近红外脑功能成像技术结合行为评估在注意缺陷多动障碍诊断中的效能分析

张茜, 任姣姣, 高红, 白瑞北, 王朝晖

中国儿童保健杂志 ›› 2025, Vol. 33 ›› Issue (4) : 402-408.

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中国儿童保健杂志 ›› 2025, Vol. 33 ›› Issue (4) : 402-408. DOI: 10.11852/zgetbjzz2024-1153
科研论著

近红外脑功能成像技术结合行为评估在注意缺陷多动障碍诊断中的效能分析

  • 张茜1, 任姣姣2, 高红2, 白瑞北2, 王朝晖1,2
作者信息 +

Diagnostic performance of functional near-infrared spectroscopy combined with behavioral assessment in attention-deficit hyperactivity disorder

  • ZHANG Xi1, REN Jiaojiao2, GAO Hong2, BAI Ruibei2, WANG Zhaohui1,2
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摘要

目的 采用近红外脑功能成像技术(fNIRS)探索注意缺陷多动障碍(ADHD)儿童在任务态下前额叶皮层(PFC)脑激活水平,及其与行为评估方法结合后在ADHD中的区分能力及诊断价值,为ADHD的早期诊断提供依据。方法 选取2023年7月—2024年7月在西安市人民医院(西安市第四医院)儿童保健科就诊的40例ADHD儿童和同期来院健康体检的30名健康儿童为研究对象,在言语流畅性任务(VFT)下采用22通道fNIRS设备采集前额叶氧合血红蛋白(HbO2)浓度变化以反映其PFC的激活情况。完成Conners父母症状问卷(PSQ),将PSQ与前额叶HbO2值进行相关性及受试者工作特征曲线(ROC)分析,以探讨HbO2联合PSQ在ADHD诊断中的敏感度和特异度。结果 ADHD儿童在右侧背外侧前额叶皮层(rDLPFC)、左侧背外侧前额叶皮层(lDLPFC)、右侧额极皮层(rFPC)、内侧前额叶皮层(mFPC)、布罗德曼8区(BA8)的HbO2水平低于健康儿童(Z=4.115、2.363、3.591、2.578、2.936,P<0.05),ADHD儿童在PSQ评估中的品行、学习问题、冲动-多动、多动指数各因子分均高于健康组儿童(Z=3.827、4.065、5.531、4.827,P<0.05)。学习问题与rDLPFC、rFPC、mFPC的HbO2呈负相关(r=-0.336、-0.412、-0.310,P<0.05),冲动-多动与mFPC的HbO2呈负相关(r=-0.382,P<0.05),多动指数与rDLPFC、rFPC、mFPC、BA8的HbO2呈负相关(r=-0.393、-0.445、-0.431、-0.339,P<0.05)。rDLPFC、lDLPFC、rPFC、lPFC、mPFC、BA8脑区的HbO2联合多动指数的ROC曲线的AUC分别为0.884、0.877、0.883、0.900、0.896、0.884,均大于HbO2及多动指数单独预测的AUC。结论 ADHD儿童PFC功能受损,前额叶HbO2值联合PSQ在ADHD诊断中具有价值。

Abstract

Objective To explore the prefrontal cortex (PFC) activation levels in children with attention deficit hyperactivity disorder (ADHD) during task states using functional near-infrared spectroscopy (fNIRS), and to evaluate the diagnostic value of combining PFC activation with behavioral assessments for distinguishing ADHD, so as to provide a basis for early diagnosis. Methods A total of 40 children diagnosed with ADHD and 30 healthy children undergoing routine health check-ups atthe Children's Health Department of Xi'an People's Hospital (Xi'an Fourth Hospital) from July 2023 to July 2024 were recruited. A 22-channel fNIRS device was used to measure changes in oxygenated hemoglobin (HbO2) concentration in the PFC during a verbal fluency task (VFT). The Conners Parent Symptom Questionnaire (PSQ) was administered, and correlations between PSQ scores and PFC HbO2 levels were analyzed. Receiver operating characteristic (ROC) curve analysis was performed to assess the sensitivity and specificity of combining HbO2 levels with PSQ scores for ADHD diagnosis. Results Compared to healthy children, ADHD children exhibited significantly lower HbO2 levels in the right dorsolateral prefrontal cortex (rDLPFC), left dorsolateral prefrontal cortex (lDLPFC), right frontopolar cortex (rFPC), medial prefrontal cortex (mFPC), and Brodmann area 8 (BA8) (Z=4.115, 2.363, 3.591, 2.578, 2.936, P<0.05). ADHD children also scored higher on the PSQ subscales for conduct problems, learning problems, impulsivity-hyperactivity, and hyperactivity index (Z=3.827, 4.065, 5.531, 4.827, P<0.05). Negative correlations were observed between learning problems and HbO2 levels in the rDLPFC, rFPC, and mFPC (r=-0.336,-0.412,-0.310, P<0.05), between impulsivity-hyperactivity and HbO2 levels in the mFPC (r=-0.382, P<0.05), and between the hyperactivity index and HbO2 levels in the rDLPFC, rFPC, mFPC, and BA8 (r=-0.393,-0.445,-0.431,-0.339, P<0.05). The area under the ROC curve (AUC) for the combined prediction of ADHD using HbO2 levels in the rDLPFC, lDLPFC, rFPC, lFPC, mFPC, and BA8 along with the hyperactivity index was 0.884, 0.877, 0.883, 0.900, 0.896, and 0.884, respectively, all of which were higher than the AUCs for HbO2 levels or the hyperactivity index alone. Conclusion Children with ADHD exhibit impaired PFC function, and the combination of PFC HbO2 levels and PSQ scores demonstrates significant diagnostic value for ADHD.

关键词

注意缺陷多动障碍 / 近红外光谱成像 / 前额叶皮层 / 氧合血红蛋白 / 行为评估

Key words

attention-deficit hyperactivity disorder / near-infrared spectroscopic imaging / prefrontal cortex / oxygenated haemoglobin / behavioral assessment

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张茜, 任姣姣, 高红, 白瑞北, 王朝晖. 近红外脑功能成像技术结合行为评估在注意缺陷多动障碍诊断中的效能分析[J]. 中国儿童保健杂志. 2025, 33(4): 402-408 https://doi.org/10.11852/zgetbjzz2024-1153
ZHANG Xi, REN Jiaojiao, GAO Hong, BAI Ruibei, WANG Zhaohui. Diagnostic performance of functional near-infrared spectroscopy combined with behavioral assessment in attention-deficit hyperactivity disorder[J]. Chinese Journal of Child Health Care. 2025, 33(4): 402-408 https://doi.org/10.11852/zgetbjzz2024-1153
中图分类号: R749.94   

参考文献

[1] Posner J, Polanczyk GV, Sonuga-Barke E. Attention-deficit hyperactivity disorder[J]. Lancet, 2020, 395(10222): 450-462.
[2] Haber SN, Robbins T. The prefrontal cortex[J]. Neuropsychopharmacol, 2022, 47(1): 1-2.
[3] Rubia K, Westwood S, Aggensteiner PM, et al. Neurotherapeutics for attention deficit/hyperactivity disorder (ADHD): A review[J]. Cells, 2021, 10(8): 2156.
[4] Carey C. DSM- 5® Guidebook: The essential companion to the diagnostic and statistical manual of mental disorders[J]. Ir J Psychol Med, 2016, 33(2):133 - 134.
[5] Yeung MK. An optical window into brain function in children and adolescents: A systematic review of functional near-infrared spectroscopy studies[J]. NeuroImage, 2021, 227: 117672.
[6] Pereira-Sanchez V, Castellanos FX. Neuroimaging in attention-deficit/hyperactivity disorder[J]. Curr Opin Psychiatry, 2021, 34(2): 105-111.
[7] Gu Y, Miao S, Han J, et al. Complexity analysis of fNIRS signals in ADHD children during working memory task[J]. Sci Rep, 2017, 7(1): 829.
[8] Kruppa JA, Reindl V, Gerloff C, et al. Brain and motor synchrony in children and adolescents with ASD-a fNIRS hyperscanning study[J]. Soc Cogn Affect Neurosci, 2021, 16(1-2): 103-116.
[9] 范娟,杜亚松,王立伟.Conners父母用症状问卷的中国城市常模和信度研究[J].上海精神医学,2005,17(6): 321-323.
Fan J, Du YS, Wang LW. The norm and reliability of the Conners Parent Symptom Questionnaire in Chinese urban children[J]. Shanghai Arch Psychiatry, 2005,17(6): 321-323. (in Chinese)
[10] Hou X, Zhang Z, Zhao C, et al. NIRS-KIT: A MATLAB toolbox for both resting-state and task fNIRS data analysis[J]. Neurophotonics, 2021, 8(1):010802.
[11] Kocsis L, Herman P, Eke A. The modified Beer-Lambert law revisited[J]. Phys Med Biol, 2006, 51(5): N91-98.
[12] Eggebrecht AT, White BR, Ferradal SL, et al. A quantitative spatial comparison of high-density diffuse optical tomography and fMRI cortical mapping[J]. NeuroImage, 2012, 61(4): 1120-1128.
[13] Zhang H, Xu L, Yu J, et al. Identification of autism spectrum disorder based on functional near-infrared spectroscopy using adaptive spatiotemporal graph convolution network[J]. Front Neurosci, 2023, 17: 1132231.
[14] Wu T, Liu X, Cheng F, et al. Dorsolateral prefrontal cortex dysfunction caused by a go/no-go task in children with attention-deficit hyperactivity disorder: A functional near-infrared spectroscopy study[J]. Front Neurosci, 2023,17: 1145485.
[15] Miao S, Han J, Gu Y, et al. Reduced prefrontal cortex activation in children with attention-deficit/hyperactivity disorder during go/no-go task: A functional near-infrared spectroscopy study[J]. Front Neurosci, 2017, 11: 367.
[16] Wagner S, Sebastian A, Lieb K, et al. A coordinate-based ALE functional MRI meta-analysis of brain activation during verbal fluency tasks in healthy control subjects[J]. BMC Neurosci, 2014, 15: 19.
[17] Li Y, Ma S, Zhang X, et al. ASD and ADHD: Divergent activating patterns of prefrontal cortex in executive function tasks?[J]. J Psychiatr Res, 2024, 172: 187-196.
[18] Sripada CS, Kessler D, Angstadt M. Lag in maturation of the brain's intrinsic functional architecture in attention-deficit/hyperactivity disorder[J]. Proc Natl Acad Sci USA, 2014, 111(39):14259-14264.
[19] Dadario NB, Tanglay O, Sughrue ME. Deconvoluting human Brodmann area 8 based on its unique structural and functional connectivity[J]. Front Neuroanat, 2023, 17: 1127143.
[20] Wu J, Xiao H, Sun H, et al. Role of Dopamine receptors in ADHD: A systematic Meta-analysis[J]. Mol Neurobiol, 2012, 45(3): 605-620.
[21] Wu ZM, Llera A, Hoogman M, et al. Linked anatomical and functional brain alterations in children with attention-deficit/hyperactivity disorder[J]. NeuroImage Clin, 2019, 23: 101851.
[22] Chiang HL, Wu CS, Chen CL, et al. Machine-learning-based feature selection to identify attention-deficit hyperactivity disorder using whole-brain white matter microstructure: A longitudinal study[J]. Asian J Psychiatry, 2024, 97: 104087.
[23] Chen Y, Huang X, Wu M, et al. Disrupted brain functional networks in drug-naïve children with attention deficit hyperactivity disorder assessed using graph theory analysis[J]. Hum Brain Mapp, 2019, 40(17): 4877-4887.
[24] Høberg A, Solberg BS, Hegvik TA, et al. Using polygenic scores in combination with Symptom Rating Scales to identify attention-deficit/hyperactivity disorder[J]. BMC Psychiatry, 2024, 24(1): 471.
[25] Hong SB. Thalamocortical functional connectivity in youth with attention-deficit/hyperactivity disorder[J].J Psychiatry Neurosci, 2023, 48(1): E50-E60.

基金

陕西省科技厅研究项目(2022SF-519)

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