Reducing instability of inter-subject covariance of FDG uptake networks using structure-weighted sparse estimation approach

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  Google Scholar 

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.

Article  PubMed  PubMed Central  Google Scholar 

留言 (0)

沒有登入
gif