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Changes in Brain Functional Network Connectivity in Adult Moyamoya Diseases

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Abstract

Moyamoya disease (MMD) is a cerebrovascular disease that is characterized by progressive stenosis or occlusion of the internal carotid arteries and its main branches, which leads to the formation of abnormal small collateral vessels. However, little is known about how these special vascular structures affect cortical network connectivity and brain function. By applying EEG analysis and graphic network analyses undergoing EEG recording of subjects with eyes-closed (EC) and eyes-open (EO) resting states, and working memory (WM) tasks, we examined the brain network features of hemorrhagic (HMMD) and ischemic MMD (IMMD) brains. For the first time, we observed that IMMD had the much lower alpha-blocking rate during EO state than healthy controls while HMMD exhibited the relatively low EEG activity rate across all the behavior states. Further, IMMD showed strong network connections in the alpha-wave band in frontal and parietal regions during EO and WM states. EEG frequency and network topological maps during both resting and WM states indicated that the left frontal lobe and left parietal lobe in HMMD patients and the right parietal lobe and temporal lobe in IMMD patients have clear differences compared with controls, which provides a new insight to understand distinct electrophysiological features of MMD. However, due to the small sample size of recruited patient subjects, the result conclusion may be limited.

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Data availability

EEG data collected in patients in this paper are owned by Huashan Hospital, Fudan University. If you have any questions about the authenticity of the data, you can contact the corresponding author by email.

References

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Baldy-Moulinier M, Ingvar DH (1968) EEG frequency content related to regional blood flow of cerebral cortex in cat. Exp Brain Res 5(1):55–60

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  • Bullmore E, Sporns O (2012) The economy of brain network organization. Nat Rev Neurosci 13(5):336

    Article  CAS  PubMed  Google Scholar 

  • Cho A, Chae J-H, Kim HM et al (2014) Electroencephalography in pediatric moyamoya disease: reappraisal of clinical value. Child’s Nervous Syst 30(3):449–459

    Article  Google Scholar 

  • Chotas H, Bourne J, Teschan P (1979) Heuristic techniques in the quantification of the electroencephalogram in renal failure. Comput Biomed Res 12(4):299–312

    Article  CAS  PubMed  Google Scholar 

  • de Oliveira JL, Ávila M, Martins TC et al (2020) Medium- and long-term functional behavior evaluations in an experimental focal ischemic stroke mouse model. Cogn Neurodynamics 14(4):473–481

    Article  Google Scholar 

  • Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134(1):9–21

    Article  PubMed  Google Scholar 

  • Frechette E, Bell-Stephens T, Steinberg G, Fisher R (2015) Electroencephalographic features of moyamoya in adults. Clin Neurophysiol 126(3):481–485

    Article  CAS  PubMed  Google Scholar 

  • Honey CJ, Kötter R, Breakspear M, Sporns O (2007) Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc Natl Acad Sci 104(24):10240–10245

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Humphries MD, Gurney K, Prescott TJ (2006) The brainstem reticular formation is a small-world, not scale-free, network. Proce Royal Soci London B: Biol Sci 273(1585):503–511

    CAS  Google Scholar 

  • Ingvar DH (1971) Cerebral blood flow and metabolism related to EEG and cerebral functions. Acta Anaesthesiol Scand 15:110–114

    Article  Google Scholar 

  • Ingvar DH, Sjölund B, Ardö A (1976) Correlation between dominant EEG frequency, cerebral oxygen uptake and blood flow. Electroencephalogr Clin Neurophysiol 41(3):268–276

    Article  CAS  PubMed  Google Scholar 

  • Jonkman E, Van Dieren A, Veering M, Ponsen L, Da Silva FL, Tulleken C. 1984 EEG and CBF in cerebral ischemia. Follow-up studies in humans and monkeys. Progress in brain research. Elsevier, Amsterdam

  • Jortner RA, Farivar SS, Laurent G (2007) A simple connectivity scheme for sparse coding in an olfactory system. J Neurosci 27(7):1659–1669

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kaiser DA (2005) Basic principles of quantitative EEG. J Adult Dev 12(2–3):99–104

    Article  Google Scholar 

  • Karzmark P, Zeifert PD, Bell-Stephens TE, Steinberg GK, Dorfman LJ (2011) Neurocognitive impairment in adults with moyamoya disease without stroke. Neurosurgery 70(3):634–638

    Article  Google Scholar 

  • Kazumata K, Tha KK, Narita H, et al. Chronic ischemia alters brain microstructural integrity and cognitive performance in adult moyamoya disease. Stroke 2014: STROKEAHA. 114.007407.

  • Kazumata K, Tha KK, Narita H et al (2016) Investigating brain network characteristics interrupted by covert white matter injury in patients with moyamoya disease: insights from graph theoretical analysis. World Neurosurg 89(654–65):e2

    Google Scholar 

  • Kim JE, Jeon JS (2014) An update on the diagnosis and treatment of adult moyamoya disease taking into consideration controversial issues. Neurol Res 36(5):407–416

    Article  PubMed  Google Scholar 

  • Kodama N, Aoki Y, Hiraga H, Wada T, Suzuki J (1979) Electroencephalographic findings in children with moyamoya disease. Arch Neurol 36(1):16–19

    Article  CAS  PubMed  Google Scholar 

  • Koessler L, Maillard L, Benhadid A et al (2009) Automated cortical projection of EEG sensors: anatomical correlation via the international 10–10 system. Neuroimage 46(1):64–72

    Article  CAS  PubMed  Google Scholar 

  • Kuroda S, Houkin K (2008) Moyamoya disease: current concepts and future perspectives. The Lancet Neurology 7(11):1056–1066

    Article  PubMed  Google Scholar 

  • Lei Y, Li Y, Ni W et al (2014) Spontaneous brain activity in adult patients with moyamoya disease: a resting-state fMRI study. Brain Res 1546:27–33

    Article  CAS  PubMed  Google Scholar 

  • Lei Y, Song B, Chen L et al (2018) Reconfigured functional network dynamics in adult moyamoya disease: a resting-state fMRI study. Brain Imaging Behav 14:1–13

    Google Scholar 

  • Liu Y, Liang M, Zhou Y et al (2008) Disrupted small-world networks in schizophrenia. Brain 131(4):945–961

    Article  PubMed  Google Scholar 

  • Marek S, Dosenbach NU (2018) The frontoparietal network: function, electrophysiology, and importance of individual precision mapping. Dialogues Clin Neurosci 20(2):133

    Article  PubMed  PubMed Central  Google Scholar 

  • Mognon A, Jovicich J, Bruzzone L, Buiatti M (2011) ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features. Psychophysiology 48(2):229–240

    Article  PubMed  Google Scholar 

  • Nagata K (1989) Topographic EEG mapping in cerebrovascular disease. Brain Topogr 2(1–2):119–128

    Article  CAS  PubMed  Google Scholar 

  • Niedermeyer E (1997) Alpha rhythms as physiological and abnormal phenomena. Int J Psychophysiol 26(1–3):31–49

    Article  CAS  PubMed  Google Scholar 

  • Niedermeyer E (2003) The clinical relevance of EEG interpretation. Clin Electroencephalogr 34(3):93–98

    Article  CAS  PubMed  Google Scholar 

  • Olshausen BA, Field DJ (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583):607

    Article  CAS  PubMed  Google Scholar 

  • Onnela J-P, Chakraborti A, Kaski K, Kertiész J (2002) Dynamic asset trees and portfolio analysis. Europ Phys J B-Condensed Mat and Complex Syst 30(3):285–288

    Article  CAS  Google Scholar 

  • Pfurtscheller G (2001) Functional brain imaging based on ERD/ERS. Vision Res 41(10–11):1257–1260

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  • Scott RM, Smith ER (2009) Moyamoya disease and moyamoya syndrome. N Engl J Med 360(12):1226–1237

    Article  CAS  PubMed  Google Scholar 

  • Simoncelli EP, Olshausen BA (2001) Natural image statistics and neural representation. Annu Rev Neurosci 24(1):1193–1216

    Article  CAS  PubMed  Google Scholar 

  • Stam CJ, Nolte G, Daffertshofer A (2007) Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources. Hum Brain Mapp 28(11):1178–1193

    Article  PubMed  PubMed Central  Google Scholar 

  • Stam C, De Haan W, Daffertshofer A et al (2008) Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer’s disease. Brain 132(1):213–224

    Article  PubMed  Google Scholar 

  • Su SH, Hai J, Zhang L, Yu F, Wu YF (2013) Assessment of cognitive function in adult patients with hemorrhagic moyamoya disease who received no surgical revascularization. Eur J Neurol 20(7):1081–1087

    Article  PubMed  Google Scholar 

  • Sulg IA. The quantitated EEG as a measure of brain dysfunction: A study by means of manual analysis of the electroencephalogram (EEG): Department of Clinical Neurophysiology, University Hospital Lund; 1969.

  • Suzuki J, Takaku A (1969) Cerebrovascular moyamoya disease: disease showing abnormal net-like vessels in base of brain. Arch Neurol 20(3):288–299

    Article  CAS  PubMed  Google Scholar 

  • Tolonen U, Ahonen A, Sulg I et al (1981) Serial measurements of quantitative EEG and cerebral blood flow and circulation time after brain infarction. Acta Neurol Scand 63(3):145–155

    Article  CAS  PubMed  Google Scholar 

  • Vinje WE, Gallant JL (2000) Sparse coding and decorrelation in primary visual cortex during natural vision. Science 287(5456):1273–1276

    Article  CAS  PubMed  Google Scholar 

  • Wan L, Huang H, Schwab N et al (2018) From eyes-closed to eyes-open: role of cholinergic projections in EC-to-EO alpha reactivity revealed by combining EEG and MRI. Human Brain Map 40:566–577

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  • Xie P, Pang X, Cheng S et al (2020) Cross-frequency and iso-frequency estimation of functional corticomuscular coupling after stroke. Cogn Neurodynamics. https://doi.org/10.1007/s11571-020-09635-0.

    Article  Google Scholar 

  • Yu Y, Migliore M, Hines ML, Shepherd GM (2014) Sparse coding and lateral inhibition arising from balanced and unbalanced dendrodendritic excitation and inhibition. J Neurosci 34(41):13701–13713

    Article  PubMed  PubMed Central  Google Scholar 

  • Yu M, Gouw AA, Hillebrand A et al (2016) Different functional connectivity and network topology in behavioral variant of frontotemporal dementia and Alzheimer’s disease: an EEG study. Neurobiol Aging 42:150–162

    Article  PubMed  Google Scholar 

  • Zeng K, Kang J, Ouyang G et al (2017) Disrupted brain network in children with autism spectrum disorder. Sci Rep 7(1):16253

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Zhang J, Wang J, Wu Q et al (2011) Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biol Psychiat 70(4):334–342

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This study was supported by the National Natural Science Foundation of China (81761128011, 81801155, and 81771237), Shanghai Science and Technology Committee support (16JC1420100, 18511102800), Shanghai Health and Family Planning Commission support (2017BR022), the program for the Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, the “Dawn” Program of Shanghai Education Commission (16SG02), and the Scientific Research Project of Huashan Hospital, Fudan University (2016QD082), the Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01) and ZJLab, the program for the Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning.

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Author notes

  1. Gaoxing Zheng and Yu Lei have contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, School of Life Sciences, Human Phenome Institute, Research Institute of Intelligent Complex Systems, Institutes of Brain Science, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China

    Gaoxing Zheng, Yuzhu Li, Wei Zhang, Xiaoying Qi & Yuguo Yu

  2. Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, 200040, China

    Yu Lei, Jiabin Su, Liang Chen, Xin Zhang, Yuxiang Gu & Ying Mao

  3. Quantitative Neuroscience with Magnetic Resonance Core Center, Yale University, New Haven, CT, 06520, USA

    Yuguo Yu

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  1. Gaoxing Zheng
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  2. Yu Lei
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  3. Yuzhu Li
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  4. Wei Zhang
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  8. Xin Zhang
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Contributions

YY, YG and YM supervised the research, YL, GZ and YY designed the research, GZ, YL, YL, WZ, JS, XQ, LC and XZ performed the research, and GZ, YL and YY wrote the paper. All authors reviewed the manuscript.

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Correspondence to Yuxiang Gu or Yuguo Yu.

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This study was approved and supervised by the Ethics Committee of Huashan Hospital.

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Zheng, G., Lei, Y., Li, Y. et al. Changes in Brain Functional Network Connectivity in Adult Moyamoya Diseases. Cogn Neurodyn 15, 861–872 (2021). https://doi.org/10.1007/s11571-021-09666-1

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  • DOI: https://doi.org/10.1007/s11571-021-09666-1

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