Brain reorganization following spinal cord injury (SCI) has been well-established using animal and human studies. Yet, much is unknown regarding functional recovery and adverse secondary outcomes after SCI. Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique that offers methodological flexibility in a real-world setting. We used fNIRS to examine the cortical functional differences between 12 males with thoracolumbar SCI (46.41 ± 11.09 years of age) and 12 healthy males (47.61 ± 11.94 years of age) during resting state and task conditions—bilateral finger tapping (FT), mental imagery of bilateral FT with action observation (FTI+AO), and bilateral ankle tapping (AT). We found an overall decrease in hemodynamic response of the SCI group during all three task conditions. Task modulated functional connectivity (FC) computed using beta series correlation technique was compared using independent sample t-tests at α = 0.05. Connectivity between the right mediolateral sensorimotor network (SMN) and the right medial SMN was reduced during the FT task in SCI. A mixed analysis of variance revealed that the FC within the right mediolateral SMN was reduced during FT but preserved during FTI+AO (i.e., comparable to controls) in the SCI group. Lower FC of these regions was associated with longer injury durations. Additionally, we found a general decrease in resting state FC of the SCI group, specifically in the Slow-3 frequency range (0.073 to 0.1 Hz). These results, though preliminary, are consistent with past studies and highlight the potential of fNIRS in SCI and rehabilitative research.
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References
1.
National Spinal Cord Injury Statistical Center. Facts and Figures at a Glance. Birmingham, AL; 2020. Available from: https://www.nscisc.uab.edu/Public/Facts%20and%20Figures%202020.pdf [Last accessed: 01/15/2022].
KarunakaranKK, AbbruzzeseK, AndrowisG, et al.A novel user control for lower extremity rehabilitation exoskeletons. Front Robot AI, 2020; 7:108.
5.
El-SayesJ, HarasymD, TurcoCV, et al.Exercise-induced neuroplasticity: a mechanistic model and prospects for promoting plasticity. Neuroscientist, 2019; 25(1):65–85.
6.
JainN, QiHX, CollinsCE, et al.Large-scale reorganization in the somatosensory cortex and thalamus after sensory loss in macaque monkeys. J Neurosci, 2008; 28(43):11042–11060.
7.
HendersonLA, GustinSM, MaceyPM, et al.Functional reorganization of the brain in humans following spinal cord injury: evidence for underlying changes in cortical anatomy. J Neurosci, 2011; 31(7):2630–2637.
8.
KokotiloKJ, EngJJ, CurtA. Reorganization and preservation of motor control of the brain in spinal cord injury: a systematic review. J Neurotrauma, 2009; 26(11):2113–2126.
9.
Oni-OrisanA, KaushalM, LiW, et al.Alterations in cortical sensorimotor connectivity following complete cervical spinal cord injury: a prospective resting-state fMRI study. PLoS One, 2016; 11(3):e0150351.
10.
HouJM, SunTS, XiangZM, et al.Alterations of resting-state regional and network-level neural function after acute spinal cord injury. Neuroscience, 2014; 277:446–454.
11.
HouJ, XiangZ, YanR, et al.Motor recovery at 6 months after admission is related to structural and functional reorganization of the spine and brain in patients with spinal cord injury. Hum Brain Mapp, 2016; 37(6):2195–2209.
12.
RaoJS, LiuZ, ZhaoC, et al.Longitudinal evaluation of functional connectivity variation in the monkey sensorimotor network induced by spinal cord injury. Acta Physiol (Oxf), 2016; 217(2):164–173.
13.
MohammedH, HollisER, 2nd. Cortical reorganization of sensorimotor systems and the role of intracortical circuits after spinal cord injury. Neurotherapeutics, 2018; 15(3):588–603.
14.
KaushalM, Oni-OrisanA, ChenG, et al.Evaluation of whole-brain resting-state functional connectivity in spinal cord injury: a large-scale network analysis using network-based statistic. J Neurotrauma. 2017; 34(6):1278–1282.
15.
KaushalM, Oni-OrisanA, ChenG, et al.Large-scale network analysis of whole-brain resting-state functional connectivity in spinal cord injury: a comparative study. Brain Connect, 2017; 7(7):413–423.
16.
PanY, DouWB, WangYH, et al.Non-concomitant cortical structural and functional alterations in sensorimotor areas following incomplete spinal cord injury. Neural Regen Res, 2017; 12(12):2059–2066.
17.
HouJ, XiangZ, YanRea. Motor recovery at 6 months after admission is related to structural and functional reorganization of the spine and brain in patients with spinal cord injury. Hum Brain Mapp, 2016; 37(6):2195–2209.
18.
MiharaM, MiyaiI. Review of functional near-infrared spectroscopy in neurorehabilitation. Neurophotonics, 2016; 3(3):031414.
19.
HuppertTJ, HogeRD, DiamondSG, et al.A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans. Neuroimage, 2006; 29(2):368–382.
20.
VillringerA, ChanceB. Non-invasive optical spectroscopy and imaging of human brain function. Trends Neurosci, 1997; 20(10):435–442.
21.
WilcoxT, BiondiM. fNIRS in the developmental sciences. Wiley Interdiscip Rev Cogn Sci, 2015; 6(3):263–283.
22.
VillringerA, PlanckJ, HockC, et al.Near infrared spectroscopy (NIRS): a new tool to study hemodynamic changes during activation of brain function in human adults. Neurosci Lett, 1993; 154(1-2):101–104.
23.
SanganiS, LamontagneA, FungJ. Cortical mechanisms underlying sensorimotor enhancement promoted by walking with haptic inputs in a virtual environment. Prog Brain Res, 2015; 218:313–330.
24.
BrigadoiS, CooperRJ. How short is short? Optimum source-detector distance for short-separation channels in functional near-infrared spectroscopy. Neurophotonics, 2015; 2(2):025005.
25.
CutiniS, BrigadoiS. Unleashing the future potential of functional near-infrared spectroscopy in brain sciences. J Neurosci Methods, 2014; 232:152–156.
26.
HoshiY. Functional near-infrared spectroscopy: potential and limitations in neuroimaging studies. Int Rev Neurobiol, 2005; 66:237–266.
27.
SimisM, SantosK, SatoJ, et al.T107. Using functional near infrared spectroscopy (fNIRS) to assess brain activity of spinal cord injury patient, during robot-assisted gait. Clin Neurophysiol, 2018; 129:e43–e44.
28.
NagaokaT, SakataniK, AwanoT, et al.Development of a new rehabilitation system based on a brain-computer interface using near-infrared spectroscopy. Adv Exp Med Biol, 2010; 662:497–503.
GhafoorU, ZafarA, YaqubMA, et al.Enhancement in classification accuracy of motor imagery signals with visual aid: an fNIRS-BCI study. 2019 19th International Conference on Control, Automation and Systems (ICCAS); Jeju, Korea (South). IEEE Press: 2019; pp. 1201–1206.
31.
KoenraadtKLM, DuysensJ, RijkenH, et al.Preserved foot motor cortex in patients with complete spinal cord injury: a functional near-infrared spectroscopic study. Neurorehabil Neural Repair, 2013; 28(2):179–187.
32.
BatulaAM, MarkJA, KimYE, et al.Comparison of brain activation during motor imagery and motor movement using fNIRS. Comput Intell Neurosci, 2017; 2017:5491296.
33.
PiperSK, KruegerA, KochSP, et al.A wearable multi-channel fNIRS system for brain imaging in freely moving subjects. Neuroimage, 2014; 85(Pt 1):64–71.
HuppertTJ, DiamondSG, FranceschiniMA, et al.HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain. Appl Opt, 2009; 48(10):D280–D298.
36.
DiX, ZhangZ, BiswalBB. Understanding psychophysiological interaction and its relations to beta series correlation. Brain Imaging Behav, 2020; 15(2):958–973.
37.
BuzsakiG, DraguhnA. Neuronal oscillations in cortical networks. Science, 2004; 304(5679):1926–1929.
38.
GohelSR, BiswalBB. Functional integration between brain regions at rest occurs in multiple-frequency bands. Brain Connect, 2015; 5(1):23–34.
39.
PenttonenM, BuzsákiG. Natural logarithmic relationship between brain oscillators. Thalamus Related Syst, 2003; 2(2):145–152.
40.
SasaiS, HomaeF, WatanabeH, et al.Frequency-specific network topologies in the resting human brain. Front Hum Neurosci, 2014; 8:1022.
41.
BenjaminiY, HochbergY. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Statist Soc, 1995; 57(1):289–300.
42.
Hotz-BoendermakerS, FunkM, SummersP, et al.Preservation of motor programs in paraplegics as demonstrated by attempted and imagined foot movements. Neuroimage, 2008; 39(1):383–394.
43.
CramerSC, LastraL, LacourseMG, et al.Brain motor system function after chronic, complete spinal cord injury. Brain, 2005; 128(Pt 12):2941–2950.
44.
AlkadhiH, BruggerP, BoendermakerSH, et al.What disconnection tells about motor imagery: evidence from paraplegic patients. Cereb Cortex, 2005; 15(2):131–140.
45.
SharpKG, GramerR, PageSJ, et al.Increased brain sensorimotor network activation after incomplete spinal cord injury. J Neurotrauma, 2017; 34(3):623–631.
46.
JurkiewiczMT, MikulisDJ, FehlingsMG, et al.Sensorimotor cortical activation in patients with cervical spinal cord injury with persisting paralysis. Neurorehabil Neural Repair, 2010; 24(2):136–140.
47.
MulderT. Motor imagery and action observation: cognitive tools for rehabilitation. J Neural Transm (Vienna), 2007; 114(10):1265–1278.
48.
MoxonKA, OlivieroA, AguilarJ, et al.Cortical reorganization after spinal cord injury: always for good?. Neuroscience, 2014; 283:78–94.
49.
HawasliAH, RutlinJ, RolandJL, et al.Spinal cord injury disrupts resting-state networks in the human brain. J Neurotrauma, 2018; 35(6):864–873.
50.
KarunakaranKD, YuanR, HeJ, et al.Resting-state functional connectivity of the thalamus in complete spinal cord injury. Neurorehabil Neural Repair, 2020; 34(2):122–133.
51.
ChoeAS, BeleguV, YoshidaS, et al.Extensive neurological recovery from a complete spinal cord injury: a case report and hypothesis on the role of cortical plasticity. Front Hum Neurosci, 2013; 7:290.
52.
AguilarJ, Humanes-ValeraD, Alonso-CalvinoE, et al.Spinal cord injury immediately changes the state of the brain. J Neurosci, 2010; 30(22):7528–7537.
53.
ZhangYJ, DuanL, ZhangH, et al.Determination of dominant frequency of resting-state brain interaction within one functional system. PLoS One. 2012; 7(12):e51584.
54.
ShaversVL. Measurement of socioeconomic status in health disparities research. J Natl Med Assoc, 2007; 99(9):1013–1023.
55.
ChenY, HeY, DeVivoMJ. Changing demographics and injury profile of new traumatic spinal cord injuries in the United States, 1972-2014. Arch Phys Med Rehabil, 2016; 97(10):1610–1619.
56.
StewartAN, MacLeanSM, StrombergAJ, et al.Considerations for studying sex as a biological variable in spinal cord injury. Front Neurol, 2020; 11:802.
57.
YaqubMA, WooS, HongK. Compact, portable, high-density functional near-infrared spectroscopy system for brain imaging. IEEE Access, 2020; 8:128224–128238.
58.
QuaresimaV, FerrariM. A mini-review on functional near-infrared spectroscopy (fNIRS): Where do we stand, and where should we go?. Photonics, 2019; 6(3):87.
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