We investigated the topology of structural brain connectivity networks and its association with outcome after mild traumatic brain injury, a major cause of permanent disability. Eighty-five patients with mild traumatic brain injury underwent magnetic resonance imaging (MRI) twice, about three weeks and eight months after injury, and 30 age-matched orthopedic trauma control subjects were scanned. Outcome was assessed with Extended Glasgow Outcome Scale on average eight months after injury. We performed constrained spherical deconvolution-based probabilistic streamlines tractography on diffusion MRI data and parcellated cortical and subcortical gray matter into 84 regions based on T1-weighted data to reconstruct structural brain connectivity networks weighted by the number of streamlines. Graph theoretical methods were employed to measure network properties in both patients and controls, and correlations between these properties and outcome were calculated. We found no global differences in the network properties between patients with mild traumatic brain injury and orthopedic control subjects at either stage. We found significantly increased betweenness centrality of the right pars opercularis in the chronic stage compared with control subjects, however. Further, both global and local network properties correlated significantly with outcome. Higher normalized global efficiency, degree, and strength as well as lower small-worldness were associated with better outcome. Correlations between the outcome and the local network properties were the most prominent in the left putamen and the left postcentral gyrus. Our results indicate that both global and local network properties provide valuable information about the outcome already in the acute/subacute stage and, therefore, are promising biomarkers for prognostic purposes in mild traumatic brain injury.
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References
1.
MenonD.K., SchwabK., WrightD.W., MaasA.I., and Demographics and Clinical Assessment Working Group of the International and Interagency Initiative toward Common Data Elements for Research on Traumatic Brain Injury and PsychologicalHealth. (2010). Position statement: Definition of Traumatic Brain injury. Arch. Phys. Med. Rehabil. 91, 1637–1640.
ShentonM.E., HamodaH.M., SchneidermanJ.S., BouixS., PasternakO., RathiY., VuM.A., PurohitM.P., HelmerK., KoerteI., LinA.P., WestinC.F., KikinisR., KubickiM., SternR.A., and ZafonteR. (2012). A review of magnetic resonance imaging and diffusion tensor imaging findings in mild traumatic brain injury. Brain Imaging Behav. 6, 137–192.
4.
Mac DonaldC.L., DikranianK., BaylyP., HoltzmanD., and BrodyD. (2007). Diffusion tensor imaging reliably detects experimental traumatic axonal injury and indicates approximate time of injury. J. Neurosci. 27, 11869–11876.
5.
BasserP.J., MattielloJ., and LeBihanD. (1994). MR diffusion tensor spectroscopy and imaging. Biophys. J. 66, 259–267.
6.
TournierJ.D., CalamanteF., and ConnellyA. (2007). Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. Neuroimage, 35, 1459–1472.
7.
TournierJ.D., CalamanteF., and ConnellyA. (2012). MRtrix: diffusion tractography in crossing fiber regions. Int. J. Imaging Syst. Technol. 22, 53–66.
8.
FarquharsonS., TournierJ.D., CalamanteF., FabinyiG., Schneider-KolskyM., JacksonG.D., and ConnellyA. (2013). White matter fiber tractography: why we need to move beyond DTI. J. Neurosurg. 118, 1367–1377.
9.
MesséA., CaplainS., ParadotG., GarrigueD., MineoJ.F., Soto AresG., DucreuxD., VignaudF., RozecG., DesalH., Pélégrini-IssacM., MontreuilM., BenaliH., and LehéricyS. (2011). Diffusion tensor imaging and white matter lesions at the subacute stage in mild traumatic brain injury with persistent neurobehavioral impairment. Hum. Brain Mapp. 32, 999–1011.
10.
WildeE.A., McCauleyS.R., HunterJ. V, BiglerE.D., ChuZ., WangZ.J., HantenG.R., TroyanskayaM., YallampalliR., LiX., ChiaJ., and LevinH.S. (2008). Diffusion tensor imaging of acute mild traumatic brain injury in adolescents. Neurology, 70, 948–955.
11.
BrandstackN., KurkiT., and TenovuoO. (2013). Quantitative diffusion-tensor tractography of long association tracts in patients with traumatic brain injury without associated findings at routine MR imaging. Radiology, 267, 231–239.
12.
BullmoreE., and SpornsO. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198.
13.
FischlB., Van Der KouweA., DestrieuxC., HalgrenE., SégonneF., SalatD.H., BusaE., SeidmanL.J., GoldsteinJ., KennedyD., CavinessV., MakrisN., RosenB., and DaleA.M. (2004). Automatically parcellating the human cerebral cortex. Cereb. Cortex, 14, 11–22.
14.
RoineT., JeurissenB., PerroneD., AeltermanJ., PhilipsW., SijbersJ., and LeemansA. (2019). Reproducibility and intercorrelation of graph theoretical measures in structural brain connectivity networks. Med. Image Anal. 52, 56–67.
15.
RoineU., RoineT., SalmiJ., Nieminen-von WendtT., TaniP., LeppämäkiS., RintahakaP., CaeyenberghsK., LeemansA., and SamsM. (2015). Abnormal wiring of the connectome in adults with high-functioning autism spectrum disorder. Mol. Autism, 6, 65.
16.
GriffaA., BaumannP.S., ThiranJ.P., and HagmannP. (2013). Structural connectomics in brain diseases. NeuroImage, 80, 515–526.
17.
RoineT., RoineU., TokolaA., BalkM.H., MannerkoskiM., ÅbergL., LönnqvistT., and AuttiT. (2019). Topological alterations of the structural brain connectivity network in children with juvenile neuronal ceroid lipofuscinosis. AJNR Am. J. Neuroradiol. 40, 2146–2153.
18.
MohammadianM., RoineT., HirvonenJ., KurkiT., Ala-SeppäläH., FrantzénJ., KatilaA., KyllönenA., MaanpääH.R., PostiJ., TakalaR., TallusJ., and TenovuoO. (2017). High angular resolution diffusion-weighted imaging in mild traumatic brain injury. Neuroimage Clin. 13, 174–180.
19.
MohammadianM., RoineT., HirvonenJ., KurkiT., PostiJ.P., KatilaA.J., TakalaR.S.-K., TallusJ., MaanpääH.-R., FrantzénJ., HutchinsonP.J., NewcombeV.F., MenonD.K., and TenovuoO. (2020). Alterations in microstructure and local fiber orientation of white matter are associated with outcome following mild traumatic brain injury. J. Neurotrauma, 37, 2616–2623.
20.
van der HornH.J., KokJ.G., de KoningM.E., ScheenenM.E., LeemansA., SpikmanJ.M., and van der NaaltJ. (2017). Altered wiring of the human structural connectome in adults with mild traumatic brain injury. J. Neurotrauma, 34, 1035–1044.
21.
IrajiA., ChenH., WisemanN., ZhangT., WelchR., O'NeilB., KulekA., AyazS.I., WangX., ZukC., HaackeE.M., LiuT., and KouZ. (2016). Connectome-scale assessment of structural and functional connectivity in mild traumatic brain injury at the acute stage. Neuroimage Clin. 12, 100–115.
22.
WatsonC.G., DeMasterD., and Ewing-CobbsL. (2019). Graph theory analysis of DTI tractography in children with traumatic injury. Neuroimage Clin. 21, 101673.
23.
Dall'AcquaP., JohannesS., MicaL., SimmenH.P., GlaabR., FandinoJ., SchwendingerM., MeierC., UlbrichE.J., MüllerA., JänckeL., and HänggiJ. (2016). Connectomic and surface-based morphometric correlates of acute mild traumatic brain injury. Front. Hum. Neurosci. 10, 127.
24.
SolmazB., TunçB., ParkerD., WhyteJ., HartT., RabinowitzA., RohrbachM., KimJ., and VermaR. (2017). Assessing connectivity related injury burden in diffuse traumatic brain injury. Hum. Brain Mapp. 38, 2913–2922.
25.
MitraJ., ShenK., GhoseS., BourgeatP., FrippJ., SalvadoO., PannekK., TaylorD.J., MathiasJ.L., and RoseS. (2016). Statistical machine learning to identify traumatic brain injury (TBI) from structural disconnections of white matter networks. NeuroImage, 129, 247–259.
26.
ImmsP., ClementeA., CookM., D'SouzaW., WilsonP.H., JonesD.K., and CaeyenberghsK. (2019). The structural connectome in traumatic brain injury: a meta-analysis of graph metrics. Neurosci. Biobehav. Rev. 99, 128–137.
27.
Ala-SeppäläH., HeinoI., FrantzénJ., TakalaR.S.K., KatilaA.J., KyllönenA., MaanpääH.R., PostiJ.P., TallusJ., and TenovuoO. (2016). Injury profiles, demography and representativeness of patients with TBI attending a regional emergency department. Brain Inj. 30, 1062–1067.
28.
CarneyN.A., and GhajarJ. (2007). Brain trauma foundation. guidelines for the management of severe traumatic brain, injury, 3rd edition. J. Neurotrauma 24, 1–106.
29.
WilsonJ.T., PettigrewL.E., and TeasdaleG.M. (1998). Structured interviews for the Glasgow Outcome Scale and the extended Glasgow Outcome Scale: guidelines for their use. J. Neurotrauma, 15, 573–585.
30.
WareJ.E., and SherbourneC.D. (1992). The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med. Care, 30, 473–483.
31.
FazekasF., ChawlukJ., AlaviA., HurtigH., and ZimmermanR. (1987). MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. AJR Am. J. Roentgenol. 149, 351–356.
32.
MarshallL.F., MarshallS.B., KlauberM.R., van Berkum ClarkM., EisenbergH.M., JaneJ.A., LuerssenT.G., MarmarouA., and FoulkesM.A. (1991). A new classification of head injury based on computerized tomography. J. Neurosurg. 75, S14–S20.
33.
VeraartJ., NovikovD.S., ChristiaensD., Ades-aronB., SijbersJ., and FieremansE. (2016). Denoising of diffusion MRI using random matrix theory. Neuroimage, 142, 394–406.
34.
ZhangY., BradyM., and SmithS. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging, 20, 45–57.
35.
LeemansA., and JonesD.K. (2009). The B-matrix must be rotated when correcting for subject motion in DTI data. Magn. Reson. Med. 61, 1336–1349.
36.
AnderssonJ.L.R., and SotiropoulosS.N. (2016). An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage, 125, 1063–1078.
IrfanogluM.O., WalkerL., SarllsJ., MarencoS., and PierpaoliC. (2012). Effects of image distortions originating from susceptibility variations and concomitant fields on diffusion MRI tractography results. Neuroimage, 61, 275–288.
39.
SmithR.E., TournierJ.D., CalamanteF., and ConnellyA. (2012). Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. Neuroimage, 62, 1924–1938.
40.
DesikanR.S., SégonneF., FischlB., QuinnB.T., DickersonB.C., BlackerD., BucknerR.L., DaleA.M., MaguireR.P., HymanB.T., AlbertM.S., and KillianyR.J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31, 968–980.
41.
PatenaudeB., SmithS.M., KennedyD.N., and JenkinsonM. (2011). A Bayesian model of shape and appearance for subcortical brain segmentation. NeuroImage, 56, 907–922.
42.
RubinovM., and SpornsO. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059–1069.
43.
CivierO., SmithR.E., YehC., ConnellyA., and CalamanteF. (2019). Is removal of weak connecCtions necessary for graph-theoretical analysis of dense weighted structural connectomes from diffusion MRI?194, 68–81.
44.
RubinovM., and SpornsO. (2011). Weight-conserving characterization of complex functional brain networks. Neuroimage, 56, 2068–2079.
45.
SchiaviS., PetraccaM., BattocchioM., El MendiliM.M., PaduriS., FleysherL., IngleseM., and DaducciA. (2020). Sensory-motor network topology in multiple sclerosis: structural connectivity analysis accounting for intrinsic density discrepancy. Hum. Brain Mapp. 41, 2951–2963.
46.
XiaM., WangJ., and HeY. (2013). BrainNet Viewer: a network visualization tool for human brain connectomics. PloS One, 8, e68910.
47.
QiuL., LuiS., KuangW., HuangX., LiJ., LiJ., ZhangJ., ChenH., SweeneyJ.A., and GongQ. (2014). Regional increases of cortical thickness in untreated, first-episode major depressive disorder. Transl. Psychiatry, 4, e378.
48.
SpielbergJ.M., MillerG.A., HellerW., and BanichM.T. (2015). Flexible brain network reconfiguration supporting inhibitory control. Proc. Natl. Acad. Sci. U. S. A. 112,10020_10025.
49.
HartwigsenG., PriceC.J., BaumgaertnerA., GeissG., KoehnkeM., UlmerS., and SiebnerH.R. (2010). The right posterior inferior frontal gyrus contributes to phonological word decisions in the healthy brain: evidence from dual-site TMS. Neuropsychologia, 48, 3155–3163.
50.
MaybergH.S. (1997). Limbic-cortical dysregulation: a proposed model of depression. J. Neuropsychiatry Clin. Neurosci. 9, 471–481.
51.
FagerholmE.D., HellyerP.J., ScottG., LeechR., and SharpD.J. (2015). Disconnection of network hubs and cognitive impairment after traumatic brain injury. Brain, 138, 1696–1709.
52.
CaeyenberghsK., LeemansA., LeunissenI., GooijersJ., MichielsK., SunaertS., and SwinnenS.P. (2014). Altered structural networks and executive deficits in traumatic brain injury patients. Brain Struct. Funct. 219, 193–209.
53.
Dall'AcquaP., JohannesS., MicaL., SimmenH.-P., GlaabR., FandinoJ., SchwendingerM., MeierC., UlbrichE.J., MüllerA., BaetschmannH., JänckeL., and HänggiJ. (2017). Functional and structural network recovery after mild traumatic brain injury: a 1-year longitudinal study. Front. Hum. Neurosci. 11, 280.
54.
DoyonJ., and BenaliH. (2005). Reorganization and plasticity in the adult brain during learning of motor skills. Curr. Opin. Neurobiol. 15, 161–167.
55.
DraganskiB., KherifF., KloppelS., CookP.A., AlexanderD.C., ParkerG.J.M., DeichmannR., AshburnerJ., and FrackowiakR.S.J. (2008). Evidence for segregated and integrative connectivity patterns in the human basal ganglia. J. Neurosci. 28, 7143–7152.
56.
ZagorchevL., MeyerC., StehleT., WenzelF., YoungS., PetersJ., WeeseJ., PaulsenK., GarlinghouseM., FordJ., RothR., FlashmanL., and McallisterT. (2016). Differences in regional brain volumes two months and one year after mild traumatic brain injury. J. Neurotrauma, 33, 29–34.
57.
GooijersJ., ChalaviS., BeeckmansK., MichielsK., LafosseC., SunaertS., and SwinnenS.P. (2016). Subcortical volume loss in the thalamus, putamen, and pallidum, induced by traumatic brain injury, is associated with motor performance deficits. Neurorehabil. Neural Repair, 30, 603–614.
58.
AndersonC.V., and BiglerE.D. (1995). Ventricular dilation, cortical atrophy, and neuropsychological outcome following traumatic brain injury. J. Neuropsychiatry Clin. Neurosci. 7, 42–48.
59.
TournierJ.D., CalamanteF., and ConnellyA. (2013). Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion-weighted imaging. NMR Biomed. 26, 1775–1786.
60.
SmithR.E., TournierJ.D., CalamanteF., and ConnellyA. (2013). SIFT: Spherical-deconvolution informed filtering of tractograms. Neuroimage, 67, 298–312.
61.
SmithR.E., TournierJ.D., CalamanteF., and ConnellyA. (2015). SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. Neuroimage, 119, 338–351.
62.
SmithR.E., TournierJ.D., CalamanteF., and ConnellyA. (2015). The effects of SIFT on the reproducibility and biological accuracy of the structural connectome. Neuroimage, 104, 253–265.
63.
CalamanteF., SmithR.E., TournierJ.D., RaffeltD., and ConnellyA. (2015). Quantification of voxel-wise total fibre density: investigating the problems associated with track-count mapping. Neuroimage, 117, 284–293.
64.
AnderssonJ.L.R., SkareS., and AshburnerJ. (2003). How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage, 20, 870–888.
65.
JeurissenB., TournierJ.D., DhollanderT., ConnellyA., and SijbersJ. (2014). Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data. Neuroimage, 103, 411–426.
LippI., ParkerG.D., TallantyreE.C., GoodallA., GramaS., PatitucciE., HeveronP., TomassiniV., and JonesD.K. (2020). Tractography in the presence of multiple sclerosis lesions. NeuroImage, 209, 116471.
69.
WildeE.A., WareA.L., LiX., WuT.C., McCauleyS.R., BarnesA., NewsomeM.R., BiekmanB.D., HunterJ.V, ChuZ.D., and LevinH.S. (2019). Orthopedic injured versus uninjured comparison groups for neuroimaging research in mild traumatic brain injury. J. Neurotrauma, 36, 239–249.
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