BACKGROUND:Structural magnetic resonance imaging (MRI) is sensitive to neurodegeneration and can be used to estimate the risk of converting to Alzheimer's disease (AD) in individuals with mild cognitive impairment (MCI). Brain changes in AD and prodromal AD involve a pattern of widespread atrophy. The use of multivariate analysis algorithms could enable the development of diagnostic tools based on structural MRI data. In this study, we investigated the possibility of combining multiple MRI features in the form of a severity index. METHODS: We used baseline MRI scans from two large multicentre cohorts (AddNeuroMed and ADNI). On the basis of volumetric and cortical thickness measures at baseline with AD cases and healthy control (CTL) subjects as training sets, we generated an MRI-based severity index using the method of orthogonal projection to latent structures (OPLS). The severity index tends to be close to 1 for AD patients and 0 for CTL subjects. Values above 0.5 indicate a more AD-like pattern. The index was then estimated for subjects with MCI, and the accuracy of classification was investigated. RESULTS: Based on the data at follow-up, 173 subjects converted to AD, of whom 112 (64.7%) were classified as AD-like and 61 (35.3%) as CTL-like. CONCLUSION: We found that joint evaluation of multiple brain regions provided accurate discrimination between progressive and stable MCI, with better performance than hippocampal volume alone, or a limited set of features. A major challenge is still to determine optimal cut-off points for such parameters and to compare their relative reliability.
RCT Entities:
BACKGROUND: Structural magnetic resonance imaging (MRI) is sensitive to neurodegeneration and can be used to estimate the risk of converting to Alzheimer's disease (AD) in individuals with mild cognitive impairment (MCI). Brain changes in AD and prodromal AD involve a pattern of widespread atrophy. The use of multivariate analysis algorithms could enable the development of diagnostic tools based on structural MRI data. In this study, we investigated the possibility of combining multiple MRI features in the form of a severity index. METHODS: We used baseline MRI scans from two large multicentre cohorts (AddNeuroMed and ADNI). On the basis of volumetric and cortical thickness measures at baseline with AD cases and healthy control (CTL) subjects as training sets, we generated an MRI-based severity index using the method of orthogonal projection to latent structures (OPLS). The severity index tends to be close to 1 for ADpatients and 0 for CTL subjects. Values above 0.5 indicate a more AD-like pattern. The index was then estimated for subjects with MCI, and the accuracy of classification was investigated. RESULTS: Based on the data at follow-up, 173 subjects converted to AD, of whom 112 (64.7%) were classified as AD-like and 61 (35.3%) as CTL-like. CONCLUSION: We found that joint evaluation of multiple brain regions provided accurate discrimination between progressive and stable MCI, with better performance than hippocampal volume alone, or a limited set of features. A major challenge is still to determine optimal cut-off points for such parameters and to compare their relative reliability.
Authors: Bruce Fischl; David H Salat; Evelina Busa; Marilyn Albert; Megan Dieterich; Christian Haselgrove; Andre van der Kouwe; Ron Killiany; David Kennedy; Shuna Klaveness; Albert Montillo; Nikos Makris; Bruce Rosen; Anders M Dale Journal: Neuron Date: 2002-01-31 Impact factor: 17.173
Authors: Serge Gauthier; Barry Reisberg; Michael Zaudig; Ronald C Petersen; Karen Ritchie; Karl Broich; Sylvie Belleville; Henry Brodaty; David Bennett; Howard Chertkow; Jeffrey L Cummings; Mony de Leon; Howard Feldman; Mary Ganguli; Harald Hampel; Philip Scheltens; Mary C Tierney; Peter Whitehouse; Bengt Winblad Journal: Lancet Date: 2006-04-15 Impact factor: 79.321
Authors: Yawu Liu; Teemu Paajanen; Eric Westman; Lars-Olof Wahlund; Andrew Simmons; Catherine Tunnard; Tomasz Sobow; Petroula Proitsi; John Powell; Patrizia Mecocci; Magda Tsolaki; Bruno Vellas; Sebastian Muehlboeck; Alan Evans; Christian Spenger; Simon Lovestone; Hilkka Soininen Journal: J Alzheimers Dis Date: 2010 Impact factor: 4.472
Authors: Christine Fennema-Notestine; Donald J Hagler; Linda K McEvoy; Adam S Fleisher; Elaine H Wu; David S Karow; Anders M Dale Journal: Hum Brain Mapp Date: 2009-10 Impact factor: 5.038
Authors: Eric Westman; Lena Cavallin; J-Sebastian Muehlboeck; Yi Zhang; Patrizia Mecocci; Bruno Vellas; Magda Tsolaki; Iwona Kłoszewska; Hilkka Soininen; Christian Spenger; Simon Lovestone; Andrew Simmons; Lars-Olof Wahlund Journal: PLoS One Date: 2011-07-21 Impact factor: 3.240
Authors: Michael W Weiner; Dallas P Veitch; Paul S Aisen; Laurel A Beckett; Nigel J Cairns; Jesse Cedarbaum; Robert C Green; Danielle Harvey; Clifford R Jack; William Jagust; Johan Luthman; John C Morris; Ronald C Petersen; Andrew J Saykin; Leslie Shaw; Li Shen; Adam Schwarz; Arthur W Toga; John Q Trojanowski Journal: Alzheimers Dement Date: 2015-06 Impact factor: 21.566
Authors: Orla M Doyle; Eric Westman; Andre F Marquand; Patrizia Mecocci; Bruno Vellas; Magda Tsolaki; Iwona Kłoszewska; Hilkka Soininen; Simon Lovestone; Steve C R Williams; Andrew Simmons Journal: PLoS One Date: 2014-08-20 Impact factor: 3.240