OBJECTIVE: When using imaging to predict time to progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD), time-to-event statistical methods account for varying lengths of follow-up times among subjects whereas two-sample t-tests in voxel-based morphometry (VBM) do not. Our objectives were to apply a time-to-event voxel-based analytic method to identify regions on MRI where atrophy is associated with significantly increased risk of future progression to AD in subjects with MCI and to compare it to traditional voxel-level patterns obtained by applying two-sample methods. We also compared the power required to detect an association using time-to-event methods versus two-sample approaches. METHODS: Subjects with MCI at baseline were followed prospectively. The event of interest was clinical diagnosis of AD. Cox proportional hazards models adjusted for age, sex, and education were used to estimate the relative hazard of progression from MCI to AD based on rank-transformed voxel-level gray matter density (GMD) estimates. RESULTS: The greatest risk of progression to AD was associated with atrophy of the medial temporal lobes. Patients ranked at the 25th percentile of GMD in these regions had more than a doubling of risk of progression to AD at a given time point compared to patients at the 75th percentile. Power calculations showed the time-to-event approach to be more efficient than the traditional two-sample approach. CONCLUSIONS: We present a new voxel-based analytic method that incorporates time-to-event statistical methods. In the context of a progressive disease like AD, time-to-event VBM seems more appropriate and powerful than traditional two-sample methods.
OBJECTIVE: When using imaging to predict time to progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD), time-to-event statistical methods account for varying lengths of follow-up times among subjects whereas two-sample t-tests in voxel-based morphometry (VBM) do not. Our objectives were to apply a time-to-event voxel-based analytic method to identify regions on MRI where atrophy is associated with significantly increased risk of future progression to AD in subjects with MCI and to compare it to traditional voxel-level patterns obtained by applying two-sample methods. We also compared the power required to detect an association using time-to-event methods versus two-sample approaches. METHODS: Subjects with MCI at baseline were followed prospectively. The event of interest was clinical diagnosis of AD. Cox proportional hazards models adjusted for age, sex, and education were used to estimate the relative hazard of progression from MCI to AD based on rank-transformed voxel-level gray matter density (GMD) estimates. RESULTS: The greatest risk of progression to AD was associated with atrophy of the medial temporal lobes. Patients ranked at the 25th percentile of GMD in these regions had more than a doubling of risk of progression to AD at a given time point compared to patients at the 75th percentile. Power calculations showed the time-to-event approach to be more efficient than the traditional two-sample approach. CONCLUSIONS: We present a new voxel-based analytic method that incorporates time-to-event statistical methods. In the context of a progressive disease like AD, time-to-event VBM seems more appropriate and powerful than traditional two-sample methods.
Authors: D P Devanand; G Pradhaban; X Liu; A Khandji; S De Santi; S Segal; H Rusinek; G H Pelton; L S Honig; R Mayeux; Y Stern; M H Tabert; M J de Leon Journal: Neurology Date: 2007-03-13 Impact factor: 9.910
Authors: W J P Henneman; J D Sluimer; J Barnes; W M van der Flier; I C Sluimer; N C Fox; P Scheltens; H Vrenken; F Barkhof Journal: Neurology Date: 2009-03-17 Impact factor: 9.910
Authors: J L Whitwell; M M Shiung; S A Przybelski; S D Weigand; D S Knopman; B F Boeve; R C Petersen; C R Jack Journal: Neurology Date: 2007-09-26 Impact factor: 9.910
Authors: Lubov E Zeifman; William F Eddy; Oscar L Lopez; Lewis H Kuller; Cyrus Raji; Paul M Thompson; James T Becker Journal: J Alzheimers Dis Date: 2015 Impact factor: 4.472
Authors: Carlos Platero; María Eugenia López; María Del Carmen Tobar; Miguel Yus; Fernando Maestu Journal: Hum Brain Mapp Date: 2018-11-19 Impact factor: 5.038
Authors: Maria Giulia Preti; Francesca Baglio; Maria Marcella Laganà; Ludovica Griffanti; Raffaello Nemni; Mario Clerici; Marco Bozzali; Giuseppe Baselli Journal: PLoS One Date: 2012-04-24 Impact factor: 3.240
Authors: Mara Ten Kate; Frederik Barkhof; Pieter Jelle Visser; Charlotte E Teunissen; Philip Scheltens; Wiesje M van der Flier; Betty M Tijms Journal: Alzheimers Res Ther Date: 2017-09-12 Impact factor: 6.982
Authors: Jung-Min Pyun; Young Ho Park; Hang-Rai Kim; Jeewon Suh; Min Ju Kang; Beom Joon Kim; Young Chul Youn; Jae-Won Jang; SangYun Kim Journal: Alzheimers Res Ther Date: 2017-12-16 Impact factor: 6.982