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• Adolf JK, Fried EI. 2019. Ergodicity is sufficient but not necessary for group-to-individual generalizability. Proc Natl Acad Sci U S A. 116:6540-6541.
• Chen JE, Lewis LD, Chang C, Tian Q, Fultz NE, Ohringer NA, Rosen BR, Polimeni JR. 2020. Resting-state "physiological networks." Neuroimage. 213:116707.
• Di X, Gohel S, Thielcke A, Wehrl HF, Biswal BB. 2017. Do all roads lead to Rome? A comparison of brain networks derived from inter-subject volumetric and metabolic covariance and moment-to-moment hemodynamic correlations in old individuals. Brain Struct Funct. 222:3833-3845.
• Eickhoff SB, Muller VI. 2015. Functional connectivity. In: Brain mapping: an encyclopedic reference. Amsterdam: Elsevier Inc, pp. 187-201.
• Garces P, Pereda E, Hernandez-Tamames JA,del-Pozo F,Maestu F, Angel Pineda-Pardo J. 2016. Multimodal description of whole brain connectivity: a comparison of resting state MEG, fMRI, and DWI. Hum Brain Mapp. 37:20-34.
• Habeck C, Steffener J, Rakitin B, Stern Y. 2012. Can the defaultmode network be described with one spatial-covariance network? Brain Res. 1468:38-51.
• Hahn A, Lanzenberger R, Kasper S. 2019. Making sense of connectivity. Int J Neuropsychopharmacol. 22(3):194-207.
• Honey CJ, Kotter R, Breakspear M, Sporns O. 2007. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc Natl Acad Sci U S A. 104:10240-10245.
• Huang S, Li J, Sun L,Ye J, Fleisher A,Wu T,Chen K,Reiman E. 2010. Learning brain connectivity of Alzheimer's disease by sparse inverse covariance estimation. Neuroimage. 50:935-949.
• Huang SY, Hsu JL, Lin KJ, Hsiao IT. 2020. A novel individual metabolic brain network for 18F-FDG PET imaging. Front Neurosci. 14:1-11.
• Jamadar SD, Ward PGD, Close TG, Fornito A, Premaratne M, O'Brien K, Stab D, Chen Z, Shah NJ, Egan GF. 2020. Simultaneous BOLD-fMRI and constant infusion FDG-PET data of the resting human brain. Sci Data. 7:363.
• Jamadar SD, Ward PGD, Liang EX, Orchard ER, Chen Z, Egan GF. 2021. Metabolic and hemodynamic resting-state connectivity of the human brain: a high-temporal resolution simultaneous BOLD-fMRI and FDG-fPET multimodality study. Cereb Cortex. 31(6):2855-2867.
• Liegeois R, Laumann TO, Snyder AZ, Zhou J, Yeo BTT. 2017. Interpreting temporal fluctuations in resting-state functional connectivity MRI. Neuroimage. 163:437-455.
• Power JD, Lynch CJ, Adeyemo B, Petersen SE. 2020. A critical, event-related appraisal of denoising in resting-state fMRI studies. Cereb Cortex. 30:5544-5559.
• Reid AT, Hoffstaedter F, Gong G, Laird AR, Fox P, Evans AC, Amunts K, Eickhoff SB. 2017. A seed-based cross-modal comparison of brain connectivity measures. Brain Struct Funct. 222:1131-1151.
• Ripp I, Stadhouders T, Savio A, Goldhardt O, Cabello J, Calhoun V, Riedl V, Hedderich D, Diehl-Schmid J, Grimmer T, et al. 2020. Integrity of neurocognitive networks in dementing disorders as measured with simultaneous PET/fMRI. J Nucl Med. 61:1341-1347.
• Roberts RP, Hach S, Tippett LJ, Addis DR. 2016. The Simpson's paradox and fMRI: similarities and differences between functional connectivity measures derived from withinsubject and across-subject correlations. Neuroimage. 135:1-15.
• Sala A, Perani D. 2019. Brain molecular connectivity in neurodegenerative diseases: recent advances and new perspectives using positron emission tomography. Front Neurosci. 13:617.
• Sasaki H, Kanno I, Murakami M, Shishido F, Uemura K. 1986. Tomographic mapping of kinetic rate constants in the fluorodeoxyglucose model using dynamic positron emission tomography. J Cereb Blood Flow Metab. 6:447-454.
• Shaffer JL, Petrella JR, Sheldon FC, Choudhury KR, Calhoun VD, Coleman RE, Doraiswamy PM. 2013. Predicting cognitive decline in subjects at risk for Alzheimer disease by using combined. Radiology. 266:583-591.
• Taylor PA, Gohel S, Di X, Walter M, Biswal BB. 2012. Functional covariance networks: obtaining resting-state networks from Intersubject variability. Brain Connect. 2:203-217.
• Titov D, Diehl-schmid J, Shi K, Perneczky R, Zou N, Grimmer T, Li J, Drzezga A, Yakushev I. 2017. Metabolic connectivity for differential diagnosis of dementing disorders. J Cereb Blood Flow Metab. 37:252-262.
• Toussaint P-J, Perlbarg V, Bellec P, Desarnaud S, Lacomblez L, Doyon J, Habert M-O, Benali H. 2012. Resting state FDG-PET functional connectivity as an early biomarker of Alzheimer's disease using conjoint univariate and independent component analyses. Neuroimage. 63:936-946.
• Veronese M, Moro L, Arcolin M, Dipasquale O, Rizzo G, Expert P, Khan W, Fisher PM, Svarer C, Bertoldo A, et al. 2019. Covariance statistics and network analysis of brain PET imaging studies. Sci Rep. 9:2496.
• Wang M, Jiang J, Yan Z, Alberts I, Ge J, Zhang H, Zuo C, Yu J, Rominger A, Shi K. 2020. 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. 47:2753-2764.
• Ward PGD, Orchard ER, Oldham S, Arnatkevic? iu? te? A, Sforazzini F, Fornito A, Storey E, Egan GF, Jamadar SD. 2020. Individual differences in haemoglobin concentration influence bold fMRI functional connectivity and its correlation with cognition. Neuroimage. 221:117196.
• Yang G, Zhou S, Bozek J, Dong HM, Han M, Zuo XN, Liu H, Gao JH. 2020. Sample sizes and population differences in brain template construction. Neuroimage. 206:116318.
• Yao Z, Hu B, Nan H, Zheng W, Xie Y. 2017. Individual metabolic network for the accurate detection of Alzheimer's disease based on FDGPET imaging. Proc - 2016 IEEE Int Conf Bioinforma Biomed BIBM 2016. 1328-1335.
• Zhang Z, Liao W, Zuo XN, Wang Z, Yuan C, Jiao Q, Chen H, Biswal BB, Lu G, Liu Y. 2011. Resting-state brain organization revealed by functional covariance networks. PLoS One. 6:e28817.