Publicado 2015-12-20
Palabras clave
Cómo citar
Descargas
Altmetrics
Citas
Resumen
La resonancia magnética funcional es una de las técnicas de neuroimagen más difundidas en las neurociencias cognitivas. Su influencia tuvo un rol central en la configuración del aspecto experimental de este campo. Frente a esto, consideramos que su estatus como evidencia no ha sido suficientemente discutido en la literatura filosófica. En este trabajo nos centramos sobre este punto abordando el problema clásico de definir el alcance que puede tener la estrategia localizacionista en neurociencias. Atendemos al modo en que este problema se manifiesta hoy, tomando algunos ejemplos recientes de abordajes neurocientíficos caracterizados por estudiar el carácter dinámico de la actividad a gran escala en el cerebro. Tomamos en cuenta un número de limitaciones que presenta la resonancia magnética funcional, distinguiendo aquellas cuyo tratamiento pone en juego problemáticas de una índole no meramente técnica. A partir del análisis de algunas maneras en que los investigadores les hacen frente, sostenemos que existe una medida importante en que este tipo de estudios de neuroimagen pueden ser orientados sobre la base de supuestos y consideraciones teóricas generales. Concluimos que esta particular permeabilidad teórica de la resonancia magnética funcional es un factor central que incide sobre su estatus como evidencia neurocientífica.
Citas
- Abrahamsen, A., & Bechtel, W. (2012). From Reactive to Endogenously Active Dynamical Conceptions of the Brain. In Philosophy of Behavioral Biology. K. S. Plaisance & T. A. C. Reydon (Eds.) (pp. 329–366). Springer Netherlands. Retrieved from http://link.springer.com/chapter/10.1007/978-94-007-1951-4_16
- Aguirre, G. K. (2010). Experimental Design Analysis. BOLD fMRI: A Guide to Functional Imaging for Neuroscientists, 55.
- Armony, J. L., Trejo-Martínez, D., & Hernández, D. (2012). Resonancia Magnética Funcional (RMf): Principios y Aplicaciones en Neuropsicología y Neurociencias Cognitivas. Neuropsicologia Latinoamericana, 4(2). Retrieved from http://www.neuropsicolatina.org/index.php/Neuropsicologia_Latinoamericana/article/view/103
- Bandettini, P. (2006). Functional magnetic resonance imaging. In Methods in Mind. C. Senior, T. Russell, & M. Gazzaniga (Eds.)(pp. 193-235). Cambridge, Mass.; London: The MIT Press.
- Beer, R. D. (2000). Dynamical Approaches to Cognitive Science. Trends in Cognitive Sciences, 4(3), 91–99.
- Bogen, J. (2002). Epistemological Custard Pies From Functional Brain Imaging. Philosophy of Science, 69(3), 59–71.
- Bressler, S. L., & Kelso, J. a. S. (2001). Cortical coordination dynamics and cognition. Trends in Cognitive Sciences, 5(1), 26–36.
- Büchel, C., & Friston, K. (2000). Assessing interactions among neuronal
- systems using functional neuroimaging. Neural Networks: The Official Journal of the International Neural Network Society, 13(8-9), 871–882.
- Bunzl, M., Hanson, S. J., & Poldrack, R. A. (2010). An Exchange about Localism. In Foundational Issues in Human Brain Mapping. S. J. Hanson & M. Bunzl (Eds.) (pp. 49–54). The MIT Press. Retrieved from http://mitpress.universitypressscholarship.com/view/10.7551/mitpress/9780262014021.001.0001/upso-9780262014021-chapter-5
- Buzsáki, G. (2006). Rhythms of the Brain. Oxford University Press. Retrieved from http://www.oxfordscholarship.com/view/10.1093/acprof:oso/9780195301069.001.0001/acprof-9780195301069
- Cabeza, R., & Moscovitch, M. (2013). Memory Systems, Processing Modes, and Components: Functional Neuroimaging Evidence. Perspectives on Psychological Science : A Journal of the Association for Psychological Science, 8(1), 49–55. http://doi.org/10.1177/1745691612469033
- Coltheart, M. (2011). Methods for modular modelling: Additive factors and cognitive neuropsychology. Cognitive Neuropsychology, 28(3-4), 224–240. h p://doi.org/10.1080/02643294.2011.587794
- Constable, R. T. (2011). Challenges in fMRI and Its Limitations. In Functional Neuroradiology. S. H. Faro, F. B. Mohamed, M. Law, & J. T. Ulmer (Eds.) (pp. 331–344). Springer US. Retrieved from http://link.springer.com/chapter/10.1007/978-1-4419-0345-7_19
- Engel, A. K., Fries, P., & Singer, W. (2001). Dynamic predictions: oscillations and synchrony in top-down processing. Nature Reviews. Neuroscience, 2(10), 704–716. http://doi.org/10.1038/35094565
- Expert, P., Lambio e, R., Chialvo, D. R., Christensen, K., Jensen, H. J., Sharp, D. J., & Turkheimer, F. (2011). Self-similar correlation function in brain resting-state functional magnetic resonance imaging. Journal of The Royal Society Interface, 8(57), 472–479. http://doi.org/10.1098/rsif.2010.0416
- Farah, M. J. (2014). Brain images, babies, and bathwater: critiquing critiques of functional neuroimaging. The Hastings Center Report, Spec No, S19–30. http://doi.org/10.1002/hast.295
- Fingelkurts, A. A., & Fingelkurts, A. A. (2004). Making complexity simpler: multivariability and metastability in the brain. The International Journal of Neuroscience, 114(7), 843–862. http://doi.org/10.1080/00207450490450046
- Fodor, J. A. (1986). La modularidad de la mente: un ensayo sobre la psicología de las facultades. Madrid: Ediciones Morata.
- Freeman, W. J. (2005). A field-theoretic approach to understanding scale-free neocortical dynamics. Biological Cybernetics, 92(6), 350–359. http://doi.org/10.1007/s00422-005-0563-1
- Friston, K. J., Rotshtein, P., Geng, J. J., Sterzer, P., & Henson, R. N. (2006). A critique of functional localisers. NeuroImage, 30(4), 1077-1087. http://doi.org/10.1016/j.neuroimage.2005.08.012
- Friston, K., & Price, C. (2011). Modules and brain mapping. Cognitive Neuropsychology, 28(3-4), 241–250. http://doi.org/10.1080/02643294.2 011.558835
- Haimovici, A., Tagliazucchi, E., Balenzuela, P., & Chialvo, D. R. (2013). Brain Organization into Resting State Networks Emerges at Criticality on a Model of the Human Connectome. Physical Review Le ers, 110(17), 178101. http://doi.org/10.1103/PhysRevLe .110.178101
- Hanson, S. J., & Bunzl, M. (Eds.) (2010). Foundational Issues in Human Brain Mapping. MIT Press.
- Hardcastle, V. G., & Stewart, C. M. (2002). What Do Brain Data Really Show? Philosophy of Science, 69(3), 572–582.
- Haxby, J. V. (2010). Multivariate Pa ern Analysis of fMRI Data: High-Dimensional Spaces for Neural and Cognitive Representations. In Foundational Issues in Human Brain Mapping (pp. 55–68). S. J. Hanson & M. Bunzl (Eds.) The MIT Press. Retrieved from http://mitpress.universitypressscholarship.com/view/10.7551/mitpress/9780262014021.001.0001/upso-9780262014021-chapter-6
- Ibáñez, A. (2007). Complexity and cognition: a meta-theoretical analysis of the mind and brain as a topological dynamical system. Nonlinear Dynamics, Psychology, and Life Sciences, 11(1), 51–90.
- Klein, C. (2010). Images Are Not the Evidence in Neuroimaging. British Journal for the Philosophy of Science, 61(2), 265–278.
- Leopold, D. A., & Wilke, M. (2005). Neuroimaging: Seeing the Trees for the Forest. Current Biology, 15(18), R766–R768. http://doi.org/10.1016/j.cub.2005.08.055
- Lindquist, M. A. (2008). The statistical analysis of fMRI data. Statistical Science, 23(4), 439–464.
- Lindquist, M. A., & Wager, T. D. (2014). Principles of functional Magnetic Resonance Imaging. In Handbook of Neuroimaging Data Analysis. London: Chapman & Hall. Retrieved from http://wagerlab.colorado.edu/les/papers/fMRIChapter.pdf.
- Norman, K. A., Polyn, S. M., Detre, G. J., & Haxby, J. V. (2006). Beyond mind-reading: multi-voxel pa ern analysis of fMRI data. Trends in Cognitive Sciences, 10(9), 424–430. http://doi.org/10.1016/j.tics.2006.07.005
- Poldrack, R. A., Mumford, J. A., & Nichols, T. E. (2011). Handbook of Functional MRI Data Analysis. New York: Cambridge University Press.
- Racine, E., Bar-Ilan, O., & Illes, J. (2005). fMRI in the public eye. Nature Reviews. Neuroscience, 6(2), 159–164. http://doi.org/10.1038/nrn1609
- Rippon, G. (2006). Electroencephalography. Retrieved from http://eprints.aston.ac.uk/24203/
- Roskies, A. L. (2007). Are Neuroimages Like Photographs of the Brain? Philosophy of Science, 74(5), 860–872. http://doi.org/10.1086/525627
- Saxe, R., Brett, M., & Kanwisher, N. (2006). Divide and conquer: a defense of functional localizers. NeuroImage, 30(4), 1088–1096; discussion 1097–1099. http://doi.org/10.1016/j.neuroimage.2005.12.062
- Skarda, C. A., & Freeman, W. J. (1987). How brains make chaos in order to make sense of the world. Behavioral and Brain Sciences, 10(02), 161-173. http://doi.org/10.1017/S0140525X00047336
- Sporns, O. (2011). Networks of the Brain. Cambridge, MA: MIT Press.
- Sternberg, S. (2011). Modular processes in mind and brain. Cognitive Neuropsychology, 28(3-4), 156–208. http://doi.org/10.1080/02643294.2011.557231
- Stroga , S. H. (1994). Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering. Westview Press.
- Tagliazucchi, E., Balenzuela, P., Fraiman, D., & Chialvo, D. R. (2012). Criticality in Large-Scale Brain fMRI Dynamics Unveiled by a Novel Point Process Analysis. Frontiers in Physiology, 3. http://doi.org/10.3389/fphys.2012.00015
- Thompson, E., & Varela, F. J. (2001). Radical embodiment: neural dynamics and consciousness. Trends in Cognitive Sciences, 5(10), 418-425.
- Tressoldi, P. E., Sella, F., Coltheart, M., & Umiltà, C. (2012). Using functional neuroimaging to test theories of cognition: a selective survey of studies from 2007 to 2011 as a contribution to the Decade of the Mind Initiative. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior, 48(9), 1247–1250. http://doi.org/10.1016/j.cortex.2012.05.024
- Uttal, W. R. (2003). The New Phrenology: The Limits of Localizing Cognitive Processes in the Brain. MIT Press.
- Venturelli, N. (2010). El enfoque dinamicista en las neurociencias cognitivas: Un abordaje histórico a partir del concepto de metaestabilidad. En Epistemología e Historia de la Ciencia, vol. 16. P. García & Massolo, A. (Eds.) (pp. 664-672). Córdoba: Imprenta de la Facultad de Filosofía y Humanidades (UNC).
- ____ (2015). Consideraciones epistemológicas alrededor del enfoque dinamicista en las neurociencias cognitivas. Ciências & Cognição, 20(1): 18-28.
- Von der Malsburg, C. von von der, Phillips, W. A., & Singer, W. (Eds.) (2010). Dynamic Coordination in the Brain: From Neurons to Mind. Cambridge, Mass.: The MIT Press.
- Wright, J. J., & Liley, D. T. J. (1996). Dynamics of the brain at global and microscopic scales: Neural networks and the EEG. Behavioral and Brain Sciences, 19(02), 285–295. http://doi.org/10.1017/S0140525X00042679
- Yarkoni, T. (2009). Big Correlations in Little Studies: Inflated fMRI Correlations Reflect Low Statistical Power—Commentary on Vul et. al. (2009). Perspectives on Psychological Science, 4(3), 294–298. http://doi.org/10.1111/j.1745-6924.2009.01127.x
- Yarkoni, T., & Braver, T. S. (2010). Cognitive neuroscience approaches to individual differences in working memory and executive control: conceptual and methodological issues. In Handbook of individual differences in cognition (pp. 87–107). Springer New York. Retrieved from http://link.springer.com/10.1007/978-1-4419-1210-7_6
- Yarkoni, T., Poldrack, R. A., Van Essen, D. C., & Wager, T. D. (2010). Cognitive neuroscience 2.0: building a cumulative science of human brain function. Trends in Cognitive Sciences, 14(11), 489–496. http://doi.org/10.1016/j.tics.2010.08.004
- Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T.D. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature Methods, 8(8), 665–670. http://doi.org/10.1038/nmeth.1635
- Yuste, R. (2015). From the neuron doctrine to neural networks. Nature Reviews. Neuroscience, 16(8), 487–497. http://doi.org/10.1038/nrn3962