There has been a recent call for longitudinal cohort studies to track the physiological recovery of sport-related concussion (SRC) and its relationship with clinical recovery. Resting-state functional magnetic resonance imaging (rs-fMRI) has shown potential for detecting subtle changes in brain function after SRC. We investigated the effects of SRC on rs-fMRI metrics assessing local connectivity (regional homogeneity; REHO), global connectivity (average nodal strength), and the relative amplitude of slow oscillations of rs-fMRI (fractional amplitude of low-frequency fluctuations; fALFF).
Athletes diagnosed with SRC (n = 92) completed visits with neuroimaging at 24–48 h post-injury (24 h), after clearance to begin the return-to-play (RTP) progression (asymptomatic), and 7 days following unrestricted RTP (post-RTP). Non-injured athletes (n = 82) completed visits yoked to the schedule of matched injured athletes and served as controls. Concussed athletes had elevated symptoms, worse neurocognitive performance, greater balance deficits, and elevated psychological symptoms at the 24-h visit relative to controls. These deficits were largely recovered by the asymptomatic visit. Concussed athletes still reported elevated psychological symptoms at the asymptomatic visit relative to controls. Concussed athletes also had elevated REHO in the right middle and superior frontal gyri at the 24-h visit that returned to normal levels by the asymptomatic visit. Additionally, REHO in these regions at 24 h predicted psychological symptoms at the asymptomatic visit in concussed athletes. Current results suggest that SRC is associated with an acute alteration in local connectivity that follows a similar time course as clinical recovery. Our results do not indicate strong evidence that concussion-related alterations in rs-fMRI persist beyond clinical recovery.
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References
1.
LangloisJ.A., Rutland-BrownW., and WaldM.M. (2006). The epidemiology and impact of traumatic brain injury: a brief overview. J. Head Trauma Rehabil. 21, 375–378.
2.
McCroryP., MeeuwisseW., DvorakJ., AubryM., BailesJ., BroglioS., CantuR.C., CassidyD., EchemendiaR.J., CastellaniR.J., DavisG.A., EllenbogenR., EmeryC., EngebretsenL., Feddermann-DemontN., GizaC.C., GuskiewiczK.M., HerringS., IversonG.L., JohnstonK.M., KissickJ., KutcherJ., LeddyJ.J., MaddocksD., MakdissiM., ManleyG.T., McCreaM., MeehanW.P., NagahiroS., PatriciosJ., PutukianM., SchneiderK.J., SillsA., TatorC.H., TurnerM., and VosP.E. (2017). Consensus statement on concussion in sport—the 5(th) International Conference on Concussion in Sport held in Berlin, October 2016. Br. J. Sport. Med. 51, 838–847.
3.
PrinsM.L., AlexanderD., GizaC.C., and HovdaD.A. (2013). Repeated mild traumatic brain injury: mechanisms of cerebral vulnerability. J. Neurotrauma, 30, 30–38.
4.
LonghiL., SaatmanK.E., FujimotoS., RaghupathiR., MeaneyD.F., DavisJ., McMillanB.S.A., ConteV., LaurerH.L., SteinS., StocchettiN., and McIntoshT.K. (2005). Temporal window of vulnerability to repetitive experimental concussive brain injury. Neurosurgery, 56, 364–374.
5.
KaminsJ., BiglerE., CovassinT., HenryL., KempS., LeddyJ.J., MayerA., McCreaM., PrinsM., SchneiderK.J., McLeodT.C.V., ZemekR., and GizaC.C. (2017). What is the physiological time to recovery after concussion?. A systematic review. Br. J. Sports Med. 51, 935–940.
6.
MayerA.R., BellgowanP.S., and HanlonF.M. (2015). Functional magnetic resonance imaging of mild traumatic brain injury. Neurosci. Biobehav. Rev. 49, 8–18.
7.
McCreaM., MeierT., HuberD., PtitoA., BiglerE., DebertC.T., ManleyG., MenonD., ChenJ.K., WallR., SchneiderK.J., and McAllisterT. (2017). Role of advanced neuroimaging, fluid biomarkers and genetic testing in the assessment of sport-related concussion: a systematic review. Br. J. Sport. Med. 51, 919–929.
8.
ChurchillN.W., HutchisonM.G., RichardsD., LeungG., GrahamS.J., and SchweizerT.A. (2017). Neuroimaging of sport concussion: persistent alterations in brain structure and function at medical clearance. Sci. Rep. 7, 8297.
9.
ManningK.Y., SchranzA., BarthaR., DekabanG.A., BarreiraC., BrownA., FischerL., AsemK., DohertyT.J., FraserD.D., HolmesJ., and MenonR.S. (2017). Multiparametric MRI changes persist beyond recovery in concussed adolescent hockey players. Neurology, 89, 2157–2166.
10.
JohnsonB., ZhangK., GayM., HorovitzS., HallettM., SebastianelliW., and SlobounovS. (2012). Alteration of brain default network in subacute phase of injury in concussed individuals: resting-state fMRI study. Neuroimage, 59, 511–518.
11.
CzerniakS.M., SikogluE.M., Liso NavarroA.A., McCaffertyJ., EisenstockJ., StevensonJ.H., KingJ.A., and MooreC.M. (2015). A resting state functional magnetic resonance imaging study of concussion in collegiate athletes. Brain Imaging Behav. 9, 323–332.
12.
MeierT.B., BellgowanP.S.F., and MayerA.R. (2017). Longitudinal assessment of local and global functional connectivity following sports-related concussion. Brain Imaging Behav. 11, 129–140.
13.
BroglioS.P., McCreaM., McAllisterT., HarezlakJ., KatzB., HackD., HainlineB., and CARE ConsortiumInvestigators. (2017). A National Study on the Effects of Concussion in Collegiate Athletes and U.S. Military Service Academy Members: The NCAA-DoD Concussion Assessment, Research and Education (CARE) Consortium Structure and Methods. Sport. Med 47, 1437–1451.
14.
ZangY., JiangT., LuY., HeY., and TianL. (2004). Regional homogeneity approach to fMRI data analysis. Neuroimage, 22, 394–400.
15.
ZhanJ., GaoL., ZhouF., KuangH., ZhaoJ., WangS., HeL., ZengX., and GongH. (2015). Decreased regional homogeneity in patients with acute mild traumatic brain injury: a resting-state fMRI study. J. Nerv. Ment. Dis. 203, 786–791.
16.
ZouQ.H., ZhuC.Z., YangY., ZuoX.N., LongX.Y., CaoQ.J., WangY.F., and ZangY.F. (2008). An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J. Neurosci. Methods, 172, 137–141.
17.
MadhavanR., JoelS.E., MullickR., CogsilT., NiogiS.N., TsiourisA.J., MukherjeeP., MasdeuJ.C., MarinelliL., and ShettyT. (2019). Longitudinal resting state functional connectivity predicts clinical outcome in mild traumatic brain injury. J. Neurotrauma, 36, 650–660.
18.
RubinovM., and SpornsO. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52, 1059–1069.
19.
KaushalM., EspañaL.Y., NenckaA.S., WangY., NelsonL.D., McCreaM.A., and MeierT.B. (2019). Resting-state functional connectivity after concussion is associated with clinical recovery. Hum. Brain Mapp. 40, 1211–1220.
20.
ZuoX.N., Di MartinoA., KellyC., ShehzadZ.E., GeeD.G., KleinD.F., CastellanosF.X., BiswalB.B., and MilhamM.P. (2010). The oscillating brain: complex and reliable. Neuroimage, 49, 1432–1445.
21.
ZuoX.N., XuT., JiangL., YangZ., CaoX.Y., HeY., ZangY.F., CastellanosF.X., and MilhamM.P. (2013). Toward reliable characterization of functional homogeneity in the human brain: preprocessing, scan duration, imaging resolution and computational space. Neuroimage, 65, 374–386.
22.
GuoC.C., KurthF., ZhouJ., MayerE.A., EickhoffS.B., KramerJ.H., and SeeleyW.W. (2012). One-year test-retest reliability of intrinsic connectivity network fMRI in older adults. Neuroimage, 61, 1471–1483.
23.
SchwarzA.J., and McGonigleJ. (2011). Negative edges and soft thresholding in complex network analysis of resting state functional connectivity data. Neuroimage, 55, 1132–1146.
24.
DoddA.B., LingJ.M., BedrickE.J., MeierT.B., and MayerA.R. (2018). Spatial distribution bias in subject-specific abnormalities analyses. Brain Imaging Behav. 12, 1828–1834.
25.
MayerA.R., BedrickE.J., LingJ.M., ToulouseT., and DoddA. (2014). Methods for identifying subject-specific abnormalities in neuroimaging data. Hum. Brain Mapp. 35, 5457–5470.
26.
MayerA.R., DoddA.B., LingJ.M., WertzC.J., ShaffN.A., BedrickE.J., and ViamonteC. (2018). An evaluation of Z-transform algorithms for identifying subject-specific abnormalities in neuroimaging data. Brain Imaging Behav. 12, 437–448.
KleinA.P., TetzlaffJ.E., BonisJ.M., NelsonL.D., MayerA.R., HuberD.L., HarezlakJ., MathewsV.P., UlmerJ.L., SinsonG.P., NenckaA.S., KochK.M., WuY.-C., SaykinA.J., DiFioriJ.P., GizaC.C., GoldmanJ., GuskiewiczK.M., MihalikJ.P., DumaS.M., RowsonS., BrooksA., BroglioS.P., McAllisterT., McCreaM.A., and MeierT.B. (2019). Prevalence of potentially clinically significant magnetic resonance imaging findings in athletes with and without sport-related concussion. J. Neurotrauma, 11, 1776–1785.
29.
WechslerD. (2001). Wechsler Test of Adult Reading: WTAR. San Antonio, TX: The Psychological. Corp.
30.
McCroryP., MeeuwisseW.H., AubryM., CantuB., DvorakJ., EchemendiaR.J., EngebretsenL., JohnstonK., KutcherJ.S., RafteryM., SillsA., BensonB.W., DavisG.A., EllenbogenR.G., GuskiewiczK., HerringS.A., IversonG.L., JordanB.D., KissickJ., McCreaM., McIntoshA.S., MaddocksD., MakdissiM., PurcellL., PutukianM., SchneiderK., TatorC.H., and TurnerM. (2013). Consensus statement on concussion in sport—the 4th International Conference on Concussion in Sport held in Zurich, November 2012. Br. J. Sport. Med. 47, 250–258.
RiemannB.L., and GuskiewiczK.M. (2000). Effects of mild head injury on postural stability as measured through clinical balance testing. . Athl. Train. 35, 19–25.
33.
McCreaM., KellyJ.P., KlugeJ., AckleyB., and RandolphC. (1997). Standardized assessment of concussion in football players. Neurology, 48, 586–588.
34.
CoxR.W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162–173.
35.
JenkinsonM., BannisterP., BradyM., and SmithS. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17, 825–841.
36.
GreveD.N., and FischlB. (2009). Accurate and robust brain image alignment using boundary-based registration. Neuroimage, 48, 63–72.
37.
TaylorP.A., and SaadZ.S. (2013). FATCAT: (an efficient) Functional and Tractographic Connectivity Analysis Toolbox. Brain Connect. 3, 523–535.
38.
GloverG.H., MuellerB.A., TurnerJ.A., van ErpT.G., LiuT.T., GreveD.N., VoyvodicJ.T., RasmussenJ., BrownG.G., KeatorD.B., CalhounV.D., LeeH.J., FordJ.M., MathalonD.H., DiazM., O'LearyD.S., GaddeS., PredaA., LimK.O., WibleC.G., SternH.S., BelgerA., McCarthyG., OzyurtB., and PotkinS.G. (2012). Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies. J. Magn. Reson. Imaging, 36, 39–54.
39.
FriedmanL., GloverG.H., KrenzD., MagnottaV., and FirstB. (2006). Reducing inter-scanner variability of activation in a multicenter fMRI study: role of smoothness equalization. Neuroimage, 32, 1656–1668.
40.
MijalkovM., KakaeiE., PereiraJ.B., WestmanE., VolpeG., and Alzheimer's Disease NeuroimagingInitiative. (2017). BRAPH: a graph theory software for the analysis of brain connectivity. PLoS One, 12, e0178798.
41.
CraddockR.C., JamesG.A., HoltzheimerP.E., HuX.P.P., and MaybergH.S. (2012). A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum. Brain Mapp. 33, 1914–1928.
42.
ChenG., SaadZ.S., BrittonJ.C., PineD.S., and CoxR.W. (2013). Linear mixed-effects modeling approach to FMRI group analysis. Neuroimage, 73, 176–190.
43.
CoxR.W., ChenG., GlenD.R., ReynoldsR.C., and TaylorP.A. (2017). FMRI clustering in AFNI: false-positive rates redux. Brain Connect. 7, 152–171.
RollsE.T., JoliotM., and Tzourio-MazoyerN. (2015). Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas. Neuroimage, 122, 1–5.
46.
YarkoniT., PoldrackR.A., NicholsT.E., Van EssenD.C., and WagerT.D. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods, 8, 665–670.
47.
WilliamsR.M., PuetzT.W., GizaC.C., and BroglioS.P. (2015). Concussion recovery time among high school and collegiate athletes: a systematic review and meta-analysis. Sport. Med. 45, 893–903.
48.
McCreaM., GuskiewiczK.M., MarshallS.W., BarrW., RandolphC., CantuR.C., OnateJ.A., YangJ., and KellyJ.P. (2003). Acute effects and recovery time following concussion in collegiate football players: the NCAA Concussion Study. JAMA, 290, 2556–2563.
49.
MeierT.B., BellgowanP.S., SinghR., KuplickiR., PolanskiD.W., and MayerA.R. (2015). Recovery of cerebral blood flow following sports-related concussion. JAMA Neurol, 72, 530–538.
50.
McCuddyW.T., EspanaL.Y., NelsonL.D., BirnR.M., MayerA.R., and MeierT.B. (2018). Association of acute depressive symptoms and functional connectivity of emotional processing regions following sport-related concussion. Neuroimage Clin. 19, 434–442.
51.
BucknerR.L., Andrews-HannaJ.R., and SchacterD.L. (2008). The brain's default network: anatomy, function, and relevance to disease. Ann. N Y Acad. Sci. 1124, 1–38.
52.
ZhuD.C., CovassinT., NogleS., DoyleS., RussellD., PearsonR.L., MonroeJ., LiszewskiC.M., DeMarcoJ.K., and KaufmanD.I. (2015). A potential biomarker in sports-related concussion: brain functional connectivity alteration of the default-mode network measured with longitudinal resting-state fMRI over thirty days. J. Neurotrauma, 32, 327–341.
53.
MilitanaA.R., DonahueM.J., SillsA.K., SolomonG.S., GregoryA.J., StrotherM.K., and MorganV.L. (2016). Alterations in default-mode network connectivity may be influenced by cerebrovascular changes within 1 week of sports related concussion in college varsity athletes: a pilot study. Brain Imaging Behav. 10, 559–568.
54.
BorichM., BabulA.N., YuanP.H., BoydL., and Virji-BabulN. (2015). Alterations in resting-state brain networks in concussed adolescent athletes. J. Neurotrauma, 32, 265–271.
55.
ColeM.W., PathakS., and SchneiderW. (2010). Identifying the brain's most globally connected regions. Neuroimage, 49, 3132–3148.
56.
SepulcreJ., LiuH., TalukdarT., MartincorenaI., YeoB.T., and BucknerR.L. (2010). The organization of local and distant functional connectivity in the human brain. PLoS Comput. Biol. 6, e1000808.
57.
BiglerE.D. (2007). Anterior and middle cranial fossa in traumatic brain injury: relevant neuroanatomy and neuropathology in the study of neuropsychological outcome. Neuropsychology, 21, 515–531.
58.
EierudC., CraddockR.C., FletcherS., AulakhM., King-CasasB., KuehlD., and LaConteS.M. (2014). Neuroimaging after mild traumatic brain injury: review and meta-analysis. Neuroimage Clin. 4, 283–294.
59.
BaylyP.V, CohenT.S., LeisterE.P., AjoD., LeuthardtE.C., and GeninG.M. (2005). Deformation of the human brain induced by mild acceleration. J. Neurotrauma, 22, 845–856.
60.
HamiltonJ.P., FarmerM., FogelmanP., and GotlibI.H. (2015). Depressive rumination, the default-mode network, and the dark matter of clinical neuroscience. Biol. Psychiatry, 78, 224–230.
van der HornH.J., LiemburgE.J., ScheenenM.E., de KoningM.E., MarsmanJ.B., SpikmanJ.M., and van der NaaltJ. (2016). Brain network dysregulation, emotion, and complaints after mild traumatic brain injury. Hum. Brain Mapp. 37, 1645–1654.
63.
BeckA.T. (2008). The evolution of the cognitive model of depression and its neurobiological correlates. Am. J. Psychiatry, 165, 969–977.
64.
GizaC.C., and HovdaD.A. (2014). The new neurometabolic cascade of concussion. Neurosurgery, 75, Suppl. 4, S24–S33.
65.
WangY., NenckaA.S., MeierT.B., GuskiewiczK., MihalikJ.P., Alison BrooksM., SaykinA.J., KochK.M., WuY.-C., NelsonL.D., McAllisterT.W., BroglioS.P., and McCreaM.A. (2018). Cerebral blood flow in acute concussion: preliminary ASL findings from the NCAA-DoD CARE consortium. Brain Imaging, Behav., 1–11.
66.
MustafiS.M., HarezlakJ., KochK.M., NenckaA.S., MeierT.B., WestJ.D., GizaC.C., DiFioriJ.P., GuskiewiczK.M., MihalikJ.P., LaConteS.M., DumaS.M., BroglioS.P., SaykinA.J., McCreaM., McAllisterT.W., and WuY.-C. (2018). Acute white-matter abnormalities in sports-related concussion: a diffusion tensor imaging study from the NCAA-DoD CARE Consortium. J. Neurotrauma, 35, 2653–2664.
67.
MeierT.B., BergaminoM., BellgowanP.S.F., TeagueT.K., LingJ.M., JerominA., and MayerA.R. (2016). Longitudinal assessment of white matter abnormalities following sports-related concussion. Hum. Brain Mapp. 37, 833–845.
68.
LancasterM.A., MeierT.B., OlsonD.V, McCreaM.A., NelsonL.D., and MuftulerL.T. (2018). Chronic differences in white matter integrity following sport-related concussion as measured by diffusion MRI: 6-month follow-up. Hum. Brain Mapp. 39, 4276–4289.
69.
NenckaA.S., MeierT.B., WangY., MuftulerL.T., WuY.C., SaykinA.J., HarezlakJ., BrooksM.A., GizaC.C., DifioriJ., GuskiewiczK.M., MihalikJ.P., LaConteS.M., DumaS.M., BroglioS., McAllisterT., McCreaM.A., and KochK.M. (2018). Stability of MRI metrics in the advanced research core of the NCAA-DoD concussion assessment, research and education (CARE) consortium. Brain Imaging Behav. 12, 1121–1140.
70.
GuskiewiczK.M., McCreaM., MarshallS.W., CantuR.C., RandolphC., BarrW., OnateJ.A., and KellyJ.P. (2003). Cumulative effects associated with recurrent concussion in collegiate football players: the NCAA Concussion Study. JAMA, 290, 2549–2555.
71.
ReynoldsB.B., StantonA.N., SoldozyS., GoodkinH.P., WintermarkM., and DruzgalT.J. (2018). Investigating the effects of subconcussion on functional connectivity using mass-univariate and multivariate approaches. Brain Imaging Behav. 12, 1332–1345.
72.
JohnsonB., NeubergerT., GayM., HallettM., and SlobounovS. (2014). Effects of subconcussive head trauma on the default mode network of the brain. J. Neurotrauma, 31, 1907–1913.
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