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Identifying ADHD and subtypes through microstates analysis and complex networks

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Abstract

The diagnosis of attention-deficit hyperactivity disorder (ADHD) is based on the health history and on the evaluation of questionnaires to identify symptoms. This evaluation can be subjective and lengthy, especially in children. Therefore, a biomarker would be of great value to assist mental health professionals in the process of diagnosing ADHD. Event-related potential (ERP) is one of the most informative and dynamic methods of monitoring cognitive processes. Previous works suggested that specific sets of ERP-microstates are selectively affected by ADHD. This paper proposes a new methodology for the ERP-microstate analysis and identification of ADHD patients based on complex networks to model the microstate topographic maps. The analysis of global and local features of ERP-microstate networks revealed topological differences between ADHD and healthy control. The classification using a neural network with a single hidden layer resulted in an average accuracy of 99.72% in binary classification and 99.31% in the classification of ADHD subtypes. The results were compared to the power band spectral densities and the energy of wavelet coefficients. The temporal features of ERP-microstates, such as frequency of occurrence, duration, coverage, and transition probabilities, were also evaluated for comparison proposes. Overall, the selected topological features of ERP-microstate networks derived from the proposed method performed significantly better classification results. The results suggest that topological features of ERP-microstate networks are promising to identify ADHD and its subtypes with a neural network model compared to power band spectrum density, wavelet transform, and temporal features of ERP-microstates.

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Data availability

The datasets analyzed during the current study are available in the OSF repository (https://osf.io/6594x/).

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Funding

The authors thank the Programa de Pós-Graduação em Engenharia Elétrica (PPGEE - UFES) and the financial support for the research from the project of the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Fundação de Amparo à Pesquisa do Espírito Santo (FAPES), number 598/2018.

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Authors and Affiliations

  1. Department of Electrical Engineering, Universidade Federal do Espírito Santo, Av. Fernando Ferrari, Vitória, 100190, ES, Brazil

    Lorraine Marques Alves, Klaus Fabian Côco & Patrick Marques Ciarelli

  2. Department of Psychology, Universidade Federal do Espírito Santo, Av. Fernando Ferrari, Vitória, 100190, ES, Brazil

    Mariane Lima De Souza

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  1. Lorraine Marques Alves
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  2. Klaus Fabian Côco
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  3. Mariane Lima De Souza
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  4. Patrick Marques Ciarelli
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Correspondence to Lorraine Marques Alves.

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Alves, L.M., Côco, K.F., De Souza, M.L. et al. Identifying ADHD and subtypes through microstates analysis and complex networks. Med Biol Eng Comput 62, 687–700 (2024). https://doi.org/10.1007/s11517-023-02948-2

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