Acknowledgement
We thank all contributors and participants for their contribution to this study.
References
- Kim SJ, Lee DK, Jang YK, Jang H, Kim SE, Cho SH, et al. The effects of longitudinal white matter hyperintensity change on cognitive decline and cortical thinning over three years. J Clin Med 2020;9:2663
- Giese AK, Schirmer MD, Dalca AV, Sridharan R, Donahue KL, Nardin M, et al. White matter hyperintensity burden in acute stroke patients differs by ischemic stroke subtype. Neurology 2020;95:e79-e88
- Venkatraman VK, Aizenstein H, Guralnik J, Newman AB, Glynn NW, Taylor C, et al. Executive control function, brain activation and white matter hyperintensities in older adults. Neuroimage 2010;49:3436-3442
- Frey BM, Petersen M, Schlemm E, Mayer C, Hanning U, Engelke K, et al. White matter integrity and structural brain network topology in cerebral small vessel disease: the Hamburg city health study. Hum Brain Mapp 2021;42:1406-1415
- Chen X, Huang L, Ye Q, Yang D, Qin R, Luo C, et al. Disrupted functional and structural connectivity within default mode network contribute to WMH-related cognitive impairment. Neuroimage Clin 2019;24:102088
- Atwi S, Metcalfe AWS, Robertson AD, Rezmovitz J, Anderson ND, MacIntosh BJ. Attention-related brain activation is altered in older adults with white matter hyperintensities using multi-echo fMRI. Front Neurosci 2018;12:748
- Gesierich B, Tuladhar AM, Ter Telgte A, Wiegertjes K, Konieczny MJ, Finsterwalder S, et al. Alterations and test-retest reliability of functional connectivity network measures in cerebral small vessel disease. Hum Brain Mapp 2020;41:2629-2641
- Ter Telgte A, Wiegertjes K, Tuladhar AM, Noz MP, Marques JP, Gesierich B, et al. Investigating the origin and evolution of cerebral small vessel disease: the RUN DMC - InTENse study. Eur Stroke J 2018;3:369-378
- Shulman RG, Rothman DL, Behar KL, Hyder F. Energetic basis of brain activity: implications for neuroimaging. Trends Neurosci 2004;27:489-495
- Evans NR, Tarkin JM, Buscombe JR, Markus HS, Rudd JHF, Warburton EA. PET imaging of the neurovascular interface in cerebrovascular disease. Nat Rev Neurol 2017;13:676-688
- Heiss WD. The additional value of PET in the assessment of cerebral small vessel disease. J Nucl Med 2018;59:1660-1664
- Tomse P, Jensterle L, Grmek M, Zaletel K, Pirtosek Z, Dhawan V, et al. Abnormal metabolic brain network associated with Parkinson's disease: replication on a new European sample. Neuroradiology 2017;59:507-515
- Huang SY, Hsu JL, Lin KJ, Hsiao IT. A novel individual metabolic brain network for 18F-FDG PET imaging. Front Neurosci 2020;14:344
- Xue X, Wu JJ, Huo BB, Xing XX, Ma J, Li YL, et al. Age-related alterations of brain metabolic network based on [18F] FDG-PET of rats. Aging (Albany NY) 2022;14:923-942
- Wang M, Yan Z, Xiao SY, Zuo C, Jiang J. A novel metabolic connectome method to predict progression to mild cognitive impairment. Behav Neurol 2020;2020:2825037
- Saggar M, Hosseini SM, Bruno JL, Quintin EM, Raman MM, Kesler SR, et al. Estimating individual contribution from group-based structural correlation networks. Neuroimage 2015;120:274-284
- Fazekas F, Chawluk JB, Alavi A, Hurtig HI, Zimmerman RA. MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. AJR Am J Roentgenol 1987;149:351-356
- Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 2013;12:822-838
- Bruno A, Shah N, Lin C, Close B, Hess DC, Davis K, et al. Improving modified Rankin Scale assessment with a simplified questionnaire. Stroke 2010;41:1048-1050
- 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;9:386
- Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002;15:273-289
- Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 2011;106:1125-1165
- Huo BB, Shen J, Hua XY, Zheng MX, Lu YC, Wu JJ, et al. Alteration of metabolic connectivity in a rat model of deafferentation pain: a 18F-FDG PET/CT study. J Neurosurg 2019;132:1295-1303
- Huang SY, Hsu JL, Lin KJ, Liu HL, Wey SP, Hsiao IT. Characteristic patterns of inter- and intra-hemispheric metabolic connectivity in patients with stable and progressive mild cognitive impairment and Alzheimer's disease. Sci Rep 2018;8:13807
- Li W, Tang Y, Wang Z, Hu S, Gao X. The reconfiguration pattern of individual brain metabolic connectome for Parkinson's disease identification. arXiv [Preprint]. 2021 [cited 2021 April 29]. Available at: https://doi.org/10.48550/arXiv.2105.02811
- Duong T. KS: kernel density estimation and kernel discriminant analysis for multivariate data in R. J Stat Softw 2007;21:1-16
- Mantel N. The detection of disease clustering and a generalized regression approach. Cancer Res 1967;27:209-220
- Tong Y, Huang X, Qi CX, Shen Y. Altered functional connectivity of the primary visual cortex in patients with iridocyclitis and assessment of its predictive value using machine learning. Front Immunol 2021;12:660554
- Mohanty R, Nair VA, Tellapragada N, Williams LM Jr, Kang TJ, Prabhakaran V. Identification of subclinical language deficit using machine learning classification based on poststroke functional connectivity derived from low frequency oscillations. Brain Connect 2019;9:194-208
- Zhang T, Zhang Y, Ren J, Yang C, Zhou H, Li L, et al. Aberrant basal ganglia-thalamo-cortical network topology in juvenile absence epilepsy: a resting-state EEG-fMRI study. Seizure 2021;84:78-83
- De Silva TM, Faraci FM. Contributions of aging to cerebral small vessel disease. Annu Rev Physiol 2020;82:275-295
- Khastkhodaei Z, Muthuraman M, Yang JW, Groppa S, Luhmann HJ. Functional and directed connectivity of the cortico-limbic network in mice in vivo. Brain Struct Funct 2021;226:685-700
- Ballarini T, Iaccarino L, Magnani G, Ayakta N, Miller BL, Jagust WJ, et al. Neuropsychiatric subsyndromes and brain metabolic network dysfunctions in early onset Alzheimer's disease. Hum Brain Mapp 2016;37:4234-4247
- Sala A, Caminiti SP, Iaccarino L, Beretta L, Iannaccone S, Magnani G, et al. Vulnerability of multiple large-scale brain networks in dementia with Lewy bodies. Hum Brain Mapp 2019;40:4537-4550
- Huber M, Beyer L, Prix C, Schonecker S, Palleis C, Rauchmann BS, et al. Metabolic correlates of dopaminergic loss in dementia with Lewy bodies. Mov Disord 2020;35:595-605