Andersen BB, Korbo L, Pakkenberg B. A quantitative study of the human cerebellum with unbiased stereological techniques. J Comp Neurol. 1992;326(4):549–60. https://doi.org/10.1002/cne.903260405.

Article  CAS  PubMed  Google Scholar 

D’Mello AM, Gabrieli JDE, Nee DE. Evidence for hierarchical cognitive control in the human cerebellum. Curr Biol. 2020;30(10):1881-1892.e3. https://doi.org/10.1016/j.cub.2020.03.028.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Sereno MI, Diedrichsen J, Tachrount M, Testa-Silva G, d’Arceuil H, De Zeeuw C. The human cerebellum has almost 80% of the surface area of the neocortex. Proc Natl Acad Sci. 2020;117(32):19538–43. https://doi.org/10.1073/pnas.2002896117.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Schmahmann JD, Guell X, Stoodley CJ, Halko MA. The theory and neuroscience of cerebellar cognition. Annu Rev Neurosci. 2019;42(1):337–64. https://doi.org/10.1146/annurev-neuro-070918-050258.

Article  CAS  PubMed  Google Scholar 

Stoodley CJ, Schmahmann JD. Evidence for topographic organization in the cerebellum of motor control versus cognitive and affective processing. Cortex. 2010;46(7):831–44. https://doi.org/10.1016/j.cortex.2009.11.008.

Article  PubMed  PubMed Central  Google Scholar 

Hoppenbrouwers SS, Schutter DJLG, Fitzgerald PB, Chen R, Daskalakis ZJ. The role of the cerebellum in the pathophysiology and treatment of neuropsychiatric disorders: a review. Brain Res Rev. 2008;59(1):185–200. https://doi.org/10.1016/j.brainresrev.2008.07.005.

Article  CAS  PubMed  Google Scholar 

Kim SH, Kim W, Li XF, Jung IJ, Kim HJ. Does interferon beta treatment exacerbate neuromyelitis optica spectrum disorder? Mult Scler J. 2012;18(10):1480–3. https://doi.org/10.1177/1352458512439439.

Article  CAS  Google Scholar 

Calabrese M, Mattisi I, Rinaldi F, et al. Magnetic resonance evidence of cerebellar cortical pathology in multiple sclerosis. J Neurol Neurosurg Psychiatry. 2010;81(4):401–4. https://doi.org/10.1136/jnnp.2009.177733.

Article  PubMed  Google Scholar 

Schoonheim MM, Douw L, Broeders TA, Eijlers AJ, Meijer KA, Geurts JJ. The cerebellum and its network: disrupted static and dynamic functional connectivity patterns and cognitive impairment in multiple sclerosis. Mult Scler J. 2021. https://doi.org/10.1177/1352458521999274.

Article  Google Scholar 

Tona F, De Giglio L, Petsas N, et al. Role of cerebellar dentate functional connectivity in balance deficits in patients with multiple sclerosis. Radiology. 2018;287(1):267–75. https://doi.org/10.1148/radiol.2017170311.

Article  PubMed  Google Scholar 

Han Y, Liu Y, Zeng C, et al. Functional connectivity alterations in neuromyelitis optica spectrum disorder: correlation with disease duration and cognitive impairment. Clin Neuroradiol. 2020;30(3):559–68. https://doi.org/10.1007/s00062-019-00802-3.

Article  PubMed  Google Scholar 

Liu Y, Jiang X, Butzkueven H, et al. Multimodal characterization of gray matter alterations in neuromyelitis optica. Mult Scler J. 2018;24(10):1308–16. https://doi.org/10.1177/1352458517721053.

Article  Google Scholar 

Sun J, Zhang N, Wang Q, et al. Normal-appearing cerebellar damage in neuromyelitis optica spectrum disorder. Am J Neuroradiol. 2019;40(7):1156–61. https://doi.org/10.3174/ajnr.A6098.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Duan Y, Zhuo Z, Li H, et al. Brain structural alterations in MOG antibody diseases: a comparative study with AQP4 seropositive NMOSD and MS. J Neurol Neurosurg Psychiatry. 2021;92(7):709–16. https://doi.org/10.1136/jnnp-2020-324826.

Article  PubMed  Google Scholar 

Lee CY, Mak HKF, Chiu PW, Chang HC, Barkhof F, Chan KH. Differential brainstem atrophy patterns in multiple sclerosis and neuromyelitis optica spectrum disorders: differential brainstem atrophy patterns. J Magn Reson Imaging. 2018;47(6):1601–9. https://doi.org/10.1002/jmri.25866.

Article  PubMed  Google Scholar 

Schneider R, Bellenberg B, Kleiter I, et al. Cervical cord and ventricle affection in neuromyelitis optica. Acta Neurol Scand. 2017;135(3):324–31. https://doi.org/10.1111/ane.12601.

Article  CAS  PubMed  Google Scholar 

Weier K, Eshaghi A, Magon S, et al. The role of cerebellar abnormalities in neuromyelitis optica – a comparison with multiple sclerosis and healthy controls. Mult Scler J. 2015;21(6):757–66. https://doi.org/10.1177/1352458514554051.

Article  Google Scholar 

Dai D, He H, Vogelstein JT, Hou Z. Accurate prediction of AD patients using cortical thickness networks. Mach Vis Appl. 2013;24(7):1445–57. https://doi.org/10.1007/s00138-012-0462-0.

Article  Google Scholar 

Raj A, Mueller SG, Young K, Laxer KD, Weiner M. Network-level analysis of cortical thickness of the epileptic brain. Neuroimage. 2010;52(4):1302–13. https://doi.org/10.1016/j.neuroimage.2010.05.045.

Article  CAS  PubMed  Google Scholar 

Zheng W, Yao Z, Hu B, Gao X, Cai H, Moore P. Novel cortical thickness pattern for accurate detection of Alzheimer’s Disease. J Alzheimers Dis. 2015;48(4):995–1008. https://doi.org/10.3233/JAD-150311.

Article  CAS  PubMed  Google Scholar 

Zhou L, Wang Y, Li Y, Yap PT, Shen D. Hierarchical anatomical brain networks for MCI prediction: revisiting volumetric measures. PLoS ONE. 2011;6(7):14.

Article  Google Scholar 

Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature. 2012;489(7416):391–9. https://doi.org/10.1038/nature11405.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Thompson AJ, Banwell BL, Barkhof F, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17(2):162–73. https://doi.org/10.1016/S1474-4422(17)30470-2.

Article  PubMed  Google Scholar 

Wingerchuk DM, Banwell B, Bennett JL, et al. International consensus diagnostic criteria for neuromyelitis optica spectrum disorders. Neurology. 2015;85(2):177–89. https://doi.org/10.1212/WNL.0000000000001729.

Article  PubMed  PubMed Central  Google Scholar 

Takahashi T, Fujihara K, Nakashima I, et al. Establishment of a new sensitive assay for anti-human aquaporin-4 antibody in neuromyelitis optica. Tohoku J Exp Med. 2006;210(4):307–13. https://doi.org/10.1620/tjem.210.307.

Article  CAS  PubMed  Google Scholar 

Romero JE, Coupé P, Giraud R, et al. CERES: a new cerebellum lobule segmentation method. Neuroimage. 2017;147:916–24. https://doi.org/10.1016/j.neuroimage.2016.11.003.

Article  PubMed  Google Scholar 

Manjón JV, Coupé P. volBrain: an online MRI brain volumetry system. Front Neuroinformatics. 2016. https://doi.org/10.3389/fninf.2016.00030.

Article  Google Scholar 

Schaefer A, Kong R, Gordon EM, et al. Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb Cortex. 2018;28(9):3095–114. https://doi.org/10.1093/cercor/bhx179.

Article  PubMed  Google Scholar 

Li Y, Wang N, Wang H, Lv Y, Zou Q, Wang J. Surface-based single-subject morphological brain networks: Effects of morphological index, brain parcellation and similarity measure, sample size-varying stability and test-retest reliability. NeuroImage. 2021;235:118018. https://doi.org/10.1016/j.neuroimage.2021.118018.

Article  PubMed  Google Scholar 

Wang H, Jin X, Zhang Y, Wang J. Single-subject morphological brain networks: connectivity mapping, topological characterization and test–retest reliability. Brain Behav. 2016. https://doi.org/10.1002/brb3.448.

Article  PubMed  PubMed Central  Google Scholar 

Wang J, Wang X, Xia M, Liao X, Evans A, He Y. GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front Hum Neurosci. 2015. https://doi.org/10.3389/fnhum.2015.00386.

Article  PubMed  PubMed Central  Google Scholar 

Fortin JP, Cullen N, Sheline YI, et al. Harmonization of cortical thickness measurements across scanners and sites. Neuroimage. 2018;167:104–20. https://doi.org/10.1016/j.neuroimage.2017.11.024.

Article  PubMed  Google Scholar 

Mucha PJ, Richardson T, Macon K, Porter MA, Onnela JP. Community structure in time-dependent, multiscale, and multiplex networks. Science. 2010;328(5980):876–8. https://doi.org/10.1126/science.1184819.

Article  CAS  PubMed  Google Scholar 

Meilă M. Comparing clusterings by the variation of information. In: Schölkopf B, Warmuth MK, editors. Lecture notes in computer science. Berlin: Springer; 2003. https://doi.org/10.1007/978-3-540-45167-9_14.

Chapter  Google Scholar 

Seidlitz J, Váša F, Shinn M, et al. Morphometric similarity networks detect microscale cortical organization and predict inter-individual cognitive variation. Neuron. 2018;97(1):231-247.e7. https://doi.org/10.1016/j.neuron.2017.11.039.

Article  CAS  PubMed  Google Scholar 

Vértes PE, Rittman T, Whitaker KJ, et al. Gene transcription profiles associated with inter-modular hubs and connection distance in human functional magnetic resonance imaging networks. Philos Trans R Soc B Biol Sci. 2016;371(1705):20150362. https://doi.org/10.1098/rstb.2015.0362.

Article  CAS  Google Scholar 

Yeo BTT, Krienen FM, Sepulcre J, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106(3):1125–65. https://doi.org/10.1152/jn.00338.2011.

Article  PubMed  Google Scholar 

Arnatkevic̆iūtė A, Fulcher BD, Fornito A. A practical guide to linking brain-wide gene expression and neuroimaging data. NeuroImage. 2019;189:353–67. https://doi.org/10.1016/j.neuroimage.2019.01.011.

Article