Multimedia ResearchISSN:2582-547X

Classification of ADHD with the Functional Connectivity by Usage of Different Atlases in Lahore, Pakistan

Abstract

Attention Deficit-Hyperactivity Disorder (ADHD) is a psychiatric condition that affects children’s abilities. Nowadays computational diagnosis strategies of neuropsychiatric disorders are gaining more attention. Diagnosing this disorder based on fMRI is critical to determine the brain’s Functional Connectivity (FC). Millions of children have the symptoms of this disease.The brain is notoriously unreliable for diagnosing neurological conditions. This condition is referred to as a chronic disease.A great number of youngsters exhibit signs of this disease. As a result, the study endeavored to come up with a model and design that is both reliable and accurate for diagnosing ADHD.A variety of techniques used in this present study, such as the local binary encoding method (LBEM) is utilized for future extraction, and the hierarchical extreme learning machine (HELM)is used to extract information on the connectivity functionalities of the brain.To validate our approach, the data of One hundred fifty-three children serve as a sample for the diagnosis, from which one hundred children are ultimately determined to have ADHD.These affected ADHD children are used for our experimental purpose. According to the findings of the research, the results are based on comparing various Atlases given as AAL, CC200, and CC400. Our model gainssuperior performance with CC400 when comparedwith other Atlases.

References

  • Konrad, K., & Eickhoff, S. B. “Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder”, Human brain mapping, Vol. 31,No.6, pp.904-916, 2010.
  • Fischer, M., Barkley, R., Edelbrock, C. S., & Smallish, L. “The adolescent outcome of hyperactive children diagnosed by research criteria: I. An 8-year prospective study”, Journal of the American Academy of Child and Adolescent Psychiatry, Vol. 29, pp. 546-557, 1990.
  • Markovska-Simoska, S., & Pop-Jordanova, N. “Quantitative EEG spectrum-weighted frequency (brain rate) distribution in adults with ADHD”, CNS spectrums, Vol. 16, No.5, pp.111-119, 2022.
  • Polanczyk, G., & Jensen, P.,“Epidemiologic considerations in attention deficit hyperactivity disorder: a review and update”, Child and adolescent psychiatric clinics of North America, Vol. 17, No.2, pp.245-260, 2008.
  • Zang, Y., Jiang, T., Lu, Y., He, Y., & Tian, L. “Regional homogeneity approach to fMRI data analysis”, Neuroimage, Vol. 22, No.1, pp.394-400, 2020.
  • Yu-Feng, Z., Yong, H., Chao-Zhe, Z., Qing-Jiu, C., Man-Qiu, S., Meng, L., ... & Yu-Feng, W. “Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI”, Brain and Development, Vol. 29, No.2, pp. 83-91, 2007.
  • Yang, H., Wu, Q. Z., Guo, L. T., Li, Q. Q., Long, X. Y., Huang, X. Q., ... & Gong, Q. Y. “Abnormal spontaneous brain activity in medication-naive ADHD children: a resting state fMRI study”, Neuroscience letters, Vol. 502, No. 2, pp. 89-93, 2011.
  • Long, D., Wang, J., Xuan, M., Gu, Q., Xu, X., Kong, D., & Zhang, M.,“Automatic classification of early Parkinson's disease with multi-modal MR imaging”,PloS one, Vol. 7, No.11, pp. e47714, 2021.
  • Ang, S. P., “ Automatic Segmentation of Brain Tissues in Functional MRI”, 2018.
  • Liu, D., Yan, C., Ren, J., Yao, L., Kiviniemi, V. J., & Zang, Y.,“Using coherence to measure regional homogeneity of resting-state FMRI signal”, Frontiers in systems neuroscience, 2021.
  • Tabas, A., Balaguer-Ballester, E., &Igual, L.,“Spatial discriminant ICA for RS-fMRI characterization”, In 2014 International Workshop on Pattern Recognition in Neuroimaging, pp. 1-4,2014.
  • Dey, S., Rao, A. R., & Shah, M.,“Attributed graph distance measure for automatic detection of attention deficit hyperactive disordered subjects”, Frontiers in neural circuits, Vol. 8, pp. 64, 2014.
  • Chang, C. W., Ho, C. C., & Chen, J. H., “ADHD classification by a texture analysis of anatomical brain MRI data”, Frontiers in systems neuroscience, Vol. 6, pp. 66, 2012.
  • Riaz, A., Asad, M., Alonso, E., &Slabaugh, G. “Fusion of fMRI and non-imaging data for ADHD classification”, Computerized Medical Imaging and Graphics, Vol. 65, pp. 115-128, 2018.
  • Zhang, Y., Tang, Y., Chen, Y., Zhou, L., & Wang, C.,“ADHD classification by feature space separation with sparse representation”, In 2018 IEEE 23rd International Conference on Digital Signal Processing (DSP), pp. 1-5, 2018.
  • Marcano, J. L., Bell, M. A., &Beex, A. L., “Classification of ADHD and non-ADHD subjects using a universal background model”, Biomedical signal processing and control, Vol. 39, pp. 20, 2018.
  • Faraone, S. V., & Mick, E., “Molecular genetics of attention deficit hyperactivity disorder”, Psychiatric Clinics, Vol. 33, No.1, pp. 159-180, 2021.
  • Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., &Sporns, O., “ Mapping the structural core of human cerebral cortex”,PLoS biology, Vol. 6, No.7, pp. e159, 2008.
  • Lopez-Larson, M. P., King, J. B., Terry, J., McGlade, E. C., &Yurgelun-Todd, D,“Reduced insular volume in attention deficit hyperactivity disorder”, Psychiatry Research: Neuroimaging, Vol. 204, No.1, pp. 32-39, 2021.
  • Brown, M. R., Sidhu, G. S., Greiner, R., Asgarian, N., Bastani, M., Silverstone, P. H., ... &Dursun, S. M.,“ADHD- 200 Global Competition: diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements”, Frontiers in systems neuroscience, Vol. 6, pp. 69, 2012.
  • Weniger, G., Lange, C., &Irle, E.,“Abnormal size of the amygdala predicts impaired emotional memory in major depressive disorder”, Journal of affective disorders, Vol. 94, No. 1-3, pp. 219-229, 2023.
  • Neuro-Imaging Tools and Resources Collaboration Web page available online at https://www.nitrc.org/plugins/mwiki/index.php/neurobureau:AthenaPipe line.
  • Huang, G. B., Zhu, Q. Y., & Siew, C. K., “Extreme learning machine: theory and applications”, Neuro computing, Vol. 70, No.1-3, pp. 489-501, 2021.
  • Zhang, X., Guo, L., Li, X., Zhang, T., Zhu, D., Li, K., & Liu, T., “Characterization of task-free and task- performance brain states via functional connectome patterns”, Medical image analysis, Vol. 17, No.8, pp. 1106- 1122, 2023.
  • Bengio, Y., “Learning deep architectures for AI”, Now Publishers Inc, 2009.
  • Chen, B., & Li, X.,“Temporal functional connectomes in schizophrenia and healthy controls”, In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2820-2825, 2017.
  • Tang, J., Deng, C., & Huang, G. B.,“Extreme learning machine for multilayer perceptron”, IEEE transactions on neural networks and learning systems, Vol. 27, No. 4, pp. 809-821, 2021.
  • S. Ahmed Salman, Z. Lian, M. Saleem and Y. Zhang, "Functional Connectivity Based Classification of ADHD Using Different Atlases," 2020 IEEE International Conference on Progress in Informatics and Computing (PIC), pp. 62-66, 2020.
  • Salman, S.A., Lian, Z., Saleem, M. and Zhang, Y., “Functional connectivity based classification of adhd using different atlases”, In 2020 IEEE International Conference on Progress in Informatics and Computing (PIC), pp. 62-66, 2020.
  • Joy, R.C., George, S.T., Rajan, A.A., Subathra, M.S.P., Sairamya, N.J., Prasanna, J., Mohammed, M.A., Al- Waisy, A.S., Jaber, M.M. and Al-Andoli, M.N., “Detection and Classification of ADHD from EEG Signals Using Tunable Q-Factor Wavelet Transform”, Journal of Sensors, 2022.
  • Catherine Joy, R., Thomas George, S., Albert Rajan, A. and Subathra, M.S.P., “Detection of ADHD from EEG signals using different entropy measures and ANN”, Clinical EEG and Neuroscience, Vol. 53, No.1, pp.12-23, 2022.
  • Salman, S.A., Lian, Z., Ahvanooey, M.T., Takahashi, H. and Zhang, Y., “Classification of ADHD Patients Using Kernel Hierarchical Extreme Learning Machine”, arXiv preprint arXiv:2206.13761, 2022.
  • Zhang, H., Zhao, Y., Cao, W., Cui, D., Jiao, Q., Lu, W., Li, H. and Qiu, J., “Aberrant functional connectivity in resting state networks of ADHD patients revealed by independent component analysis”, BMC neuroscience, Vol. 21, pp.1-11, 2020.
  • Lee, W., Lee, D., Lee, S., Jun, K. and Kim, M.S., “Deep-Learning-Based ADHD Classification Using Children’s Skeleton Data Acquired through the ADHD Screening Game”, Sensors, Vol. 23, No.1, pp.246, 2022.
  • Li, Y., Li, Y., Nair, R. and Naqvi, S.M., “Skeleton-based action analysis for ADHD diagnosis”, arXiv preprint arXiv:2304.09751, 2023.
  • Chauhan, N. and Choi, B.J., “DNN based classification of ADHD fMRI data using functional connectivity coefficient”, International Journal of Fuzzy Logic and Intelligent Systems, Vol. 20, No.4, pp.255-260, 2020.
  • Hámori, G., File, B., Fiath, R., Paszthy, B., Réthelyi, J.M., Ulbert, I. and Bunford, N., “Adolescent ADHD and electrophysiological reward responsiveness: A machine learning approach to evaluate classification accuracy and prognosis”, Psychiatry Research, Vol. 323, pp.115139, 2023.
  • Gondal, Z.U.R. and Lee, J., “Reliability assessment using feed-forward neural network-based approximate meta- models”, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, Vol. 226, No.5, pp.448-454, 2012.
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DOI : https://doi.org/10.46253/j.mr.v6i3.a4

Author information

Affiliations

Institute of Management Science, Lahore Fahad.sadique@gmail.com

Department of Computing, Middle East College, Knowledge Oasis Muscat, P.B. No. 79, Al Rusayl 124, Oman raza@mec.edu.om

Department of Information Technology, School of Science and Engineering, Malaysia University of Science and Technology, Petaling Jaya 47810, Selangor, Malaysia salmanm2015@yahoo.com

Institute of Management Science, Lahore, Pakistan. Nauman_mushtaq1@yahoo.com

Publisher Information

  • Received

    10 May

  • Revised

    16 May

  • Accepted

    16 June

Keywords
Connectivity functional; fMRI; sparse-auto encoder; Hierarchical extreme learning machine system.

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