A new method for structural volume analysis of longitudinal brain MRI data and its application in studying the growth trajectories of anatomical brain structures in childhood

被引:129
作者
Aubert-Broche, B. [1 ]
Fonov, V. S. [1 ]
Garcia-Lorenzo, D. [1 ,2 ]
Mouiha, A. [1 ]
Guizard, N. [1 ]
Coupe, P. [1 ,3 ]
Eskildsen, S. F. [1 ,4 ,5 ]
Collins, D. L. [1 ]
机构
[1] McGill Univ, Montreal Neurol Inst, Brain Imaging Ctr, Montreal, PQ H3A 2T5, Canada
[2] Hop La Pitie Salpetriere, ICM, UPMC INSERM CNRS UMR7225 UMR975, Paris, France
[3] Unite Mixte Rech CNRS UMR 5800, Lab Bordelais Rech Informat, Bordeaux, France
[4] Aarhus Univ, Ctr Functionally Integrat Neurosci, Aarhus, Denmark
[5] Aarhus Univ, MINDLab, Aarhus, Denmark
基金
加拿大健康研究院;
关键词
Longitudinal brain MRI; Longitudinal analysis; Structural volume analysis; Brain development; Growth trajectories; REGISTRATION; TEMPLATE; SEGMENTATION; VALIDATION; AGE;
D O I
10.1016/j.neuroimage.2013.05.065
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Cross-sectional analysis of longitudinal anatomical magnetic resonance imaging (MRI) data may be suboptimal as each dataset is analyzed independently. In this study, we evaluate how much variability can be reduced by analyzing structural volume changes in longitudinal data using longitudinal analysis. We propose a two-part pipeline that consists of longitudinal registration and longitudinal classification. The longitudinal registration step includes the creation of subject-specific linear and nonlinear templates that are then registered to a population template. The longitudinal classification step comprises a four-dimensional expectation-maximization algorithm, using a priori classes computed by averaging the tissue classes of all time points obtained cross-sectionally. To study the impact of these two steps, we apply the framework completely ("LL method": Longitudinal registration and Longitudinal classification) and partially ("LC method": Longitudinal registration and Cross-sectional classification) and compare these with a standard cross-sectional framework ("CC method": Cross-sectional registration and Cross-sectional classification). The three methods are applied to (1) a scan-rescan database to analyze reliability and (2) the NIH pediatric population to compare gray matter growth trajectories evaluated with a linear mixed model. The LL method, and the LC method to a lesser extent, significantly reduced the variability in the measurements in the scan-rescan study and gave the best-fitted gray matter growth model with the NIH pediatric MRI database. The results confirm that both steps of the longitudinal framework reduce variability and improve accuracy in comparison with the cross-sectional framework, with longitudinal classification yielding the greatest impact. Using the improved method to analyze longitudinal data, we study the growth trajectories of anatomical brain structures in childhood using the NIH pediatric MRI database. We report age- and gender-related growth trajectories of specific regions of the brain during childhood that could be used as a reference in studying the impact of neurological disorders on brain development. (C) 2013 Elsevier Inc. All rights reserved.
引用
收藏
页码:393 / 402
页数:10
相关论文
共 28 条
[1]   Geometric means in a novel vector space structure on symmetric positive-definite matrices [J].
Arsigny, Vincent ;
Fillard, Pierre ;
Pennec, Xavier ;
Ayache, Nicholas .
SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS, 2007, 29 (01) :328-347
[2]   Total and Regional Brain Volumes in a Population-Based Normative Sample from 4 to 18 Years: The NIH MRI Study of Normal Brain Development [J].
Ball, William S. ;
Byars, Anna Weber ;
Schapiro, Mark ;
Bommer, Wendy ;
Carr, April ;
German, April ;
Dunn, Scott ;
Rivkin, Michael J. ;
Waber, Deborah ;
Mulkern, Robert ;
Vajapeyam, Sridhar ;
Chiverton, Abigail ;
Davis, Peter ;
Koo, Julie ;
Marmor, Jacki ;
Mrakotsky, Christine ;
Robertson, Richard ;
McAnulty, Gloria ;
Brandt, Michael E. ;
Fletcher, Jack M. ;
Kramer, Larry A. ;
Yang, Grace ;
McCormack, Cara ;
Hebert, Kathleen M. ;
Volero, Hilda ;
Botteron, Kelly ;
McKinstry, Robert C. ;
Warren, William ;
Nishino, Tomoyuki ;
Almli, C. Robert ;
Todd, Richard ;
Constantino, John ;
McCracken, James T. ;
Levitt, Jennifer ;
Alger, Jeffrey ;
O'Neill, Joseph ;
Toga, Arthur ;
Asarnow, Robert ;
Fadale, David ;
Heinichen, Laura ;
Ireland, Cedric ;
Wang, Dah-Jyuu ;
Moss, Edward ;
Zimmerman, Robert A. ;
Bintliff, Brooke ;
Bradford, Ruth ;
Newman, Janice ;
Evans, Alan C. ;
Arnaoutelis, Rozalia ;
Pike, G. Bruce .
CEREBRAL CORTEX, 2012, 22 (01) :1-12
[3]  
Bhatia KK, 2004, 2004 2ND IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: MACRO TO NANO, VOLS 1 AND 2, P908
[4]   Real longitudinal data analysis for real people: Building a good enough mixed model [J].
Cheng, Jing ;
Edwards, Lloyd J. ;
Maldonado-Molina, Mildred M. ;
Komro, Kelli A. ;
Muller, Keith E. .
STATISTICS IN MEDICINE, 2010, 29 (04) :504-520
[5]   A fully automatic and robust brain MRI tissue classification method [J].
Cocosco, CA ;
Zijdenbos, AP ;
Evans, AC .
MEDICAL IMAGE ANALYSIS, 2003, 7 (04) :513-527
[6]   AUTOMATIC 3D INTERSUBJECT REGISTRATION OF MR VOLUMETRIC DATA IN STANDARDIZED TALAIRACH SPACE [J].
COLLINS, DL ;
NEELIN, P ;
PETERS, TM ;
EVANS, AC .
JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY, 1994, 18 (02) :192-205
[7]   Animal: Validation and applications of nonlinear registration-based segmentation [J].
Collins, DL ;
Evans, AC .
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 1997, 11 (08) :1271-1294
[8]  
Collins DL, 1999, LECT NOTES COMPUT SC, V1613, P210
[9]   An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images [J].
Coupe, Pierrick ;
Yger, Pierre ;
Prima, Sylvain ;
Hellier, Pierre ;
Kervrann, Charles ;
Barillot, Christian .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2008, 27 (04) :425-441
[10]   Robust Rician noise estimation for MR images [J].
Coupe, Pierrick ;
Manjon, Jose V. ;
Gedamu, Elias ;
Arnold, Douglas ;
Robles, Montserrat ;
Collins, D. Louis .
MEDICAL IMAGE ANALYSIS, 2010, 14 (04) :483-493