Proceedings of International Conference on Applied Innovation in IT  ·  2026/03/31  ·  Vol. 14  ·  Issue 1  ·  pp. 1499–1508
A Brief Review of Resting-State Functional Magnetic Resonance Imaging Analysis in ADHD
Dalia Abdulhadi Al-Ubaidi, Azurah A. Samah, Ahmed Talib Abdulameer and Mahdi Nsaif Jasim
📄 Download PDF DOI: 10.25673/123769
Abstract
Attention Deficit/Hyperactivity Disorder (ADHD) represents a brain-based disorder that is typically diagnosed based on reported behavioral symptoms, which is challenging due to the broad spectrum of symptoms and the presence of multiple subtypes. Thus, there is a growing interest in exploring its underlying mechanisms using advancements in brain imaging such as resting-state functional Magnetic Resonance Imaging (rs-fMRI). This technique) has become a central tool in investigating ADHD, enabling researchers to explore how intrinsic functional connectivity (FC) contributes to the disorder. Various methodologies, including model-driven and data-driven approaches, are available for analyzing rs-fMRI data. This research provides a brief overview of rs-fMRI analysis methodologies in ADHD research. It highlights the principal analytical methods and critically discusses their advantages and drawbacks in contributing to a more profound understanding of ADHD-specific FC profiles. By summarizing the recent trends and methodological advancements, this short review will help researchers familiarize themselves with these techniques and guide them in applying them effectively in clinical settings. Ultimately, this will improve the diagnostic potential of rs-fMRI in ADHD studies.
Keywords
ADHD Functional Connectivity Analysis Human Brain Mapping Resting-State fMRI.
References
  1. B. Sutcubasi, B. Metin, M. K. Kurban, Z. E. Metin,
  2. B. Beser, and E. Sonuga-Barke, “Resting-state network dysconnectivity in ADHD: A system-neuroscience-based meta-analysis,” World J Biol Psychiatry, vol. 21, no. 9, pp. 662-672, Nov 2020, [Online]. Available: https://doi.org/10.1080/15622975.2020.1775889.
  3. A. consortium, “The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience,” Frontiers in Systems Neuroscience, vol. 6, p. 62, 2012.
  4. V. Pereira-Sanchez et al., “Resting-State fMRI to Identify the Brain Correlates of Treatment Response to Medications in Children and Adolescents With Attention-Deficit/Hyperactivity Disorder: Lessons From the CUNMET Study,” Frontiers in Psychiatry, vol. 12, 2021, [Online]. Available: https://doi.org/10.3389/fpsyt.2021.759696.
  5. G. H. Glover, “Overview of functional magnetic resonance imaging,” Neurosurg Clin N Am, vol. 22, no. 2, pp. 133-vii, 2011, [Online]. Available: https://doi.org/10.1016/j.nec.2010.11.001.
  6. A. Harikumar, D. W. Evans, C. C. Dougherty,
  7. K. L. H. Carpenter, and A. M. Michael, “A Review of the Default Mode Network in Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder,” Brain Connect, vol. 11, no. 4, pp. 253-263, May 2021, [Online]. Available: https://doi.org/10.1089/brain.2020.0865.
  8. M. E. Shenton, M. Kubicki, and N. Makris, “Understanding alterations in brain connectivity in attention-deficit/hyperactivity disorder using imaging connectomics,” Biological Psychiatry, vol. 76, no. 8, pp. 601-602, 2014.
  9. B. Sutcubasi, B. Metin, M. K. Kurban, Z. E. Metin,
  10. B. Beser, and E. Sonuga-Barke, “Resting-state network dysconnectivity in ADHD: A system-neuroscience-based meta-analysis,” The World Journal of Biological Psychiatry, vol. 21, no. 9, pp. 662-672, 2020, [Online]. Available: https://doi.org/10.1080/15622975.2020.1775889.
  11. K. Smitha et al., “Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks,” The Neuroradiology Journal, vol. 30, no. 4, pp. 305-317, 2017, [Online]. Available: https://doi.org/10.1177/1971400917697342.
  12. P. Thomson et al., “Longitudinal maturation of resting state networks: Relevance to sustained attention and attention deficit/hyperactivity disorder,” Cognitive, Affective, & Behavioral Neuroscience, vol. 22, no. 6, pp. 1432-1446, 2022, [Online]. Available: https://doi.org/10.3758/s13415-022-01017-9.
  13. A. dos Santos Siqueira, C. E. Biazoli Junior,
  14. W. E. Comfort, L. A. Rohde, and J. R. Sato, “Abnormal functional resting-state networks in ADHD: graph theory and pattern recognition analysis of fMRI data,” BioMed Research International, vol. 2014, 2014, [Online]. Available: https://doi.org/10.1155/2014/380531.
  15. P. Sörös et al., “Hyperactivity/restlessness is associated with increased functional connectivity in adults with ADHD: a dimensional analysis of resting state fMRI,” BMC Psychiatry, vol. 19, no. 1, pp. 1-11, 2019.
  16. Y. Wang, C. Zuo, Q. Xu, S. Liao, M. Kanji, and
  17. D. Wang, “Altered resting functional network topology assessed using graph theory in youth with attention-deficit/hyperactivity disorder,” Progress in Neuro-Psychopharmacology and Biological Psychiatry, vol. 98, p. 109796, 2020, [Online]. Available: https://doi.org/10.1016/j.pnpbp.2019.109796.
  18. Y. I. H. Aljanabi, “Sensor Networks and Monitoring Applications in Environmental Sensing Systems,” Al-Turath Journal of Cyber Security, vol. 1, no. 1, pp. 1-5, 2024.
  19. B. Iravani, A. Arshamian, P. Fransson, and
  20. N. Kaboodvand, “Whole-brain modelling of resting state fMRI differentiates ADHD subtypes and facilitates stratified neuro-stimulation therapy,” NeuroImage, vol. 231, p. 117844, 2021, [Online]. Available: https://doi.org/10.1016/j.neuroimage.2021.117844.
  21. H. Zhang et al., “Aberrant functional connectivity in resting state networks of ADHD patients revealed by independent component analysis,” BMC Neuroscience, vol. 21, no. 1, p. 39, 2020, [Online]. Available: https://doi.org/10.1186/s12868-020-00589-x.
  22. G. Liu, W. Lu, J. Qiu, and L. Shi, “Identifying individuals with attention-deficit/hyperactivity disorder based on multisite resting-state functional magnetic resonance imaging: A radiomics analysis,” Hum Brain Mapp, vol. 44, no. 8, pp. 3433-3445, 2023, [Online]. Available: https://doi.org/10.1002/hbm.26290.
  23. K. W. Lange, S. Reichl, K. M. Lange, L. Tucha, and O. Tucha, “The history of attention deficit hyperactivity disorder,” Atten Defic Hyperact Disord, vol. 2, no. 4, pp. 241-255, 2010, [Online]. Available: https://doi.org/10.1007/s12402-010-0045-8.
  24. A. Thapar, M. Cooper, R. Jefferies, and
  25. E. Stergiakouli, “What causes attention deficit hyperactivity disorder?,” Arch Dis Child, vol. 97, no. 3, pp. 260-265, 2012, [Online]. Available: https://doi.org/10.1136/archdischild-2011-300482.
  26. A. P. Association, Diagnostic and Statistical Manual of Mental Disorders: DSM-5. Washington, DC: American Psychiatric Association, 2013.
  27. E. M. Mahone and M. B. Denckla, “Attention-Deficit/Hyperactivity Disorder: A Historical Neuropsychological Perspective,” J Int Neuropsychol Soc, vol. 23, no. 9-10, pp. 916-929, 2017, [Online]. Available: https://doi.org/10.1017/s1355617717000807.
  28. S. L. James et al., “Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017,” The Lancet, vol. 392, no. 10159, pp. 1789-1858, 2018, [Online]. Available: https://doi.org/10.1016/S0140-6736(18)32279-7.
  29. P. S. Wang et al., “Use of mental health services for anxiety, mood, and substance disorders in 17 countries in the WHO world mental health surveys,” The Lancet, vol. 370, no. 9590, pp. 841-850, 2007, [Online]. Available: https://doi.org/10.1016/S0140-6736(07)61414-7.
  30. World Health Organization, “Mental disorders,” [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/mental-disorders, [Accessed: 2020].
  31. Y. Paloyelis, M. A. Mehta, J. Kuntsi, and P. Asherson, “Functional MRI in ADHD: a systematic literature review,” Expert Review of Neurotherapeutics, vol. 7, no. 10, pp. 1337-1356, 2007, [Online]. Available: https://doi.org/10.1586/14737175.7.10.1337.
  32. M. Ahmadlou, H. Adeli, and A. Adeli, “Graph theoretical analysis of organization of functional brain networks in ADHD,” Clinical EEG and Neuroscience, vol. 43, no. 1, pp. 5-13, 2012, [Online]. Available: https://doi.org/10.1177/1550059411428555.
  33. A. Fornito, A. Zalesky, and E. T. Bullmore, “An Introduction to Brain Networks,” in Fundamentals of Brain Network Analysis, San Diego: Academic Press, 2016, pp. 1-35.
  34. S.-F. Liang et al., “Differentiation between resting-state fMRI data from ADHD and normal subjects: based on functional connectivity and machine learning,” in 2012 International Conference on Fuzzy Theory and Its Applications (iFUZZY2012), pp. 294-298, 2012, [Online]. Available: https://doi.org/10.1109/iFUZZY.2012.6409719.
  35. B. Biswal, F. Z. Yetkin, V. M. Haughton, and
  36. J. S. Hyde, “Functional connectivity in the motor cortex of resting human brain using echo-planar MRI,” Magn Reson Med, vol. 34, no. 4, pp. 537-541, 1995, [Online]. Available: https://doi.org/10.1002/mrm.1910340409.
  37. L. Zhang, K. Qin, N. Pan, H. Xu, and Q. Gong, “Shared and distinct patterns of default mode network dysfunction in major depressive disorder and bipolar disorder: A comparative meta-analysis,” Journal of Affective Disorders, vol. 368, pp. 23-32, 2025, [Online]. Available: https://doi.org/10.1016/j.jad.2024.09.021.
  38. F. Li et al., “Intrinsic Brain Abnormalities in Attention Deficit Hyperactivity Disorder: A Resting-State Functional MR Imaging Study,” Radiology, vol. 272, no. 2, pp. 514-523, 2014, [Online]. Available: https://doi.org/10.1148/radiol.14131622.
  39. H. Yang et al., “Abnormal spontaneous brain activity in medication-naïve ADHD children: A resting state fMRI study,” Neuroscience Letters, vol. 502, no. 2, pp. 89-93, 2011, [Online]. Available: https://doi.org/10.1016/j.neulet.2011.07.028.
  40. J. Sato, M. Hoexter, A. Fujita, and L. Rohde, “Evaluation of Pattern Recognition and Feature Extraction Methods in ADHD Prediction,” Frontiers in Systems Neuroscience, vol. 6, 2012, [Online]. Available: https://doi.org/10.3389/fnsys.2012.00068.
  41. B. d. C. Alonso, S. H. Tobón, P. D. Suarez,
  42. J. G. Flores, B. d. C. Carrillo, and E. B. Pérez, “Correction: A multi-methodological MR resting state network analysis to assess the changes in brain physiology of children with ADHD,” PLoS One, vol. 10, no. 11, p. e0143785, 2015, [Online]. Available: https://doi.org/10.1371/journal.pone.0143785.
  43. X. Wang, Y. Jiao, T. Tang, H. Wang, and Z. Lu, “Altered regional homogeneity patterns in adults with attention-deficit hyperactivity disorder,” European Journal of Radiology, vol. 82, no. 9, pp. 1552-1557, 2013, [Online]. Available: https://doi.org/10.1016/j.ejrad.2013.04.009.
  44. L. Tan, X. Guo, S. Ren, J. N. Epstein, and L. J. Lu, “A computational model for the automatic diagnosis of attention deficit hyperactivity disorder based on functional brain volume,” Frontiers in Computational Neuroscience, vol. 11, p. 75, 2017, [Online]. Available: https://doi.org/10.3389/fncom.2017.00075.
  45. Z. Wang, X. Zhou, Y. Gui, M. Liu, and H. Lu, “Multiple measurement analysis of resting-state fMRI for ADHD classification in adolescent brain from the ABCD study,” Translational Psychiatry, vol. 13, no. 1, p. 45, 2023, [Online]. Available: https://doi.org/10.1038/s41398-023-02309-5.
  46. K. Jiang et al., “Functional network connectivity changes in children with attention-deficit hyperactivity disorder: A resting-state fMRI study,” International Journal of Developmental Neuroscience, vol. 78, pp. 1-6, 2019, [Online]. Available: https://doi.org/10.1016/j.ijdevneu.2019.07.003.
  47. Z. W. Zhou et al., “Inconsistency in Abnormal Functional Connectivity Across Datasets of ADHD-200 in Children With Attention Deficit Hyperactivity Disorder,” Frontiers in Psychiatry, vol. 10, p. 692, 2019, [Online]. Available: https://doi.org/10.3389/fpsyt.2019.00692.
  48. F. X. Yan, C. W. Wu, S. Y. Cheng, K. E. Lim,
  49. Y. Y. Hsu, and H. L. Liu, “Resting-state functional magnetic resonance imaging analysis with seed definition constrained by regional homogeneity,” Brain Connect, vol. 3, no. 4, pp. 438-449, 2013, [Online]. Available: https://doi.org/10.1089/brain.2013.0164.
  50. S. Genon and J. Li, “Brain networks atlases,” in Advances in Resting-State Functional MRI, Academic Press, 2023, pp. 59-85.
  51. L. Fan et al., “The human brainnetome atlas: a new brain atlas based on connectional architecture,” Cerebral Cortex, vol. 26, no. 8, pp. 3508-3526, 2016, [Online]. Available: https://doi.org/10.1007/s11427-016-5110-x.
  52. J. K. Mai, M. Majtanik, and G. Paxinos, Atlas of the Human Brain. Academic Press, 2015.
  53. M. Yu et al., “Meta-analysis of structural and functional alterations of brain in patients with attention-deficit/hyperactivity disorder,” Frontiers in Psychiatry, vol. 13, 2023, [Online]. Available: https://doi.org/10.3389/fpsyt.2022.1070142.
  54. N. Tzourio-Mazoyer et al., “Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain,” NeuroImage, vol. 15, no. 1, pp. 273-289, 2002, [Online]. Available: https://doi.org/10.1006/nimg.2001.0978.
  55. A. Riaz, M. Asad, E. Alonso, and G. Slabaugh, “Fusion of fMRI and non-imaging data for ADHD classification,” Computerized Medical Imaging and Graphics, vol. 65, pp. 115-128, 2018, [Online]. Available: https://doi.org/10.1016/j.compmedimag.2017.10.002.
  56. A. Y. Revell et al., “A framework for brain atlases: Lessons from seizure dynamics,” NeuroImage, vol. 254, p. 118986, 2022, [Online]. Available: https://doi.org/10.1016/j.neuroimage.2022.118986.
  57. R. C. Craddock, G. A. James, P. E. Holtzheimer,
  58. X. P. Hu, and H. S. Mayberg, “A whole brain fMRI atlas generated via spatially constrained spectral clustering,” Hum Brain Mapp, vol. 33, no. 8, pp. 1914-1928, 2012, [Online]. Available: https://doi.org/10.1002/hbm.21333.
  59. T. Yeo et al., “The organization of the human cerebral cortex estimated by intrinsic functional connectivity,” Journal of Neurophysiology, vol. 106, no. 3, pp. 1125-1165, 2011, [Online]. Available: https://doi.org/10.1152/jn.00338.2011.
  60. NeuroBureau, “Atlas visualization,” [Online]. Available: https://www.nitrc.org/plugins/mwiki/index.php/neurobureau:AthenaPipeline, [Accessed: 18 Oct 2023].
  61. B. Thirion, G. Varoquaux, E. Dohmatob, and
  62. J.-B. Poline, “Which fMRI clustering gives good brain parcellations?,” Frontiers in Neuroscience, vol. 8, p. 167, 2014, [Online]. Available: https://doi.org/10.3389/fnins.2014.00167.
  63. T. H. Hsieh, F. Z. Shaw, C. C. Kung, and S. F. Liang, “Seed correlation analysis based on brain region activation for ADHD diagnosis in a large-scale resting state data set,” Frontiers in Human Neuroscience, vol. 17, p. 1082722, 2023, [Online]. Available: https://doi.org/10.3389/fnhum.2023.1082722.
  64. V. G. van de Ven, E. Formisano, D. Prvulovic,
  65. C. H. Roeder, and D. E. Linden, “Functional connectivity as revealed by spatial independent component analysis of fMRI measurements during rest,” Human Brain Mapping, vol. 22, no. 3, pp. 165-178, 2004, [Online]. Available: https://doi.org/10.1002/hbm.20022.
  66. F. X. Castellanos and Y. Aoki, “Intrinsic Functional Connectivity in Attention-Deficit/Hyperactivity Disorder: A Science in Development,” Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, vol. 1, no. 3, pp. 253-261, 2016, [Online]. Available: https://doi.org/10.1016/j.bpsc.2016.03.004.
  67. H. Lv et al., “Resting-State Functional MRI: Everything That Nonexperts Have Always Wanted to Know,” AJNR Am J Neuroradiol, vol. 39, no. 8, pp. 1390-1399, 2018, [Online]. Available: https://doi.org/10.3174/ajnr.A5527.
  68. M. Brown et al., “ADHD-200 Global Competition: diagnosing ADHD using personal characteristic data can outperform resting state fMRI measurements,” Frontiers in Systems Neuroscience, vol. 6, 2012, [Online]. Available: https://doi.org/10.3389/fnsys.2012.00069.
  69. J. E. Scofield, J. D. Johnson, P. K. Wood, and
  70. D. C. Geary, “Latent resting-state network dynamics in boys and girls with attention-deficit/hyperactivity disorder,” PLoS One, vol. 14, no. 6, p. e0218891, 2019, [Online]. Available: https://doi.org/10.1371/journal.pone.0218891.
  71. S. B. Eickhoff, B. Yeo, and S. Genon, “Imaging-based parcellations of the human brain,” Nature Reviews Neuroscience, vol. 19, no. 11, pp. 672-686, 2018, [Online]. Available: https://doi.org/10.1038/s41583-018-0071-7.
  72. S. Arslan, S. I. Ktena, A. Makropoulos,
  73. E. C. Robinson, D. Rueckert, and S. Parisot, “Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex,” NeuroImage, vol. 170, pp. 5-30, 2018, [Online]. Available: https://doi.org/10.1016/j.neuroimage.2017.04.014.
  74. A. Kaur and Y. Kumar, “Healthcare data analysis using water wave optimization-based diagnostic model,” Journal of Information and Communication Technology, vol. 20, no. 4, pp. 457-488, 2021, [Online]. Available: https://doi.org/10.32890/jict2021.20.4.1.
  75. M. Ahmadi, K. Kazemi, K. Kuc, A. Cybulska-Klosowicz, M. S. Helfroush, and A. Aarabi, “Resting state dynamic functional connectivity in children with attention deficit/hyperactivity disorder,” J Neural Eng, vol. 18, no. 4, 2021, [Online]. Available: https://doi.org/10.1088/1741-2552/ac16b3.
  76. Y. Sun et al., “Brain state-dependent dynamic functional connectivity patterns in attention-deficit/hyperactivity disorder,” Journal of Psychiatric Research, vol. 138, pp. 569-575, 2021, [Online]. Available: https://doi.org/10.1016/j.jpsychires.2021.05.010.
  77. W. Cai, T. Chen, L. Szegletes, K. Supekar, and
  78. V. Menon, “Aberrant Time-Varying Cross-Network Interactions in Children With Attention-Deficit/Hyperactivity Disorder and the Relation to Attention Deficits,” Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, vol. 3, no. 3, pp. 263-273, 2018, [Online]. Available: https://doi.org/10.1016/j.bpsc.2017.10.005.
  79. O. Sporns, “Graph theory methods: applications in brain networks,” Dialogues in Clinical Neuroscience, 2022, [Online]. Available: https://doi.org/10.31887/DCNS.2018.20.2/osporns.
  80. L. Wang et al., “Altered small-world brain functional networks in children with attention-deficit/hyperactivity disorder,” Human Brain Mapping, vol. 30, no. 2, pp. 638-649, 2009, [Online]. Available: https://doi.org/10.1002/hbm.20530.
  81. Z. Yao, B. Hu, Y. Xie, P. Moore, and J. Zheng, “A review of structural and functional brain networks: small world and atlas,” Brain Informatics, vol. 2, no. 1, pp. 45-52, 2015, [Online]. Available: https://doi.org/10.1007/s40708-015-0009-z.
  82. J. D. Power et al., “Functional network organization of the human brain,” Neuron, vol. 72, no. 4, pp. 665-678, 2011.
  83. P. Moghimi, A. T. Dang, Q. Do, T. I. Netoff,
  84. K. O. Lim, and G. Atluri, “Evaluation of functional MRI-based human brain parcellation: a review,” J Neurophysiol, vol. 128, no. 1, pp. 197-217, 2022, [Online]. Available: https://doi.org/10.1152/jn.00411.2021.

Proceedings of the International Conference on Applied Innovations in IT by Anhalt University of Applied Sciences is licensed under CC BY-SA 4.0  ·  This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License

ICAIIT 2026
International Conference on Applied Innovation in IT
Navigation
Publisher
ISSN2199-8876
Publisher Edition Hochschule Anhalt
Location Anhalt University of Applied Sciences
Email leiterin.hsb@hs-anhalt.de
Phone +49 (0) 3496 67 5611
Address Building 01, Room 425
Bernburger Str. 55
D-06366 Köthen, Germany
Open Access License
CC BY-SA 4.0

All works are licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0), unless otherwise noted.

Published by ICAIIT in cooperation with Anhalt University of Applied Sciences.

© 2026 ICAIIT — International Conference on Applied Innovations in IT. Anhalt University of Applied Sciences, Köthen, Germany.
Visitors: site traffic counter