Abstract
The use of functional near-infrared spectroscopy (fNIRS) for brain imaging during human movement continues to increase. This technology measures brain activity non-invasively using near-infrared light, is highly portable, and robust to motion artifact. However, the spatial resolution of fNIRS is lower than that of other imaging modalities. It is unclear whether fNIRS has sufficient spatial resolution to differentiate nearby areas of the cortex, such as the leg areas of the motor cortex. Therefore, the purpose of this study was to determine fNIRS’ ability to discern laterality of lower body contractions. Activity in the primary motor cortex was recorded in forty participants (mean = 23.4 years, SD = 4.5, female = 23, male = 17) while performing unilateral lower body contractions. Contractions were performed at 30% of maximal force against a handheld dynamometer. These contractions included knee extension, knee flexion, dorsiflexion, and plantar flexion of the left and right legs. fNIRS signals were recorded and stored for offline processing and analysis. Channels of fNIRS data were grouped into regions of interest, with five tolerance conditions ranging from strict to lenient. Four of five tolerance conditions resulted in significant differences in cortical activation between hemispheres. During right leg contractions, the left hemisphere was more active than the right hemisphere. Similarly, during left leg contractions, the right hemisphere was more active than the left hemisphere. These results suggest that fNIRS has sufficient spatial resolution to distinguish laterality of lower body contractions. This makes fNIRS an attractive technology in research and clinical applications in which laterality of brain activity is required during lower body activity.
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Data availability
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
Abbreviations
- fNIRS:
-
Functional near-infrared spectroscopy
- fMRI:
-
Functional magnetic resonance imaging
- EEG:
-
Electroencephalography
- oxy-Hb:
-
Oxygenated hemoglobin
- deoxy-Hb:
-
Deoxygenated hemoglobin
- total-Hb:
-
Total hemoglobin
- ROI:
-
Region of interest
References
Ayaz H, Onaral B, Izzetoglu K, Shewokis PA, McKendrick R, Parasuraman R (2013) Continuous monitoring of brain dynamics with functional near infrared spectroscopy as a tool for neuroergonomic research: Empirical examples and a technological development. Front Human Neurosci. https://doi.org/10.3389/fnhum.2013.00871
Balardin JB, Zimeo Morais GA, Furucho RA, Trambaiolli L, Vanzella P, Biazoli C, Sato JR (2017) Imaging brain function with functional near-infrared spectroscopy in unconstrained environments. Front Hum Neurosci 11:258. https://doi.org/10.3389/fnhum.2017.00258
Batula AM, Mark JA, Kim YE, Ayaz H (2017) Comparison of brain activation during motor imagery and motor movement using fNIRS. Comput Intell Neurosci 2017:1–12. https://doi.org/10.1155/2017/5491296
Boas DA, Gaudette T, Strangman G, Cheng X, Marota JJA, Mandeville JB (2001) The accuracy of near infrared spectroscopy and imaging during focal changes in cerebral hemodynamics. Neuroimage 13(1):76–90. https://doi.org/10.1006/nimg.2000.0674
Boas DA, Elwell CE, Ferrari M, Taga G (2014) Twenty years of functional near-infrared spectroscopy: Introduction for the special issue. Neuroimage 85:1–5. https://doi.org/10.1016/j.neuroimage.2013.11.033
Brigadoi S, Cooper RJ (2015) How short is short? Optimum source–detector distance for short-separation channels in functional near-infrared spectroscopy. Neurophotonics 2(2):025005. https://doi.org/10.1117/1.NPh.2.2.025005
Cockx H, Oostenveld R, Tabor M, Savenco E, van Setten A, Cameron I, van Wezel R (2023) fNIRS is sensitive to leg activity in the primary motor cortex after systemic artifact correction. Neuroimage 269:119880. https://doi.org/10.1016/j.neuroimage.2023.119880
Delpy DT, Cope M, van der Zee P, Arridge S, Wray S, Wyatt JS (1988) Estimation of optical pathlength through tissue from direct time of flight measurement. Phys Med Biol 33(12):1433. https://doi.org/10.1088/0031-9155/33/12/008
Ferrari M, Quaresima V (2012) A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application. Neuroimage 63(2):921–935. https://doi.org/10.1016/j.neuroimage.2012.03.049
Fischl B, Dale AM (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci 97(20):11050–11055. https://doi.org/10.1073/pnas.200033797
Gil-Castillo J, Alnajjar F, Koutsou A, Torricelli D, Moreno JC (2020) Advances in neuroprosthetic management of foot drop: A review. J Neuroeng Rehabil 17(1):46. https://doi.org/10.1186/s12984-020-00668-4
Graziano MSA, Aflalo TN (2007) Mapping behavioral repertoire onto the cortex. Neuron 56(2):239–251. https://doi.org/10.1016/j.neuron.2007.09.013
Graziano MSA, Taylor CSR, Moore T (2002) Complex movements evoked by microstimulation of precentral cortex. Neuron 34(5):841–851
Herold F, Orlowski K, Börmel S, Müller NG (2017a) Cortical activation during balancing on a balance board. Hum Mov Sci 51:51–58. https://doi.org/10.1016/j.humov.2016.11.002
Herold F, Wiegel P, Scholkmann F, Thiers A, Hamacher D, Schega L (2017b) Functional near-infrared spectroscopy in movement science: a systematic review on cortical activity in postural and walking tasks. Neurophotonics 4(4):041403. https://doi.org/10.1117/1.NPh.4.4.041403
Herold F, Wiegel P, Scholkmann F, Müller N (2018) Applications of functional near-infrared spectroscopy (fNIRS) neuroimaging in exercise-cognition science: a systematic. Methodology-Focused Rev J Clin Med 7(12):466. https://doi.org/10.3390/jcm7120466
Hiyamizu M, Maeoka H, Matsuo A, Morioka S (2014) Effects of self-action observation on standing balance learning: a change of brain activity detected using functional near-infrared spectroscopy. NeuroRehabilitation 35(3):579–585. https://doi.org/10.3233/NRE-141153
Hocke L, Oni I, Duszynski C, Corrigan A, Frederick B, Dunn J (2018) Automated processing of fnirs data—a visual guide to the pitfalls and consequences. Algorithms 11(5):67. https://doi.org/10.3390/a11050067
Holtzer R, Yuan J, Verghese J, Mahoney JR, Izzetoglu M, Wang C (2017) Interactions of subjective and objective measures of fatigue defined in the context of brain control of locomotion. J Gerontol Ser A Biol Sci Med Sci. https://doi.org/10.1093/gerona/glw167
Huppert TJ, Diamond SG, Franceschini MA, Boas DA (2009) HomER: A review of time-series analysis methods for near-infrared spectroscopy of the brain. Appl Opt 48(10):D280. https://doi.org/10.1364/AO.48.00D280
Islam MK, Rastegarnia A, Yang Z (2016) Methods for artifact detection and removal from scalp EEG: a review. Neurophysiologie Clinique/clin Neurophysiol 46(4–5):287–305. https://doi.org/10.1016/j.neucli.2016.07.002
Khan RA, Naseer N, Qureshi NK, Noori FM, Nazeer H, Khan MU (2018) fNIRS-based neurorobotic Interface for gait rehabilitation. J Neuroeng Rehabil 15(1):7. https://doi.org/10.1186/s12984-018-0346-2
Koenraadt KLM, Roelofsen EGJ, Duysens J, Keijsers NLW (2014) Cortical control of normal gait and precision stepping: an fNIRS study. Neuroimage 85:415–422. https://doi.org/10.1016/j.neuroimage.2013.04.070
Li T, Gong H, Luo Q (2011) Visualization of light propagation in visible Chinese human head for functional near-infrared spectroscopy. J Biomed Opt 16(04):1. https://doi.org/10.1117/1.3567085
Lloyd-Fox S, Blasi A, Elwell CE (2010) Illuminating the developing brain: the past, present and future of functional near infrared spectroscopy. Neurosci Biobehav Rev 34(3):269–284. https://doi.org/10.1016/j.neubiorev.2009.07.008
Logothetis NK (2008) What we can do and what we cannot do with fMRI. Nature 453(7197):869–878. https://doi.org/10.1038/nature06976
Lu H, Lam LCW, Ning Y (2019) Scalp-to-cortex distance of left primary motor cortex and its computational head model: Implications for personalized neuromodulation. CNS Neurosci Ther 25(11):1270–1276. https://doi.org/10.1111/cns.13204
McKendrick R, Parasuraman R, Murtza R, Formwalt A, Baccus W, Paczynski M, Ayaz H (2016) Into the wild: neuroergonomic differentiation of hand-held and augmented reality wearable displays during outdoor navigation with functional near infrared spectroscopy. Front Human Neurosci. https://doi.org/10.3389/fnhum.2016.00216
Menon V, Crottaz-Herbette S (2005) Combined EEG and fMRI studies of human brain function. Int Rev Neurobiol. https://doi.org/10.1016/S0074-7742(05)66010-2
Mentiplay BF, Perraton LG, Bower KJ, Adair B, Pua Y-H, Williams GP, McGaw R, Clark RA (2015) Assessment of lower limb muscle strength and power using hand-held and fixed dynamometry: a reliability and validity study. PLoS ONE 10(10):e0140822. https://doi.org/10.1371/journal.pone.0140822
Molavi B, Dumont GA (2012) Wavelet-based motion artifact removal for functional near-infrared spectroscopy. Physiol Meas 33(2):259–270. https://doi.org/10.1088/0967-3334/33/2/259
Niu R, Yu Y, Li Y, Liu Y (2019) Use of fNIRS to Characterize the Neural Mechanism of Inter-Individual Rhythmic Movement Coordination. Front Physiol 10:781. https://doi.org/10.3389/fphys.2019.00781
Osofundiya O, Benden ME, Dowdy D, Mehta RK (2016) Obesity-specific neural cost of maintaining gait performance under complex conditions in community-dwelling older adults. Clin Biomech 35:42–48. https://doi.org/10.1016/j.clinbiomech.2016.03.011
Penfield W, Boldrey E (1937) Somatic motor and sensory representation in the cerebral cortex of man as studied by electrical stimulation. Brain 60(4):389–443. https://doi.org/10.1093/brain/60.4.389
Perrey S (2014) Possibilities for examining the neural control of gait in humans with fNIRS. Front Physiol. https://doi.org/10.3389/fphys.2014.00204
Pinti P, Aichelburg C, Gilbert S, Hamilton A, Hirsch J, Burgess P, Tachtsidis I (2018) A review on the use of wearable functional near-infrared spectroscopy in naturalistic environments: review of fNIRS measurements in naturalistic environments. Jpn Psychol Res 60(4):347–373. https://doi.org/10.1111/jpr.12206
Piper SK, Krueger A, Koch SP, Mehnert J, Habermehl C, Steinbrink J, Obrig H, Schmitz CH (2014) A wearable multi-channel fNIRS system for brain imaging in freely moving subjects. Neuroimage 85:64–71. https://doi.org/10.1016/j.neuroimage.2013.06.062
Quaresima V, Ferrari M (2019) Functional near-infrared spectroscopy (fNIRS) for assessing cerebral cortex function during human behavior in natural/social situations: a concise review. Organ Res Methods 22(1):46–68. https://doi.org/10.1177/1094428116658959
Rea M, Rana M, Lugato N, Terekhin P, Gizzi L, Brötz D, Fallgatter A, Birbaumer N, Sitaram R, Caria A (2014) Lower limb movement preparation in chronic stroke: a pilot study toward an fnirs-bci for gait rehabilitation. Neurorehabil Neural Repair 28(6):564–575. https://doi.org/10.1177/1545968313520410
Rosso AL, Cenciarini M, Sparto PJ, Loughlin PJ, Furman JM, Huppert TJ (2017) Neuroimaging of an attention demanding dual-task during dynamic postural control. Gait Posture 57:193–198. https://doi.org/10.1016/j.gaitpost.2017.06.013
Scarapicchia V, Brown C, Mayo C, Gawryluk JR (2017) Functional magnetic resonance imaging and functional near-infrared spectroscopy: insights from combined recording studies. Front Hum Neurosci 11:419. https://doi.org/10.3389/fnhum.2017.00419
Strangman GE, Li Z, Zhang Q (2013) Depth sensitivity and source-detector separations for near infrared spectroscopy based on the colin27 brain template. PLoS ONE 8(8):e66319. https://doi.org/10.1371/journal.pone.0066319
Tachtsidis I, Scholkmann F (2016) False positives and false negatives in functional near-infrared spectroscopy: Issues, challenges, and the way forward. Neurophotonics 3(3):031405. https://doi.org/10.1117/1.NPh.3.3.031405
Thompson T, Steffert T, Ros T, Leach J, Gruzelier J (2008) EEG applications for sport and performance. Methods 45(4):279–288. https://doi.org/10.1016/j.ymeth.2008.07.006
Uludağ K, Steinbrink J, Villringer A, Obrig H (2004) Separability and cross talk: Optimizing dual wavelength combinations for near-infrared spectroscopy of the adult head. Neuroimage 22(2):583–589. https://doi.org/10.1016/j.neuroimage.2004.02.023
von Lühmann A, Zheng Y, Ortega-Martinez A, Kiran S, Somers DC, Cronin-Golomb A, Awad LN, Ellis TD, Boas DA, Yücel MA (2021) Toward neuroscience of the everyday World (NEW) using functional near-infrared spectroscopy. Curr Opinion Biomed Eng 18:100272. https://doi.org/10.1016/j.cobme.2021.100272
Yücel MA, Selb JJ, Huppert TJ, Franceschini MA, Boas DA (2017) Functional near infrared spectroscopy: enabling routine functional brain imaging. Curr Opinion in Biomed Eng 4:78–86. https://doi.org/10.1016/j.cobme.2017.09.011
Yücel MA, Lühmann A, Scholkmann F, Gervain J, Dan I, Ayaz H, Boas DA, Cooper RJ, Culver J, Elwell CE, Eggebrecht A, Franceschini MA, Grova C, Homae F, Lesage F, Obrig H, Tachtsidis I, Tak S, Tong Y, Torricelli A, Wabnitz H, Wolf M (2021) Best practices for fNIRS publications. Neurophotonics 8:1–34. https://doi.org/10.1117/1.NPh.8.1.012101
Acknowledgements
We would like to thank Dr. Meryem A. Yücel for her input on signal processing and analysis.
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Conceptualization: JAHS, RJM, JMD; Methodology: JAHS, RJML, JMD; Data acquisition: RJML, JAHS, JMD, SMR, JCES, CMS, KS, JC, AAO, TLD, DLML, JIP, RMG, KKH, NC, JCC, XY, JWP, MSS. Formal analysis and investigation: RJML, JAHS, JMD, SMR, JCES, CMS, KS, JC, AAO, TLD, DLML, JIP, RMG, KKH, NC, JCC, XY, JWP, MSS; Writing—original draft preparation: RJML, JMD, JAHS; Writing—review and editing: RJML, JMD, JAHS, SMR, JCES, CMS, KS, JC, AAO, TLD, DLML, JIP, RMG, KKH, NC, JCC, XY, JWP, MSS.
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K.S. works at the (f)NIRS manufacturer Artinis Medical Systems B.V. The other authors have no relevant financial or non-financial interests to disclose.
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This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committees of Oklahoma State University (IRB-20-388) and University of Central Florida (STUDY00001263).
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MacLennan, R.J., Hernandez-Sarabia, J.A., Reese, S.M. et al. fNIRS is capable of distinguishing laterality of lower body contractions. Exp Brain Res (2024). https://doi.org/10.1007/s00221-024-06798-8
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DOI: https://doi.org/10.1007/s00221-024-06798-8