Graph convolution-based brain network analysis of visual mismatch negativity in adults with attention-deficit/hyperactivity disorder_Journal of Biomedical Engineering

Authors：

ZHENG Min ¹ , DANG Chen ² , SUN Li ² ,  ZOU Ling ¹

1. Wang Zheng College of Microelectronics, Changzhou University, Changzhou, Jiangsu 213159, P. R. China;
2. Institute of Mental Health, Peking University Sixth Hospital, Beijing 100191, P.R. China;

Corresponding?author：

ZOU Ling, Email: zouling@cczu.edu.cn

Keywords：

Attention deficit hyperactivity disorder; Source localization; Functional connectivity; Graph convolutional network

DOI：

10.7507/1001-5515.202507079

Video：

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Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Adults with ADHD continue to exhibit deficits in attention and executive function, whereas the neural mechanisms underlying visual mismatch negativity (vMMN)-related processing remain unclear. This study investigated the functional brain connectivity characteristics of adults with ADHD by combining standardized low-resolution brain electromagnetic tomography (sLORETA), phase-locking value (PLV) analysis, and a graph convolutional network (GCN) based on a visual Oddball paradigm that elicited vMMN responses. Electroencephalography (EEG) data were collected from 10 adults with ADHD and 10 healthy controls using a 128-channel recording system. Source signals from 68 brain regions defined by the Desikan-Killiany atlas were reconstructed using sLORETA. Functional connectivity networks were constructed using PLV and subsequently classified by the GCN model. The results showed that the accuracy, precision, recall, and F1-score of the GCN model under five-fold cross-validation were (85.13 ± 1.94)%, (80.58 ± 2.08)%, (86.04 ± 1.76)%, and (83.21 ± 1.89)%, respectively. Node feature weights and classification contribution analyses identified the lingual gyrus, calcarine fissure and surrounding cortex, parahippocampal gyrus, and precuneus as highly discriminative brain regions. These findings indicate that adults with ADHD exhibit abnormal functional connectivity patterns during vMMN-related processing and provide evidence for the auxiliary identification and neural mechanism investigation of ADHD.

Citation： ZHENG Min, DANG Chen, SUN Li, ZOU Ling. Graph convolution-based brain network analysis of visual mismatch negativity in adults with attention-deficit/hyperactivity disorder. Journal of Biomedical Engineering, 2026, 43(3): 521-529. doi: 10.7507/1001-5515.202507079 Copy

1.	Chaulagain D, Pandit A, Yadav D K, et al. A systematic meta-review of systematic reviews on attention deficit hyperactivity disorder. Eur Psychiatry, 2023, 66(1): e66.
2.	Kooij J J S, Bijlenga D, Salerno L, et al. Updated European consensus statement on diagnosis and treatment of adult ADHD. Eur Psychiatry, 2019, 56: 14-34.
3.	Ceroni M, Rossi S, Zerboni G, et al. Attentive-executive functioning and compensatory strategies in adult ADHD: a retrospective case series study. Front Psychol, 2022, 13: 1015102.
4.	Di Lorenzo R, Balducci J, Poppi C, et al. Children and adolescents with ADHD followed up to adulthood: a systematic review of long-term outcomes. Acta Neuropsychiatr, 2021, 33(6): 283-298.
5.	Onandia-Hinchado I, Pardo-Palenzuela N, Díaz-Orueta U. Cognitive characterization of adult ADHD by domains: a systematic review. J Neural Transm, 2021, 128: 893-937.
6.	Yu M, Gao X, Niu X, et al. Meta-analysis of structural and functional alterations of brain in patients with attention-deficit/hyperactivity disorder. Front Psychiatry, 2023, 13: 1070142.
7.	Frost-Karlsson M, Capusan A J, Olausson H, et al. Altered somatosensory processing in adult attention deficit hyperactivity disorder. BMC Psychiatry, 2024, 24(1): 558.
8.	Panagiotidi M, Overton P G, Stafford T. The relationship between ADHD traits and sensory sensitivity in the general population. Compr Psychiatry, 2018, 80: 179-185.
9.	楊姣姣, 郭倩, 李文杰, 等. 基于腦電主成分分析和K-最近鄰的多動癥兒童與正常兒童分類研究. 生物醫學工程學雜志, 2016, 33(2): 232-238.
10.	Arns M, Conners C K, Kraemer H C. A decade of EEG theta/beta ratio research in ADHD: a meta-analysis. J Atten Disord, 2013, 17(5): 374-383.
11.	Da Costa J D, Pastura G M C, Do Carmo C N, et al. Clinical and sociodemographic factors associated with overweight in children and adolescents with attention deficit hyperactivity disorder (ADHD): a cross-sectional study. PharmaNutrition, 2024, 28: 100375.
12.	Liu Y, Wang J, Li Y, et al. Neural oscillations in theta, alpha, beta, and gamma bands and their roles in cognitive processing: a comprehensive review. Neurosci Biobehav Rev, 2023, 145: 104961.
13.	Qin X, Du L, Jiao X, et al. Evaluation of brain source localization methods based on test-retest reliability with multiple session EEG data. IEEE Trans Biomed Eng, 2023, 70(7): 2080-2090.
14.	Uddin M S, Al Mamun A, Alghamdi B S, et al. Brain-computer interface and EEG-based diagnostic biomarkers for ADHD: a systematic review. Biomed Pharmacother, 2022, 152: 113206.
15.	McCracken H S, Murphy B A, Ambalavanar U, et al. Source localization of audiovisual multisensory neural generators in young adults with attention-deficit/hyperactivity disorder. Brain Sci, 2022, 12(6): 809.
16.	Cannon R, Kerson C, Hampshire A. sLORETA and fMRI detection of medial prefrontal default network anomalies in adult ADHD. J Neurother, 2011, 15(4): 358-373.
17.	Elahian B, Yeasin M, Mudigoudar B, et al. Identifying seizure onset zone from electrocorticographic recordings: a machine learning approach based on phase locking value. Seizure, 2017, 51: 35-42.
18.	Ansari Nasab S, Panahi S, Ghassemi F, et al. Functional neuronal networks reveal emotional processing differences in children with ADHD. Cogn Neurodyn, 2022, 16(1): 91-100.
19.	Ahmadi Moghadam E, Abedinzadeh Torghabeh F, Hosseini S A, et al. Improved ADHD diagnosis using EEG connectivity and deep learning through combining Pearson correlation coefficient and phase-locking value. Neuroinformatics, 2024, 22(3): 521-537.
20.	Bakhtyari M, Mirzaei M. ADHD detection from EEG signals using GCN-based multi-domain features. Front Neurosci, 2025, 19: 1561994.
21.	Li R, Yang X, Lou J, et al. A temporal-spectral graph convolutional neural network model for EEG emotion recognition within and across subjects. Brain Inform, 2024, 11: 30.
22.	Dang C, Luo X, Zhu Y, et al. Automatic sensory change processing in adults with attention deficit and hyperactivity disorder: a visual mismatch negativity study. Eur Arch Psychiatry Clin Neurosci, 2024, 274(7): 1651-1660.
23.	American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Arlington: American Psychiatric Publishing, 2013.
24.	Ferree T C, Luu P, Russell G S, et al. Scalp electrode impedance, infection risk, and EEG data quality. Clin Neurophysiol, 2001, 112(3): 536-544.
25.	Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods, 2004, 134(1): 9-21.
26.	Makeig S, Bell A J, Jung T P, et al. Independent component analysis of electroencephalographic data//Advances in Neural Information Processing Systems 8. Cambridge: MIT Press, 1996: 145-151.
27.	Gramfort A, Papadopoulo T, Olivi E, et al. OpenMEEG: open-source software for quasistatic bioelectromagnetics. Biomed Eng Online, 2010, 9: 45.
28.	Pascual-Marqui R D. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol, 2002, 24(Suppl D): 5-12.
29.	Fan C, Song Y, Mao X. A classification method of motor imagery based on brain functional networks by fusing PLV and ECSP. Neural Netw, 2025, 190: 107684.
30.	Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B, 1995, 57(1): 289-300.
31.	Hu Y, Ran J, Qiao R, et al. Identifying ADHD-related abnormal functional connectivity with a graph convolutional neural network. Neural Plast, 2024, 2024: 8862647.
32.	Jiang H, Cao P, Xu M Y, et al. Hi-GCN: a hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction. Comput Biol Med, 2020, 127: 104096.
33.	Ying R, Bourgeois D, You J, et al. GNNExplainer: generating explanations for graph neural networks//Advances in Neural Information Processing Systems 32. Vancouver: NeurIPS, 2019: 9244-9256.
34.	Kremlacek J, Kreegipuu K, Tales A, et al. Visual mismatch negativity (vMMN): a review and meta-analysis of studies in psychiatric and neurological disorders. Cortex, 2016, 80: 76-112.
35.	Su Y, Wang Y, Wang X, et al. Executive function and neural oscillations in adults with attention-deficit/hyperactivity disorder: a systematic review. Front Hum Neurosci, 2025, 19: 1638094.
36.	Castellanos F X, Proal E. Large-scale brain systems in ADHD: beyond the prefrontal-striatal model. Trends Cogn Sci, 2012, 16(1): 17-26.
37.	Buckner R L, Andrews-Hanna J R, Schacter D L. The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci, 2008, 1124: 1-38.
38.	Zhou J, Cui G, Hu S, et al. Graph neural networks: a review of methods and applications. AI Open, 2020, 1: 57-81.

1. Chaulagain D, Pandit A, Yadav D K, et al. A systematic meta-review of systematic reviews on attention deficit hyperactivity disorder. Eur Psychiatry, 2023, 66(1): e66.
2. Kooij J J S, Bijlenga D, Salerno L, et al. Updated European consensus statement on diagnosis and treatment of adult ADHD. Eur Psychiatry, 2019, 56: 14-34.
3. Ceroni M, Rossi S, Zerboni G, et al. Attentive-executive functioning and compensatory strategies in adult ADHD: a retrospective case series study. Front Psychol, 2022, 13: 1015102.
4. Di Lorenzo R, Balducci J, Poppi C, et al. Children and adolescents with ADHD followed up to adulthood: a systematic review of long-term outcomes. Acta Neuropsychiatr, 2021, 33(6): 283-298.
5. Onandia-Hinchado I, Pardo-Palenzuela N, Díaz-Orueta U. Cognitive characterization of adult ADHD by domains: a systematic review. J Neural Transm, 2021, 128: 893-937.
6. Yu M, Gao X, Niu X, et al. Meta-analysis of structural and functional alterations of brain in patients with attention-deficit/hyperactivity disorder. Front Psychiatry, 2023, 13: 1070142.
7. Frost-Karlsson M, Capusan A J, Olausson H, et al. Altered somatosensory processing in adult attention deficit hyperactivity disorder. BMC Psychiatry, 2024, 24(1): 558.
8. Panagiotidi M, Overton P G, Stafford T. The relationship between ADHD traits and sensory sensitivity in the general population. Compr Psychiatry, 2018, 80: 179-185.
9. 楊姣姣, 郭倩, 李文杰, 等. 基于腦電主成分分析和K-最近鄰的多動癥兒童與正常兒童分類研究. 生物醫學工程學雜志, 2016, 33(2): 232-238.
10. Arns M, Conners C K, Kraemer H C. A decade of EEG theta/beta ratio research in ADHD: a meta-analysis. J Atten Disord, 2013, 17(5): 374-383.
11. Da Costa J D, Pastura G M C, Do Carmo C N, et al. Clinical and sociodemographic factors associated with overweight in children and adolescents with attention deficit hyperactivity disorder (ADHD): a cross-sectional study. PharmaNutrition, 2024, 28: 100375.
12. Liu Y, Wang J, Li Y, et al. Neural oscillations in theta, alpha, beta, and gamma bands and their roles in cognitive processing: a comprehensive review. Neurosci Biobehav Rev, 2023, 145: 104961.
13. Qin X, Du L, Jiao X, et al. Evaluation of brain source localization methods based on test-retest reliability with multiple session EEG data. IEEE Trans Biomed Eng, 2023, 70(7): 2080-2090.
14. Uddin M S, Al Mamun A, Alghamdi B S, et al. Brain-computer interface and EEG-based diagnostic biomarkers for ADHD: a systematic review. Biomed Pharmacother, 2022, 152: 113206.
15. McCracken H S, Murphy B A, Ambalavanar U, et al. Source localization of audiovisual multisensory neural generators in young adults with attention-deficit/hyperactivity disorder. Brain Sci, 2022, 12(6): 809.
16. Cannon R, Kerson C, Hampshire A. sLORETA and fMRI detection of medial prefrontal default network anomalies in adult ADHD. J Neurother, 2011, 15(4): 358-373.
17. Elahian B, Yeasin M, Mudigoudar B, et al. Identifying seizure onset zone from electrocorticographic recordings: a machine learning approach based on phase locking value. Seizure, 2017, 51: 35-42.
18. Ansari Nasab S, Panahi S, Ghassemi F, et al. Functional neuronal networks reveal emotional processing differences in children with ADHD. Cogn Neurodyn, 2022, 16(1): 91-100.
19. Ahmadi Moghadam E, Abedinzadeh Torghabeh F, Hosseini S A, et al. Improved ADHD diagnosis using EEG connectivity and deep learning through combining Pearson correlation coefficient and phase-locking value. Neuroinformatics, 2024, 22(3): 521-537.
20. Bakhtyari M, Mirzaei M. ADHD detection from EEG signals using GCN-based multi-domain features. Front Neurosci, 2025, 19: 1561994.
21. Li R, Yang X, Lou J, et al. A temporal-spectral graph convolutional neural network model for EEG emotion recognition within and across subjects. Brain Inform, 2024, 11: 30.
22. Dang C, Luo X, Zhu Y, et al. Automatic sensory change processing in adults with attention deficit and hyperactivity disorder: a visual mismatch negativity study. Eur Arch Psychiatry Clin Neurosci, 2024, 274(7): 1651-1660.
23. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Arlington: American Psychiatric Publishing, 2013.
24. Ferree T C, Luu P, Russell G S, et al. Scalp electrode impedance, infection risk, and EEG data quality. Clin Neurophysiol, 2001, 112(3): 536-544.
25. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods, 2004, 134(1): 9-21.
26. Makeig S, Bell A J, Jung T P, et al. Independent component analysis of electroencephalographic data//Advances in Neural Information Processing Systems 8. Cambridge: MIT Press, 1996: 145-151.
27. Gramfort A, Papadopoulo T, Olivi E, et al. OpenMEEG: open-source software for quasistatic bioelectromagnetics. Biomed Eng Online, 2010, 9: 45.
28. Pascual-Marqui R D. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Methods Find Exp Clin Pharmacol, 2002, 24(Suppl D): 5-12.
29. Fan C, Song Y, Mao X. A classification method of motor imagery based on brain functional networks by fusing PLV and ECSP. Neural Netw, 2025, 190: 107684.
30. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B, 1995, 57(1): 289-300.
31. Hu Y, Ran J, Qiao R, et al. Identifying ADHD-related abnormal functional connectivity with a graph convolutional neural network. Neural Plast, 2024, 2024: 8862647.
32. Jiang H, Cao P, Xu M Y, et al. Hi-GCN: a hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction. Comput Biol Med, 2020, 127: 104096.
33. Ying R, Bourgeois D, You J, et al. GNNExplainer: generating explanations for graph neural networks//Advances in Neural Information Processing Systems 32. Vancouver: NeurIPS, 2019: 9244-9256.
34. Kremlacek J, Kreegipuu K, Tales A, et al. Visual mismatch negativity (vMMN): a review and meta-analysis of studies in psychiatric and neurological disorders. Cortex, 2016, 80: 76-112.
35. Su Y, Wang Y, Wang X, et al. Executive function and neural oscillations in adults with attention-deficit/hyperactivity disorder: a systematic review. Front Hum Neurosci, 2025, 19: 1638094.
36. Castellanos F X, Proal E. Large-scale brain systems in ADHD: beyond the prefrontal-striatal model. Trends Cogn Sci, 2012, 16(1): 17-26.
37. Buckner R L, Andrews-Hanna J R, Schacter D L. The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci, 2008, 1124: 1-38.
38. Zhou J, Cui G, Hu S, et al. Graph neural networks: a review of methods and applications. AI Open, 2020, 1: 57-81.

Journal of Biomedical Engineering

Graph convolution-based brain network analysis of visual mismatch negativity in adults with attention-deficit/hyperactivity disorder

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