Application of the concept of neural networks surgery in cerebrovascular disease treatment


Cerebrovascular disease
Neural networks surgery
Neural networks
Cognitive function




Based on advanced techniques, both the brain structural network and functional network can be reflected, giving rise to a new field: neural networks. Entering the 21st century, along with the extensive research on neural networks and the digital brain imaging field of neuromodulation, the neurosurgical field has entered into a novel stage: neural networks surgery. Neural networks surgery was developed to devote to protecting the cognitive function of patients with central nervous system diseases. By lucubrate, multiple new views of cerebrovascular disease have emerged. In this paper, we review the applications of this novel concept in treating cerebrovascular diseases, primarily through three aspects: disease mechanism, progression, and treatment strategy. Based on recent research, the development of a novel treatment system for cerebrovascular diseases might help clarify the course of these diseases, provide optimal treatment strategies, and protect the cognitive function of patients to the greatest extent.


Sakas DE, Panourias IG, Simpson BA, 2007, An introduction to neural networks surgery, a field of neuromodulation which is based on advances in neural networks science and digitised brain imaging. Acta Neurochir Suppl, 97: 3–13.

Hart MG, Ypma RJ, Romero-Garcia R, et al., 2016, Graph theory analysis of complex brain networks: New concepts in brain mapping applied to neurosurgery. J Neurosurg, 124: 1665–1678.

Herbet G, Duffau H, 2020, Revisiting the functional anatomy of the human brain: Toward a meta-networking theory of cerebral functions. Physiol Rev, 100: 1181–1228.

Sporns O, Tononi G, Kötter R, 2005, The human connectome: A structural description of the human brain. PLoS Comput Biol, 1: e42.

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

Xu Y, He Y, Bi Y, 2017, A tri-network model of human semantic processing. Front Psychol, 8: 1538.

Chen X, Liu M, Wu Z, et al., 2020, Topological abnormalities of functional brain network in early-stage Parkinson’s disease patients with mild cognitive impairment. Front Neurosci, 14: 616872.

Thom T, Haase N, Rosamond W, et al., 2006, Heart disease and stroke statistics--2006 update: A report from the American heart association statistics committee and stroke statistics subcommittee. Circulation, 113: e85–e151.

Caplan LR, Searls DE, Hon FK, 2009, Cerebrovascular disease. Med Clin North Am, 93: 353–369.

Ma Q, Li R, Wang L, et al., 2021, Temporal trend and attributable risk factors of stroke burden in China, 1990- 2019: An analysis for the global burden of disease study 2019. Lancet Public Health, 6: e897–e906.

O’Donnell MJ, Chin SL, Rangarajan S, et al., 2016, Global and regional effects of potentially modifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): A case-control study. Lancet, 388: 761–775.

Chan A, Ho S, Poon WS, 2002, Neuropsychological sequelae of patients treated with microsurgical clipping or endovascular embolization for anterior communicating artery aneurysm. Eur Neurol, 47: 37–44.

Li C, Zhu H, Zong X, et al., 2018, History, current situation, and future development of endoscopic neurosurgery in China. World Neurosurg, 110: 270–275.

Baldassarre A, Ramsey L, Rengachary J, et al., 2016, Dissociated functional connectivity profiles for motor and attention deficits in acute right-hemisphere stroke. Brain, 139: 2024–2038.

Baldassarre A, Ramsey L, Hacker CL, et al., 2014, Large-scale changes in network interactions as a physiological signature of spatial neglect. Brain, 137: 3267–3283.

Gordon EM, Laumann TO, Adeyemo B, et al., 2016. Generation and evaluation of a cortical area parcellation from resting-state correlations. Cereb Cortex, 26: 288–303.

Varsou O, Macleod MJ, Schwarzbauer C, 2014, Functional connectivity magnetic resonance imaging in stroke: An evidence-based clinical review. Int J Stroke, 9: 191–198.

Martinez-Ramirez S, Greenberg SM, Viswanathan A, 2014, Cerebral microbleeds: Overview and implications in cognitive impairment. Alzheimers Res Ther, 6: 33.

Charidimou A, Krishnan A, Werring DJ, et al., 2013, Cerebral microbleeds: A guide to detection and clinical relevance in different disease settings. Neuroradiology, 55: 655–674.

Al-Masni MA, Kim WR, Kim EY, et al., 2020, A two cascaded network integrating regional-based YOLO and 3D-CNN for cerebral microbleeds detection. Annu Int Conf IEEE Eng Med Biol Soc, 2020: 1055–1058.

Wadi LC, Grigoryan MM, Kim RC, et al., 2020, Mechanisms of cerebral microbleeds. J Neuropathol Exp Neurol, 42: 1093–1099.

Crouzet C, Jeong G, Chae RH, et al., 2021, Spectroscopic and deep learning–based approaches to identify and quantify cerebral microhemorrhages. Sci Rep, 11: 10725.

Jayaraman MV, Mayo-Smith WW, Tung GA, et al., 2004, Detection of intracranial aneurysms: Multi-detector row CT angiography compared with DSA. Radiology, 230: 510–518.

Brown RD Jr., Broderick JP, 2014, Unruptured intracranial aneurysms: Epidemiology, natural history, management options, and familial screening. Lancet Neurol, 13: 393–404.

Nemoto M, Hayashi N, Hanaoka S, et al., 2017, Feasibility study of a generalized framework for developing computer-aided detection systems-a new paradigm. J Digit Imaging, 30: 629–639.

Ueda D, Yamamoto A, Nishimori M, et al., 2019, Deep learning for MR angiography: Automated detection of cerebral aneurysms. Radiology, 290: 187–194.

Westerlaan HE, van Dijk JM, der Weide MC, et al., 2011, Intracranial aneurysms in patients with subarachnoid hemorrhage: CT angiography as a primary examination tool for diagnosis--systematic review and meta-analysis. Radiology, 258: 134–145.

Bo ZH, Qiao H, Tian C, et al., 2021, Toward human intervention-free clinical diagnosis of intracranial aneurysm via deep neural network. Patterns (N Y), 2: 100197.

Chen G, Wei X, Lei H, et al., 2020, Automated computer-assisted detection system for cerebral aneurysms in time-of-flight magnetic resonance angiography using fully convolutional network. Biomed Eng Online, 19: 38.

Findlay JM, Nisar J, Darsaut T, 2016, Cerebral vasospasm: A review. Can J Neurol Sci, 43: 15–32.

Dumont TM, 2016, Prospective assessment of a symptomatic cerebral vasospasm predictive neural network model. World Neurosurg, 94: 126–130.

Boulouis G, Charidimou A, Greenberg SM, 2016, Sporadic cerebral amyloid angiopathy: Pathophysiology, neuroimaging features, and clinical implications. Semin Neurol, 36: 233–243.

Charidimou A, Gang Q, Werring DJ, 2012, Sporadic cerebral amyloid angiopathy revisited: Recent insights into pathophysiology and clinical spectrum. J Neurol Neurosurg Psychiatry, 83, 124–137.

Arvanitakis Z, Leurgans SE, Wang Z, et al., 2011, Cerebral amyloid angiopathy pathology and cognitive domains in older persons. Ann Neurol, 69: 320–327.

Boyle PA, Yu L, Nag S, et al., 2015, Cerebral amyloid angiopathy and cognitive outcomes in community-based older persons. Neurology, 85: 1930–1936.

Reijmer YD, Fotiadis P, Martinez-Ramirez S, et al., 2015, Structural network alterations and neurological dysfunction in cerebral amyloid angiopathy. Brain, 138: 179–188.

Drenth N, van der Grond J, Rombouts SA, et al., 2021, Cerebral amyloid angiopathy is associated with decreased functional brain connectivity. Neuroimage Clin, 29: 102546.

Yonas H, Smith HA, Durham SR, et al., 1993, Increased stroke risk predicted by compromised cerebral blood flow reactivity. J Neurosurg, 79: 483–489.

Lin CJ, Tu PC, Chern CM, et al., 2014, Connectivity features for identifying cognitive impairment in presymptomatic carotid stenosis. PLoS One, 9: e85441.

Chang TY, Huang KL, Ho MY, et al., 2016, Graph theoretical analysis of functional networks and its relationship to cognitive decline in patients with carotid stenosis. J Cereb Blood Flow Metab, 36: 808–818.

Calabrò RS, Bramanti P, Baglieri A, et al., 2015, Functional cortical and cerebellar reorganization in a case of moyamoya disease. Innov Clin Neurosci, 12: 24–28.

Jefferson AL, Glosser G, Detre JA, et al., 2006, Neuropsychological and perfusion MR imaging correlates of revascularization in a case of moyamoya syndrome. AJNR Am J Neuroradiol, 27: 98–100.

Aben HP, Biessels GJ, Weaver NA, et al., 2019, Extent to which network hubs are affected by ischemic stroke predicts cognitive recovery. Stroke, 50: 2768–2774.

Gong G, He Y, Concha L, et al., 2009, Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb Cortex, 19: 524–536.

Hagmann P, Cammoun L, Gigandet X, et al., 2008, Mapping the structural core of human cerebral cortex. PLoS Biol, 6: e159.

Tomasi D,Volkow ND, 2010, Functional connectivity density mapping. Proc Natl Acad Sci U S A, 107: 9885–9890.

Raichle ME, Macleod AM, Snyder AZ, et al., 2001, A default mode of brain function. Proc Natl Acad Sci U S A, 98: 676–682.

Power JD, Schlaggar BL, Lessov-Schlaggar CN, et al., 2013, Evidence for hubs in human functional brain networks. Neuron, 79: 798–813.

Várkuti B, Cavusoglu M, Kullik A, et al., 2011, Quantifying the link between anatomical connectivity, gray matter volume and regional cerebral blood flow: An integrative MRI study. PLoS One, 6: e14801.

Liang X, Zou Q, He Y, et al., 2013, Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain. Proc Natl Acad Sci U S A, 110: 1929–1934.

Yuan B, Fang Y, Han Z, et al., 2017, Brain hubs in lesion models: Predicting functional network topology with lesion patterns in patients. Sci Rep, 7: 17908.

Buckner RL, Sepulcre J, Talukdar T, et al., 2009, Cortical hubs revealed by intrinsic functional connectivity: Mapping, assessment of stability, and relation to Alzheimer’s disease. J Neurosci, 29: 1860–1873.

Hilgetag CC, Burns GA, O’Neill MA, et al., 2000, Anatomical connectivity defines the organization of clusters of cortical areas in the macaque monkey and the cat. Philos Trans R Soc Lond B Biol Sci, 355: 91–110.

Hilgetag CC, O’Neill MA, Young MP, 2000, Hierarchical organization of macaque and cat cortical sensory systems explored with a novel network processor. Philos Trans R Soc Lond B Biol Sci, 355: 71–89.

Sporns O, Zwi JD, 2004, The small world of the cerebral cortex. Neuroinformatics, 2: 145–162.

Sporns O, Kötter R, 2004, Motifs in brain networks. PLoS Biol, 2: e369.

Li Y, Liu Y, Li J, et al., 2009, Brain anatomical network and intelligence. PLoS Comput Biol, 5: e1000395.

Lee S, Kim D, Youn H, et al., 2021, Brain network analysis reveals that amyloidopathy affects comorbid cognitive dysfunction in older adults with depression. Sci Rep, 11: 4299.

Cuadrado-Godia E, Dwivedi P, Sharma S, et al., 2018, Cerebral small vessel disease: A review focusing on pathophysiology, biomarkers, and machine learning strategies. J Stroke, 20: 302–320.

Bosnell RA, Kincses T, Stagg CJ, et al., 2011, Motor practice promotes increased activity in brain regions structurally disconnected after subcortical stroke. Neurorehabil Neural Repair, 25: 607–616.

Carrera E, Tononi G, 2014, Diaschisis: Past, present, future. Brain, 137l: 2408–2422.

Boukrina O, Barrett AM, 2017, Disruption of the ascending arousal system and cortical attention networks in post-stroke delirium and spatial neglect. Neurosci Biobehav Rev, 83: 1–10.

Bonilha L, Hillis AE, Wilmskoetter J, et al., 2019, Neural structures supporting spontaneous and assisted (entrained) speech fluency. Brain, 142: 3951–3962.

Altinbas A, Van Zandvoort MJ, van den Berg E, et al., 2011, Cognition after carotid endarterectomy or stenting: A randomized comparison. Neurology, 77: 1084–1090.

Nauta IM, Kulik SD, Breedt LC, et al., 2021, Functional brain network organization measured with magnetoencephalography predicts cognitive decline in multiple sclerosis. Mult Scler, 27: 1727–1737.

Zhang X, Liu J, Chen Y, et al., 2021, Brain network construction and analysis for patients with mild cognitive impairment and Alzheimer’s disease based on a highly-available nodes approach. Brain Behav, 11: e02027.

Jiao Y, Lin F, Wu J, et al., 2016, Lesion-to-eloquent fiber distance is a crucial risk factor in presurgical evaluation of arteriovenous malformations in the temporo–occipital junction. World Neurosurg, 93: 355–364.

Kazumata K, Tha KK, Narita H, et al., 2015, Chronic ischemia alters brain microstructural integrity and cognitive performance in adult moyamoya disease. Stroke, 46: 354–360.

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.

Lei Y, Song B, Chen L, et al., 2020, Reconfigured functional network dynamics in adult moyamoya disease: A resting-state fMRI study. Brain Imaging Behav, 14: 715–727.

He S, Liu Z, Xu Z, et al., 2020, Brain functional network in chronic asymptomatic carotid artery stenosis and occlusion: Changes and compensation. Neural Plast, 2020: 9345602.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.