Yakushev I, Chételat G, Fischer FU, Landeau B, Bastin C, Scheurich A, et al. Metabolic and structural connectivity within the default mode network relates to working memory performance in young healthy adults. Neuroimage. 2013;79:184–90. https://doi.org/10.1016/j.neuroimage.2013.04.069.
Wang M, Jiang J, Yan Z, Alberts I, Ge J, Zhang H, et al. Individual brain metabolic connectome indicator based on Kullback-Leibler divergence similarity estimation predicts progression from mild cognitive impairment to Alzheimer's dementia. Eur J Nucl Med Mol Imaging. 2020;47(12):2753–64. https://doi.org/10.1007/s00259-020-04814-x.
Article PubMed PubMed Central Google Scholar
Huang S, Li J, Sun L, Ye J, Fleisher A, Wu T, et al. Learning brain connectivity of Alzheimer's disease by sparse inverse covariance estimation. Neuroimage. 2010;50(3):935–49. https://doi.org/10.1016/j.neuroimage.2009.12.120.
Yakushev I, Drzezga A, Habeck C. Metabolic connectivity: methods and applications. Current opinion in neurology. 2017;30(6):677–85. https://doi.org/10.1097/wco.0000000000000494.
Morbelli S, Perneczky R, Drzezga A, Frisoni GB, Caroli A, van Berckel BN, et al. Metabolic networks underlying cognitive reserve in prodromal Alzheimer disease: a European Alzheimer disease consortium project. J Nucl Med. 2013;54(6):894–902. https://doi.org/10.2967/jnumed.112.113928.
Article CAS PubMed Google Scholar
Perani D, Farsad M, Ballarini T, Lubian F, Malpetti M, Fracchetti A, et al. The impact of bilingualism on brain reserve and metabolic connectivity in Alzheimer's dementia. Proc Natl Acad Sci U S A. 2017;114(7):1690–5. https://doi.org/10.1073/pnas.1610909114.
Article CAS PubMed PubMed Central Google Scholar
Titov D, Diehl-Schmid J, Shi K, Perneczky R, Zou N, Grimmer T, et al. Metabolic connectivity for differential diagnosis of dementing disorders. J Cereb Blood Flow Metab. 2017;37(1):252–62. https://doi.org/10.1177/0271678X15622465.
Article CAS PubMed Google Scholar
Jeong Y, Cho SS, Park JM, Kang SJ, Lee JS, Kang E, et al. 18F-FDG PET findings in frontotemporal dementia: an SPM analysis of 29 patients. J Nucl Med. 2005;46(2):233–9.
Toussaint PJ, Perlbarg V, Bellec P, Desarnaud S, Lacomblez L, Doyon J, et al. Resting state FDG-PET functional connectivity as an early biomarker of Alzheimer's disease using conjoint univariate and independent component analyses. Neuroimage. 2012;63(2):936–46. https://doi.org/10.1016/j.neuroimage.2012.03.091.
Friedman J, Hastie T, Tibshirani R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics. 2008;9(3):432–41. https://doi.org/10.1093/biostatistics/kxm045.
Tucholka A, Grau-Rivera O, Falcon C, Rami L, Sánchez-Valle R, Lladó A, et al. Structural connectivity alterations along the Alzheimer's disease continuum: reproducibility across two independent samples and correlation with cerebrospinal fluid amyloid-β and Tau. J Alzheimer's Dis : JAD. 2018;61(4):1575–87. https://doi.org/10.3233/jad-170553.
Article CAS PubMed Google Scholar
Alm KH, Bakker A. Relationships between diffusion tensor imaging and cerebrospinal fluid metrics in early stages of the Alzheimer's disease continuum. J Alzheimer's Dis : JAD. 2019;70(4):965–81. https://doi.org/10.3233/jad-181210.
Article CAS PubMed Google Scholar
Yakushev I, Ripp I, Wang M, Savio A, Schutte M, Lizarraga A, et al. Mapping covariance in brain FDG uptake to structural connectivity. Eur J Nucl Med Mol Imaging. 2021. https://doi.org/10.1007/s00259-021-05590-y.
Broser PJ, Groeschel S, Hauser TK, Lidzba K, Wilke M. Functional MRI-guided probabilistic tractography of cortico-cortical and cortico-subcortical language networks in children. Neuroimage. 2012;63(3):1561–70. https://doi.org/10.1016/j.neuroimage.2012.07.060.
Sreedharan RM, Menon AC, James JS, Kesavadas C, Thomas SV. Arcuate fasciculus laterality by diffusion tensor imaging correlates with language laterality by functional MRI in preadolescent children. Neuroradiology. 2015;57(3):291–7. https://doi.org/10.1007/s00234-014-1469-1.
Zhu D, Li K, Guo L, Jiang X, Zhang T, Zhang D, et al. DICCCOL: dense individualized and common connectivity-based cortical landmarks. Cereb Cortex. 2013;23(4):786–800. https://doi.org/10.1093/cercor/bhs072.
Bowman FD, Zhang L, Derado G, Chen S. Determining functional connectivity using fMRI data with diffusion-based anatomical weighting. Neuroimage. 2012;62(3):1769–79. https://doi.org/10.1016/j.neuroimage.2012.05.032.
Ng B, Varoquaux G, Poline JB, Thirion B. A novel sparse graphical approach for multimodal brain connectivity inference. Med Image Comput Comput Assist Interv. 2012;15(Pt 1):707–14. https://doi.org/10.1007/978-3-642-33415-3_87.
McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology. 1984;34(7):939–44. https://doi.org/10.1212/wnl.34.7.939.
Article CAS PubMed Google Scholar
Neary D, Snowden JS, Gustafson L, Passant U, Stuss D, Black S, et al. Frontotemporal lobar degeneration: a consensus on clinical diagnostic criteria. Neurology. 1998;51(6):1546–54. https://doi.org/10.1212/wnl.51.6.1546.
Article CAS PubMed Google Scholar
Gonzalez-Escamilla G, Lange C, Teipel S, Buchert R, Grothe MJ. PETPVE12: an SPM toolbox for partial volume effects correction in brain PET - application to amyloid imaging with AV45-PET. Neuroimage. 2017;147:669–77. https://doi.org/10.1016/j.neuroimage.2016.12.077.
Hammers A, Allom R, Koepp MJ, Free SL, Myers R, Lemieux L, et al. Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe. Hum Brain Mapp. 2003;19(4):224–47. https://doi.org/10.1002/hbm.10123.
Article PubMed PubMed Central Google Scholar
Wilkins B, Lee N, Gajawelli N, Law M, Leporé N. Fiber estimation and tractography in diffusion MRI: development of simulated brain images and comparison of multi-fiber analysis methods at clinical b-values. Neuroimage. 2015;109:341–56. https://doi.org/10.1016/j.neuroimage.2014.12.060.
Thirion B, Varoquaux G, Dohmatob E, Poline JB. Which fMRI clustering gives good brain parcellations? Front Neurosci. 2014;8:167. https://doi.org/10.3389/fnins.2014.00167.
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