ucaldas.edu.co/handle/ucaldas/27018
https://doi.org/10.17151/biosa.2021.20.1.1
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1
20
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Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
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spelling Perspectivas bibliométricas sobre la conectómica: enfoques desde la química y la neurocienciaBibliometric Insights into Connectomics: Chemistry and Neuroscience PerspectivesAnálisis bibliométricoConexiones cerebralesResonancia magnéticaRedes neuronalesTrastornos neurodegenerativosBibliometric analysisBrain connectionsMagnetic resonance imagingNeurodegenerative disordersNeural networksIntroducción. La conectómica, una ciencia ómica emergente, ha contribuido al diagnóstico preventivo de enfermedades neurodegenerativas. Este campo integra información química y neurocientífica, analizando las funciones de los neurotransmisores y las alteraciones dentro de las conexiones cerebrales. Además, permite realizar simulaciones teóricas que vinculan la estructura biofísica del cerebro con las interacciones neuronales, proporcionando conocimientos sobre la función cerebral. Objetivo. Este estudio explora la conectómica como ciencia ómica y sus contribuciones a la química y las neurociencias. Metodología. Se llevó a cabo un análisis bibliométrico utilizando Scopus, RStudio y VOSviewer para identificar las tendencias globales de la investigación en conectómica. Resultados. La co-citación y la co-ocurrencia de palabras clave analizan aspectos clave de la investigación en conectómica. Los hallazgos resaltan la necesidad de promover estudios sobre esta ciencia ómica emergente en Colombia. Conclusiones. La conectómica juega un papel crucial en la comprensión de las redes neuronales y en el avance de tratamientos para trastornos neurodegenerativos. Este campo se beneficia de técnicas instrumentales como la resonancia magnética y los modelos de aprendizaje automático para el procesamiento de datos. Estas herramientas enfatizan la importancia de la investigación conectómica en la neurociencia y las ciencias cognitivas.Introduction. Connectomics, an emerging omics science, has contributed to the preventive diagnosis of neurodegenerative diseases. This field integrates chemical and neuroscientific information, analyzing neurotransmitter functions and alterations within brain connections. Additionally, it enables theoretical simulations that link the brain´s biophysical structure with neuronal interactions, providing insights into brain function. Objective. This study explores connectomics as an omics science and its contributions to chemistry and neurosciences. Methodology. A bibliometric analysis was conducted using Scopus, RStudio and VOSviewer to identify global research trends in connectomics. Results. Co-citation and keyword co-occurrence analyses key aspects of connectomics research. The findings highlight the need to promote studies on this emerging omics science in Colombia. Conclusions. Connectomics plays a crucial role in understanding neural networks and advancing treatments for neurodegenerative disorders. The field benefits from instrumental techniques such as magnetic resonance imaging and machine learning models for data processing. 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