Functional Near-Infrared Spectroscopy as Promising Method for Studying Cognitive Functions in Children

The description of new promising method of functional neuroimaging, functional near-infrared spectroscopy (fNIRS), is presented. General information on functional tomography and its features in children are given. Brief description on the history of fNIRS development, the method itself, its advantages and disadvantages are covered. fNIRS implementation areas in science and clinical practice are clarified. fNIRS features are described, and the role of this method among others in functional tomography is determined. It was noted that fNIRS significantly complements other research and diagnostic methods, including functional magnetic resonance imaging, electroencephalography, induced potentials, thereby expanding the range of scientific and clinical issues that can be solved by functional neuroimaging.

1. Thompson PM, Stein JL, Medland SE, et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 2014;8(2):153–182. doi: https://doi.org/10.1007/s11682-013-9269-5

2. Nelson CA 3rd, McCleery JP. Use of event-related potentials in the study of typical and atypical development. J Am Acad Child Adolesc Psychiatry. 2008;47(11):1252–1261. doi: https://doi.org/10.1097/CHI.0b013e318185a6d8

3. Nunez PL. Electric fields of the brain. New York: Oxford University Press; 1981.

4. Cuffin BN, Cohen D. Comparison of the magnetoencepha logram and electroencephalogram. Electroencephalogr Clin Neurophysiol. 1979;47(2):132–146. doi: https://doi.org/10.1016/0013-4694(79)90215-3

5. Chugani HT, Phelps ME. Imaging human brain development with positron emission tomography. J Nucl Med. 1991;32(1):23–26.

6. Suhonen-Polvi H, Ruotsalainen U, Ahonen A, et al. Positron emission tomography in asphyxiated infants. Preliminary studies of regional cerebral glucose metabolism using 18F-FDG. Acta Radiol Suppl. 1991;376:173.

7. Shan ZY, Leiker AJ, Onar-Thomas A, et al. Cerebral glucose metabolism on positron emission tomography of children. Hum Brain Mapp. 2014;35(5):2297–2309. doi: https://doi.org/10.1002/hbm.22328

8. Chugani HT. Positron Emission Tomography in Pediatric Neurodegenerative Disorders. Pediatr Neurol. 2019;100:12–25. doi: https://doi.org/10.1016/j.pediatrneurol.2019.07.003

9. Chin WC, Liu FY, Huang YS, et al. Different positron emission tomography findings in schizophrenia and narcolepsy type 1 in adolescents and young adults: a preliminary study. J Clin Sleep Med. 2021; 17(4):739–748. doi: https://doi.org/10.5664/jcsm.9032

10. Turpin S, Martineau P, Levasseur MA, et al. 18F-Flurodeoxyglucose positron emission tomography with computed tomography (FDG PET/CT) findings in children with encephalitis and comparison to conventional imaging. Eur J Nucl Med Mol Imaging. 2019;46(6): 1309–1324. doi: https://doi.org/10.1007/s00259-019-04302-x

11. Glover GH. Overview of functional magnetic resonance imaging. Neurosurg Clin N Am. 2011;22(2):133–139, vii. doi: https://doi.org/10.1016/j.nec.2010.11.001

12. Enge A, Friederici AD, Skeide MA. A meta-analysis of fMRI studies of language comprehension in children. Neuroimage. 2020;215: 116858. doi: https://doi.org/10.1016/j.neuroimage.2020.116858

13. Yaple Z, Arsalidou M. N-back Working Memory Task: Metaanalysis of Normative fMRI Studies With Children. Child Dev. 2018;89(6):2010–2022. doi: https://doi.org/10.1111/cdev.13080

14. Benischek A, Long X, Rohr CS, et al. Pre-reading language abilities and the brain’s functional reading network in young children. Neuroimage. 2020;217:116903. doi: https://doi.org/10.1016/j.neuroimage.2020.116903

15. Cohen D, Cuffin BN. Demonstration of useful differences between magnetoencephalogram and electroencephalogram. Electro encephalogr Clin Neurophysiol. 1983;56(1):38–51. doi: https://doi.org/10.1016/0013-4694(83)90005-6

16. Hämäläinen MS, Hari R, Ilmoniemi RJ, et al. Magneto encephalography — theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys. 1993;65(2):413–497. doi: https://doi.org/10.1103/REVMODPHYS.65.413

17. Cohen D, Cuffin BN. EEG versus MEG localization accuracy: theory and experiment. Brain Topogr. 1991;4(2):95–103. doi: https://doi.org/10.1007/BF01132766

18. Cohen D. Magnetoencephalography: evidence of magnetic fields produced by alpha-rhythm currents. Science. 1968;161(3843): 784–786. doi: https://doi.org/10.1126/science.161.3843.784

19. Ressel V, Wilke M, Lidzba K, et al. Increases in language lateralization in normal children as observed using magnetoencephalography. Brain Lang. 2008;106(3):167–176. doi: https://doi.org/10.1016/j.bandl.2008.01.004

20. Foley E, Cross JH, Thai NJ, et al. MEG Assessment of Expressive Language in Children Evaluated for Epilepsy Surgery. Brain Topogr. 2019;32(3):492–503. doi: https://doi.org/10.1007/s10548-019-00703-1

21. Kostas D, Pang EW, Rudzicz F. Machine learning for MEG during speech tasks. Sci Rep. 2019;9(1):1609. doi: https://doi.org/10.1038/s41598-019-38612-9

22. Quaresima V, Bisconti S, Ferrari M. A brief review on the use of functional near-infrared spectroscopy (fNIRS) for language imaging studies in human newborns and adults. Brain Lang. 2012;121(2):79–89. doi: https://doi.org/10.1016/j.bandl.2011.03.009

23. Ferrari M, Quaresima V. A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application. Neuroimage. 2012;63(2):921–935. doi: https://doi.org/10.1016/j.neuroimage.2012.03.049

24. Millikan GA. Muscle hemoglobin. Proc R Soc Lond Ser B Biol Sci. 1936;120(818):366–388.

25. Chance В. Optical Method. Ann Rev Biophys Biophys Chem. 1991;20:1–30. doi: https://doi.org/10.1146/annurev.bb.20.060191.000245

26. Jöbsis FF. Noninvasive, infrared monitoring of cerebral and myocardial oxygen sufficiency and circulatory parameters. Science. 1977;198(4323):1264–1267. doi: https://doi.org/10.1126/science.929199

27. Jo Bsis-Vandervliet FF. Discovery of the near-infrared window into the body and the early development of near-infrared spectroscopy. J Biomed Opt. 1999;4(4):392–396. doi: https://doi.org/10.1117/1.429952

28. Brazy JE, Lewis DV, Mitnick MH, Jöbsis vander Vliet FF. Noninvasive monitoring of cerebral oxygenation in preterm infants: preliminary observations. Pediatrics. 1985;75(2):217–225.

29. Ferrari M, Giannini I, Carpi A, Fasella P. Near I.R. spectroscopy in non invasive monitoring of cerebral function. In: World Congress on Medical Physics and Biomedical Engineering. Hamburg, 1982, September 5–11. Bleifeld W, Harder D, Leetz HK, Schaldach M., eds. Hamburg; 1982.

30. Ferrari M, Giannini I, Sideri G, Zanette E. Continuous non invasive monitoring of human brain by near infrared spectroscopy. Adv Exp Med Biol. 1985;191: 873–882. doi: https://doi.org/10.1007/978-1-4684-3291-6_88

31. Wyatt JS, Cope M, Delpy DT, et al. Quantification of cerebral oxygenation and haemodynamics in sick newborn infants by near infrared spectrophotometry. Lancet. 1986;2(8515):1063–1066. doi: https://doi.org/10.1016/s0140-6736(86)90467-8

32. Reynolds EO, Wyatt JS, Azzopardi D, et al. New non-invasive methods for assessing brain oxygenation and haemodynamics. Br Med Bull. 1988;44(4):1052–1075. doi: https://doi.org/10.1093/oxfordjournals.bmb.a072289

33. Delpy DT, Cope M. Quantification in tissue near-infrared spectroscopy. Philos Trans R Soc. Lond B Biol Sci. 1997;352(1354): 649–659. doi: https://doi.org/10.1098/rstb.1997.0046

34. Ito H, Kanno I, Fukuda H. Human cerebral circulation: positron emission tomography studies. Ann Nucl Med. 2005;19(2):65–74. doi: https://doi.org/10.1007/BF03027383

35. Custo A, Wells WM 3rd, Barnett AH, et l. Effective scattering coefficient of the cerebral spinal fluid in adult head models for diffuse optical imaging. Appl Opt. 2006;45(19):4747–4755. doi: https://doi.org/10.1364/ao.45.004747

36. Okada E, Delpy DT. Near-infrared light propagation in an adult head model. I. Modeling of low-level scattering in the cerebrospinal fluid layer. Appl Opt. 2003;42(16):2906–2914. doi: https://doi.org/10.1364/ao.42.002906

37. Okada E, Delpy DT. Near-infrared light propagation in an adult head model. II. Effect of superficial tissue thickness on the sensitivity of the near-infrared spectroscopy signal. Appl Opt. 2003;42(16):2915–2922. doi: https://doi.org/10.1364/ao.42.002915

38. Watanabe E, Maki A, Kawaguchi F, et al. Non-invasive assessment of language dominance with near-infrared spectroscopic mapping. Neurosci Lett. 1998;256(1):49–52. doi: https://doi.org/10.1016/s0304-3940(98)00754-x

39. Peña M, Maki A, Kovacić D, et al. Sounds and silence: an optical topography study of language recognition at birth. Proc Natl Acad Sci U S A. 2003;100(20):11702–11705. doi: https://doi.org/10.1073/pnas.1934290100

40. Grossmann T, Oberecker R, Koch SP, Friederici AD. The developmental origins of voice processing in the human brain. Neuron. 2010;65(6):852–858. doi: https://doi.org/10.1016/j.neuron.2010.03.001

41. Chen S, Sakatani K, Lichty W, et al. Auditory-evoked cerebral oxygenation changes in hypoxic-ischemic encephalopathy of newborn infants monitored by near infrared spectroscopy. Early Hum Dev. 2002;67(1-2):113–121. doi: https://doi.org/10.1016/s0378-3782(02)00004-x

42. Kovelman I, Shalinsky MH, White KS, et al. Dual language use in sign-speech bimodal bilinguals: fNIRS brain-imaging evidence. Brain Lang. 2009;109(2-3):112–123. doi: https://doi.org/10.1016/j.bandl.2008.09.008

43. Minagawa-Kawai Y, Mori K, Sato Y. Different brain strategies underlie the categorical perception of foreign and native phonemes. J Cogn Neurosci. 2005;17(9):1376–1385. doi: https://doi.org/10.1162/0898929054985482

44. Aryadoust V, Ng LY, Foo S, Esposito G. A neurocognitive investigation of test methods and gender effects in listening assessment. Comput Assist Lang Lear. 2022;35(4):743–763. doi: https://doi.org/10.1080/09588221.2020.1744667

45. Cannizzaro MS, Stephens SR, Breidenstein M, Crovo C. Prefrontal Cortical Activity During Discourse Processing: An Observational fNIRS Study. Topics in Language Disorders. 2916;36(1):65–79. doi: https://doi.org/10.1097/TLD.0000000000000082

46. Lei M, Miyoshi T, Niwa Y, et al. Comprehension-Dependent Cortical Activation During Speech Comprehension Tasks with Multiple Languages: Functional Near-Infrared Spectroscopy Study. Japanese Psychological Research. 2018;60(4):300–310. doi: https://doi.org/10.1111/JPR.12218

47. Hasegawa M, Carpenter PA, Just MA. An fMRI study of bilingual sentence comprehension and workload. Neuroimage. 2002;15(3):647–660. doi: https://doi.org/10.1006/nimg.2001.1001

48. Nakai T, Matsuo K, Kato C, et al. A functional magnetic resonance imaging study of listening comprehension of languages in human at 3 tesla-comprehension level and activation of the language areas. Neurosci Lett. 1999;263(1):33–36. doi: https://doi.org/10.1016/s0304-3940(99)00103-2

49. Miyai I, Tanabe HC, Sase I, et al. Cortical mapping of gait in humans: a near-infrared spectroscopic topography study. Neuroimage. 2001;14(5):1186–1192. doi: https://doi.org/10.1006/nimg.2001.0905

50. Murata Y, Sakatani K, Katayama Y, Fukaya C. Increase in focal concentration of deoxyhaemoglobin during neuronal activity in cerebral ischaemic patients. J Neurol Neurosurg Psychiatry. 2002;73(2):182–184. doi: https://doi.org/10.1136/jnnp.73.2.182

51. Kato H, Izumiyama M, Koizumi H, et al. Near-infrared spectroscopic topography as a tool to monitor motor reorganization after hemiparetic stroke: a comparison with functional MRI. Stroke. 2002;33(8):2032–2036. doi: https://doi.org/10.1161/01.str.0000021903.52901.97

52. Arenth PM, Ricker JH, Schultheis MT. Applications of functional near-infrared spectroscopy (fNIRS) to Neurorehabilitation of cognitive disabilities. Clin Neuropsychol. 2007;21(1):38–57. doi: https://doi.org/10.1080/13854040600878785