孤独症谱系障碍男童感觉运动网络最优频率异常发育模式

卢春赢; 张浅阅; 陈雪; 李博文; 贺碧芳; 叶绍兵; 陈恒

doi:10.16835/j.cnki.1000-9817.2023.03.006

孤独症谱系障碍男童感觉运动网络最优频率异常发育模式

doi: 10.16835/j.cnki.1000-9817.2023.03.006

卢春赢¹,
张浅阅¹,
陈雪¹,
李博文¹,
贺碧芳¹,
叶绍兵^2, ,,
陈恒^1, ,

1.
贵州大学医学院，贵阳 550025
2.
重庆市开州区人民医院普通外科

基金项目:

国家自然科学基金 61901129

国家自然科学基金 61901130

贵州省科技厅科学技术基金一般项目 [2020]1Y345

详细信息

作者简介:
卢春赢(1999-)，女，浙江磐安人，在读硕士，主要研究方向为精神疾病脑功能图像、静息态功能磁共振图像分析

通讯作者:
叶绍兵，E-mail: yesb1971@163.com

陈恒，E-mail: hchen13@gzu.edu.cn

利益冲突声明 所有作者声明无利益冲突。
中图分类号: R748 R179
计量
- 文章访问数: 441
- HTML全文浏览量: 253
- PDF下载量: 22
- 被引次数: 0
出版历程
- 收稿日期: 2022-10-14
- 修回日期: 2022-12-18
- 刊出日期: 2023-03-25

Atypical developmental of the sensorimotor network optimal frequency in children with autism spectrum disorder

1.
School of Medicine, Guizhou University, Guiyang(550025), China

摘要

摘要: 目的基于功能连接优势频率指标，采用“脑年龄”分析方法，探索孤独症谱系障碍(autism spectrum disorder, ASD)男童感觉运动相关网络的异常发育模式。方法筛选来自ABIDE数据库中105例ASD男童与匹配的102例正常发育男童的静息态功能磁共振数据(7~12岁)。每个被试构建感觉运动相关脑区不同频段下的功能连接网络，然后计算每条连接最强连接值所在的频率作为该连接的最优频率。采用脑年龄分析方法探索ASD男童脑年龄相对于生理年龄的年龄差。结果 ASD男童大脑感觉运动网络出现“过发育”到“欠发育”的异常发育模式，2种模式的转换在7.8岁左右。“欠发育”趋势得到抑制的大龄ASD男童(10岁以上)病情严重程度相对更轻(r=-0.43，P < 0.05)。结论 ASD男童大脑感觉运动网络存在异常发育过程，感觉运动网络脑-生理年龄差有作为衡量ASD疾病发展过程的神经影像学标记物潜力。
- 孤独性障碍 /
- 感觉 /
- 运动活动 /
- 生长和发育 /
- 儿童 /
- 男(雄)性
Abstract: Objective On the basis of the dominant frequency index of functional connectivity, the "brain age" analysis method was used to explore abnormal development patterns of sensorimotor networks in boys with autism spectrum disorder(ASD). Methods The resting state functional magnetic resonance data (7-12 years old) for 105 boys with ASD and 102 matched boys with normal development from the ABIDE public database were screened. Functional connection networks in different frequency bands of sensorimotor related brain regions were constructed for each individual, and the frequency of the strongest connection were constructed as the optimal frequency of the connection. Brain age analysis was used to explore the difference between brain age and chronological age in boys with ASD. Results The brain sensorimotor network of boys with ASD showed an abnormal development pattern of overdevelopment followed by underdevelopment, and the transition between the two patterns occurred at approximately 7.8 years of age. Older boys with ASD (older than 10 years) whose underdevelopment trend was suppressed had lower ASD severity(r=-0.43, P < 0.05). Conclusion The brain sensorimotor network in boys with ASD has an abnormal development process, and the brain-chronological age difference in the sensorimotor network has potential as a neuroimaging marker to measure the development of ASD.
- Autistic disorder /
- Sensation /
- Motor activity /
- Growth and development /
- Child /
- Male
注释:

1) 利益冲突声明 所有作者声明无利益冲突。

HTML全文

图 1 正常发育儿童脑年龄与生理年龄分析模型效果

Figure 1. Effect of brain age and chronological age analysis model of normal developing child

下载: 全尺寸图片幻灯片

图 2 正常发育儿童脑-生理年龄差与生理年龄的关联

Figure 2. Correlation between brain-chronological age difference and chronological age in normal developing child

下载: 全尺寸图片幻灯片

图 3 ASD患儿脑年龄与生理年龄分析模型效果

Figure 3. Effect of brain age and chronological age analysis model for child with ASD

下载: 全尺寸图片幻灯片

图 4 ASD患者生理年龄与脑-生理年龄差的关联

Figure 4. Correlation between physiological age and brain-physiological age difference in patients with autism spectrum disorder

下载: 全尺寸图片幻灯片

参考文献(21)

[1]	LAI M C, LOMBARDO M V, BARON-COHEN S. Autism[J]. Lancet, 2014, 383(9920): 896-910. doi: 10.1016/S0140-6736(13)61539-1
[2]	BISWAL B B, MENNES M, ZUO X N, et al. Toward discovery science of human brain function[J]. Proc Natl Acad Sci USA, 2010, 107(10): 4734-4739. doi: 10.1073/pnas.0911855107
[3]	WANG J, FU K, CHEN L, et al. Increased gray matter volume and resting-state functional connectivity in somatosensory cortex and their relationship with autistic symptoms in young boys with autism spectrum disorder[J]. Front Physiol, 2017, 8: 588. doi: 10.3389/fphys.2017.00588
[4]	CHEN H, WANG J, UDDIN L Q, et al. Aberrant functional connectivity of neural circuits associated with social and sensorimotor deficits in young children with autism spectrum disorder[J]. Autism Res, 2018, 11(12): 1643-1652. doi: 10.1002/aur.2029
[5]	SUPEKAR K, MUSEN M, MENON V. Development of large-scale functional brain networks in children[J]. PLoS Biol, 2009, 7(7): e1000157. doi: 10.1371/journal.pbio.1000157
[6]	BROWN T T, KUPERMAN J M, CHUNG Y, et al. Neuroanatomical assessment of biological maturity[J]. Curr Biol, 2012, 22(18): 1693-1698. doi: 10.1016/j.cub.2012.07.002
[7]	ZUO X N, DI MARTINO A, KELLY C, et al. The oscillating brain: complex and reliable[J]. Neuroimage, 2010, 49(2): 1432-1445. doi: 10.1016/j.neuroimage.2009.09.037
[8]	CHEN H, DUAN X, LIU F, et al. Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity-a multi-center study[J]. Prog Neuropsychopharmacol Biol Psychiatry, 2016, 64: 1-9. doi: 10.1016/j.pnpbp.2015.06.014
[9]	DUAN X, CHEN H, HE C, et al. Resting-state functional under-connectivity within and between large-scale cortical networks across three low-frequency bands in adolescents with autism[J]. Prog Neuropsychopharmacol Biol Psychiatry, 2017, 79(PtB): 434-441.
[10]	DI MARTINO A, YAN C G, LI Q, et al. The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism[J]. Mol Psychiatry, 2014, 19(6): 659-667. doi: 10.1038/mp.2013.78
[11]	DI MARTINO A, O'CONNOR D, CHEN B, et al. Enhancing studies of the connectome in autism using the autism brain imaging data exchange Ⅱ[J]. Sci Data, 2017, 4: 170010. doi: 10.1038/sdata.2017.10
[12]	GUO X, DUAN X, CHEN H, et al. Altered inter-and intrahemispheric functional connectivity dynamics in autistic children[J]. Hum Brain Mapp, 2020, 41(2): 419-428. doi: 10.1002/hbm.24812
[13]	YAN C G, WANG X D, ZUO X N, et al. DPABI: data processing & analysis for (resting-state) brain imaging[J]. Neuroinformatics, 2016, 14(3): 339-351. doi: 10.1007/s12021-016-9299-4
[14]	DOSENBACH N U, NARDOS B, COHEN A L, et al. Prediction of individual brain maturity using fMRI[J]. Science, 2010, 329(5997): 1358-1361. doi: 10.1126/science.1194144
[15]	SHANNON C E. The mathematical theory of communication. 1963[J]. MD Comput, 1997, 14(4): 306-317.
[16]	NENADIC I, DIETZEK M, LANGBEIN K, et al. Brain AGE score indicates accelerated brain aging in schizophrenia, but not bipolar disorder[J]. Psychiatry Res Neuroimag, 2017, 266: 86-89. doi: 10.1016/j.pscychresns.2017.05.006
[17]	LIANG H, ZHANG F, NIU X. Investigating systematic bias in brain age estimation with application to post-traumatic stress disorders[J]. Hum Brain Mapp, 2019, 40(11): 3143-3152. doi: 10.1002/hbm.24588
[18]	BISWAL B, YETKIN F Z, HAUGHTON V M, et al. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI[J]. Magn Reson Med, 1995, 34(4): 537-541. doi: 10.1002/mrm.1910340409
[19]	CHEN H, DUAN X, LIU F, et al. Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity: a multi-center study[J]. Prog Neuropsychopharmacol Biol Psychiatry, 2016, 64: 1-9. doi: 10.1016/j.pnpbp.2015.06.014
[20]	LONG J, LU F, YANG S, et al. Different functional connectivity optimal frequency in autism compared with HCs and the relationship with social communication deficits: evidence from gene expression and behavior symptom analysis[J]. Human Brain Mapp, 2023, 44(1): 258-268. doi: 10.1002/hbm.26011
[21]	AMARAL D G, SCHUMANN C M, NORDAHL C W. Neuroanatomy of autism[J]. Trends Neurosci, 2008, 31(3): 137-145. doi: 10.1016/j.tins.2007.12.005