孤独症谱系障碍患儿脑白质异常的弥散张量成像观察

doi:10.3969/j.issn.1006-9771.2018.00.007

摘要/Abstract

摘要： 目的探讨孤独症谱系障碍(ASD)患儿与正常儿童脑白质连接的差异。方法 2017年9月至2018年7月,选取ASD患儿10例(ASD组)和正常儿童10例(对照组)。ASD组采用Gesell发展量表(GDS)评定发育商,并采用孤独症儿童心理教育评核第3版(PEP-3)进行评定。所有儿童均行MRI弥散张量成像(DTI)扫描,比较两组各向异性分数(FA)、轴向弥散系数(AD)、径向弥散系数(RD)和平均弥散系数(MD)差异;以及ASD组临床量表评分与DTI各参数行相关性分析。结果与对照组相比,ASD组左侧小脑中脚、左侧胼胝体体部、左侧内囊后肢、双侧放射冠和双侧上纵束FA降低,左侧小脑中脚、左侧胼胝体体部及压部、双侧放射冠和双侧上纵束RD增高。智龄与左侧小脑中脚FA呈正相关(r = 0.686, P < 0.05),发育商与左侧放射冠(r = 0.720, P < 0.05)和左侧胼胝体压部(r = 0.744, P < 0.05) FA呈正相关,语言表达(r = 0.711, P < 0.05)和语言理解(r = 0.754, P < 0.05)与左侧胼胝体体部FA呈正相关,精细运动与左侧小脑中脚FA呈正相关(r = 0.723, P < 0.05),情感表达与左侧胼胝体体部RD呈负相关(r = -0.902, P < 0.05)。结论 ASD患儿脑白质发育异常,且与智力、发育和临床表现相关。

关键词: 孤独症谱系障碍, 弥散张量成像, 脑白质, 基于纤维束追踪的空间统计

Abstract: Objective To explore the central pathogenesis of autism spectrum disorder (ASD) by comparing the difference between children with ASD and typically developing children. Methods From September, 2017 to July, 2018, ten children with ASD (ASD group) and ten typically developing children (control group) were included. ASD group was assessed with Gesell Developmental Schedules (GDS) and Psychoeducational Profile-Third Edition (PEP-3). All the children accepted diffusion tensor imaging, and the differences in fraction anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) between them were compared, and the correlation between abnormal data and the clinical scores was explored in ASD group. Results Compared with those in the control group, FA decreased in left middle cerebellar, left peduncle body of corpus callosum, left posterior limb of internal capsule, bilateral corona radiate and bilateral longitudinal fasciculus in ASD group; while RD increased in left middle cerebellar, left peduncle body and splenium of corpus callosum, bilateral corona radiata and bilateral longitudinal fasciculus. The mental age correlated with FA of left middle cerebellar (r = 0.686, P < 0.05), and the development quotient correlated with FA of left corona radiate (r = 0.720, P < 0.05) and left peduncle body of corpus callosum (r = 0.744, P < 0.05). The scores of Expressive Language (r = 0.711, P < 0.05) and Receptive Language (r = 0.754, P < 0.05) were correlated with FA of left peduncle body of corpus callosum, while the scores of Fine Motor were correlated with FA of left middle cerebellar (r = 0.723, P < 0.05). The scores of Affective Expression were correlated with RD of left peduncle body of corpus callosum (r = -0.902, P < 0.05). Conclusion There are developmental disorders of brain white matter in children with ASD, which may involve in mentality, development and clinical features.

Key words: autism spectrum disorder, diffusion tension imaging, white matter, tract-based spatial statistics

中图分类号:

R749.93

刘洋, 张通. 孤独症谱系障碍患儿脑白质异常的弥散张量成像观察[J]. 《中国康复理论与实践》, 2018, 24(11): 1296-1301.

LIU Yang, ZHANG Tong. Brain Connection in Children with Autism Spectrum Disorder: A Diffusion Tensor Imaging Study[J]. 《Chinese Journal of Rehabilitation Theory and Practice》, 2018, 24(11): 1296-1301.

参考文献

[1] Baio J, Wiggins L, Christensen DL, et al.Prevalence of autism spectrum disorder among children aged 8 years–Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2014[J]. MMWR Surveill Summ, 2018, 67(6): 1-23.
[2] Tonnsen BL, Boan AD, Bradley CC, et al.Prevalence of autism spectrum disorders among children with intellectual disability[J]. Am J Intellect, 2016, 121(6): 487-500.
[3] Ji B, Zhao I, Turner C, et al.Predictors of health-related quality of life in Chinese caregivers of children with autism spectrum disorders: a cross-sectional study[J]. Arch Psychiatr Nurs, 2014, 28(5): 327-332.
[4] Basser PJ, Pierpaoli C.Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI[J]. J Magn Reson B, 1996, 111(3): 209-219.
[5] Bakhtiari R, Zürcher NR, Rogier O, et al.Differences in white matter reflect atypical developmental trajectory in autism: a tract-based spatial statistics study[J]. Neuroimage Clin, 2012, 1(1): 48-56.
[6] Itahashi T, Yamada T, Watanabe H, et al.Alterations of local spontaneous brain activity and connectivity in adults with high-functioning autism spectrum disorder[J]. Mol Autism, 2015, 6(30): 1-14.
[7] Fitzgerald J, Gallagher L, McGrath J. Widespread disrupted white matter microstructure in autism spectrum disorders[J]. J Autism Dev Disord, 2016. [Epub ahead of print].
[8] Nickel K, Tebartz van Elst L, Perlov E, et al. Altered white matter integrity in adults with autism spectrum disorder and an IQ > 100: a diffusion tensor imaging study[J]. Acta Psychiatr Scand, 2017, 135(6): 573-583.
[9] Chan AS, Cheung MC, Han YM, et al.Executive function deficits and neural discordance in children with autism spectrum disorders[J]. Clin Neurophysiol, 2009, 120(6): 1107-1115.
[10] Cheng Y, Chou KH, Chen IY, et al.Atypical development of white matter microstructure in adolescents with autism spectrum disorders[J]. Neuroimage, 2010, 50(3): 873-882.
[11] Di X, Azeez A, Li X, et al.Disrupted focal white matter integrity in autism spectrum disorder: a voxel-based meta-analysis of diffusion tensor imaging studies[J]. Prog Neuropsychopharmacol Biol Psychiatry, 2018, 82(1): 242-248.
[12] Ball RS.The Gesell Developmental Schedules: Arnold Gesell (1880-1961)[J]. J Abnorm Child Psychol, 1977, 5(3): 233-239.
[13] de Giacomo A, Craig F, Cristella A, et al. Can PEP-3 provide a cognitive profile in children with ASD? A comparison between the developmental ages of PEP-3 and IQ of Leiter-R[J]. J Appl Res Intellect Disabil, 2016, 29(6): 566-573.
[14] Chen KL, Chiang FM, Tseng MH, et al.Responsiveness of the Psychoeducational Profile-third edition for children with autism spectrum disorders[J]. J Autism Dev Disord, 2011, 41(12): 1658-1664.
[15] Smith SM, Jenkinson M, Johansen-Berg H, et al.Tract-based spatial statistics: voxel wise analysis of multi-subject diffusion data[J]. Neuroimage, 2006, 31(4): 1487-1505.
[16] Smith SM, Nichols TE.Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference[J]. Neuroimage, 2009, 44(1): 83-98.
[17] Lilja Y, Gustafsson O, Ljungberg M, et al.Impact of region-of-interest method on quantitative analysis of DTI data in the optic tracts[J]. BMC Med Imaging, 2016, 16(1): 42-52.
[18] Bach M, Laun FB, Leemans A, et al.Methodological considerations on tract-based spatial statistics (TBSS)[J]. Neuroimage, 2014, 100: 358-369.
[19] Liacu D, Idy-Peretti I, Ducreux D, et al.Diffusion tensor imaging tractography parameters of limbic system bundles in temporal lobe epilepsy patients[J]. J Magn Reson Imaging, 2012, 36(3): 561-568.
[20] Libero LE, DeRamus TP, Lahti AC, et al. Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates[J]. Cortex, 2015, 66(1): 46-59.
[21] Ameis SH, Fan J, Rockel C, et al.Impaired structural connectivity of socio-emotional circuits in autism spectrum disorders: a diffusion tensor imaging study[J]. PLoS One, 2011, 6(11): 1-9.
[22] Travers BG, Tromp DP, Adluru N, et al.Atypical development of white matter microstructure of the corpus callosum in males with autism: a longitudinal investigation[J]. Mol Autism, 2015, 6(1): 1-15.
[23] Gibbard CR, Ren J, Seunarine KK, et al.White matter microstructure correlates with autism trait severity in a combined clinical control sample of high-functioning adults[J]. Neuroimage Clin, 2013, 3(1): 106-114.
[24] Kleinhans NM, Pauley G, Richards T, et al.Age-related abnormalities in white matter microstructure in autism spectrum disorders[J]. Brain Res, 2012, 1479(1): 1-16.
[25] Anderson JS, Druzgal TJ, Froehlich A, et al.Decreased interhemispheric functional connectivity in autism[J]. Cerebral Cortex, 2011, 21(5): 1134-1146.
[26] Herbert MR, Ziegler DA, Deutsch CK, et al.Brain asymmetries in autism and developmental language disorder: a nested wholebrain analysis[J]. Brain, 2005, 128(1): 213-226.
[27] Just MA, Cherkassky VL, Keller TA, et al.Functional and anatomical cortical under connectivity in autism: evidence from an fMRI study of an executive function task and corpus callosum morphometry[J]. Cerebral Cortex, 2007, 17(4): 951-961.
[28] Treit S, Chen Z, Rasmussen C, et al.White matter correlates of cognitive inhibition during development: a diffusion tensor imaging study[J]. Neuroscience, 2014, 276(1): 87-97.
[29] van der Knaap LJ, van der Ham IJ. How does the corpus callosum mediate interhemispheric transfer? A review[J]. Behav Brain Res, 2011, 223(1): 211-221.
[30] Hanaie R, Mohri I, Kagitani-Shimono K, et al.Altered microstructural connectivity of the superior cerebellar peduncle is related to motor dysfunction in children with autistic spectrum disorders[J]. Cerebellum, 2013, 12(5): 645-656.
[31] Mosconi MW, Luna B, Kay-Stacey M, et al.Saccade adaptation abnormalities implicate dysfunction of cerebellar-dependent learning mechanisms in autism spectrum disorders (ASD)[J]. PLoS One, 2013, 8(5): 1-9.
[32] Ridley NJ, Homewood J, Walters J.Cerebellar dysfunction, cognitive flexibility and autistic traits in a non-clinical sample[J]. Autism, 2011, 15(6): 728-745.