Resumen
Antecedentes: en años recientes, la psicometría de redes surge de la aplicación de la teoría de redes al análisis de instrumentos psicológicos, la cual deriva del enfoque de redes aplicado a la psicopatología. Uno de los aportes de este último es la comprensión de la comorbilidad y el contagio entre síndromes como la acción de síntomas compartidos entre estos: los síntomas puente. Objetivo: ilustrar la identificación de los síntomas puente analizando las dimensiones orientación y memoria del test addenbrooke’s cognitive examination III en una muestra de adultos mayores. Identificar síntomas que actúen como factores clave que conectan diferentes áreas cognitivas, podría llevar a diseñar intervenciones más dirigidas y personalizadas para enlentecer su progresión. Método: se utiliza el lenguaje r para estimar la red como un modelo de ising. Resultados: en una muestra de 1.164 adultos mayores identificamos cinco nodos puente entre ambas dimensiones de acuerdo con la fuerza del puente: dos pertenecen a orientación (año y mes) y tres a memoria (gobierno militar, Copiapó 3 y presidente actual). Conclusiones: el avance del deterioro cognitivo desde el área de la memoria hacia la de orientación se daría principalmente desde problemas para recuperar información espacial general y temporal de mediano y largo plazo hacia dificultades para situarse temporalmente en términos globales (año y mes).
Referencias
Álvarez-Díaz, M., Gallego-Acedo, C., Fernández-Alonso, R., Muñiz, J. y Fonseca-Pedrero, E. (2022). Análisis de redes: Una alternativa a los enfoques clásicos de evaluación de los sistemas educativos. Educational Psychology, 28(2), 165–173. https://doi.org/10.5093/psed2021a16
Blanco, I., Contreras, A., Valiente, C., Espinosa, R., Nieto, I. y Vázquez, C. (2019). El análisis de redes en psicopatología: Conceptos y metodología. Psicología Conductual, 27(1), 87–106.
Blanken, T. F., Borsboom, D., Penninx, B. W. y Van Someren, E. J. (2020). Network outcome analysis identifies difficulty initiating sleep as a primary target for prevention of depression: A 6-year prospective study. Sleep, 43(5). https://doi.org/10.1093/sleep/zsz288
Blanken, T. F., Deserno, M. K., Dalege, J., Borsboom, D., Blanken, P., Kerkhof, G. A. y Cramer, A. O. J. (2018). The role of stabilizing and communicating symptoms given overlapping communities in psychopathology networks. Scientific Reports, 8(1), 5854. https://doi.org/10.1038/s41598-018-24224-2
Blanken, T. F., Van Der Zweerde, T., Van Straten, A., Van Someren, E. J. W., Borsboom, D. y Lancee, J. (2019). Introducing network intervention analysis to investigate sequential, symptom-specific treatment effects: A demonstration in co-occurring insomnia and depression. Psychotherapy and Psychosomatics, 88(1), 52–54. https://doi.org/10.1159/000495045
Borsboom, D. (2017). A network theory of mental disorders. World Psychiatry, 16(1), 5–13. https://doi.org/10.1002/wps.20375
Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G., Isvoranu, A.-M., Wysocki, A. C., van Borkulo, C. D., van Bork, R. y Waldorp, L. J. (2021). Network analysis of multivariate data in psychological science. Nature Reviews Methods Primers, 1(1), Article 1. https://doi.org/10.1038/s43586-021-00055-w
Borsboom, D. y Cramer, A. O. J. (2013). Network analysis: An integrative approach to the structure of psychopathology. Annual Review of Clinical Psychology, 9(1), Article 1. https://doi.org/10.1146/annurev-clinpsy-050212-185608
Borsboom, D., Cramer, A. O. J., Schmittmann, V. D., Epskamp, S. y Waldorp, L. J. (2011). The small world of psychopathology. PLOS ONE, 6(11), e27407. https://doi.org/10.1371/journal.pone.0027407
Borsboom, D., Fried, E. I., Epskamp, S., Waldorp, L. J., van Borkulo, C. D., van der Maas, H. L. J. y Cramer, A. O. J. (2017). False alarm? A comprehensive reanalysis of “Evidence that psychopathology symptom networks have limited replicability” by Forbes, Wright, Markon, and Krueger (2017). Journal of Abnormal Psychology, 126(7), 989–999. https://doi.org/10.1037/abn0000306
Briganti, G., Scutari, M., Epskamp, S., Borsboom, D., Hoekstra, R. H. A., Golino, H. F., Christensen, A. P., Morvan, Y., Ebrahimi, O. V., Costantini, G., Heeren, A., Ron, J. de, Bringmann, L. F., Huth, K., Haslbeck, J. M. B., Isvoranu, A., Marsman, M., Blanken, T., Gilbert, A., Henry, T. R., Fried, E. I. y McNally, R. J. (2024). Network analysis: An overview for mental health research. International Journal of Methods in Psychiatric Research, 33(4), e2034. https://doi.org/10.1002/mpr.2034
Bringmann L. F. (2024). The future of dynamic networks in research and clinical practice. World psychiatry, 23(2), 288-289. https://doi.org/10.1002/wps.21209
Bruno, D., Slachevsky, A., Fiorentino, N., Rueda, D. S., Bruno, G., Tagle, A. R., Olavarria, L., Flores, P., Lillo, P., Roca, M. y Torralva, T. (2020). Validación argentino-chilena de la versión en español del test Addenbrooke’s Cognitive Examination III para el diagnóstico de demencia. Neurología, 35(2), Article 2. https://doi.org/10.1016/j.nrl.2017.06.004
Calderón, C., Beyle, C., Véliz-García, O. y Bekios-Calfa, J. (2021). Psychometric properties of Addenbrooke’s Cognitive Examination III (ACE-III): An item response theory approach. PLOS ONE, 16(5), e0251137. https://doi.org/10.1371/journal.pone.0251137
Calderón, C., Palominos, D., Véliz-García, Ó., Ramos-Henderson, M., Bekios-Canales, N., Beyle, C., Ávalos-Tejeda, M. y Domic-Siede, M. (2025). Using a nonparametric item response theory model to identify patterns of cognitive decline: The Mokken scale analysis. Journal of Neuropsychology, 19(1), 1–14. https://doi.org/10.1111/jnp.12381
Cammisuli, D. M. y Crowe, S. (2018). Spatial disorientation and executive dysfunction in elderly nondemented patients with Parkinson’s disease. Neuropsychiatric Disease and Treatment, 14, 2531–2539. https://doi.org/10.2147/NDT.S173820
Castro, D., Ferreira, F., de Castro, I., Rodrigues, A. R., Correia, M., Ribeiro, J. y Ferreira, T. B. (2019). The differential role of central and bridge symptoms in deactivating psychopathological networks. Frontiers in Psychology, 10. https://doi.org/10.3389/fpsyg.2019.02448
Contreras, A., Nieto, I., Valiente, C., Espinosa, R. y Vazquez, C. (2019). The study of psychopathology from the network analysis perspective: A systematic review. Psychotherapy and Psychosomatics, 88(2), 71–83. https://doi.org/10.1159/000497425
Cramer, A. O. J., Waldorp, L. J., Maas, H. L. J. van der y Borsboom, D. (2010). Comorbidity: A network perspective. Behavioral and Brain Sciences, 33(2–3), 137–150. https://doi.org/10.1017/S0140525X09991567
David, S. J., Marshall, A. J., Evanovich, E. K. y Mumma, G. H. (2018). Intraindividual dynamic network analysis – implications for clinical assessment. Journal of Psychopathology and Behavioral Assessment, 40(2), 235–248. https://doi.org/10.1007/s10862-017-9632-8
Dumurgier, J., Dartigues, J.-F., Gabelle, A., Paquet, C., Prevot, M., Hugon, J. y Tzourio, C. (2016). Time orientation and 10 years risk of dementia in elderly adults: The three-city study. Journal of Alzheimer’s Disease, 53(4), 1411–1418. https://doi.org/10.3233/JAD-160295
Domínguez D., J. F., Singh, M., Firman-Sadler, L., Guarnera, J., Simpson-Kent, I. L., Imms, P., Irimia, A., Caeyenberghs, K. y TRACK-TBI Investigators. (2025). Bridging mental health, cognition and the brain in mild traumatic brain injury: A multilayer network analysis of the TRACK-TBI study [Preprint]. medRxiv. https://doi.org/10.1101/2025.02.16.25322263
Epskamp, S. (2020). Psychometric network models from time-series and panel data. Psychometrika, 85(1), Article 1. https://doi.org/10.1007/s11336-020-09697-3
Epskamp, S., Borsboom, D. y Fried, E. I. (2018). Estimating psychological networks and their accuracy: A tutorial paper. Behavior Research Methods, 50(1), 195–212. https://doi.org/10.3758/s13428-017-0862-1
Epskamp, S., Costantini, G., Haslbeck, J., Isvoranu, A., Cramer, A. O. J., Waldorp, L. J., Schmittmann, V. D. y Borsboom, D. (2023). qgraph: Graph plotting methods, psychometric data visualization and graphical model estimation (Versión 1.9.8) [Software]. https://cran.r-project.org/web/packages/qgraph/index.html
Epskamp, S. y Fried, E. I. (2024). bootnet: Bootstrap methods for various network estimation routines (Versión 1.6) [Software]. https://cran.r-project.org/web/packages/bootnet/index.html
Epskamp, S., Maris, G., Waldorp, L. J. y Borsboom, D. (2018). Network psychometrics. En P. Irwing, T. Booth y D. J. Hughes (Eds.), The Wiley Handbook of Psychometric Testing (pp. 953–986). John Wiley & Sons. https://doi.org/10.1002/9781118489772.ch30
Fonseca-Pedrero, E. (2017). Análisis de redes: ¿una nueva forma de comprender la psicopatología? Revista de Psiquiatría y Salud Mental, 10(4), 206–215. https://doi.org/10.1016/j.rpsm.2017.06.004
Fonseca-Pedrero, E. (2018). Análisis de redes en psicología. Papeles del Psicólogo, 39(1), 1–12. https://doi.org/10.23923/pap.psicol2018.2852
Girvan, M. y Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12), 7821–7826. https://doi.org/10.1073/pnas.122653799
Golino, H. F. y Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLOS ONE, 12(6), e0174035. https://doi.org/10.1371/journal.pone.0174035
Hermida, R. (2015). The problem of allowing correlated errors in structural equation modeling: Concerns and considerations. Computational Methods in Social Sciences, 3(1), 05–17.
Jones, P. (2021). networktools: Tools for identifying important nodes in networks (Versión 1.4.0) [Software]. https://CRAN.R-project.org/package=networktools
Jones, P. J., Ma, R. y McNally, R. J. (2021). Bridge centrality: A network approach to understanding comorbidity. Multivariate Behavioral Research, 56(2), 353–367. https://doi.org/10.1080/00273171.2019.1614898
Jones, P. J., Mair, P. y McNally, R. J. (2018). Visualizing psychological networks: A tutorial in R. Frontiers in Psychology, 9. https://doi.org/10.3389/fpsyg.2018.01742
Joray, S., Herrmann, F., Mulligan, R. y Schnider, A. (2004). Mechanism of disorientation in Alzheimer’s disease. European Neurology, 52(4), 193–197. https://doi.org/10.1159/000082034
Larson, R. y Csikszentmihalyi, M. (2014). The experience sampling method. En M. Csikszentmihalyi (Ed.), Flow and the Foundations of Positive Psychology: The Collected Works of Mihaly Csikszentmihalyi (pp. 21–34). Springer Netherlands. https://doi.org/10.1007/978-94-017-9088-8_2
Marsman, M., Borsboom, D., Kruis, J., Epskamp, S., Bork, R. van, Waldorp, L. J., Maas, H. L. J. van der y Maris, G. (2018). An introduction to network psychometrics: Relating ising network models to item response theory models. Multivariate Behavioral Research, 53(1), 15–35. https://doi.org/10.1080/00273171.2017.1379379
McNally, R. J. (2021). Network analysis of psychopathology: Controversies and challenges. Annual Review of Clinical Psychology, 17(1), Article 1. https://doi.org/10.1146/annurev-clinpsy-081219-092850
Muñiz, J. (2018). Introducción a la psicometría. Pirámide.
R Core Team. (2024). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/
Ruiz-Ruano García, A. M. y López Puga, J. (2020). Modelos gráficos y redes en psicología. Revista de Historia de la Psicología, 41(4), 24–33. https://doi.org/10.5093/rhp2020a18
Scheffer, M., Bockting, C. L., Borsboom, D., Cools, R., Delecroix, C., Hartmann, J. A., Kendler, K. S., van de Leemput, I., van der Maas, H. L. J., van Nes, E., Mattson, M., McGorry, P. D. y Nelson, B. (2024). A dynamical systems view of psychiatric disorders—practical implications: A review. JAMA Psychiatry, 81(6), 624–630. https://doi.org/10.1001/jamapsychiatry.2024.0228
Scoville, W. B. y Milner, B. (1957). Loss of recent memory after bilateral hippocampal lesions. Journal of Neurology, Neurosurgery & Psychiatry, 20(1), 11–21. https://doi.org/10.1136/jnnp.20.1.11
Sekiguchi, T., Sugimoto, H., Tokunaga, S. y Otake-Matsuura, M. (2024). Time-orientations of older adults in group conversations and their association with memory functioning. Current Psychology, 43(7), 5854–5867. https://doi.org/10.1007/s12144-023-04545-w
Sousa, A., Gomar, J. J., Goldberg, T. E. y Alzheimer’s Disease Neuroimaging Initiative. (2015). Neural and behavioral substrates of disorientation in mild cognitive impairment and Alzheimer’s disease. Alzheimer’s & Dementia: Translational Research & Clinical Interventions, 1(1), 37–45. https://doi.org/10.1016/j.trci.2015.04.002
van Berkel, N., Ferreira, D. y Kostakos, V. (2017). The experience sampling method on mobile devices. ACM Computing Surveys, 50(6), 1–40. https://doi.org/10.1145/3123988
van Borkulo, C. D., Borsboom, D., Epskamp, S., Blanken, T. F., Boschloo, L., Schoevers, R. A. y Waldorp, L. J. (2014). A new method for constructing networks from binary data. Scientific Reports, 4(1), Article 1. https://doi.org/10.1038/srep05918
van Borkulo, C. D., Constantin, S. E., Robitzsch, A. y Constantin, M.A. (2023). IsingFit: Fitting Ising models using the elasso method (Versión 0.4) [Software]. https://cran.r-project.org/web/packages/IsingFit/index.html
van Borkulo, C. D., van Bork, R., Boschloo, L., Kossakowski, J. J., Tio, P., Schoevers, R. A., Borsboom, D. y Waldorp, L. J. (2023). Comparing network structures on three aspects: A permutation test. Psychological Methods, 28(6), 1273–1285. https://doi.org/10.1037/met0000476
Véliz García, Ó., Calderón Carvajal, C. y Beyle Sandoval, C. (2020). Psychometric properties of the Addenbrooke’s Cognitive Examination III (ACE-III) for the detection of dementia. Revista médica de Chile, 148(9), Article 9. https://doi.org/10.4067/S0034-98872020000901279
Witowska, J. y Zajenkowski, M. (2021). Cognitive consequences of timeframe bias. On the link between working memory, cognitive switching, and time perspective. Current Psychology, 40(7), 3532–3545. https://doi.org/10.1007/s12144-019-00302-0