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Brain Imaging and Behavior

, Volume 9, Issue 2, pp 302–311 | Cite as

Neuroimaging and cognition using functional near infrared spectroscopy (fNIRS) in multiple sclerosis

  • Jelena Stojanovic-Radic
  • Glenn WylieEmail author
  • Gerald Voelbel
  • Nancy Chiaravalloti
  • John DeLuca
Original Research

Abstract

The present study utilized functional near infrared spectroscopy (fNIRS) to detect neural activation differences in the orbitofrontal brain region between individuals with multiple sclerosis (MS) and healthy controls (HCs) during a working memory (WM) task. Thirteen individuals with MS and 12 HCs underwent fNIRS recording while performing the n-back WM task with four levels of difficulty (0-, 1-, 2-, and 3-back). Subjects were fitted with the fNIRS cap consisting of 30 ‘optodes’ positioned over the forehead. The results revealed different patterns of brain activation in MS and HCs. The MS group showed an increase in brain activation, as measured by the concentration of oxygenated hemoglobin (oxyHb), in the left superior frontal gyrus (LSFG) at lower task difficulty levels (i.e. 1-back), followed by a decrease at higher task difficulty (2- and 3-back) as compared with the HC group. HC group achieved higher accuracy than the MS group on the lower task loads (i.e. 0- and 1-back), however there were no performance differences between the groups at the higher task loads (i.e. 2- and 3-back). Taken together, the results suggest that individuals with MS experience a task with the lower cognitive load as more difficult than the HC group, and the brain activation patterns observed during the task confirm some of the previous findings from functional magnetic resonance imaging (fMRI) studies. This study is the first to investigate brain activation by utilizing the method of fNIRS in MS during the performance of a cognitive task.

Keywords

Multiple sclerosis Working memory Near infrared spectroscopy 

Notes

Acknowledgments

This project was supported by the National Multiple Sclerosis Society Grant MB 0024 (JS-R) and by a grant from the NIH (1F32NS055509 to GV)

Conflict of Interest

Jelena Stojanovic-Radic, Glenn Wylie, Gerald Voelbel, Nancy Chiaravalloti, and John DeLuca report no conflicts of interest.

Informed Consent Statement

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, and the applicable revisions at the time of the investigation. Informed consent was obtained from all patients for being included in the study.

References

  1. Amann, M., Doessegar, L. S., Penner, I. K., Hirsch, J. G., Raselli, C., Calabrese, P., et al. (2011). Altered functional adaptation to attention and working memory tasks with increasing complexity in relapsing-remitting multiple sclerosis patients. Human Brain Mapping, 32, 1704–19.CrossRefPubMedGoogle Scholar
  2. Ances, B. M., Leontiev, O., Perthen, J. E., Liang, C., Lansing, A. E., & Buxton, R.B. (2008). Regional differences in the coupling of cerebral blood flow and oxygen metabolism changes in response to activation: Implications for BOLD-fMRI. Neuroimage, 39, 1510–21.Google Scholar
  3. Aries, M. J. H., Coumou, A. D., Elting, J. W. J., van der Haarst, J. J., Kremer, B. P. H., et al. (2012). Near infrared spectroscopy for the detection of desaturations in vulnerable ischemic brain tissue: a pilot study at the stroke unit bedside. Stroke, 43, 1134–1136.CrossRefPubMedGoogle Scholar
  4. Audoin, B., Reuter, F., Duong, M. V. A., Malikova, I., Confort-Gouny, S., Cherif, A. A., et al. (2008). Efficiency of cognitive control recruitment in the very early stage of multiple sclerosis: a one year fMRI follow-up study. Multiple Sclerosis, 14, 786–92.CrossRefPubMedGoogle Scholar
  5. Azechi, M., Iwase, M., Ikezawa, K., Takahashi, H., Canuet, L., et al. (2010). Discriminant analysis in schizophrenia and healthy subjects using prefrontal activation during frontal lobe tasks: a near-infrared spectroscopy. Schizophrenia Research, 117, 52–60.CrossRefPubMedGoogle Scholar
  6. Bandettini, P. A., Kwong, K. K., Davis, T. L., Tootell, R. B. H., Wong, E. C., Fox, P. T., et al. (1997). Characterization of cerebral blood oxygenation and flow changes during prolonged brain activation. Human Brain Mapping, 5, 93–109.CrossRefPubMedGoogle Scholar
  7. Barber, P. A., Rushforth, D., Agrawal, S., & Tuor, U. I. (2012). Infrared optical imaging of matrix metalloproteinases (MMPs) up regulation following ischemia reperfusion is ameliorated by hypothermia. BMC Neuroscience, 13, 1–8.CrossRefGoogle Scholar
  8. Bermel, R. A., & Bakshi, R. (2006). The measurement and clinical relevance of brain atrophy in multiple sclerosis. Lancet Neurology, 5, 158–70.CrossRefGoogle Scholar
  9. Burgess, P. W., Dumontheil, I., & Gilbert, S. J. (2007). The gateway hypothesis of rostral prefrontal cortex (area 10) function. Trends in Cognitive Sciences, 11, 290–298.CrossRefPubMedGoogle Scholar
  10. Buxton, R. B. (2010). Interpreting oxygenation-based neuroimaging signals: the importance and the challenge of understanding brain oxygen metabolism. Frontiers in Neuroenergetics, 2, 1–16.Google Scholar
  11. Cader, S., Cifelli, A., Abu-Omar, Y., Palace, J., & Matthews, P. M. (2006). Reduced brain functional reserve and altered functional connectivity in patients with multiple sclerosis. Brain, 129, 527–537.CrossRefPubMedGoogle Scholar
  12. Chard, D., & Miller, D. (2009). Grey matter pathology in clinically early multiple sclerosis: evidence from magnetic resonance imaging. Journal of the Neurological Sciences, 282, 5–11.CrossRefPubMedGoogle Scholar
  13. Chen, G., Saad, Z.S., Britton, J.C., Pine, D.S., Cox, R.W. (2013). Linear Mixed-Effects Modeling Approach to FMRI Group Analysis. NeuroImage, http://dx.doi.org/ 10.1016/j.neuroimage.2013.01.047
  14. Chiaravalloti, N. D., & DeLuca, J. (2008). Cognitive impairment in multiple sclerosis. Lancet Neurology, 7, 1139–51.CrossRefGoogle Scholar
  15. Chiaravalloti, N. D., Hillary, F. G., Ricker, J. H., Christodoulou, C., Kalnin, A. J., Liu, W. C., et al. (2005). Cerebral activation patterns during working memory performance in multiple sclerosis using fMRI. Journal of Clinical and Experimental Neuropsychology, 27, 1–23.CrossRefGoogle Scholar
  16. Christodoulou, C., DeLuca, J., Ricker, J. H., Madigan, N. K., Bly, B. M., Lange, G., et al. (2001). Functional magnetic resonance imaging of working memory impairment after traumatic brain injury. Journal of Neurology, Neurosurgery, and Psychiatry, 71, 161–68.CrossRefPubMedCentralPubMedGoogle Scholar
  17. Colorado, R. A., Shukla, K., Chou, Y., Wolinsky, J. S., & Narayana, P. A. (2012). Multi-task functional MRI in multiple sclerosis patients without clinical disability. NeuroImage, 59, 573–81.CrossRefPubMedCentralPubMedGoogle Scholar
  18. Comi, G. (2010). The physiopathology of multiple sclerosis. In J. Kesselring, G. Comi, & A. J. Thompson (Eds.), Multiple sclerosis: recovery of function and neurorehabilitation. New York: Cambridge University Press.Google Scholar
  19. Corporation, T. P. (1997). WAIS-III, WMS-III technical manual. San Antonio: The Psychological Corporation.Google Scholar
  20. Cox, R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29, 162–73.CrossRefPubMedGoogle Scholar
  21. DeLuca, J., Chelune, G. J., Tulsky, D. S., Lengenfelder, J., & Chiaravalloti, N. D. (2004). Is speed of processing or working memory the primary information processing deficit in multiple sclerosis? Journal of Clinical and Experimental Neuropsychology, 26, 550–562.CrossRefPubMedGoogle Scholar
  22. Fisher, E., Lee, J. C., Nakamura, K., & Rudick, R. A. (2008). Gray matter atrophy in multiple sclerosis: a longitudinal study. Annals of Neurology, 64, 255–65.CrossRefPubMedGoogle Scholar
  23. Forn, C., Barros-Loscertales, A., Escudero, J., Benlloch, V., Campos, S., Parcet, M. A., & Avila, C. (2006). Cortical reorganization during PASAT task in MS patients with preserved working memory functions. NeuroImage, 31, 686–691.CrossRefPubMedGoogle Scholar
  24. Forn, C., Barros-Loscertales, A., Escudero, J., Benlloch, V., Campos, S., Parcet, M. A., & Avila, C. (2007). Compensatory activations in patients with multiple sclerosis during preserved performance on the auditory n-back task. Human Brain Mapping, 28, 424–30.CrossRefPubMedGoogle Scholar
  25. Forn, C., Rocca, M. A., Valsasina, P., Bosca, I., Casanova, B., et al. (2012). Functional magnetic resonance imaging correlates of cognitive performance in patients with clinically isolated syndrome suggestive of multiple sclerosis at presentation: an activation and connectivity study. Multiple Sclerosis Journal, 18, 153–163.CrossRefPubMedGoogle Scholar
  26. Griffeth, V. E., & Buxton, R. B. (2011). A theoretical framework for estimating cerebral oxygen metabolism changes using the calibrated-BOLD method: modeling the effects of blood volume distribution, hematocrit, oxygen extraction fraction, and tissue signal properties on the BOLD signal. Neuroimage, 58, 198–212.Google Scholar
  27. 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, 837–47.CrossRefPubMedGoogle Scholar
  28. Hoshi, Y., Oda, I., Wada, Y., Ito, Y., Yamashita, Y., et al. (2000). Visuospatial imagery is a fruitful strategy for the digit span backward task: a study with near-infrared optical tomography. Cognitive Brain Research, 9, 339–342.CrossRefPubMedGoogle Scholar
  29. Hoshi, Y., Tsou, B. H., Billock, V. A., Tanosaki, M., Iguchi, Y., et al. (2003). Spatiotemporal characteristics of hemodynamic changes in the human lateral prefrontal cortex during working memory tasks. NeuroImage, 20, 1493–1504.CrossRefPubMedGoogle Scholar
  30. Huettel, S. A., Song, A. W., & McCarthy, G. (2009). Functional Magnetic Resonance Imaging. Sunderland: Sinauer Associates, Inc.Google Scholar
  31. Hulst, H. H., & Geurts, J. J. G. (2011). Gray matter imaging in multiple sclerosis: what have we learned? BMC Neurology, 11, 1–11.CrossRefGoogle Scholar
  32. Hulst, H. H., Schoonheim, M. M., Roosendaal, S. D., Popescu, V., Schweren, L. J. S., et al. (2012). Functional adaptive changes within the hippocampal memory system of patients with multiple sclerosis. Human Brain Mapping, 33, 2268–2280.CrossRefPubMedGoogle Scholar
  33. Jaeggi, S., Seewer, R., Nirkko, A. C., Eckstein, D., Schroth, G., et al. (2003). Does excessive memory load attenuate activation in the prefrontal cortex? Load-dependent processing in single and dual tasks: functional magnetic resonance imaging study. NeuroImage, 19, 210–225.CrossRefPubMedGoogle Scholar
  34. James, D. R. C., Leff, D. R., Orihuela-Espina, F., Kwok, K.-W., Mylonas, G. P., et al. (2013). Enhanced frontoparietal network architectures following “gaze-contingent” versus “free hand” motor learning. NeuroImage, 64, 267–276.CrossRefPubMedGoogle Scholar
  35. Kobashi, N., Holper, L., Scholkmann, F., Kiper, D., & Eng, K. (2012). Enhancement of motor imagery-related cortical activation during first-person observation measured by functional near-infrared spectroscopy. European Journal of Neuroscience, 35, 1513–1521.CrossRefPubMedGoogle Scholar
  36. Kwee, I. L., & Nakada, T. (2003). Dorsolateral prefrontal lobe activation declines significantly with age: functional NIRS study. Journal of Neurology, 250, 525–529.CrossRefPubMedGoogle Scholar
  37. Lengenfelder, J., Chiaravalloti, N. D., Ricker, J. H., & DeLuca, J. (2003). Deciphering components of impaired working memory in multiple sclerosis. Cognitive and Behavioral Neurology, 16(1), 28–39.Google Scholar
  38. Lin, P.-Y., Chen, J.-J. J., & Lin, S.-I. (2012). The cortical control of cycling exercise in stroke patients: an fNIRS study. Human Brain Mapping. doi: 10.1002/hbm.22072. Epub ahead of print.Google Scholar
  39. Logothetis, N. K., & Wandell, B. A. (2004). Interpreting the BOLD signal. Annual Review of Physiology, 66, 735–69.Google Scholar
  40. Mainero, C., Caramia, F., Pozzilli, C., et al. (2004). fMRI evidence of brain reorganization during attention and memory tasks in multiple sclerosis. NeuroImage, 21, 858–67.CrossRefPubMedGoogle Scholar
  41. Matsuo, K., Taneichi, K., Matsumoto, A., Ohtani, T., Yamasue, H., et al. (2003). Hypoactivation of the prefrontal cortex during verbal fluency test in PTSD: a near-infrared spectroscopy study. Psychiatry Research: Neuroimaging, 124, 1–10.CrossRefPubMedGoogle Scholar
  42. McAllister, T. W., Sparling, M. B., Flashman, L. A., Guerin, S. J., Mamourian, A. C., & Saykin, A. J. (2001). Differential working memory load effects after mild traumatic brain injury. NeuroImage, 14, 1004–12.CrossRefPubMedGoogle Scholar
  43. McDonald, W. I., Compston, A., Edan, G., Goodkin, D., Lublin, H. P., et al. (2001). Recommended diagnostic criteria for multiple sclerosis: guidelines from the international panel on the diagnosis of multiple sclerosis. Annals of Neurology, 50, 121–127.CrossRefPubMedGoogle Scholar
  44. Mehagnoul-Schipper, D.J., van der Kallen, B.F.W., Colier, W.N.J.M., van der Sluijs, M.C., ven Erning, L.J.Th.O., et al. (2002). Simultaneous measurements of cerebral oxygenation changes during brain activation by near-infrared spectroscopy and functional magnetic resonance imaging in healthy young and elderly subjects. Human Brain Mapping, 16, 14–23.Google Scholar
  45. Mihara, M., Miyai, I., Hattori, N., Hatakenaka, M., Yagura, H., et al. (2012). Neurofeedback using real-time near-infrared spectroscopy enhances motor imagery related cortical activation. PloS One, 7, e32234.CrossRefPubMedCentralPubMedGoogle Scholar
  46. Molteni, E., Butti, M., Bianchi, A.M., & Reni, G. (2008, August 20–24). Activation of the prefrontal cortex during a visual n-back working memory task with varying memory load: a near infrared spectroscopy study. Paper presented at the 30th Annual International IEEE EMBS Conference. doi:  10.1109/IEMBS.2008.4650092
  47. Nakahachi, T., Ishii, R., Iwase, M., Canuet, L., Takahashi, H., et al. (2010). Frontal cortex activation associated with speeded processing of visuospatial working memory revealed by multichannel near-infrared spectroscopy during advanced trail making test performance. Behavioural Brain Research, 215, 21–27.CrossRefPubMedGoogle Scholar
  48. Nobre, A. C., Coull, J. T., Frith, C. D., & Mesulam, M. M. (1999). Orbitofrontal cortex is activated during breaches of expectation in tasks of visual attention. Nature Neuroscience, 2, 11–12.CrossRefPubMedGoogle Scholar
  49. Obrig, H., Israel, H., Kohl-Bareis, M., Uludag, K., Wenzel, R., Mueller, B., & Villringer, A. (2002). Habituation of the visually evoked potential and its vascular response: implications for neurovascular coupling in the healthy adult. NeuroImage, 17, 1–18.CrossRefPubMedGoogle Scholar
  50. Oldag, A., Goertler, M., Bertz, A. K., Schreiber, S., Stoppel, C., et al. (2012). Assessment of cortical hemodynamics by multichannel near-infrared spectroscopy in steno-occlusive disease of the middle cerebral artery. Stroke, 43, 2980–2985.CrossRefPubMedGoogle Scholar
  51. Penner, I. K., Rausch, M., Kappos, L., Opwis, K., & Radu, E. W. (2003). Analysis of impairment related functional architecture in MS patients during performance of different attention tasks. Journal of Neurology, 250, 461–72.CrossRefPubMedGoogle Scholar
  52. Reingold, S. C. (1995). Research Directions in Multiple Sclerosis. New York: National Multiple Sclerosis Society.Google Scholar
  53. Strangman, G., Culver, J. P., Thompson, J. H., & Boas, D. A. (2002). A quantitative comparison of simultaneous BOLD fMRI and NIRS recordings during functional brain activation. NeuroImage, 17, 719–731.CrossRefPubMedGoogle Scholar
  54. Sweet, L. H., Rao, S. M., Primeau, M., Mayer, A. R., & Cohen, R. A. (2004). Functional magnetic resonance imaging of working memory among multiple sclerosis patients. Journal of Neuroimaging, 14, 150–157.CrossRefPubMedGoogle Scholar
  55. Sweet, L. H., Rao, S. M., Primeau, M., Durgerian, S., & Cohen, R. A. (2006). Functional magnetic resonance imaging response to increased verbal working memory demands among patients with multiple sclerosis. Human Brain Mapping, 27, 28–36.CrossRefPubMedGoogle Scholar
  56. Trapp, B. D., Peterson, J., Ransohoff, R. M., Rudick, R., Mörk, S., & Bö, L. (1998). Axonal transection in the lesions of multiple sclerosis. The New England Journal of Medicine, 338, 278–85.CrossRefPubMedGoogle Scholar
  57. Viola, S., Viola, P., Litterio, P., Buongarzone, M. P., & Fiorelli, L. (2012). Stroke risk and migraine: near-infrared spectroscopy study. Neurological Sciences, 33, S173–S175.CrossRefPubMedGoogle Scholar
  58. White, B. R., Snyder, A. Z., Cohen, A. L., Petersen, S. E., Raichle, M. E., et al. (2009). Resting state functional connectivity in the human brain revealed with diffuse optical tomography. NeuroImage, 47, 148–156.CrossRefPubMedCentralPubMedGoogle Scholar
  59. Wishart, H. A., Saykin, A. J., McDonald, B. C., Mamourian, A. C., Flashman, L. A., Schuschu, K. R., et al. (2004). Brain activation patterns associated with working memory in relapsing-remitting MS. Neurology, 62, 234–38.CrossRefPubMedGoogle Scholar
  60. Wylie, G. R., Graber, H. L., Voelbel, G. T., Kohl, A. D., DeLuca, J., Pei, Y., et al. (2009). Using co-variations in the Hb signal to detect visual activation: a near infrared spectroscopic imaging study. NeuroImage, 47, 473–81.CrossRefPubMedGoogle Scholar
  61. Wylie, G. R., Genova, H., DeLuca, J., Chiaravalloti, N., & Sumowski, J. (2012). Functional magnetic resonance imaging movers and shakers: does subject movement cause sampling bias? Human Brain Mapping. doi: 10.1002/hbm.22150 (Epub ahead of print).PubMedCentralPubMedGoogle Scholar
  62. Zhang, X., Toronov, V. Y., Fabiani, M., Gratton, G., & Webb, A. G. (2005). The study of cerebral hemodynamic and neural response to visual stimulation using simultaneous NIR optical tomography and BOLD fMRI in humans. Proceedings – Society of Photo-Optical Instrumentation Engineers, 5686, 566–572.Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Jelena Stojanovic-Radic
    • 1
    • 2
  • Glenn Wylie
    • 1
    • 2
    Email author
  • Gerald Voelbel
    • 1
    • 4
    • 5
  • Nancy Chiaravalloti
    • 1
    • 2
  • John DeLuca
    • 1
    • 2
    • 3
  1. 1.Kessler FoundationNeuropsychology and Neuroscience LaboratoryWest OrangeUSA
  2. 2.Department of Physical Medicine and RehabilitationRutgers University –New Jersey Medical SchoolNew BrunswickUSA
  3. 3.Department of NeurosciencesRutgers University –New Jersey Medical SchoolNew BrunswickUSA
  4. 4.Department of Occupational Therapy, Steinhardt School of Culture, Education, and Human DevelopmentNew York UniversityNew YorkUSA
  5. 5.Rusk Institute of Rehabilitation MedicineNew York University Langone Medical CenterNew YorkUSA

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