The SACT Template: A Human Brain Diffusion Tensor Template for School-age Children

Neuroscience Bulletin - Tập 38 - Trang 607-621 - 2022
Congying Chu1,2, Haoran Guan1, Sangma Xie3, Yanpei Wang1, Jie Luo1, Gai Zhao1, Zhiying Pan1, Mingming Hu1, Weiwei Men4,5, Shuping Tan6, Jia-Hong Gao4,5,7, Shaozheng Qin1,8, Yong He1,8, Lingzhong Fan9, Qi Dong1, Sha Tao1
1National Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
2Division of Molecular Neuroimaging, Institute of Neuroscience and Medicine-2, Research Center Jülich, Jülich, Germany
3Institute of Biomedical Engineering and Instrumentation, School of Automation, Hangzhou Dianzi University, Hangzhou, China
4Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
5Beijing City Key Laboratory for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China
6Psychiatry Research Center, Beijing HuiLongGuan Hospital, Peking University, Beijing, China
7McGovern Institute for Brain Research, Peking University, Beijing, China
8Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China
9Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

Tóm tắt

School-age children are in a specific development stage corresponding to juvenility, when the white matter of the brain experiences ongoing maturation. Diffusion-weighted magnetic resonance imaging (DWI), especially diffusion tensor imaging (DTI), is extensively used to characterize the maturation by assessing white matter properties in vivo. In the analysis of DWI data, spatial normalization is crucial for conducting inter-subject analyses or linking the individual space with the reference space. Using tensor-based registration with an appropriate diffusion tensor template presents high accuracy regarding spatial normalization. However, there is a lack of a standardized diffusion tensor template dedicated to school-age children with ongoing brain development. Here, we established the school-age children diffusion tensor (SACT) template by optimizing tensor reorientation on high-quality DTI data from a large sample of cognitively normal participants aged 6–12 years. With an age-balanced design, the SACT template represented the entire age range well by showing high similarity to the age-specific templates. Compared with the tensor template of adults, the SACT template revealed significantly higher spatial normalization accuracy and inter-subject coherence upon evaluation of subjects in two different datasets of school-age children. A practical application regarding the age associations with the normalized DTI-derived data was conducted to further compare the SACT template and the adult template. Although similar spatial patterns were found, the SACT template showed significant effects on the distributions of the statistical results, which may be related to the performance of spatial normalization. Looking forward, the SACT template could contribute to future studies of white matter development in both healthy and clinical populations. The SACT template is publicly available now ( https://figshare.com/articles/dataset/SACT_template/14071283 ).

Tài liệu tham khảo

DelGiudice M. Middle Childhood: An Evolutionary-developmental Synthesis: Handbook of Life Course Health Development, Cham: Springer, 2018, pp 95–107. Ghetti S, Bunge SA. Neural changes underlying the development of episodic memory during middle childhood. Dev Cogn Neurosci 2012, 2: 381–395. Lebel C, Deoni S. The development of brain white matter microstructure. Neuroimage 2018, 182: 207–218. Pierpaoli C, Basser PJ. Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med 1996, 36: 893–906. Kochunov P, Hong LE, Dennis EL, Morey RA, Tate DF, Wilde EA, et al. ENIGMA-DTI: Translating reproducible white matter deficits into personalized vulnerability metrics in cross-diagnostic psychiatric research. Hum Brain Mapp 2020: hbm.24998. Lebel C, Treit S, Beaulieu C. A review of diffusion MRI of typical white matter development from early childhood to young adulthood. NMR Biomed 2019, 32: e3778. https://doi.org/10.1002/nbm.3778. Liang SG, Wang Q, Kong XZ, Deng W, Yang X, Li XJ. White matter abnormalities in major depression biotypes identified by diffusion tensor imaging. Neurosci Bull 2019, 35: 867–876. Qiu AQ, Mori S, Miller MI. Diffusion tensor imaging for understanding brain development in early life. Annu Rev Psychol 2015, 66: 853–876. Mori S, Oishi K, Faria AV. White matter atlases based on diffusion tensor imaging. Curr Opin Neurol 2009, 22: 362–369. Bach M, Laun FB, Leemans A, Tax CM, Biessels GJ, Stieltjes B, et al. Methodological considerations on tract-based spatial statistics (TBSS). Neuroimage 2014, 100: 358–369. Irfanoglu MO, Nayak A, Jenkins J, Hutchinson EB, Sadeghi N, Thomas CP, et al. DR-TAMAS: Diffeomorphic registration for tensor accurate alignment of anatomical structures. Neuroimage 2016, 132: 439–454. Wang Y, Gupta A, Liu ZX, Zhang H, Escolar ML, Gilmore JH, et al. DTI registration in atlas based fiber analysis of infantile Krabbe disease. Neuroimage 2011, 55: 1577–1586. Wang Y, Yu Q, Liu ZX, Lei T, Guo Z, Qi M, et al. Evaluation on diffusion tensor image registration algorithms. Multimed Tools Appl 2016, 75: 8105–8122. Timmers I, Roebroeck A, Bastiani M, Jansma B, Rubio-Gozalbo E, Zhang H. Assessing microstructural substrates of white matter abnormalities: a comparative study using DTI and NODDI. PLoS One 2016, 11: e0167884. Hsu YC, Lo YC, Chen YJ, Wedeen VJ, Isaac Tseng WY. NTU-DSI-122: a diffusion spectrum imaging template with high anatomical matching to the ICBM-152 space. Hum Brain Mapp 2015, 36: 3528–3541. Mori S, Oishi K, Jiang HY, Jiang L, Li X, Akhter K, et al. Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage 2008, 40: 570–582. Peng HL, Orlichenko A, Dawe RJ, Agam G, Zhang SW, Arfanakis K. Development of a human brain diffusion tensor template. Neuroimage 2009, 46: 967–980. Zhang H, Yushkevich PA, Rueckert D, Gee JC. A computational white matter atlas for aging with surface-based representation of fasciculi. Biomed Image Regist 2010, https://doi.org/10.1007/978-3-642-14366-3_8. Zhang S, Arfanakis K. Evaluation of standardized and study-specific diffusion tensor imaging templates of the adult human brain: template characteristics, spatial normalization accuracy, and detection of small inter-group FA differences. Neuroimage 2018, 172: 40–50. Zhang SW, Peng HL, Dawe RJ, Arfanakis K. Enhanced ICBM diffusion tensor template of the human brain. Neuroimage 2011, 54: 974–984. Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL, et al. Unbiased average age-appropriate atlases for pediatric studies. Neuroimage 2011, 54: 313–327. Yoon U, Fonov VS, Perusse D, Evans AC, Brain Development Cooperative Group. The effect of template choice on morphometric analysis of pediatric brain data. Neuroimage 2009, 45: 769–777. Yang GY, Zhou SZ, Bozek J, Dong HM, Han MZ, Zuo XN, et al. Sample sizes and population differences in brain template construction. Neuroimage 2020, 206: 116318. Zhang SW, Arfanakis K. Role of standardized and study-specific human brain diffusion tensor templates in inter-subject spatial normalization. J Magn Reson Imaging 2013, 37: 372–381. Zhang H, Avants BB, Yushkevich PA, Woo JH, Wang SM, McCluskey LF, et al. High-dimensional spatial normalization of diffusion tensor images improves the detection of white matter differences: an example study using amyotrophic lateral sclerosis. IEEE Trans Med Imaging 2007, 26: 1585–1597. Wang Y, Shen Y, Liu DY, Li GQ, Guo Z, Fan YY, et al. Evaluations of diffusion tensor image registration based on fiber tractography. Biomed Eng Online 2017, 16: 9. Zhang H, Yushkevich PA, Alexander DC, Gee JC. Deformable registration of diffusion tensor MR images with explicit orientation optimization. Med Image Anal 2006, 10: 764–785. Zhang H, Yushkevich PA, Rueckert D, Gee JC. Unbiased white matter atlas construction using diffusion tensor images. Med Image Comput Comput Assist Interv 2007, 10: 211–218. Zhao TD, Liao XH, Fonov VS, Wang QS, Men WW, Wang YP, et al. Unbiased age-specific structural brain atlases for Chinese pediatric population. Neuroimage 2019, 189: 55–70. Sha Tao. Intelligence development and school adjustment of school-age children and adolescents: a follow-up cohort study. Psychol Commun 2019, 2: 88–90. Dong Q, Lin C. Standardized Tests of the National Children’s Study of China, Beijing, Science Press, 2011, pp 1–14. Nooner KB, Colcombe SJ, Tobe RH, Mennes M, Benedict MM, Moreno AL, et al. The NKI-rockland sample: a model for accelerating the pace of discovery science in psychiatry. Front Neurosci 2012, 6: 152. Xu JQ, Moeller S, Auerbach EJ, Strupp J, Smith SM, Feinberg DA, et al. Evaluation of slice accelerations using multiband echo planar imaging at 3 T. Neuroimage 2013, 83: 991–1001. Oguz I, Farzinfar M, Matsui J, Budin F, Liu ZX, Gerig G, et al. DTIPrep: quality control of diffusion-weighted images. Front Neuroinform 2014, 8: 4. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. Fsl. Neuroimage 2012, 62: 782–790. Andersson JLR, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 2016, 125: 1063–1078. Andersson JLR, Graham MS, Zsoldos E, Sotiropoulos SN. Incorporating outlier detection and replacement into a non-parametric framework for movement and distortion correction of diffusion MR images. Neuroimage 2016, 141: 556–572. Andersson JLR, Graham MS, Drobnjak I, Zhang H, Filippini N, Bastiani M. Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement. Neuroimage 2017, 152: 450–466. Lutkenhoff ES, Rosenberg M, Chiang J, Zhang KY, Pickard JD, Owen AM, et al. Optimized brain extraction for pathological brains (optiBET). PLoS One 2014, 9: e115551. https://doi.org/10.1371/journal.pone.0115551. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 2001, 20: 45–57. Jones DK, Griffin LD, Alexander DC, Catani M, Horsfield MA, Howard R, et al. Spatial normalization and averaging of diffusion tensor MRI data sets. Neuroimage 2002, 17: 592–617. van Hecke W, Leemans A, Emsell L. DTI analysis methods: Voxel-based analysis. Diffusion Tensor Imaging. New York: Springer, 2016: 183–203. Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, MacKay CE, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 2006, 31: 1487–1505. Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE. Permutation inference for the general linear model. Neuroimage 2014, 92: 381–397. Smith SM, Nichols TE. Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 2009, 44: 83–98. Fritz CO, Morris PE, Richler JJ. Effect size estimates: current use, calculations, and interpretation. J Exp Psychol Gen 2012, 141: 2–18. Zalesky A. Moderating registration misalignment in voxelwise comparisons of DTI data: a performance evaluation of skeleton projection. Magn Reson Imaging 2011, 29: 111–125. Cetin Karayumak S, Bouix S, Ning LP, James A, Crow T, Shenton M, et al. Retrospective harmonization of multi-site diffusion MRI data acquired with different acquisition parameters. Neuroimage 2019, 184: 180–200. Liu BL, Zhu T, Zhong JH. Comparison of quality control software tools for diffusion tensor imaging. Magn Reson Imaging 2015, 33: 276–285. Serag A, Aljabar P, Ball G, Counsell SJ, Boardman JP, Rutherford MA, et al. Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression. Neuroimage 2012, 59: 2255–2265. Wilke M, Holland SK, Altaye M, Gaser C. Template-O-Matic: a toolbox for creating customized pediatric templates. Neuroimage 2008, 41: 903–913. Adluru N, Zhang H, Fox AS, Shelton SE, Ennis CM, Bartosic AM, et al. A diffusion tensor brain template for rhesus macaques. Neuroimage 2012, 59: 306–318. Peterson M, Warf BC, Schiff SJ. Normative human brain volume growth. J Neurosurg Pediatr 2018, 21: 478–485. Evans AC, Janke AL, Collins DL, Baillet S. Brain templates and atlases. Neuroimage 2012, 62: 911–922. Jones DK, Cercignani M. Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed 2010, 23: 803–820. Park BY, Byeon K, Lee MJ, Chung CS, Kim SH, Morys F, et al. Whole-brain functional connectivity correlates of obesity phenotypes. Hum Brain Mapp 2020, 41: 4912–4924. Cabeen RP, Bastin ME, Laidlaw DH. A Comparative evaluation of voxel-based spatial mapping in diffusion tensor imaging. Neuroimage 2017, 146: 100–112. Spisák T, Spisák Z, Zunhammer M, Bingel U, Smith S, Nichols T, et al. Probabilistic TFCE: a generalized combination of cluster size and voxel intensity to increase statistical power. Neuroimage 2019, 185: 12–26. Krogsrud SK, Fjell AM, Tamnes CK, Grydeland H, Mork L, Due-Tønnessen P, et al. Changes in white matter microstructure in the developing brain–a longitudinal diffusion tensor imaging study of children from 4 to 11 years of age. Neuroimage 2016, 124: 473–486. Tamnes CK, Roalf DR, Goddings AL, Lebel C. Diffusion MRI of white matter microstructure development in childhood and adolescence: methods, challenges and progress. Dev Cogn Neurosci 2018, 33: 161–175.