Brain Imaging and Behavior

, Volume 8, Issue 3, pp 435–445 | Cite as

Examining network dynamics after traumatic brain injury using the extended unified SEM approach

  • F. G. Hillary
  • J. D. Medaglia
  • K. M. Gates
  • P. C. Molenaar
  • D. C. Good
Neuroimaging and Rehabilitation SPECIAL ISSUE

Abstract

The current study uses effective connectivity modeling to examine how individuals with traumatic brain injury (TBI) learn a new task. We make use of recent advancements in connectivity modeling (extended unified structural equation modeling, euSEM) and a novel iterative grouping procedure (Group Iterative Multiple Model Estimation, GIMME) in order to examine network flexibility after injury. The study enrolled 12 individuals sustaining moderate and severe TBI to examine the influence of task practice on connections between 8 network nodes (bilateral prefrontal cortex, anterior cingulate, inferior parietal lobule, and Crus I in the cerebellum). The data demonstrate alterations in networks from pre to post practice and differences in the models based upon distinct learning trajectories observed within the TBI sample. For example, better learning in the TBI sample was associated with diminished connectivity within frontal systems and increased frontal to parietal connectivity. These findings reveal the potential for using connectivity modeling and the euSEM to examine dynamic networks during task engagement and may ultimately be informative regarding when networks are moving in and out of periods of neural efficiency.

Keywords

fMRI TBI Brain injury Rehabilitation Working memory Cognitive control 

References

  1. Albantakis, L., & Deco, G. (2011). Changes of mind in an attractor network of decision-making. PLoS Comput Biol, 7(6). doi:10.1371/journal.pcbi.1002086
  2. Army Individual Test Battery:Manual of directions and scoring (1944). Washington, D.C.: War Department, Adjutant General’s Office.Google Scholar
  3. Baddeley, A., & Della Sala, S. (1996). Working memory and executive control. Philos Trans R Soc Lond B Biol Sci., 351(1346), 1397–1403; discussion 1403–1404. Review.Google Scholar
  4. Bergerbest, D., Ghahremani, D. G., Gabrieli, J. D. (2004). Neural correlates of auditory repetition priming: reduced fMRI activation in the auditory cortex. Journal of Cognitive Neuroscience, 16(6), 966–977.Google Scholar
  5. Braver, T. S., et al. (1997). A parametric study of prefrontal cortex involvement in human working memory. Neuroimage, 5(1), 49–62.Google Scholar
  6. Cohen, J. D., Botvinick, M., & Carter, C. S. (2000). Anterior cingulate and prefrontal cortex: who’s in control? Nature Neuroscience, 3(5), 421–423. doi:10.1038/74783.PubMedCrossRefGoogle Scholar
  7. Collette, F., Van der Linden, M., Bechet, S., & Salmon, E. (1999). Phonological loop and central executive functioning in Alzheimer’s disease. Neuropsychologia, 37(8), 905–918.PubMedCrossRefGoogle Scholar
  8. DeLuca, J., Schultheis, M. T., Madigan, N. K., Christodoulou, C., & Averill, A. (2000). Acquisition versus retrieval deficits in traumatic brain injury: implications for memory rehabilitation. Archives of Physical Medicine and Rehabilitation, 81(10), 1327–1333.PubMedCrossRefGoogle Scholar
  9. Demaree, H. A., DeLuca, J., Gaudino, E. A., & Diamond, B. J. (1999). Speed of information processing as a key deficit in multiple sclerosis: implications for rehabilitation. Journal of Neurology, Neurosurgery & Psychiatry, 67(5), 661–663.CrossRefGoogle Scholar
  10. Friston, K. (2009). Causal Modelling and Brain Connectivity in Functional Magnetic Resonance Imaging. PLoS Biol, 7(2). doi:10.1371/journal.pbio.1000033
  11. Friston, K. J., Price, C. J., Fletcher, P., Moore, C., Frackowiak, R. S. J., & Dolan, R. J. (1996). The trouble with cognitive subtraction. NeuroImage, 4(2), 97–104.PubMedCrossRefGoogle Scholar
  12. Gates, K. M., & Molenaar, P. C. (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. NeuroImage, 63(1), 310–319.PubMedCrossRefGoogle Scholar
  13. Gates, K. M., Molenaar, P. C., Hillary, F. G., Ram, N., & Rovine, M. J. (2010). Automatic search for fMRI connectivity mapping: an alternative to Granger causality testing using formal equivalences among SEM path modeling, VAR, and unified SEM. NeuroImage, 50(3), 1118–1125.PubMedCrossRefGoogle Scholar
  14. Gates, K. M., Molenaar, P. C., Hillary, F. G., & Slobounov, S. (2011). Extended unified SEM approach for modeling event-related fMRI data. NeuroImage, 54(2), 1151–1158.PubMedCrossRefGoogle Scholar
  15. Gazzaniga, M. S. (2000). Cerebral specialization and interhemispheric communication: does the corpus callosum enable the human condition? Brain, 123(Pt 7), 1293–1326.PubMedCrossRefGoogle Scholar
  16. Hillary, F. G. (2008). Neuroimaging of working memory dysfunction and the dilemma with brain reorganization hypotheses. Journal of the International Neuropsychological Society, 14(4), 526–534.PubMedCrossRefGoogle Scholar
  17. Hillary, F. G., Genova, H. M., Chiaravalloti, N. D., Rypma, B., & DeLuca, J. (2006). Prefrontal modulation of working memory performance in brain injury and disease. Human Brain Mapping, 27(11), 837–847.PubMedCrossRefGoogle Scholar
  18. Hillary, F. G., Genova, H. M., Medaglia, J. D., Fitzpatrick, N. M., Chiou, K. S., Wardecker, B. M., et al. (2010). The nature of processing speed deficits in traumatic brain injury: is less brain more? Brain Imaging and Behavior, 4(2), 141–154. doi:10.1007/s11682-010-9094-z.PubMedCrossRefGoogle Scholar
  19. Hillary, F., Medaglia, J. D., Gates, K., Molenaar, P., Slocomb, J., Peechatka, A., et al. (2011). Examining working memory task acquisition in a disrupted neural network. Brain, 134(Pt 5).Google Scholar
  20. Honma, M., Soshi, T., Kim, Y., & Kuriyama, K. (2010). Right prefrontal activity reflects the ability to overcome sleepiness during working memory tasks: a functional near-infrared spectroscopy study. PLoS One. doi:10.1371/journal.pone.0012923.
  21. Jensen, A. R., & Rohwer, W. D. (1966). The stroop color-word test: a review. Acta Psychologica, 25, 36–93.PubMedCrossRefGoogle Scholar
  22. Jöreskog, K. G., Sörbom, D. (1992). LISREL. Scientific Software International, Inc.Google Scholar
  23. Katori, Y., Sakamoto, K., Saito, N., Tanji, J., Mushiake, H., & Aihara, K. (2011). Representational Switching by Dynamical Reorganization of Attractor Structure in a Network Model of the Prefrontal Cortex. PLoS Biol, 7(11). doi:10.1371/journal.pcbi.1002266
  24. Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007). Unified structural equation modeling approach for the analysis of multisubject, multivariate functional MRI data. Human Brain Mapping, 28(2), 85–93. doi:10.1002/hbm.20259.PubMedCrossRefGoogle Scholar
  25. Kirchner, W. K. (1958). Age differences in short-term retention of rapidly changing information. Journal of Experimental Psychology, 55(4), 352–358.PubMedCrossRefGoogle Scholar
  26. Leavitt, V., Wylie, G., Genova, H. M., Chiaravalloti, N., & Deluca, J. (2012). Altered effective connectivity during performance of an information processing speed task in multiple sclerosis. Multiple Sclerosis, 18(4), 409–417.PubMedCrossRefGoogle Scholar
  27. Maruishi, M., Miyatani, M., Nakao, T., & Muranaka, H. (2007). Compensatory cortical activation during performance of an attention task by patients with diffuse axonal injury: a functional magnetic resonance imaging study. Journal of Neurology, Neurosurgery & Psychiatry, 78(2), 168–173. doi:10.1136/jnnp.2006.097345.CrossRefGoogle Scholar
  28. McDowell, S., Whyte, J., & D’Esposito, M. (1997). Working memory impairments in traumatic brain injury: evidence from a dual-task paradigm. Neuropsychologia, 35(10), 1341–1353.PubMedCrossRefGoogle Scholar
  29. McIntosh, A. R., & Gonzalez-Lima, F. (1991). Structural modeling of functional neural pathways mapped with 2-deoxyglucose: effects of acoustic startle habituation on the auditory system. Brain Research, 547(2), 295–302.PubMedCrossRefGoogle Scholar
  30. McIntosh, A. R., & Gonzalez-Lima, F. (1992). Structural modeling of functional visual pathways mapped with 2-deoxyglucose: effects of patterned light and footshock. Brain Research, 578(1–2), 75–86.PubMedCrossRefGoogle Scholar
  31. Medaglia, J. D., Chiou, K., Slocomb, J., Fitzpatrick, N., Wardecker, B. M., Ramanathan, D. M., et al. (2011). The less BOLD, the wiser: support for the latent resource hypothesis after traumatic brain injury. Human Brain Mapping, 33(4), 979–993. doi:10.1002/hbm.21264.PubMedCrossRefGoogle Scholar
  32. Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, 167–202. doi:10.1146/annurev.neuro.24.1.167.PubMedCrossRefGoogle Scholar
  33. Morris, R. G., & Baddeley, A. D. (1988). Primary and working memory functioning in Alzheimer-type dementia. Journal of Clinical and Experimental Neuropsychology, 10(2), 279–296.PubMedCrossRefGoogle Scholar
  34. Pardo, J. V., Fox, P. T., & Raichle, M. E. (1991). Localization of a human system for sustained attention by positron emission tomography. Nature, 349(6304), 61–64.PubMedCrossRefGoogle Scholar
  35. Price, C. J., & Friston, K. J. (1999). Scanning patients with tasks they can perform. Human Brain Mapping, 8(2–3), 102–108. doi:10.1002/(SICI)1097-0193(1999).PubMedCrossRefGoogle Scholar
  36. Price, C. J., & Friston, K. J. (2002). Functional imaging studies of neuropsychological patients: applications and limitations. Neurocase, 8(5), 345–354.PubMedCrossRefGoogle Scholar
  37. Price, C. J., Crinion, J., & Friston, K. J. (2006). Design and analysis of fMRI studies with neurologically impaired patients. Journal of Magnetic Resonance Imaging, 23(6), 816–826.PubMedCrossRefGoogle Scholar
  38. Rosanova, M., Gpsseries, O., Casarotto, S., Boly, M., Casali, A., Bruno, M., et al. (2012). Recovery of cortical effective connectivity and recovery of consciousness in vegetative patients. Brain, 135, 1308–1320.PubMedCrossRefPubMedCentralGoogle Scholar
  39. Rypma, B., Berger, J. S., Prabhakaran, V., Bly, B. M., Kimberg, D. Y., Biswal, B. B., et al. (2006). Neural correlates of cognitive efficiency. NeuroImage, 33(3), 969–979.PubMedCrossRefGoogle Scholar
  40. Sanchez-Carrion, R., Fernandez-Espejo, D., Junque, C., Falcon, C., Bargallo, N., Roig, T., et al. (2008). A longitudinal fMRI study of working memory in severe TBI patients with diffuse axonal injury. NeuroImage, 43(3), 421–429.PubMedCrossRefGoogle Scholar
  41. Sanchez-Carrion, R., Vendrell Gomez, P., Junque, C., Fernandez-Espejo, D., Falcon, C., Bargallo, N., et al. (2008). Frontal hypoactivation on functional magnetic resonance imaging in working memory after severe diffuse traumatic brain injury. Journal of Neurotrauma, 25(5), 15. doi:10.1089/neu.2007.0417.CrossRefGoogle Scholar
  42. Scheibel, R. S., Newsome, M. R., Steinberg, J. L., Pearson, D. A., Rauch, R. A., Mao, H., et al. (2007). Altered brain activation during cognitive control in patients with moderate to severe traumatic brain injury. Neurorehabilitation and Neural Repair, 21(1), 36–45. doi:10.1177/1545968306294730.PubMedCrossRefGoogle Scholar
  43. Smith, S., Miller, K., Salimi-Khorshidi, G., Webster, M., Beckmann, C. F., Nichols, T., et al. (2011). Network modelling methods for FMRI. NeuroImage, 54(2), 875–891.PubMedCrossRefGoogle Scholar
  44. Speck, O., Ernst, T., Braun, J., Koch, C., Miller, E., & Chang, L. (2000). Gender differences in the functional organization of the brain for working memory. Neuroreport, 11(11), 2581–2585.PubMedCrossRefGoogle Scholar
  45. Sporns, O. (2011). Networks of the Brain (pp. 207–231). London: MIT Press.Google Scholar
  46. Stroop, J. R. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18(6), 643–662.CrossRefGoogle Scholar
  47. Teasdale, G., & Jennett, B. (1974). Assessment of command impaired consciousness: a practical scale. The Lancet, 304(7872), 81–84.CrossRefGoogle Scholar
  48. Turner, G. R., & Levine, B. (2008). Augmented neural activity during executive control processing following diffuse axonal injury. Neurology, 71(11), 812–818.PubMedCrossRefPubMedCentralGoogle Scholar
  49. Turner, G. R., McIntosh, A. R., & Levine, B. (2011). Prefrontal compensatory engagement in TBI is due to altered functional engagement of existing networks and not functional reorganization. Frontal Systems Neuroscience, 24(5), 9.Google Scholar
  50. Wechsler, D. (1997). WAIS-III administration and scoring manual. San Antonio: The Psychological Corporation.Google Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • F. G. Hillary
    • 1
    • 3
  • J. D. Medaglia
    • 1
  • K. M. Gates
    • 2
  • P. C. Molenaar
    • 2
  • D. C. Good
    • 3
  1. 1.Department of PsychologyThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Human Development and Family StudiesUniversity ParkUSA
  3. 3.Department of NeurologyHershey Medical CenterHersheyUSA

Personalised recommendations