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Multimodal neuroimaging study reveals dissociable processes between structural and functional networks in patients with subacute intracerebral hemorrhage

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Abstract

Emerging evidence has revealed widespread stroke-induced brain dysconnectivity, which leads to abnormal network organization. However, there are apparent discrepancies in dysconnectivity between structural connectivity and functional connectivity studies. In this work, resting-state fMRI and structural diffusion tensor imaging were obtained from 26 patients with subacute (10–14 days) intracerebral hemorrhage (ICH) and 20 matched healthy participants (patients/controls = 21/18 after head motion rejection). Graph theoretical approaches were applied to multimodal brain networks to quantitatively compare topological properties between both groups. Prominent small-world properties were found in the structural and functional brain networks of both groups. However, a significant deficit in global integration was revealed in the structural brain networks of the patient group and was associated with more severe clinical manifestations of ICH. Regarding ICH-related nodal deficits, reduced nodal interconnectivity was mainly detected in motor-related regions. Moreover, in the functional brain network, topological properties were mostly comparable between patients with ICH and healthy participants. Beyond the prominent small-world architecture in multimodal brain networks, there are dissociable alterations between structural and functional brain networks in patients with ICH. These findings highlight the potential for using aberrant network metrics as neural biomarkers for evaluation of the severity of ICH.

Intracerebral hemorrhage (ICH) also known as cerebral bleed, a major type of stroke, would significantly affect brain structure and function. Using multimodal neuroimaging, Zhang et al. investigate the ICH-related dysconnectivity in structural and functional brain networks and show a significantly disintegrated structural brain network with a preserved functional network topology in subacute phase (10–14 days).

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References

  1. Sporns O (2011) The human connectome: a complex network. Ann N Y Acad Sci 1224:109–125

    Article  Google Scholar 

  2. Sun Y, Lim J, Dai Z, Wong K, Taya F, Chen Y, Li J, Thakor N, Bezerianos A (2017) The effects of a mid-task break on the brain connectome in healthy participants: a resting-state functional MRI study. Neuroimage 152:19–30

    Article  Google Scholar 

  3. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):440–442

    Article  CAS  Google Scholar 

  4. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186–198

    Article  CAS  Google Scholar 

  5. Stam CJ (2014) Modern network science of neurological disorders. Nat Rev Neurosci 15(10):683–695

    Article  CAS  Google Scholar 

  6. Grefkes C, Fink GR (2011) Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches. Brain 134(Pt 5):1264–1276

    Article  Google Scholar 

  7. Feeney DM, Baron JC (1986) Diaschisis. Stroke 17(5):817–830

    Article  CAS  Google Scholar 

  8. Alstott J, Breakspear M, Hagmann P, Cammoun L, Sporns O (2009) Modeling the impact of lesions in the human brain. PLoS Comput Biol 5(6):e1000408

    Article  Google Scholar 

  9. Dijkhuizen RM, van der Marel K, Otte WM, Hoff EI, van der Zijden JP, van der Toorn A, van Meer MP (2012) Functional MRI and diffusion tensor imaging of brain reorganization after experimental stroke. Transl Stroke Res 3:36–43

    Article  Google Scholar 

  10. Wang L, Yu C, Chen H, Qin W, He Y, Fan F, Zhang Y, Wang M, Li K, Zang Y, Woodward TS, Zhu C (2010) Dynamic functional reorganization of the motor execution network after stroke. Brain 133(Pt 4):1224–1238

    Article  Google Scholar 

  11. Cheng L, Wu Z, Sun J, Fu Y, Wang X, Yang GY, Miao F, Tong S (2015) Reorganization of motor execution networks during sub-acute phase after stroke. IEEE Trans Neural Syst Rehabil Eng 23(4):713–723

    Article  Google Scholar 

  12. Kothari RU, Brott T, Broderick JP, Barsan WG, Sauerbeck LR, Zuccarello M, Khoury J (1996) The ABCs of measuring intracerebral hemorrhage volumes. Stroke 27(8):1304–1305

    Article  CAS  Google Scholar 

  13. Gladstone DJ, Danells CJ, Black SE (2002) The Fugl-Meyer assessment of motor recovery after stroke: a critical review of its measurement properties. Neurorehabil Neural Repair 16(3):232–240

    Article  Google Scholar 

  14. Shah S, Vanclay F, Cooper B (1989) Improving the sensitivity of the Barthel Index for stroke rehabilitation. J Clin Epidemiol 42(8):703–709

    Article  CAS  Google Scholar 

  15. Sun Y, Dai Z, Li J, Collinson SL, Sim K (2017) Modular-level alterations of structure-function coupling in schizophrenia connectome. Hum Brain Mapp 38(4):2008–2025

    Article  Google Scholar 

  16. Sun Y, Yin Q, Fang R, Yan X, Wang Y, Bezerianos A, Tang H, Miao F, Sun J (2014) Disrupted functional brain connectivity and its association to structural connectivity in amnestic mild cognitive impairment and Alzheimer’s disease. PLoS One 9(5):e96505

    Article  Google Scholar 

  17. Sun Y, Li J, Suckling J, Feng L (2017) Asymmetry of hemispheric network topology reveals dissociable processes between functional and structural brain connectome in community-living elders. Front Aging Neurosci 9:361

    Article  Google Scholar 

  18. Yan CG, Zang YF (2010) DPARSF: a Matlab toolbox for “pipeline” data analysis of resting-state fMRI. Front Syst Neurosci 4:13

    Google Scholar 

  19. Friston KJ, Williams S, Howard R, Frackowiak RS, Turner R (1996) Movement-related effects in fMRI time-series. Magn Reson Med 35(3):346–355

    Article  CAS  Google Scholar 

  20. Yan CG, Cheung B, Kelly C, Colcombe S, Craddock RC, Di Martino A, Li Q, Zuo XN, Castellanos FX, Milham MP (2013) A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. NeuroImage 76:183–201

    Article  Google Scholar 

  21. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15(1):273–289

    Article  CAS  Google Scholar 

  22. Zalesky A, Fornito A, Bullmore E (2012) On the use of correlation as a measure of network connectivity. NeuroImage 60(4):2096–2106

    Article  Google Scholar 

  23. Cui Z, Zhong S, Xu P, He Y, Gong G (2013) PANDA: a pipeline toolbox for analyzing brain diffusion images. Front Hum Neurosci 7:42

    PubMed  PubMed Central  Google Scholar 

  24. Mori S, Crain BJ, Chacko VP, van Zijl PC (1999) Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 45(2):265–269

    Article  CAS  Google Scholar 

  25. Sun Y, Chen Y, Collinson SL, Bezerianos A, Sim K (2017) Reduced hemispheric asymmetry of brain anatomical networks is linked to schizophrenia: a connectome study. Cereb Cortex 27(1):602–615

    PubMed  Google Scholar 

  26. Buchanan CR, Pernet CR, Gorgolewski KJ, Storkey AJ, Bastin ME (2014) Test-retest reliability of structural brain networks from diffusion MRI. NeuroImage 86:231–243

    Article  Google Scholar 

  27. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3):1059–1069

    Article  Google Scholar 

  28. Humphries MD, Gurney K, Prescott TJ (2006) The brainstem reticular formation is a small-world, not scale-freenetwork. Proc Biol Sci 273(1585):503–511

    Article  CAS  Google Scholar 

  29. Maslov S, Sneppen K (2002) Specificity and stability in topology of protein networks. Science 296(5569):910–913

    Article  CAS  Google Scholar 

  30. Achard S, Bullmore E (2007) Efficiency and cost of economical brain functional networks. PLoS Comput Biol 3(2):e17

    Article  Google Scholar 

  31. Latora V, Marchiori M (2001) Efficient behavior of small-world networks. Phys Rev Lett 87(19):198701

    Article  CAS  Google Scholar 

  32. Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40:35–41

    Article  Google Scholar 

  33. van den Heuvel MP, de Lange SC, Zalesky A, Seguin C, Yeo BTT, Schmidt R (2017) Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies: issues and recommendations. NeuroImage 152:437–449

    Article  Google Scholar 

  34. He Y, Wang J, Wang L, Chen ZJ, Yan C, Yang H, Tang H, Zhu C, Gong Q, Zang Y, Evans AC (2009) Uncovering intrinsic modular organization of spontaneous brain activity in humans. PLoS One 4(4):e5226

    Article  Google Scholar 

  35. Honey CJ, Sporns O (2008) Dynamical consequences of lesions in cortical networks. Hum Brain Mapp 29(7):802–809

    Article  Google Scholar 

  36. Park CH, Chang WH, Ohn SH, Kim ST, Bang OY, Pascual-Leone A, Kim YH (2011) Longitudinal changes of resting-state functional connectivity during motor recovery after stroke. Stroke 42(5):1357–1362

    Article  Google Scholar 

  37. Toga AW, Thompson PM (2003) Mapping brain asymmetry. Nat Rev Neurosci 4(1):37–48

    Article  CAS  Google Scholar 

  38. Nichols TE, Holmes AP (2002) Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 15(1):1–25

    Article  Google Scholar 

  39. Caliandro P, Vecchio F, Miraglia F, Reale G, Della Marca G, La Torre G, Lacidogna G, Iacovelli C, Padua L, Bramanti P, Rossini PM (2017) Small-world characteristics of cortical connectivity changes in acute stroke. Neurorehabil Neural Repair 31(1):81–94

    Article  Google Scholar 

  40. Bassett DS, Bullmore E (2006) Small-world brain networks. Neuroscientist 12(6):512–523

    Article  Google Scholar 

  41. Grefkes C, Nowak DA, Eickhoff SB, Dafotakis M, Kust J, Karbe H, Fink GR (2008) Cortical connectivity after subcortical stroke assessed with functional magnetic resonance imaging. Ann Neurol 63(2):236–246

    Article  Google Scholar 

  42. Nomura EM, Gratton C, Visser RM, Kayser A, Perez F, D’Esposito M (2010) Double dissociation of two cognitive control networks in patients with focal brain lesions. Proc Natl Acad Sci U S A 107(26):12017–12022

    Article  CAS  Google Scholar 

  43. de Vico Fallani F, Astolfi L, Cincotti F, Mattia D, la Rocca D, Maksuti E, Salinari S, Babiloni F, Vegso B, Kozmann G, Nagy Z (2009) Evaluation of the brain network organization from EEG signals: a preliminary evidence in stroke patient. Anat Rec (Hoboken) 292:2023–2031

    Article  Google Scholar 

  44. Dum RP, Strick PL (2002) Motor areas in the frontal lobe of the primate. Physiol Behav 77(4–5):677–682

    Article  CAS  Google Scholar 

  45. van den Heuvel MP, Sporns O (2013) Network hubs in the human brain. Trends Cogn Sci 17(12):683–696

    Article  Google Scholar 

  46. Gerloff C, Bushara K, Sailer A, Wassermann EM, Chen R, Matsuoka T, Waldvogel D, Wittenberg GF, Ishii K, Cohen LG, Hallett M (2006) Multimodal imaging of brain reorganization in motor areas of the contralesional hemisphere of well recovered patients after capsular stroke. Brain 129(Pt 3):791–808

    Article  Google Scholar 

  47. Jones DK, Knosche TR, Turner R (2013) White matter integrity, fiber count, and other fallacies: the do's and don'ts of diffusion MRI. NeuroImage 73:239–254

    Article  Google Scholar 

  48. Gong G, He Y, Concha L, Lebel C, Gross DW, Evans AC, Beaulieu C (2009) Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb Cortex 19(3):524–536

    Article  Google Scholar 

  49. Behrens TE, Berg HJ, Jbabdi S, Rushworth MF, Woolrich MW (2007) Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? NeuroImage 34(1):144–155

    Article  CAS  Google Scholar 

  50. Smith SM, Miller KL, Salimi-Khorshidi G, Webster M, Beckmann CF, Nichols TE, Ramsey JD, Woolrich MW (2011) Network modelling methods for FMRI. NeuroImage 54(2):875–891

    Article  Google Scholar 

  51. Zalesky A, Fornito A, Harding IH, Cocchi L, Yucel M, Pantelis C, Bullmore ET (2010) Whole-brain anatomical networks: does the choice of nodes matter? NeuroImage 50(3):970–983

    Article  Google Scholar 

  52. Xia M, Wang J, He Y (2013) BrainNet viewer: a network visualization tool for human brain connectomics. PLoS One 8(7):e68910

    Article  CAS  Google Scholar 

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Acknowledgments

We would like to convey our appreciation to all the participants, especially patients with ICH and their families.

Funding

This work was supported by the General Research Plan B of Zhejiang province (Grant no. 2017KY661 awarded to X. Z.), the ‘Hundred Talents Program’ of Zhejiang University (awarded to Y. S.), the National Natural Science Foundation of China (Grant no. 81801785 awarded to Y. S.), and the Fundamental Research Funds for the Central Universities (Grant no. 2018QNA5017 awarded to Y. S.).

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Correspondence to Yu Sun or Peng Qi.

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The study was approved by the local ethics committee in Shaoxing People’s Hospital, and written informed consent was obtained from each participant (control group) or from the patient’s first degree relatives (patient group).

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The authors declare that they have no conflict of interest.

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Zhang, X., Yu, X., Bao, Q. et al. Multimodal neuroimaging study reveals dissociable processes between structural and functional networks in patients with subacute intracerebral hemorrhage. Med Biol Eng Comput 57, 1285–1295 (2019). https://doi.org/10.1007/s11517-019-01953-8

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