Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2018 (2018), Article ID 4835676, 12 pages
https://doi.org/10.1155/2018/4835676
Research Article

Automated Extraction of Human Functional Brain Network Properties Associated with Working Memory Load through a Machine Learning-Based Feature Selection Algorithm

Faculty of Life and Medical Sciences, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe-shi, Kyoto, Japan

Correspondence should be addressed to Satoru Hiwa; pj.ca.ahsihsod.liam@awihs

Received 9 November 2017; Revised 23 February 2018; Accepted 1 March 2018; Published 10 April 2018

Academic Editor: Hasan Ayaz

Copyright © 2018 Satoru Hiwa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. A. D. Baddeley and G. Hitch, “Working memory,” in The Psychology of Learning and Motivation, vol. 8, pp. 47–89, Academic Press, New York, NY, USA, 1974. View at Publisher · View at Google Scholar
  2. A. Baddeley, “The episodic buffer: a new component of working memory,” Trends in Cognitive Sciences, vol. 4, no. 11, pp. 417–423, 2000. View at Publisher · View at Google Scholar · View at Scopus
  3. M. A. Just and P. A. Carpenter, “A capacity theory of comprehension: Individual differences in working memory,” Psychological Review, vol. 99, no. 1, pp. 122–149, 1992. View at Publisher · View at Google Scholar · View at Scopus
  4. P. C. Kyllonen and R. E. Christal, “Reasoning ability is (little more than) working-memory capacity,” Intelligence, vol. 14, no. 4, pp. 389–433, 1990. View at Publisher · View at Google Scholar · View at Scopus
  5. T. Klingberg, “Limitations in information processing in the human brain: Neuroimaging of dual task performance and working memory tasks,” Progress in Brain Research, vol. 126, pp. 95–102, 2000. View at Publisher · View at Google Scholar · View at Scopus
  6. A. R. A. Conway, N. Cowan, and M. F. Bunting, “The cocktail party phenomenon revisited: The importance of working memory capacity,” Psychonomic Bulletin & Review, vol. 8, no. 2, pp. 331–335, 2001. View at Publisher · View at Google Scholar · View at Scopus
  7. M. J. Kane, “The intelligent brain in conflict,” Trends in Cognitive Sciences, vol. 7, no. 9, pp. 375–377, 2003. View at Publisher · View at Google Scholar
  8. J. Duncan, “An adaptive coding model of neural function in prefrontal cortex,” Nature Reviews Neuroscience, vol. 2, no. 11, pp. 820–829, 2001. View at Publisher · View at Google Scholar · View at Scopus
  9. E. K. Miller and J. D. Cohen, “An integrative theory of prefrontal cortex function,” Annual Review of Neuroscience, vol. 24, pp. 167–202, 2001. View at Publisher · View at Google Scholar · View at Scopus
  10. F. McNab and T. Klingberg, “Prefrontal cortex and basal ganglia control access to working memory,” Nature Neuroscience, vol. 11, no. 1, pp. 103–107, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Gevins and B. Cutillo, “Spatiotemporal dynamics of component processes in human working memory,” Electroencephalography and Clinical Neurophysiology, vol. 87, no. 3, pp. 128–143, 1993. View at Publisher · View at Google Scholar · View at Scopus
  12. S. M. Courtney, L. Petit, J. V. Haxby, and L. G. Ungerleider, “The role of prefrontal cortex in working memory: examining the contents of consciousness,” Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 353, no. 1377, pp. 1819–1828, 1998. View at Publisher · View at Google Scholar · View at Scopus
  13. G.-R. PS, “Working memory dysfunction in schizophrenia,” The Journal of Neuropsychiatry and Clinical Neurosciences, vol. 6, no. 4, pp. 348–357, 1994. View at Publisher · View at Google Scholar
  14. A. M. Owen, K. M. McMillan, A. R. Laird, and E. Bullmore, “N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies,” Human Brain Mapping, vol. 25, no. 1, pp. 46–59, 2005. View at Publisher · View at Google Scholar · View at Scopus
  15. T. S. Braver, D. M. Barch, W. M. Kelley et al., “Direct comparison of prefrontal cortex regions engaged by working and long-term memory tasks,” NeuroImage, vol. 14, no. 1 I, pp. 48–59, 2001. View at Publisher · View at Google Scholar · View at Scopus
  16. J. H. Callicott, V. S. Mattay, A. Bertolino et al., “Physiological characteristics of capacity constraints in working memory as revealed by functional MRI,” Cerebral Cortex, vol. 9, no. 1, pp. 20–26, 1999. View at Publisher · View at Google Scholar · View at Scopus
  17. T. S. Braver, J. D. Cohen, L. E. Nystrom, J. Jonides, E. E. Smith, and D. C. Noll, “A parametric study of prefrontal cortex involvement in human working memory,” NeuroImage, vol. 5, no. 1, pp. 49–62, 1997. View at Publisher · View at Google Scholar · View at Scopus
  18. E. Volle, S. Kinkingnéhun, J.-B. Pochon et al., “The functional architecture of the left posterior and lateral prefrontal cortex in humans,” Cerebral Cortex, vol. 18, no. 10, pp. 2460–2469, 2008. View at Publisher · View at Google Scholar · View at Scopus
  19. A. T. Newton, V. L. Morgan, B. P. Rogers, and J. C. Gore, “Modulation of steady state functional connectivity in the default mode and working memory networks by cognitive load,” Human Brain Mapping, vol. 32, no. 10, pp. 1649–1659, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. R. L. Bluhm, C. R. Clark, A. C. McFarlane, K. A. Moores, M. E. Shaw, and R. A. Lanius, “Default network connectivity during a working memory task,” Human Brain Mapping, vol. 32, no. 7, pp. 1029–1035, 2011. View at Publisher · View at Google Scholar
  21. G. D. Honey, C. H. Y. Fu, J. Kim et al., “Effects of verbal working memory load on corticocortical connectivity modeled by path analysis of functional magnetic resonance imaging data,” NeuroImage, vol. 17, no. 2, pp. 573–582, 2002. View at Publisher · View at Google Scholar · View at Scopus
  22. N. S. Narayanan, V. Prabhakaran, S. A. Bunge, K. Christoff, E. M. Fine, and J. D. E. Gabrieli, “The role of the prefrontal cortex in the maintenance of verbal working memory: An event-related fMRI analysis,” Neuropsychology, vol. 19, no. 2, pp. 223–232, 2005. View at Publisher · View at Google Scholar · View at Scopus
  23. N. Axmacher, D. P. Schmitz, T. Wagner, C. E. Elger, and J. Fell, “Interactions between medial temporal lobe, prefrontal cortex, and inferior temporal regions during visual working memory: a combined intracranial EEG and functional magnetic resonance imaging study,” The Journal of Neuroscience, vol. 28, no. 29, pp. 7304–7312, 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. J. McGrath, K. Johnson, E. O'Hanlon, H. Garavan, A. Leemans, and L. Gallagher, “Atypical functional connectivity in autism spectrum disorder is associated with disrupted white matter microstructural organisation,” Frontiers in Human Neuroscience, vol. 7, no. 434, 2013. View at Publisher · View at Google Scholar · View at Scopus
  25. M. Schurz, H. Wimmer, F. Richlan, P. Ludersdorfer, J. Klackl, and M. Kronbichler, “Resting-state and task-based functional brain connectivity in developmental dyslexia,” Cerebral Cortex, vol. 25, no. 10, pp. 3502–3514, 2015. View at Publisher · View at Google Scholar · View at Scopus
  26. M. Lee, C. Smyser, and J. Shimony, “Resting-State fMRI: A review of methods and clinical applications,” American Journal of Neuroradiology, vol. 34, no. 10, pp. 1866–1872, 2013. View at Publisher · View at Google Scholar
  27. E. Bullmore and O. Sporns, “Complex brain networks: Graph theoretical analysis of structural and functional systems,” Nature Reviews Neuroscience, vol. 10, no. 4, pp. 186–198, 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. O. Sporns, “Structure and function of complex brain networks,” Dialogues in clinical neuroscience, vol. 15, no. 3, pp. 247–262, 2013. View at Publisher · View at Google Scholar
  29. D. Vatansever, D. K. Menon, A. E. Manktelow, B. J. Sahakian, and E. A. Stamatakis, “Default mode dynamics for global functional integration,” The Journal of Neuroscience, vol. 35, no. 46, pp. 15254–15262, 2015. View at Publisher · View at Google Scholar · View at Scopus
  30. J. R. Cohen, C. L. Gallen, E. G. Jacobs, T. G. Lee, and M. DEsposito, “Quantifying the reconfiguration of intrinsic networks during working memory,” PLOS ONE, vol. 9, no. 9, pp. 1–8, 2014. View at Publisher · View at Google Scholar
  31. P. Metzak, E. Feredoes, Y. Takane et al., “Constrained principal component analysis reveals functionally connected load-dependent networks involved in multiple stages of working memory,” Human Brain Mapping, vol. 32, no. 6, pp. 856–871, 2011. View at Publisher · View at Google Scholar · View at Scopus
  32. H. Frohlich, O. Chapelle, and B. Scholkopf, “Feature selection for support vector machines by means of genetic algorithm,” in Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence, pp. 142–148, Sacramento, Calif, USA, 2003. View at Publisher · View at Google Scholar
  33. L. Li, W. Jiang, X. Li et al., “A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset,” Genomics, vol. 85, no. 1, pp. 16–23, 2005. View at Publisher · View at Google Scholar
  34. N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou et al., “Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain,” NeuroImage, vol. 15, no. 1, pp. 273–289, 2002. View at Publisher · View at Google Scholar · View at Scopus
  35. C. Chih-Chung and C. Chih-Jen, “LIBSVM: a Library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article 27, pp. 1–27, 2011. View at Publisher · View at Google Scholar · View at Scopus
  36. I. Braga, L. P. do Carmo, C. C. Benatti, and M. C. Monard, “A note on parameter selection for support vector machines,” in Advances in Soft Computing and Its Applications, F. Castro, A. Gelbukh, and M. González, Eds., vol. 8266 of Lecture Notes in Computer Science, pp. 233–244, Springer, Heidelberg, Berlin, 2013. View at Publisher · View at Google Scholar
  37. F.-A. Fortin, F.-M. De Rainville, M.-A. Gardner, M. Parizeau, and C. Gagné, “DEAP: Evolutionary algorithms made easy,” Journal of Machine Learning Research, vol. 13, pp. 2171–2175, 2012. View at Google Scholar · View at MathSciNet
  38. T. Back, D. B. Fogel, D. Whitley, and P. J. Angeline, “Evolutionary computation 1 basic algorithms and operators,” T. Baeck, D. B. Fogel, and Z. Michalewicz, Eds., vol. 32, pp. 237–255, Institute of Physics Publishing, Bristol, England, 2000. View at Google Scholar
  39. S. Whitfield-Gabrieli and A. Nieto-Castanon, “Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks,” Brain Connectivity, vol. 2, no. 3, pp. 125–141, 2012. View at Publisher · View at Google Scholar
  40. Y. Behzadi, K. Restom, J. Liau, and T. T. Liu, “A component based noise correction method (CompCor) for BOLD and perfusion based fMRI,” NeuroImage, vol. 37, no. 1, pp. 90–101, 2007. View at Publisher · View at Google Scholar · View at Scopus
  41. M. Rubinov and O. Sporns, “Weight-conserving characterization of complex functional brain networks,” NeuroImage, vol. 56, no. 4, pp. 2068–2079, 2011. View at Publisher · View at Google Scholar · View at Scopus
  42. A. J. Schwarz and J. McGonigle, “Negative edges and soft thresholding in complex network analysis of resting state functional connectivity data,” NeuroImage, vol. 55, no. 3, pp. 1132–1146, 2011. View at Publisher · View at Google Scholar · View at Scopus
  43. M. Rubinov and O. Sporns, “Complex network measures of brain connectivity: uses and interpretations,” NeuroImage, vol. 52, no. 3, pp. 1059–1069, 2010. View at Publisher · View at Google Scholar · View at Scopus
  44. M. E. J. Newman, “Modularity and community structure in networks,” Proceedings of the National Acadamy of Sciences of the United States of America, vol. 103, no. 23, pp. 8577–8582, 2006. View at Publisher · View at Google Scholar · View at Scopus
  45. M. Xia, J. Wang, and Y. He, “BrainNet viewer: a network visualization tool for human brain connectomics,” PLoS ONE, vol. 8, no. 7, 2013. View at Publisher · View at Google Scholar
  46. T. Klingberg, The Overflowing Brain: Information Overload and the Limits of Working Memory, Oxford University Press, 2009.
  47. N. Osaka, M. Osaka, H. Kondo, M. Morishita, H. Fukuyama, and H. Shibasaki, “The neural basis of executive function in working memory: an fMRI study based on individual differences,” NeuroImage, vol. 21, no. 2, pp. 623–631, 2004. View at Publisher · View at Google Scholar · View at Scopus
  48. M. D. Rosenberg, E. S. Finn, D. Scheinost et al., “A neuromarker of sustained attention from whole-brain functional connectivity,” Nature Neuroscience, vol. 19, no. 1, pp. 165–171, 2015. View at Publisher · View at Google Scholar · View at Scopus
  49. N. U. F. Dosenbach, B. Nardos, A. L. Cohen et al., “Prediction of individual brain maturity using fMRI,” Science, vol. 329, no. 5997, pp. 1358–1361, 2010. View at Publisher · View at Google Scholar · View at Scopus
  50. J. D. Power, A. L. Cohen, S. M. Nelson et al., “Functional network organization of the human brain,” Neuron, vol. 72, no. 4, pp. 665–678, 2011. View at Publisher · View at Google Scholar · View at Scopus
  51. R. C. Craddock, G. A. James, P. E. Holtzheimer, X. P. Hu, and H. S. Mayberg, “A whole brain fMRI atlas generated via spatially constrained spectral clustering,” Human Brain Mapping, vol. 33, no. 8, pp. 1914–1928, 2012. View at Publisher · View at Google Scholar · View at Scopus
  52. T. T. Erguzel, S. Ozekes, O. Tan, and S. Gultekin, “Feature selection and classification of electroencephalographic signals: An artificial neural network and genetic algorithm based approach,” Clinical EEG and Neuroscience, vol. 46, no. 4, pp. 321–326, 2015. View at Publisher · View at Google Scholar · View at Scopus
  53. Y. Wang and K. C. Veluvolu, “Evolutionary algorithm based feature optimization for multi-channel EEG classification,” Frontiers in Neuroscience, vol. 11, article no. 28, 2017. View at Publisher · View at Google Scholar · View at Scopus
  54. X. Ma, C.-A. Chou, H. Sayama, and W. A. Chaovalitwongse, “Brain response pattern identification of fMRI data using a particle swarm optimization-based approach,” Brain Informatics, vol. 3, no. 3, pp. 181–192, 2016. View at Publisher · View at Google Scholar
  55. L. Baldassarre, M. Pontil, and J. Mourão-Miranda, “Sparsity is better with stability: Combining accuracy and stability for model selection in brain decoding,” Frontiers in Neuroscience, vol. 11, article no. 62, 2017. View at Publisher · View at Google Scholar · View at Scopus