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Betancourt Arango, J. P., Patiño Ospina, A., Suárez Millán, M. D. C., & Taborda Ocampo, G. (2026). Perspectivas bibliométricas sobre la conectómica: enfoques desde la química y la neurociencia. Biosalud, 20(1). https://doi.org/10.17151/biosa.2021.20.1.1
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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.
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Albarracín, S. L., Baldeón, M. E., Sangronis, E., Petruschina, A. C., & Reyes, F. G. R. (2016). L-Glutamato: Un aminoácido clave para las funciones sensoriales y metabólicas. Archivos Latinoamericanos de Nutricion, 66(2), 101–112.
Arshadi, C., Günther, U., Eddison, M., Harrington, K. I. S., & Ferreira, T. A. (2021). SNT: a unifying toolbox for quantification of neuronal anatomy. Nature Methods, 18(4), 374–377. https://doi.org/10.1038/s41592-021-01105-7
Barsotti, E., Correia, A., & Cardona, A. (2021). Neural architectures in the light of comparative connectomics. Current Opinion in Neurobiology, 71, 139–149. https://doi.org/10.1016/j.conb.2021.10.006
Bates, A. S., & Jefferis, G. (2022). Systems neuroscience: Auditory processing at synaptic resolution. Current Biology, 32(15), R830–R833. https://doi.org/10.1016/j.cub.2022.07.001
Betancourt-Arango, J. P., Patiño-Ospina, A., Taborda-Ocampo, G., & Fiscal-Ladino, J. A. (2025). Aplicaciones de la xenometabolómica para la identificación de biomarcadores de toxicidad: una revisión del tema. Revista Biosalud, 19(1), 7–30. https://doi.org/10.17151/biosa.2020.19.1.1
Betancourt-Arango, J. P., Suárez-Millán, M., & Álvarez-Márquez, D. (2022). Revisión sistemática de literatura sobre la relación entre la teoría y la práctica en estudiantes de biología y química de Colombia. Luna Azul, 114–142. https://doi.org/10.17151/luaz.2022.54.7
Betancourt-Arango, J. P., Villaroel-Solis, E. E., Fiscal-Ladino, J. A., & Taborda-Ocampo, G. (2024). Volatilomics: An emerging discipline within Omics Sciences - A systematic review [version 1; peer review: awaiting peer review]. F1000Research, 13(991). https://doi.org/10.12688/f1000research.149773.1
Betzel, R. F. (2021). Network neuroscience and the connectomics revolution. In Connectomic Deep Brain Stimulation (pp. 25–58). Elsevier. https://doi.org/10.1016/B978-0-12-821861-7.00002-6
Bloomingdale, P., Karelina, T., Cirit, M., Muldoon, S. F., Baker, J., McCarty, W. J., Geerts, H., & Macha, S. (2021). Quantitative systems pharmacology in neuroscience: Novel methodologies and technologies. CPT: Pharmacometrics and Systems Pharmacology, 10(5), 412–419. https://doi.org/10.1002/psp4.12607
Burbano, M. (2024). Neurociencia y sus Campos de Acción. Ciencia Latina Revista Científica Multidisciplinar, 8, 396–408. https://doi.org/10.37811/cl_rcm.v8i4.12228
Chen, P. B., & Flint, J. (2021). What connectomics can learn from genomics. PLoS Genetics, 17(7). https://doi.org/10.1371/journal.pgen.1009692
Chung, J., Bridgeford, E., Arroyo, J., Pedigo, B. D., Saad-Eldin, A., Gopalakrishnan, V., Xiang, L., Priebe, C. E., & Vogelstein, J. T. (2021). Statistical connectomics. Annual Review of Statistics and Its Application, 8, 463–492. https://doi.org/10.1146/annurev-statistics-042720-023234
Cwiek, A., Rajtmajer, S. M., Wyble, B., Honavar, V., Grossner, E., & Hillary, F. G. (2022). Feeding the machine: Challenges to reproducible predictive modeling in resting-state connectomics. Network Neuroscience, 6(1), 29–48. https://doi.org/10.1162/netn_a_00212
D’Souza, N. S., & Venkataraman, A. (2023). Network comparisons and their applications in connectomics. In Connectome Analysis: Characterization, Methods, and Analysis (pp. 173–199). Elsevier. https://doi.org/10.1016/B978-0-323-85280-7.00009-9
DeFelipe, J. (2010). From the connectome to the synaptome: An epic love story. Science, 330(6008), 1198–1201. https://doi.org/10.1126/science.1193378
Duffau, H. (2021). Brain connectomics applied to oncological neuroscience: from a traditional surgical strategy focusing on glioma topography to a meta-network approach. Acta Neurochirurgica, 163(4), 905–917. https://doi.org/10.1007/s00701-021-04752-z
Egea, J., Bravo-Cordero, J. J., García, A., & Eguiagaray, J. (2004). Neurotransmisores, señales de calcio y comunicación neuronal. Neurocirugía: Organo Oficial de La Sociedad Española de Neurocirugía, ISSN 1130-1473, Vol. 15, No. 2, 2004, Pags. 109-118, 15. https://doi.org/10.1016/S1130-1473(04)70489-3
Emwas, A. H., Szczepski, K., Al-Younis, I., Lachowicz, J. I., & Jaremko, M. (2022). Fluxomics - New Metabolomics Approaches to Monitor Metabolic Pathways. Frontiers in Pharmacology, 13(March), 1–13. https://doi.org/10.3389/fphar.2022.805782
Flores-Soto, M., & Segura-Torres, J. (2005). Estructura y función de los receptores nicotinicos. Rev Mex Neuroci, 6(4), 315–326.
Frigolet, M. E., & Gutiérrez-Aguilar, R. (2017). Ciencias “ómicas”, ¿cómo ayudan a las ciencias de la salud? Revista Digital Universitaria, 18(7), 0–15. https://doi.org/10.22201/codeic.16076079e.2017.v18n7.a3
Galaburda, A., & Wong, B. (2017). Neuropsicología: mirando hacia adelante. Panamerican Journal of Neuropsychology, 11(2), 5.
Galindo, S. E., Toharia, P., Robles, O. D., & Pastor, L. (2021). SynCoPa: Visualizing Connectivity Paths and Synapses Over Detailed Morphologies. Frontiers in Neuroinformatics, 15. https://doi.org/10.3389/fninf.2021.753997
Gomez-Marin, A. (2021). Promisomics and the short-circuiting of mind. ENeuro, 8(2). https://doi.org/10.1523/ENEURO.0521-20.2021
Goyal, N., Moraczewski, D., Bandettini, P. A., Finn, E. S., & Thomas, A. G. (2022). The positive-negative mode link between brain connectivity, demographics and behaviour: a pre-registered replication of Smith et al. (2015). Royal Society Open Science, 9(2). https://doi.org/10.1098/rsos.201090
Grospietsch, F., Krack, P., & Kühn, A. (2022). Investigating cognitive neuroscience concepts using connectomic DBS (pp. 483–504). https://doi.org/10.1016/B978-0-12-821861-7.00013-0
Gutierrez Zuniga, R., Diez, I., Bueichekú, E., Kim, C.-M., Orwig, W., Montal, V., Fuentes, B., Díez‐Tejedor, E., Gutiérrez-Fernández, M., & Sepulcre, J. (2022). Connectomic-genetic signatures in the cerebral small vessel disease. Neurobiology of Disease, 167, 105671. https://doi.org/10.1016/j.nbd.2022.105671
Han, W., Ward, J. L., Kong, Y., & Li, X. (2023). Editorial : Targeted and untargeted metabolomics for the evaluation of plant metabolites in response to the environment. March, 2021–2023. https://doi.org/10.3389/fpls.2023.1167513
Hansen, J., Vogel, J., Smart, K., Bearden, C., Franke, B., Mcdonald, C., Stein, D., & Thompson, P. (2022). Molecular and connectomic vulnerability shape cross-disorder cortical abnormalities. https://doi.org/https://doi.org/10.1101/2022.01.21.476409
Hasegawa, E., Truman, J. W., & Nose, A. (2016). Identification of excitatory premotor interneurons which regulate local muscle contraction during Drosophila larval locomotion. Scientific Reports, 6(1), 30806. https://doi.org/10.1038/srep30806
Hirabayashi, Y., Tapia, J. C., & Polleux, F. (2018). Correlated Light-Serial Scanning Electron Microscopy (CoLSSEM) for ultrastructural visualization of single neurons in vivo. Scientific Reports, 8(1), 14491. https://doi.org/10.1038/s41598-018-32820-5
Hoy, R. R. (2021). Quantitative skills in undergraduate neuroscience education in the age of big data. Neuroscience Letters, 759. https://doi.org/10.1016/j.neulet.2021.136074
Hua, Y., Loomba, S., Pawlak, V., Voit, K.-M., Laserstein, P., Boergens, K. M., Wallace, D. J., Kerr, J. N. D., & Helmstaedter, M. (2022). Connectomic analysis of thalamus-driven disinhibition in cortical layer 4. Cell Reports, 41(2). https://doi.org/10.1016/j.celrep.2022.111476
Jütten, K., Weninger, L., Mainz, V., Gauggel, S., Binkofski, F., Wiesmann, M., Merhof, D., Clusmann, H., & Na, C.-H. (2021). Dissociation of structural and functional connectomic coherence in glioma patients. Scientific Reports, 11(1), 16790. https://doi.org/10.1038/s41598-021-95932-5
Kamagata, K., Zalesky, A., Yokoyama, K., Andica, C., Hagiwara, A., Shimoji, K., Kumamaru, K. K., Takemura, M. Y., Hoshino, Y., Kamiya, K., Hori, M.,
Pantelis, C., Hattori, N., & Aoki, S. (2019). MR g-ratio-weighted connectome analysis in patients with multiple sclerosis. Scientific Reports, 9(1), 13522. https://doi.org/10.1038/s41598-019-50025-2
Khan, A. M., D’Arcy, C. E., & Olimpo, J. T. (2021). A historical perspective on training students to create standardized maps of novel brain structure: Newly-uncovered resonances between past and present research-based neuroanatomy curricula. Neuroscience Letters, 759. https://doi.org/10.1016/j.neulet.2021.136052
Krempl, G., Kottke, D., & Pham, T. (2021). Statistical Analysis of Pairwise Connectivity. In S. C. & T. L. (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 12986 LNAI (pp. 138–148). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-88942-5_11
Li, G., & Yap, P. T. (2022). From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis. Frontiers in Human Neuroscience, 16(August), 1–17. https://doi.org/10.3389/fnhum.2022.940842
Liao, W., Ji, G.-J., Xu, Q., Wei, W., Wang, J., Wang, Z., Yang, F., Sun, K., Jiao, Q., Richardson, M. P., Zang, Y.-F., Zhang, Z., & Lu, G. (2016). Functional Connectome before and following Temporal Lobectomy in Mesial Temporal Lobe Epilepsy. Scientific Reports, 6(1), 23153. https://doi.org/10.1038/srep23153
Martinez de Paz, J. M., & Macé, E. (2021). Functional ultrasound imaging: A useful tool for functional connectomics? NeuroImage, 245. https://doi.org/10.1016/j.neuroimage.2021.118722
McGrath, H., Zaveri, H. P., Collins, E., Jafar, T., Chishti, O., Obaid, S., Ksendzovsky, A., Wu, K., Papademetris, X., & Spencer, D. D. (2022). High-resolution cortical parcellation based on conserved brain landmarks for localization of multimodal data to the nearest centimeter. Scientific Reports, 12(1), 18778. https://doi.org/10.1038/s41598-022-21543-3
Meamardoost, S., Bhattacharya, M., Hwang, E. J., Komiyama, T., Mewes, C., Wang, L., Zhang, Y., & Gunawan, R. (2021). FARCI: Fast and Robust Connectome Inference. Brain Sciences, 11(12). https://doi.org/10.3390/BRAINSCI11121556
Mithani, K., Boutet, A., Germann, J., Elias, G. J. B., Weil, A. G., Shah, A., Guillen, M., Bernal, B., Achua, J. K., Ragheb, J., Donner, E., Lozano, A. M., Widjaja, E., & Ibrahim, G. M. (2019). Lesion Network Localization of Seizure Freedom following MR-guided Laser Interstitial Thermal Ablation. Scientific Reports, 9(1), 18598. https://doi.org/10.1038/s41598-019-55015-y
Mohanty, R., Sethares, W. A., Nair, V. A., & Prabhakaran, V. (2020). Rethinking Measures of Functional Connectivity via Feature Extraction. Scientific Reports, 10(1), 1298. https://doi.org/10.1038/s41598-020-57915-w
Nalls, M. A., Bras, J., Hernandez, D. G., Keller, M. F., Majounie, E., Renton, A. E., Saad, M., Jansen, I., Guerreiro, R., Lubbe, S., Plagnol, V., Gibbs, J. R., Schulte, C., Pankratz, N., Sutherland, M., Bertram, L., Lill, C. M., Destefano, A. L., Faroud, T., … Tzourio, C. (2015). NeuroX, a fast and efficient genotyping platform for investigation of neurodegenerative diseases. Neurobiology of Aging, 36(3), 1605.e7-1605.e12. https://doi.org/10.1016/j.neurobiolaging.2014.07.028
Nenning, K.-H., & Langs, G. (2022). Machine learning in neuroimaging: from research to clinical practice. Radiologie, 62, 1–10. https://doi.org/10.1007/s00117-022-01051-1
Ochoa-de la Paz, L. D., Gulias-Cañizo, R., D´Abril Ruíz-Leyja, E., Sánchez-Castillo, H., & Parodí, J. (2021). The role of GABA neurotransmitter in the human central nervous system, physiology, and pathophysiology. Revista Mexicana de Neurociencia, 22(2), 67–76. https://doi.org/10.24875/rmn.20000050
Ortiz-Teran, E., Diez, I., Sepulcre, J., Lopez-Pascual, J., & Ortiz, T. (2021). Connectivity adaptations in dopaminergic systems define the brain maturity of investors. Scientific Reports, 11(1), 11671. https://doi.org/10.1038/s41598-021-91227-x
Plosnić, G., Raguž, M., Deletis, V., & Chudy, D. (2023). Dysfunctional connectivity as a neurophysiologic mechanism of disorders of consciousness: a systematic review. Frontiers in Neuroscience, 17(July), 1–11. https://doi.org/10.3389/fnins.2023.1166187
Rodríguez-caso, C., & López-rodríguez, D. (2016). ¿ Qué es la conectómica ? Encuentros En La Biología, IX(159), 123–126.
Rodriguez-Cruces, R., Royer, J., Larivière, S., Bassett, D. S., Caciagli, L., & Bernhardt, B. C. (2022). Multimodal connectome biomarkers of cognitive and affective dysfunction in the common epilepsies. Network Neuroscience, 6(2), 320–338. https://doi.org/10.1162/netn_a_00237
Rodríguez-Méndez, D. A., San-Juan, D., Hallett, M., Antonopoulos, C. G., López-Reynoso, E., & Lara-Ramírez, R. (2022). A new model for freedom of movement using connectomic analysis. PeerJ, 10. https://doi.org/10.7717/peerj.13602
Roffet, F., Delrieux, C., & Patow, G. (2022). Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory. Brain Sciences, 12(9). https://doi.org/10.3390/brainsci12091219
Rojas-Leguizamon, M., Molina, J., Hidalgo-Aguirre, R., & Figueroa-Jiménez, M. (2021). Una mirada a las neurociencias De las neuronas a la cognición. In Una mirada a las neurociencias: de las neuronas a la cognición. https://doi.org/10.47377/neurociencias.6
Saenger, V. M., Kahan, J., Foltynie, T., Friston, K., Aziz, T. Z., Green, A. L., van Hartevelt, T. J., Cabral, J., Stevner, A. B. A., Fernandes, H. M., Mancini, L., Thornton, J., Yousry, T., Limousin, P., Zrinzo, L., Hariz, M., Marques, P., Sousa, N., Kringelbach, M. L., & Deco, G. (2017). Uncovering the underlying mechanisms and whole-brain dynamics of deep brain stimulation for Parkinson’s disease. Scientific Reports, 7(1), 9882. https://doi.org/10.1038/s41598-017-10003-y
Sala, A., Lizarraga, A., Caminiti, S. P., Calhoun, V. D., Eickhoff, S. B., Habeck, C., Jamadar, S. D., Perani, D., Pereira, J. B., Veronese, M., & Yakushev, I. (2023). Brain connectomics: time for a molecular imaging perspective? Trends in Cognitive Sciences, 27(4), 353–366. https://doi.org/10.1016/j.tics.2022.11.015
Schirmer, M. D., Arichi, T., & Chung, A. W. (2023). Connectome Analysis: Characterization, Methods, and Analysis. In Connectome Analysis: Characterization, Methods, and Analysis. Elsevier. https://doi.org/10.1016/C2020-0-01236-3
Schmidt, M., Motta, A., Sievers, M., & Helmstaedter, M. (2024). RoboEM: automated 3D flight tracing for synaptic-resolution connectomics. Nature Methods, 21(5), 908–913. https://doi.org/10.1038/s41592-024-02226-5
Sebastián Domingo, J. J., & Sebastián Sánchez, B. (2018). Serotonin and the two brains: Conductor of orchestra of intes-tinal physiology and mood Role in irritable bowel syndrome MEDICINA NATURISTA · 2018; Vol. 12 · No 2. Medicina Naturista, 12, 1576–3080.
Shah, H. A., Mehta, N. H., Saleem, M. I., & D’Amico, R. S. (2022). Connecting the connectome: A bibliometric investigation of the 50 most cited articles. Clinical Neurology and Neurosurgery, 223. https://doi.org/10.1016/j.clineuro.2022.107481
Sheikh, S. R., McKee, Z. A., Ghosn, S., Jeong, K.-S., Kattan, M., Burgess, R. C., Jehi, L., & Saab, C. Y. (2024). Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography. Scientific Reports, 14(1), 21771. https://doi.org/10.1038/s41598-024-72249-7
Shimono, M., & Hatano, N. (2018). Efficient communication dynamics on macro-connectome, and the propagation speed. Scientific Reports, 8(1), 2510. https://doi.org/10.1038/s41598-018-20591-y
Stefanovski, L., Meier, J. M., Pai, R. K., Triebkorn, P., Lett, T., Martin, L., Bülau, K., Hofmann-Apitius, M., Solodkin, A., McIntosh, A. R., & Ritter, P. (2021). Bridging Scales in Alzheimer’s Disease: Biological Framework for Brain Simulation With The Virtual Brain. Frontiers in Neuroinformatics, 15. https://doi.org/10.3389/fninf.2021.630172
Stürner, T., Brooks, P., Capdevila, L. S., Morris, B. J., Javier, A., Fang, S., Gkantia, M., Cachero, S., Beckett, I. R., Champion, A. S., Moitra, I., Richards, A., Klemm, F., Kugel, L., Namiki, S., Cheong, H. S. J., Kovalyak, J., Tenshaw, E., Parekh, R., … Eichler, K. (2024). Comparative connectomics of the descending and ascending neurons of the Drosophila nervous system: stereotypy and sexual dimorphism. BioRxiv, 2024.06.04.596633.
Tellez Vargas, J. (2000). La noradrenalina. Su rol en la depresión. Revista Colombiana de Psiquiatría, XXIX(1), 59–73.
Timonidis, N., Bakker, R., & Tiesinga, P. (2020). Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data. Neuroinformatics, 18(4), 611–626. https://doi.org/10.1007/s12021-020-09471-x
Tsai, D., Morley, J. W., Suaning, G. J., & Lovell, N. H. (2017). Survey of electrically evoked responses in the retina - stimulus preferences and oscillation among neurons. Scientific Reports, 7(1), 13802. https://doi.org/10.1038/s41598-017-14357-1
Ugolini, G. (2020). Viruses in connectomics: Viral transneuronal tracers and genetically modified recombinants as neuroscience research tools. Journal of Neuroscience Methods, 346. https://doi.org/10.1016/j.jneumeth.2020.108917
Val-Laillet, D., Aarts, E., Weber, B., Ferrari, M., Quaresima, V., Stoeckel, L. E., Alonso-Alonso, M., Audette, M., Malbert, C. H., & Stice, E. (2015). Neuroimaging and neuromodulation approaches to study eating behavior and prevent and treat eating disorders and obesity. NeuroImage: Clinical, 8, 1–31. https://doi.org/https://doi.org/10.1016/j.nicl.2015.03.016
Wende, T., Güresir, E., Wach, J., Vychopen, M., Hoffmann, A., Prasse, G., Wilhelmy, F., & Kasper, J. (2024). Radiomic white matter parameters of functional integrity of the corticospinal tract in high-grade glioma. Scientific Reports, 14(1), 12891. https://doi.org/10.1038/s41598-024-63813-2
an, H., Elkaim, L. M., Gouveia, F. V., Huber, J. F., Germann, J., Loh, A., Benedetti-Isaac, J. C., Doshi, P. K., Torres, C. V., Segar, D. J., Elias, G. J.
B., Boutet, A., Rees, G. C., Fasano, A., Lozano, A. M., Kulkarni, A. V., & Ibrahim, G. M. (2022). Deep brain stimulation for extreme behaviors associated with autism spectrum disorder converges on a common pathway: a systematic review and connectomic analysis. Journal of Neurosurgery, 137(3), 699–708. https://doi.org/10.3171/2021.11.JNS21928
Yokoyama, C., Autio, J. A., Ikeda, T., Sallet, J., Mars, R. B., Van Essen, D. C., Glasser, M. F., Sadato, N., & Hayashi, T. (2021). Comparative connectomics of the primate social brain. NeuroImage, 245. https://doi.org/10.1016/j.neuroimage.2021.118693
Yuan, B., Fang, Y., Han, Z., Song, L., He, Y., & Bi, Y. (2017). Brain hubs in lesion models: Predicting functional network topology with lesion patterns in patients. Scientific Reports, 7(1), 17908. https://doi.org/10.1038/s41598-017-17886-x
Zhou, D., Lynn, C. W., Cui, Z., Ciric, R., Baum, G. L., Moore, T. M., Roalf, D. R., Detre, J. A., Gur, R. C., Gur, R. E., Satterthwaite, T. D., & Bassett, D. S. (2022). Efficient coding in the economics of human brain connectomics. Network Neuroscience, 6(1), 234–274. https://doi.org/10.1162/netn_a_00223
Arshadi, C., Günther, U., Eddison, M., Harrington, K. I. S., & Ferreira, T. A. (2021). SNT: a unifying toolbox for quantification of neuronal anatomy. Nature Methods, 18(4), 374–377. https://doi.org/10.1038/s41592-021-01105-7
Barsotti, E., Correia, A., & Cardona, A. (2021). Neural architectures in the light of comparative connectomics. Current Opinion in Neurobiology, 71, 139–149. https://doi.org/10.1016/j.conb.2021.10.006
Bates, A. S., & Jefferis, G. (2022). Systems neuroscience: Auditory processing at synaptic resolution. Current Biology, 32(15), R830–R833. https://doi.org/10.1016/j.cub.2022.07.001
Betancourt-Arango, J. P., Patiño-Ospina, A., Taborda-Ocampo, G., & Fiscal-Ladino, J. A. (2025). Aplicaciones de la xenometabolómica para la identificación de biomarcadores de toxicidad: una revisión del tema. Revista Biosalud, 19(1), 7–30. https://doi.org/10.17151/biosa.2020.19.1.1
Betancourt-Arango, J. P., Suárez-Millán, M., & Álvarez-Márquez, D. (2022). Revisión sistemática de literatura sobre la relación entre la teoría y la práctica en estudiantes de biología y química de Colombia. Luna Azul, 114–142. https://doi.org/10.17151/luaz.2022.54.7
Betancourt-Arango, J. P., Villaroel-Solis, E. E., Fiscal-Ladino, J. A., & Taborda-Ocampo, G. (2024). Volatilomics: An emerging discipline within Omics Sciences - A systematic review [version 1; peer review: awaiting peer review]. F1000Research, 13(991). https://doi.org/10.12688/f1000research.149773.1
Betzel, R. F. (2021). Network neuroscience and the connectomics revolution. In Connectomic Deep Brain Stimulation (pp. 25–58). Elsevier. https://doi.org/10.1016/B978-0-12-821861-7.00002-6
Bloomingdale, P., Karelina, T., Cirit, M., Muldoon, S. F., Baker, J., McCarty, W. J., Geerts, H., & Macha, S. (2021). Quantitative systems pharmacology in neuroscience: Novel methodologies and technologies. CPT: Pharmacometrics and Systems Pharmacology, 10(5), 412–419. https://doi.org/10.1002/psp4.12607
Burbano, M. (2024). Neurociencia y sus Campos de Acción. Ciencia Latina Revista Científica Multidisciplinar, 8, 396–408. https://doi.org/10.37811/cl_rcm.v8i4.12228
Chen, P. B., & Flint, J. (2021). What connectomics can learn from genomics. PLoS Genetics, 17(7). https://doi.org/10.1371/journal.pgen.1009692
Chung, J., Bridgeford, E., Arroyo, J., Pedigo, B. D., Saad-Eldin, A., Gopalakrishnan, V., Xiang, L., Priebe, C. E., & Vogelstein, J. T. (2021). Statistical connectomics. Annual Review of Statistics and Its Application, 8, 463–492. https://doi.org/10.1146/annurev-statistics-042720-023234
Cwiek, A., Rajtmajer, S. M., Wyble, B., Honavar, V., Grossner, E., & Hillary, F. G. (2022). Feeding the machine: Challenges to reproducible predictive modeling in resting-state connectomics. Network Neuroscience, 6(1), 29–48. https://doi.org/10.1162/netn_a_00212
D’Souza, N. S., & Venkataraman, A. (2023). Network comparisons and their applications in connectomics. In Connectome Analysis: Characterization, Methods, and Analysis (pp. 173–199). Elsevier. https://doi.org/10.1016/B978-0-323-85280-7.00009-9
DeFelipe, J. (2010). From the connectome to the synaptome: An epic love story. Science, 330(6008), 1198–1201. https://doi.org/10.1126/science.1193378
Duffau, H. (2021). Brain connectomics applied to oncological neuroscience: from a traditional surgical strategy focusing on glioma topography to a meta-network approach. Acta Neurochirurgica, 163(4), 905–917. https://doi.org/10.1007/s00701-021-04752-z
Egea, J., Bravo-Cordero, J. J., García, A., & Eguiagaray, J. (2004). Neurotransmisores, señales de calcio y comunicación neuronal. Neurocirugía: Organo Oficial de La Sociedad Española de Neurocirugía, ISSN 1130-1473, Vol. 15, No. 2, 2004, Pags. 109-118, 15. https://doi.org/10.1016/S1130-1473(04)70489-3
Emwas, A. H., Szczepski, K., Al-Younis, I., Lachowicz, J. I., & Jaremko, M. (2022). Fluxomics - New Metabolomics Approaches to Monitor Metabolic Pathways. Frontiers in Pharmacology, 13(March), 1–13. https://doi.org/10.3389/fphar.2022.805782
Flores-Soto, M., & Segura-Torres, J. (2005). Estructura y función de los receptores nicotinicos. Rev Mex Neuroci, 6(4), 315–326.
Frigolet, M. E., & Gutiérrez-Aguilar, R. (2017). Ciencias “ómicas”, ¿cómo ayudan a las ciencias de la salud? Revista Digital Universitaria, 18(7), 0–15. https://doi.org/10.22201/codeic.16076079e.2017.v18n7.a3
Galaburda, A., & Wong, B. (2017). Neuropsicología: mirando hacia adelante. Panamerican Journal of Neuropsychology, 11(2), 5.
Galindo, S. E., Toharia, P., Robles, O. D., & Pastor, L. (2021). SynCoPa: Visualizing Connectivity Paths and Synapses Over Detailed Morphologies. Frontiers in Neuroinformatics, 15. https://doi.org/10.3389/fninf.2021.753997
Gomez-Marin, A. (2021). Promisomics and the short-circuiting of mind. ENeuro, 8(2). https://doi.org/10.1523/ENEURO.0521-20.2021
Goyal, N., Moraczewski, D., Bandettini, P. A., Finn, E. S., & Thomas, A. G. (2022). The positive-negative mode link between brain connectivity, demographics and behaviour: a pre-registered replication of Smith et al. (2015). Royal Society Open Science, 9(2). https://doi.org/10.1098/rsos.201090
Grospietsch, F., Krack, P., & Kühn, A. (2022). Investigating cognitive neuroscience concepts using connectomic DBS (pp. 483–504). https://doi.org/10.1016/B978-0-12-821861-7.00013-0
Gutierrez Zuniga, R., Diez, I., Bueichekú, E., Kim, C.-M., Orwig, W., Montal, V., Fuentes, B., Díez‐Tejedor, E., Gutiérrez-Fernández, M., & Sepulcre, J. (2022). Connectomic-genetic signatures in the cerebral small vessel disease. Neurobiology of Disease, 167, 105671. https://doi.org/10.1016/j.nbd.2022.105671
Han, W., Ward, J. L., Kong, Y., & Li, X. (2023). Editorial : Targeted and untargeted metabolomics for the evaluation of plant metabolites in response to the environment. March, 2021–2023. https://doi.org/10.3389/fpls.2023.1167513
Hansen, J., Vogel, J., Smart, K., Bearden, C., Franke, B., Mcdonald, C., Stein, D., & Thompson, P. (2022). Molecular and connectomic vulnerability shape cross-disorder cortical abnormalities. https://doi.org/https://doi.org/10.1101/2022.01.21.476409
Hasegawa, E., Truman, J. W., & Nose, A. (2016). Identification of excitatory premotor interneurons which regulate local muscle contraction during Drosophila larval locomotion. Scientific Reports, 6(1), 30806. https://doi.org/10.1038/srep30806
Hirabayashi, Y., Tapia, J. C., & Polleux, F. (2018). Correlated Light-Serial Scanning Electron Microscopy (CoLSSEM) for ultrastructural visualization of single neurons in vivo. Scientific Reports, 8(1), 14491. https://doi.org/10.1038/s41598-018-32820-5
Hoy, R. R. (2021). Quantitative skills in undergraduate neuroscience education in the age of big data. Neuroscience Letters, 759. https://doi.org/10.1016/j.neulet.2021.136074
Hua, Y., Loomba, S., Pawlak, V., Voit, K.-M., Laserstein, P., Boergens, K. M., Wallace, D. J., Kerr, J. N. D., & Helmstaedter, M. (2022). Connectomic analysis of thalamus-driven disinhibition in cortical layer 4. Cell Reports, 41(2). https://doi.org/10.1016/j.celrep.2022.111476
Jütten, K., Weninger, L., Mainz, V., Gauggel, S., Binkofski, F., Wiesmann, M., Merhof, D., Clusmann, H., & Na, C.-H. (2021). Dissociation of structural and functional connectomic coherence in glioma patients. Scientific Reports, 11(1), 16790. https://doi.org/10.1038/s41598-021-95932-5
Kamagata, K., Zalesky, A., Yokoyama, K., Andica, C., Hagiwara, A., Shimoji, K., Kumamaru, K. K., Takemura, M. Y., Hoshino, Y., Kamiya, K., Hori, M.,
Pantelis, C., Hattori, N., & Aoki, S. (2019). MR g-ratio-weighted connectome analysis in patients with multiple sclerosis. Scientific Reports, 9(1), 13522. https://doi.org/10.1038/s41598-019-50025-2
Khan, A. M., D’Arcy, C. E., & Olimpo, J. T. (2021). A historical perspective on training students to create standardized maps of novel brain structure: Newly-uncovered resonances between past and present research-based neuroanatomy curricula. Neuroscience Letters, 759. https://doi.org/10.1016/j.neulet.2021.136052
Krempl, G., Kottke, D., & Pham, T. (2021). Statistical Analysis of Pairwise Connectivity. In S. C. & T. L. (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 12986 LNAI (pp. 138–148). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-88942-5_11
Li, G., & Yap, P. T. (2022). From descriptive connectome to mechanistic connectome: Generative modeling in functional magnetic resonance imaging analysis. Frontiers in Human Neuroscience, 16(August), 1–17. https://doi.org/10.3389/fnhum.2022.940842
Liao, W., Ji, G.-J., Xu, Q., Wei, W., Wang, J., Wang, Z., Yang, F., Sun, K., Jiao, Q., Richardson, M. P., Zang, Y.-F., Zhang, Z., & Lu, G. (2016). Functional Connectome before and following Temporal Lobectomy in Mesial Temporal Lobe Epilepsy. Scientific Reports, 6(1), 23153. https://doi.org/10.1038/srep23153
Martinez de Paz, J. M., & Macé, E. (2021). Functional ultrasound imaging: A useful tool for functional connectomics? NeuroImage, 245. https://doi.org/10.1016/j.neuroimage.2021.118722
McGrath, H., Zaveri, H. P., Collins, E., Jafar, T., Chishti, O., Obaid, S., Ksendzovsky, A., Wu, K., Papademetris, X., & Spencer, D. D. (2022). High-resolution cortical parcellation based on conserved brain landmarks for localization of multimodal data to the nearest centimeter. Scientific Reports, 12(1), 18778. https://doi.org/10.1038/s41598-022-21543-3
Meamardoost, S., Bhattacharya, M., Hwang, E. J., Komiyama, T., Mewes, C., Wang, L., Zhang, Y., & Gunawan, R. (2021). FARCI: Fast and Robust Connectome Inference. Brain Sciences, 11(12). https://doi.org/10.3390/BRAINSCI11121556
Mithani, K., Boutet, A., Germann, J., Elias, G. J. B., Weil, A. G., Shah, A., Guillen, M., Bernal, B., Achua, J. K., Ragheb, J., Donner, E., Lozano, A. M., Widjaja, E., & Ibrahim, G. M. (2019). Lesion Network Localization of Seizure Freedom following MR-guided Laser Interstitial Thermal Ablation. Scientific Reports, 9(1), 18598. https://doi.org/10.1038/s41598-019-55015-y
Mohanty, R., Sethares, W. A., Nair, V. A., & Prabhakaran, V. (2020). Rethinking Measures of Functional Connectivity via Feature Extraction. Scientific Reports, 10(1), 1298. https://doi.org/10.1038/s41598-020-57915-w
Nalls, M. A., Bras, J., Hernandez, D. G., Keller, M. F., Majounie, E., Renton, A. E., Saad, M., Jansen, I., Guerreiro, R., Lubbe, S., Plagnol, V., Gibbs, J. R., Schulte, C., Pankratz, N., Sutherland, M., Bertram, L., Lill, C. M., Destefano, A. L., Faroud, T., … Tzourio, C. (2015). NeuroX, a fast and efficient genotyping platform for investigation of neurodegenerative diseases. Neurobiology of Aging, 36(3), 1605.e7-1605.e12. https://doi.org/10.1016/j.neurobiolaging.2014.07.028
Nenning, K.-H., & Langs, G. (2022). Machine learning in neuroimaging: from research to clinical practice. Radiologie, 62, 1–10. https://doi.org/10.1007/s00117-022-01051-1
Ochoa-de la Paz, L. D., Gulias-Cañizo, R., D´Abril Ruíz-Leyja, E., Sánchez-Castillo, H., & Parodí, J. (2021). The role of GABA neurotransmitter in the human central nervous system, physiology, and pathophysiology. Revista Mexicana de Neurociencia, 22(2), 67–76. https://doi.org/10.24875/rmn.20000050
Ortiz-Teran, E., Diez, I., Sepulcre, J., Lopez-Pascual, J., & Ortiz, T. (2021). Connectivity adaptations in dopaminergic systems define the brain maturity of investors. Scientific Reports, 11(1), 11671. https://doi.org/10.1038/s41598-021-91227-x
Plosnić, G., Raguž, M., Deletis, V., & Chudy, D. (2023). Dysfunctional connectivity as a neurophysiologic mechanism of disorders of consciousness: a systematic review. Frontiers in Neuroscience, 17(July), 1–11. https://doi.org/10.3389/fnins.2023.1166187
Rodríguez-caso, C., & López-rodríguez, D. (2016). ¿ Qué es la conectómica ? Encuentros En La Biología, IX(159), 123–126.
Rodriguez-Cruces, R., Royer, J., Larivière, S., Bassett, D. S., Caciagli, L., & Bernhardt, B. C. (2022). Multimodal connectome biomarkers of cognitive and affective dysfunction in the common epilepsies. Network Neuroscience, 6(2), 320–338. https://doi.org/10.1162/netn_a_00237
Rodríguez-Méndez, D. A., San-Juan, D., Hallett, M., Antonopoulos, C. G., López-Reynoso, E., & Lara-Ramírez, R. (2022). A new model for freedom of movement using connectomic analysis. PeerJ, 10. https://doi.org/10.7717/peerj.13602
Roffet, F., Delrieux, C., & Patow, G. (2022). Assessing Multi-Site rs-fMRI-Based Connectomic Harmonization Using Information Theory. Brain Sciences, 12(9). https://doi.org/10.3390/brainsci12091219
Rojas-Leguizamon, M., Molina, J., Hidalgo-Aguirre, R., & Figueroa-Jiménez, M. (2021). Una mirada a las neurociencias De las neuronas a la cognición. In Una mirada a las neurociencias: de las neuronas a la cognición. https://doi.org/10.47377/neurociencias.6
Saenger, V. M., Kahan, J., Foltynie, T., Friston, K., Aziz, T. Z., Green, A. L., van Hartevelt, T. J., Cabral, J., Stevner, A. B. A., Fernandes, H. M., Mancini, L., Thornton, J., Yousry, T., Limousin, P., Zrinzo, L., Hariz, M., Marques, P., Sousa, N., Kringelbach, M. L., & Deco, G. (2017). Uncovering the underlying mechanisms and whole-brain dynamics of deep brain stimulation for Parkinson’s disease. Scientific Reports, 7(1), 9882. https://doi.org/10.1038/s41598-017-10003-y
Sala, A., Lizarraga, A., Caminiti, S. P., Calhoun, V. D., Eickhoff, S. B., Habeck, C., Jamadar, S. D., Perani, D., Pereira, J. B., Veronese, M., & Yakushev, I. (2023). Brain connectomics: time for a molecular imaging perspective? Trends in Cognitive Sciences, 27(4), 353–366. https://doi.org/10.1016/j.tics.2022.11.015
Schirmer, M. D., Arichi, T., & Chung, A. W. (2023). Connectome Analysis: Characterization, Methods, and Analysis. In Connectome Analysis: Characterization, Methods, and Analysis. Elsevier. https://doi.org/10.1016/C2020-0-01236-3
Schmidt, M., Motta, A., Sievers, M., & Helmstaedter, M. (2024). RoboEM: automated 3D flight tracing for synaptic-resolution connectomics. Nature Methods, 21(5), 908–913. https://doi.org/10.1038/s41592-024-02226-5
Sebastián Domingo, J. J., & Sebastián Sánchez, B. (2018). Serotonin and the two brains: Conductor of orchestra of intes-tinal physiology and mood Role in irritable bowel syndrome MEDICINA NATURISTA · 2018; Vol. 12 · No 2. Medicina Naturista, 12, 1576–3080.
Shah, H. A., Mehta, N. H., Saleem, M. I., & D’Amico, R. S. (2022). Connecting the connectome: A bibliometric investigation of the 50 most cited articles. Clinical Neurology and Neurosurgery, 223. https://doi.org/10.1016/j.clineuro.2022.107481
Sheikh, S. R., McKee, Z. A., Ghosn, S., Jeong, K.-S., Kattan, M., Burgess, R. C., Jehi, L., & Saab, C. Y. (2024). Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography. Scientific Reports, 14(1), 21771. https://doi.org/10.1038/s41598-024-72249-7
Shimono, M., & Hatano, N. (2018). Efficient communication dynamics on macro-connectome, and the propagation speed. Scientific Reports, 8(1), 2510. https://doi.org/10.1038/s41598-018-20591-y
Stefanovski, L., Meier, J. M., Pai, R. K., Triebkorn, P., Lett, T., Martin, L., Bülau, K., Hofmann-Apitius, M., Solodkin, A., McIntosh, A. R., & Ritter, P. (2021). Bridging Scales in Alzheimer’s Disease: Biological Framework for Brain Simulation With The Virtual Brain. Frontiers in Neuroinformatics, 15. https://doi.org/10.3389/fninf.2021.630172
Stürner, T., Brooks, P., Capdevila, L. S., Morris, B. J., Javier, A., Fang, S., Gkantia, M., Cachero, S., Beckett, I. R., Champion, A. S., Moitra, I., Richards, A., Klemm, F., Kugel, L., Namiki, S., Cheong, H. S. J., Kovalyak, J., Tenshaw, E., Parekh, R., … Eichler, K. (2024). Comparative connectomics of the descending and ascending neurons of the Drosophila nervous system: stereotypy and sexual dimorphism. BioRxiv, 2024.06.04.596633.
Tellez Vargas, J. (2000). La noradrenalina. Su rol en la depresión. Revista Colombiana de Psiquiatría, XXIX(1), 59–73.
Timonidis, N., Bakker, R., & Tiesinga, P. (2020). Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data. Neuroinformatics, 18(4), 611–626. https://doi.org/10.1007/s12021-020-09471-x
Tsai, D., Morley, J. W., Suaning, G. J., & Lovell, N. H. (2017). Survey of electrically evoked responses in the retina - stimulus preferences and oscillation among neurons. Scientific Reports, 7(1), 13802. https://doi.org/10.1038/s41598-017-14357-1
Ugolini, G. (2020). Viruses in connectomics: Viral transneuronal tracers and genetically modified recombinants as neuroscience research tools. Journal of Neuroscience Methods, 346. https://doi.org/10.1016/j.jneumeth.2020.108917
Val-Laillet, D., Aarts, E., Weber, B., Ferrari, M., Quaresima, V., Stoeckel, L. E., Alonso-Alonso, M., Audette, M., Malbert, C. H., & Stice, E. (2015). Neuroimaging and neuromodulation approaches to study eating behavior and prevent and treat eating disorders and obesity. NeuroImage: Clinical, 8, 1–31. https://doi.org/https://doi.org/10.1016/j.nicl.2015.03.016
Wende, T., Güresir, E., Wach, J., Vychopen, M., Hoffmann, A., Prasse, G., Wilhelmy, F., & Kasper, J. (2024). Radiomic white matter parameters of functional integrity of the corticospinal tract in high-grade glioma. Scientific Reports, 14(1), 12891. https://doi.org/10.1038/s41598-024-63813-2
an, H., Elkaim, L. M., Gouveia, F. V., Huber, J. F., Germann, J., Loh, A., Benedetti-Isaac, J. C., Doshi, P. K., Torres, C. V., Segar, D. J., Elias, G. J.
B., Boutet, A., Rees, G. C., Fasano, A., Lozano, A. M., Kulkarni, A. V., & Ibrahim, G. M. (2022). Deep brain stimulation for extreme behaviors associated with autism spectrum disorder converges on a common pathway: a systematic review and connectomic analysis. Journal of Neurosurgery, 137(3), 699–708. https://doi.org/10.3171/2021.11.JNS21928
Yokoyama, C., Autio, J. A., Ikeda, T., Sallet, J., Mars, R. B., Van Essen, D. C., Glasser, M. F., Sadato, N., & Hayashi, T. (2021). Comparative connectomics of the primate social brain. NeuroImage, 245. https://doi.org/10.1016/j.neuroimage.2021.118693
Yuan, B., Fang, Y., Han, Z., Song, L., He, Y., & Bi, Y. (2017). Brain hubs in lesion models: Predicting functional network topology with lesion patterns in patients. Scientific Reports, 7(1), 17908. https://doi.org/10.1038/s41598-017-17886-x
Zhou, D., Lynn, C. W., Cui, Z., Ciric, R., Baum, G. L., Moore, T. M., Roalf, D. R., Detre, J. A., Gur, R. C., Gur, R. E., Satterthwaite, T. D., & Bassett, D. S. (2022). Efficient coding in the economics of human brain connectomics. Network Neuroscience, 6(1), 234–274. https://doi.org/10.1162/netn_a_00223
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