Structural Brain Morphometry in Children with Attention Deficit Hyperactivity Disorder and Comorbid Mild Cognitive Impairments
https://doi.org/10.15690/vsp.v23i6.2838
Abstract
Background. Even though mild cognitive impairments are common in patients with attention deficit hyperactivity disorder (ADHD), there are no studies of morphometric brain parameters in children with ADHD and comorbid mild cognitive impairments. Objective. The aim of the study is to determine and perform comparative analysis of MR-morphometric brain parameters in children with ADHD depending on the presence or absence of comorbid mild cognitive impairments. Methods. Participants are children aged from 7 to 8 years with ADHD without comorbid pathology (CP), ADHD with mild cognitive impairment (MCI), MCI without ADHD, and healthy children. All participants underwent brain magnetic resonance imaging followed by morphometry to obtain quantitative parameters of large brain structures, cerebral cortex gyri, basal ganglia, cerebellum, and lateral ventricles. Results. 90 children were examined. ADHD with MCI group has shown significant decrease in the volumes of caudate nuclei bilaterally and hippocampus on the right, as well as decrease in the volumes of right superior parietal gyrus, supramarginal gyrus, and frontal cortex. ADHD without CP group has shown different changes: decrease in the volume of putamen on both sides and thalamus on the left, increase in the volume of six and decrease in the volume of the cortex of four gyri, cortex thinning of four gyri with cortex thickening of one gyrus, volume increase of four cerebellar lobules. MCI without ADHD group has shown bilateral enlargement of lateral ventricles, decrease in the volume of right pallidum and seven gyri cortex, mostly on the right side, as well as decrease in the volume of four cerebellar lobules. Direct comparison between the two ADHD groups has shown significant differences up to lower total cortex volume with 5 gyri of left hemisphere and 7 gyri of right hemisphere in the ADHD with MCI group. Participants of ADHD groups compared to healthy individuals and the MCI without ADHD group did not show any age-related dynamic decrease in the volumes of cerebral cortex. Conclusion. ADHD is characterized by parallel presence of two pathogenetic processes: cerebral cortex hypoplasia and delayed age-related changes in other areas. Significant differences in morphometric parameters were revealed between ADHD without CP and ADHD with MCI. It suggests individual treatment for such patients and revision of approaches to morphometric brain studies in patients with ADHD. Enlargement of lateral ventricles in MCI may indicate the effect of perinatal pathology on these conditions’ etiology.
Keywords
About the Authors
George A. KarkashadzeRussian Federation
Moscow
Disclosure of interest:
George A. Karkashadze — lecturing for pharmaceutical
companies Opella Healthcare Russia, Materia Medica Holding, GEROPHARM, Organon, Sotex
Alexey I. Firumyants
Russian Federation
Moscow
Disclosure of interest:
Author confirmed the absence of a reportable
conflict of interests.
Nikita S. Shilko
Russian Federation
Moscow
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Author confirmed the absence of a reportable
conflict of interests.
Nataliya S. Sergienko
Russian Federation
Moscow
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Author confirmed the absence of a reportable
conflict of interests.
Yulia V. Nesterova
Russian Federation
Moscow
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Author confirmed the absence of a reportable
conflict of interests.
Leonid M. Yatsyk
Russian Federation
Moscow
Disclosure of interest:
Author confirmed the absence of a reportable
conflict of interests.
Elena N. Rudenko
Russian Federation
Moscow
Disclosure of interest:
Author confirmed the absence of a reportable
conflict of interests.
Mikhail I. Polle
Russian Federation
Moscow
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Author confirmed the absence of a reportable
conflict of interests.
Tatiana A. Salimgareeva
Russian Federation
Moscow
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Author confirmed the absence of a reportable
conflict of interests.
Tinatin Yu. Gogberashvili
Russian Federation
Moscow
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Author confirmed the absence of a reportable
conflict of interests.
Nataliya E. Sergeeva
Russian Federation
Moscow
Disclosure of interest:
Author confirmed the absence of a reportable
conflict of interests.
Tatiana A. Konstantinidi
Russian Federation
Moscow
Disclosure of interest:
Author confirmed the absence of a reportable
conflict of interests.
Safarbegim Kh. Sadilloeva
Russian Federation
Moscow
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Author confirmed the absence of a reportable
conflict of interests.
Marina A. Kurakina
Russian Federation
Moscow
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Author confirmed the absence of a reportable
conflict of interests.
Viktor V. Dyachenko
Russian Federation
Moscow
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Author confirmed the absence of a reportable
conflict of interests.
Inessa A. Povalyaeva
Russian Federation
Moscow
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Author confirmed the absence of a reportable
conflict of interests.
Evgeniy V. Bogdanov
Russian Federation
Moscow
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Author confirmed the absence of a reportable
conflict of interests.
Anastasia I. Rykunova
Russian Federation
Moscow
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Author confirmed the absence of a reportable
conflict of interests.
Elena A. Vishneva
Russian Federation
Moscow
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Author confirmed the absence of a reportable
conflict of interests.
Elena V. Kaytukova
Russian Federation
Moscow
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Author confirmed the absence of a reportable
conflict of interests.
Kamilla E. Efendieva
Russian Federation
Moscow
Disclosure of interest:
Author confirmed the absence of a reportable
conflict of interests.
Leyla S. Namazova-Baranova
Russian Federation
Moscow
Disclosure of interest:
Leyla S. Namazova-Baranova — receiving research
grants from pharmaceutical companies Pierre Fabre,
Genzyme Europe B.V., Astra Zeneca PLC, Gilead / PRA
“Pharmaceutical Research Associates CIS”, Bionorica,
Teva Branded Pharmaceutical products R&D, Inc / “PPD
Development (Smolensk)” LLC, “Stallerzhen S.A.” / “Quintiles GMBH” (Austria).
References
1. Opel N, Goltermann J, Hermesdorf M, et al. Cross-Disorder Analysis of Brain Structural Abnormalities in Six Major Psychiatric Disorders: A Secondary Analysis of Mega- and Meta-analytical Findings From the ENIGMA Consortium. Biol Psychiatry. 2020;88(9):678–686. doi: https://doi.org/10.1016/j.biopsych.2020.04.027
2. Dipietro L, Gonzalez-Mego P, Ramos-Estebanez C, et al. The evolution of Big Data in neuroscience and neurology. J Big Data. 2023;10(1):116. doi: https://doi.org/10.1186/s40537-023-00751-2
3. Firumyants AI, NamazovaBaranova LS, Karkashadze GA, et al. Brain Morphometry is an Advanced Method of Neuroimaging Mapping in Children. Voprosy sovremennoi pediatrii — Current Pediatrics. 2023;22(6):521–527. (In Russ). doi: https://doi.org/10.15690/vsp.v22i6.2707
4. 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. doi: https://doi.org/10.3389/fpsyt.2022.1070142
5. Albajara Sáenz A, Villemonteix T, Massat I. Structural and functional neuroimaging in attention-deficit/hyperactivity disorder. Dev Med Child Neurol. 2019;61(4):399–405. doi: https://doi.org/10.1111/dmcn.14050
6. Bedford SA, Lai MC, Lombardo MV, et al. Brain-charting autism and attention deficit hyperactivity disorder reveals distinct and overlapping neurobiology. medRxiv [Preprint]. 20237:2023.12.06.23299587. doi: https://doi.org/10.1101/2023.12.06.23299587
7. Hoogman M, Muetzel R, Guimaraes JP, et al. Brain Imaging of the Cortex in ADHD: A Coordinated Analysis of Large-Scale Clinical and Population-Based Samples. Am J Psychiatry. 2019;176(7): 531–542. doi: https://doi.org/10.1176/appi.ajp.2019.18091033
8. Narr KL, Woods RP, Lin J, et al. Widespread cortical thinning is a robust anatomical marker for attention-deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry. 2009;48(10): 1014–1022. doi: https://doi.org/10.1097/CHI.0b013e3181b395c0
9. Silk TJ, Beare R, Malpas C, et al. Cortical morphometry in attention deficit/hyperactivity disorder: contribution of thickness and surface area to volume. Cortex. 2016;82:1–10. doi: https://doi.org/10.1016/j.cortex.2016.05.012
10. Shaw P, Eckstrand K, Sharp W, et al. Attention-deficit/ hyperactivity disorder is characterized by a delay in cortical maturation. Proc Natl Acad Sci U S A. 2007;104(49):19649–19654. doi: https://doi.org/10.1073/pnas.0707741104
11. Almeida L, Ricardo-Garcell J, Prado H, et al. Reduced right frontal cortical thickness in children, adolescents and adults with ADHD and its correlation to clinical variables: a crosssectional study. J Psychiatr Res. 2010;44(16):1214–1223. doi: https://doi.org/10.1016/j.jpsychires.2010.04.026
12. Ambrosino S, de Zeeuw P, Wierenga LM, et al. What can cortical development in attention-deficit/hyperactivity disorder teach us about the early developmental mechanisms involved? Cereb Cortex. 2017;27(9):4624–4634. doi: https://doi.org/10.1093/cercor/bhx182
13. Wolosin SM, Richardson ME, Hennessey JG, et al. Abnormal cerebral cortex structure in children with ADHD. Hum Brain Mapp. 2009;30(1):175–184. doi: https://doi.org/10.1002/hbm.20496
14. Almeida Montes L, Prado Alcántara H, Martínez García R, et al. Brain cortical thickness in ADHD: age, sex, and clinical correlations. J Atten Disord. 2012;17(8):641–654. doi: https://doi.org/10.1177/1087054711434351
15. Luo X, Lin X, Ide JS, et al. Male-specific, replicable and functional roles of genetic variants and cerebral gray matter volumes in ADHD: a gene-wide association study across KTN1 and a region-wide functional validation across brain. Child Adolesc Psychiatry Ment Health. 2023;17(1):4. doi: https://doi.org/10.1186/s13034-022-00536-0
16. Chiang HL, Lin HY, Tseng WI, et al. Neural substrates underpinning intra-individual variability in children with ADHD: A voxel-based morphometry study. J Formos Med Assoc. 2022;121(2):546–556. doi: https://doi.org/10.1016/j.jfma.2021.06.003
17. Kaya BS, Metin B, Tas ZC, et al. Gray matter increase in motor cortex in pediatric ADHD: a voxel-based morphometry study. J Atten Disord. 2018;22(7):611–618. doi: https://doi.org/10.1177/1087054716659139
18. Makris N, Liang L, Biederman J, et al. Toward Defining the Neural Substrates of ADHD: A Controlled Structural MRI Study in Medication-Naïve Adults. J Atten Disord. 2015;19(11):944–953. doi: https://doi.org/10.1177/1087054713506041
19. Moreno-Alcázar A, Ramos-Quiroga JA, Radua J, et al. Brain abnormalities in adults with Attention Deficit Hyperactivity Disorder revealed by voxel-based morphometry. Psychiatry Res Neuroimaging. 2016;254:41–47. doi: https://doi.org/10.1016/j.pscychresns.2016.06.002
20. Backhausen LL, Herting MM, Tamnes CK, Vetter NC. Best Practices in Structural Neuroimaging of Neurodevelopmental Disorders. Neuropsychol Rev. 2022;32(2):400–418. doi: https://doi.org/10.1007/s11065-021-09496-2
21. Mueller KL, Tomblin JB. Examining the comorbidity of language disorders and ADHD. Top Lang Disord. 2012;32(3):228–246. doi: https://doi.org/10.1097/TLD.0b013e318262010d
22. Katsarou DV, Efthymiou E, Kougioumtzis GA, et al. Identifying Language Development in Children with ADHD: Differential Challenges, Interventions, and Collaborative Strategies. Children (Basel). 2024;11(7):841. doi: https://doi.org/10.3390/children11070841
23. Tsui KW, Lai KY, Lee MM, et al. Prevalence of motor problems in children with attention deficit hyperactivity disorder in Hong Kong. Hong Kong Med J. 2016;22(2):98–105. doi: https://doi.org/10.12809/hkmj154591
24. DuPaul GJ, Gormley MJ, Laracy SD. Comorbidity of LD and ADHD: implications of DSM-5 for assessment and treatment. J Learn Disabil. 2013;46(1):43–51. doi: https://doi.org/10.1177/0022219412464351
25. Chan ESM, Shero JA, Hand ED, et al. Are Reading Interventions Effective for At-Risk Readers with ADHD? A Meta-Analysis. J Atten Disord. 2023;27(2):182–200. doi: https://doi.org/10.1177/10870547221130111
26. McGrath LM, Stoodley CJ. Are there shared neural correlates between dyslexia and ADHD? A meta-analysis of voxel-based morphometry studies. J Neurodev Disord. 2019;11(1):31. doi: https://doi.org/10.1186/s11689-019-9287-8
27. Jednoróg K, Gawron N, Marchewka A, et al. Cognitive subtypes of dyslexia are characterized by distinct patterns of grey matter volume. Brain Struct Funct. 2014;219(5):1697–1707. doi: https://doi.org/10.1007/s00429-013-0595-6.
28. Xia Z, Hoeft F, Zhang L, Shu H. Neuroanatomical anomalies of dyslexia: Disambiguating the effects of disorder, performance, and maturation. Neuropsychologia. 2016;81:8–78. doi: https://doi.org/10.1016/j.neuropsychologia.2015.12.003
29. Liégeois F, Mayes A, Morgan A. Neural Correlates of Developmental Speech and Language Disorders: Evidence from Neuroimaging. Curr Dev Disord Rep. 2014;1(3):215–227. doi: https://doi.org/10.1007/s40474-014-0019-1
30. Morgan A, Bonthrone A, Liégeois FJ. Brain basis of childhood speech and language disorders: are we closer to clinically meaningful MRI markers? Curr Opin Pediatr. 2016;28(6):725–730. doi: https://doi.org/10.1097/MOP.0000000000000420
31. Ullman MT, Clark GM, Pullman MY, et al. The neuroanatomy of developmental language disorder: a systematic review and metaanalysis. Nat Hum Behav. 2024;8(5):962–975. doi: https://doi.org/10.1038/s41562-024-01843-6
32. Lee MM, Drury BC, McGrath LM, Stoodley CJ. Shared grey matter correlates of reading and attention. Brain Lang. 2023;237:105230. doi: https://doi.org/10.1016/j.bandl.2023.105230
33. Jagger-Rickels AC, Kibby MY, Constance JM. Global gray matter morphometry differences between children with reading disability, ADHD, and comorbid reading disability/ADHD. Brain Lang. 2018;185:54–66. doi: https://doi.org/10.1016/j.bandl.2018.08.004
34. Langer N, Benjamin C, Becker BLC, Gaab N. Comorbidity of reading disabilities and ADHD: Structural and functional brain characteristics. Hum Brain Mapp. 2019;40(9):2677–2698. doi: https://doi.org/10.1002/hbm.24552
35. Brown TT, Jernigan TL. Brain development during the preschool years. Neuropsychol Rev. 2012;22(4):313–333. doi: https://doi.org/10.1007/s11065-012-9214-1
36. Vijayakumar N, Mills KL, Alexander-Bloch A, et al. Structural brain development: A review of methodological approaches and best practices. Dev Cogn Neurosci. 2018;33:129–148. doi: https://doi.org/10.1016/j.dcn.2017.11.008
37. O’Brien LM, Ziegler DA, Deutsch CK, et al. Statistical adjustments for brain size in volumetric neuroimaging studies: Some practical implications in methods. Psychiatry Res. 2011;193(2):113–122. doi: https://doi.org/10.1016/j.pscychresns.2011.01.007
38. Desikan RS, Ségonne F, Fischl B, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31(3):968–980. https://doi.org/10.1016/j.neuroimage.2006.01.021
39. Backhausen LL, Herting MM, Buse J, et al. Quality Control of Structural MRI Images Applied Using FreeSurfer-A Hands-On Workflow to Rate Motion Artifacts. Front Neurosci. 2016;10:558. doi: https://doi.org/10.3389/fnins.2016.00558
40. Hoogman M, Bralten J, Hibar DP, et al. Subcortical brain volume differences in participants with attention deficit hyperactivity disorder in children and adults: a cross-sectional mega-analysis. Lancet Psychiatry. 2017;4(4):310–319. doi: 10.1016/S2215-0366(17)30049-4
41. Klein M, Walters RK, Demontis D, et al. Genetic Markers of ADHD-Related Variations in Intracranial Volume. Am J Psychiatry. 2019;176(3):228–238. doi: https://doi.org/10.1176/appi.ajp.2018.18020149
42. Houk JC, Adams JL, Barto AG. A Model of How the Basal Ganglia Generate and Use Neural Signals that Predict Reinforcement. In: Models of Information Processing in the Basal Ganglia. Houk JC, Davis JL Beiser DG, eds. Cambridge, MA: MIT Press; 1995. Ch. 13. pp. 249–270. doi: https://doi.org/10.7551/mitpress/4708.001.0001
43. Haruno M, Kuroda T, Doya K, et al. A neural cor-relate of reward-based behavioral learning in caudate nucleus: a functional magnetic resonance imaging study of a stochastic decision task. J Neurosci. 2004;24(7):1660–1665. doi: https://doi.org/10.1523/JNEUROSCI.3417-03.2004
44. O’Doherty J, Dayan P, Friston K, et al. Temporal difference models and reward-related learning in the human brain. Neuron. 2003;38:329–337
45. Doidge JL, Flora DB, Toplak ME. A Meta-Analytic Review of Sex Differences on Delay of Gratification and Temporal Discounting Tasks in ADHD and Typically Developing Samples. J Atten Disord. 2021;25(4):540–561. doi: https://doi.org/10.1177/1087054718815588
46. Ziegler S, Pedersen ML, Mowinckel AM, Biele G. Modelling ADHD: A review of ADHD theories through their predictions for computational models of decision-making and reinforcement learning. Neurosci Biobehav Rev. 2016;71:633–656. doi: https://doi.org/10.1016/j.neubiorev.2016.09.002
47. Luman M, Tripp G, Scheres A. Identifying the neurobiology of altered reinforcement sensitivity in ADHD: a review and research agenda. Neurosci Biobehav Rev. 2010;34(5):744–754. doi: https://doi.org/10.1016/j.neubiorev.2009.11.021
48. Lisman JE, Grace AA. The Hippocampal-VTA Loop: Controlling the Entry of Information into Long-Term Memory. Neuron. 2005;46(5): 703–713. doi: https://doi.org/10.1016/j.neuron.2005.05.002
49. Prigge MBD, Lange N, Bigler ED, et al. A 16-year study of longitudinal volumetric brain development in males with autism. Neuroimage. 2021;236:118067. doi: https://doi.org/10.1016/j.neuroimage.2021.118067
50. Brovelli A, Nazarian B, Meunier M, Boussaoud D. Differential roles of caudate nucleus and putamen during instrumental learning. Neuroimage. 2011;57(4):1580–1590. doi: https://doi.org/10.1016/j.neuroimage.2011.05.059
51. Haruno M, Kawato M. Different neural correlates of reward expectation and reward expectation error in the putamen and caudate nucleus during stimulus-action-reward association learning. J Neurophysiol. 2006;95(2):948–959. doi: https://doi.org/10.1152/jn.00382.2005
52. Guida P, Michiels M, Redgrave P, et al. An fMRI metaanalysis of the role of the striatum in everyday-life vs laboratorydeveloped habits. Neurosci Biobehav Rev. 2022;141:104826. doi: https://doi.org/10.1016/j.neubiorev.2022.104826
53. Sowell ER, Thompson PM, Welcome SE, et al. Cortical abnormalities in children and adolescents with attention-deficit hyperactivity disorder. Lancet. 2003;362(9397):1699–1707. doi: https://doi.org/10.1016/S0140-6736(03)14842-8
54. Valera EM, Faraone SV, Murray KE, Seidman LJ. Meta-analysis of structural imaging findings in attention-deficit/hyperactivity disorder. Biol Psychiatry. 2007;61(12):1361–1369. doi: https:// doi.org/10.1016/j.biopsych.2006.06.011
55. Hart H, Radua J, Nakao T, et al. Meta-analysis of functional magnetic resonance imaging studies of inhibition and attention in attention-deficit/hyperactivity disorder: exploring task-specific, stimulant medication, and age effects. JAMA Psychiatry. 2013;70(2):185–198. doi: https://doi.org/10.1001/jamapsychiatry.2013.277
56. Karkashadze GA, Kaitukova EV, Gogberashvili TY, et al. A Single-Stage Population-Based Study of the Relationship between Cognitive and Somatic Health Parameters in Children of Secondary School Age. Annals of the Russian Academy of Medical Sciences. 2023;78(5):408–430. doi: https://doi.org/10.15690/vramn14392]
57. Shiohama T, Ortug A, Warren JLA, et al. Small Nucleus Accumbens and Large Cerebral Ventricles in Infants and Toddlers Prior to Receiving Diagnoses of Autism Spectrum Disorder. Cereb Cortex. 2022;32(6):1200–1211. doi: https://doi.org/10.1093/cercor/bhab283
58. Chao CP, Zaleski CG, Patton AC. Neonatal hypoxic-ischemic encephalopathy: multimodality imaging findings. Radiographics. 2006;26(Suppl 1):S159–S172. doi: https://doi.org/10.1148/rg.26si065504
59. Fichera G, Stramare R, Bisogno G, et al. Neonatal cerebral ultrasound: anatomical variants and age-related diseases. J Ultrasound. 2024;27(4):993–1002. doi: https://doi.org/10.1007/s40477-024-00914-8
60. Varghese B, Xavier R, Manoj VC, et al. Magnetic resonance imaging spectrum of perinatal hypoxic-ischemic brain injury. Indian J Radiol Imaging. 2016;26(3):316–327. doi: https://doi.org/10.4103/0971-3026.190421
61.
Review
For citations:
Karkashadze G.A., Firumyants A.I., Shilko N.S., Sergienko N.S., Nesterova Yu.V., Yatsyk L.M., Rudenko E.N., Polle M.I., Salimgareeva T.A., Gogberashvili T.Yu., Sergeeva N.E., Konstantinidi T.A., Sadilloeva S.Kh., Kurakina M.A., Dyachenko V.V., Povalyaeva I.A., Bogdanov E.V., Rykunova A.I., Vishneva E.A., Kaytukova E.V., Efendieva K.E., Namazova-Baranova L.S. Structural Brain Morphometry in Children with Attention Deficit Hyperactivity Disorder and Comorbid Mild Cognitive Impairments. Current Pediatrics. 2024;23(6):466-482. https://doi.org/10.15690/vsp.v23i6.2838