摘要探索相位同步和复杂网络方法在注意缺陷多动障碍(ADHD)脑网络机制研究中的应用, 选取135例ADHD患者和102例正常对照作为研究对象。以这237例被试的功能磁共振图像时间序列作为研究数据, 利用相位同步分析方法获得脑区间的连接关系, 并在此基础上构建脑网络。然后, 利用复杂网络的局部效率指标评估静息态脑功能, 并采用多元线性回归和方差分析等统计方法, 分析ADHD患者和正常对照在静息态下脑区的局部效率可能存在的差异。结果表明, ADHD患者与正常对照在年龄、性别、量表分值(注意力和自制力)、3种智商值(语言智商、操作智商和总智商)等方面均无统计学差异, 在诊断和头动参数上有显著差异(P<0.05, 校正后)。诊断方面发现, 11个局部效率正常对照组与ADHD组具有统计学差异的脑区(P<0.05), 其中主要的脑区为左侧尾状核(0.118±0.317 vs 278±0.433)、丘脑(0.345±0.425 vs 0.541±0.435)、颞横回(0.467±0.476 vs 0.654±0.444)和右侧背外侧额上回(0.536±0.401 vs 0.681±0.333)、额中回(0.505±0.377vs 0.641±0.331)、尾状核(0.144±0.329 vs 0.298±0.423)。在静息态下, ADHD患者和正常对照在左侧中央前回、尾状核、丘脑等脑区的局部效率差异可能与患者尾状核、丘脑等特定脑区的功能异常有关, 也可能与患者注意和执行有关的神经网络损伤有关。
Abstract：In this study, we explored the application of complex network methods based on phase synchronization of brain network mechanisms for attention deficit hyperactivity disorder (ADHD). A total of 135 patients with ADHD and 102 normal controls were selected as subjects. The time series of functional magnetic resonance images of these 237 subjects were used as research data to study the brain network of children with ADHD. The phase synchronization method was used to obtain the connection relationship of each pair of brain regions. The brain network was constructed by using this connection relationship. Then, the resting state brain function was evaluated by using the local efficiency index of the complex network, and statistical methods such as multiple linear regression and variance analysis are used to analyze whether there was a significant difference in the local efficiencies of patients with ADHD and normal controls in the resting brain region. There were no significant differences in age, gender, scale scores (inattention and impulsivity), and three IQ values (verbal IQ, performance IQ and full IQ) between patients with ADHD and normal controls. The significance of the study was statistically significant (P<0.05) in the diagnosis labels and head movement parameters. In terms of diagnosis, 11 brain regions with statistical difference (P<0.05) between the control group with normal local efficiency and the ADHD group were found, among which the main brain regions were: left cauda nucleus (0.118±0.317 vs 278±0.433), thalamus (0.345±0.425 vs 0.541±0.435), heschl gyrus (0.467±0.476 vs 0.654±0.444) and right dorsolateral superior frontal gyrus (0.536±0.401 vs 0.681±0.333), middle frontal gyrus (0.505±0.377 vs 0.641±0.331), caudate nucleus (0.144±0.329 vs 0.298±0.423). There is a significant difference in the local efficiency of the left anterior gyrus, caudate nucleus, thalamus between patients with ADHD and normal controls. These differences might be related to functional abnormalities in specific brain regions such as the caudate nucleusandthalamus, or neural network damage associated with patient attention and execution.
徐杰, 王训恒, 厉力华. 基于静息态fMRI相位同步的ADHD儿童脑网络研究[J]. 中国生物医学工程学报, 2020, 39(3): 265-270.
Xu Jie, Wang Xunheng, Li Lihua. Investigating Brain Networks for ADHD Children Based on Phase Synchronization of Resting State fMRI. Chinese Journal of Biomedical Engineering, 2020, 39(3): 265-270.
[1] 刘秀梅, 冉霓. 注意缺陷多动障碍神经影像学研究进展[J].齐鲁医学杂志, 2005, 20(3):86-87, 90. [2] Dias TG, Iyer SP, Carpenter SD, et al. Characterizing heterogeneity in children with and without ADHD based on reward system connectivity [J]. Dev Cogn Neurosci, 2015(11): 155-174. [3] Rakesh J, Saundra JC, Brendan M. Addressing diagnosis and treatment gaps in adults with attention-deficit/hyperactivity disorder[J]. Prim Care Companion CNS Disorder, 2017, 19(5):17nr02153. [4] Barry R, Clarke A, Johnstone S. A review of electrophysiology in [5] attention-deficit hyperactivity disorder:I.qualitative and quantitative el-ectroencephalography [J]. Clinical Neurophysiology, 2003, 114(2):184-198. [5] 李艳苓, 汤艳清, 杨华彬, 等.注意缺陷多动障碍患儿感觉统合能力的比较[J].中国行为医学科学, 2005, 14(3):240-241. [6] Chang LY, Wang MY, Tsai PS. Diagnostic accuracy of rating scales for attention-deficit/hyperactivity disorder:A meta-analysis [J].Pediatrics, 2016, 137(3):1-13. [7] Hoogman M, Bralten J, Hibar DP, et al. Subcortical brain volume differences in participants with attention deficit hyperactivity disorder inchildren and adults: A cross-sectional mega-analysis [J].Lancet Psychiatry, 2017, 4(4):310-319. [8] Seidman LJ, Valera EM, Makris N, et al. Dorsolateral prefrontal and anterior cingulate cortex volumetric abnormalitiesin adults with attention-deficit/hyperactivity disorder identified by magnetic resonance imaging [J].Biological Psychiatry, 2006, 60(10): 1071-1080. [9] Castellanos FX. Toward a pathophysiology of attention deficit/hyperactivity disorder [J]. Clinical Pediatrics. 1997, 36(7): 381-393. [10] 田丽霞. 基于图论的复杂脑网络分析[J]. 北京生物医学工程. 2010, 4(1):96-100. [11] 胡巧莉. 基于相位同步分析的抑郁症脑电信号的研究[D]. 上海:上海交通大学, 2010. [12] 胡巧莉. 基于相位同步分析方法的抑郁症脑电信号的研究[J]. 中国医疗器械杂志, 2010, 4:246-249. [13] Bob P, Palus M, Susta M, et al. EEG phase synchronization in patients with paranoid schizophrenia[J]. Neuroscience Letters, 2008, 447(1): 73-77. [14] Lehnertz K, Elger CE. Can epileptic seizures be predicted? Evidence from nonlinear time series analysis of brain electrical activity [J].Physical Review Letters, 1998, 80(22): 5019-5022. [15] Kim D, Timothy QD, Kim SG, et al. High-resolution mapping of iso-orientation columns by fMRI [J]. Nature Neurosciencce, 2000, 3(2):164-169. [16] 叶子骏, 黄慧芳, 刘杰.不同大脑分区下注意缺陷多动障碍分类研究[J], 北京生物医学工程, 2016, 10(4):389-394. [17] 赵丽娜, 王保强, 尧德中. 基于信号处理的脑电相位同步分析方法研究[J]. 生物医学工程学杂志, 2008, 7(2): 250-254. [18] Cingulate BG. Frontaland parietal cortietal dysfunction in attention deficit/hyperactivity disorder [J]. Biol Psychiatry, 2011, 69(12):1160-1167. [19] Castellanos FX, Proal E. Large-scale brain systems in ADHD: Beyond the prefrontal-Striatal model [J].Trends Cognitive Science, 2012, 16(1):17-26. [20] Dickstein SG, Bannon K, Castellanos FX, et al. The neural correlates of attention deficit/hyperactivity disorder:An ALE meta-analysis [J]. Journal of Child Psychology Psychiatry. 2006, 47(10):1051-1062. [21] 姚燕滨. ADHD的病因与病程和预后的研究进展[J]. 中国妇幼保健, 2007, 22(3):428-430. [22] Castellanos FX, Giedd JN, Marsh WL, et al. Quantitative brain magnetic resonance imaging in attention-deficit hyperactivity disorder [J]. Arch Gen Psychiaty, 1996, 53:607-616. [23] Sheline YI, Price JL, Yan Z, et al. Resting-state functional MRI in depression unmakes increased connectivity between networks via the dorsal nexus [J].Proceedings National Academy of Science, 2010, 107(24):11020-11025.
