References

vsp

Вопросы современной педиатрии

Current Pediatrics

1682-55271682-5535

Издательство «ПедиатрЪ»

10.15690/vsp.v22i6.2707

vsp-3360

Research Article

ОБЗОР ЛИТЕРАТУРЫ

REVIEW

Морфометрия головного мозга — передовой метод нейровизуализационного картирования у детей

Brain Morphometry is an Advanced Method of Neuroimaging Mapping in Children

https://orcid.org/0000-0002-5282-6504

Фирумянц

А. И.

Firumyants

Alexey I.

Фирумянц Алексей Игоревич - врач-рентгенолог, младший научный сотрудник отдела инновационных диагностических методов исследования НИИ педиатрии и охраны здоровья детей НКЦ №2 ФГБНУ «РНЦХ им. акад. Б.В. Петровского» Минобрнауки России.

119333, Москва, ул. Фотиевой, д. 10, стр. 1

Moscow

alexfirum@gmail.com

https://orcid.org/0000-0002-2209-7531

Намазова-Баранова

Л. С.

Namazova-Baranova

Leyla S.

Москва

Moscow

https://orcid.org/0000-0002-8540-3858

Каркашадзе

Г. А.

Karkashadze

George A.

Москва

Moscow

https://orcid.org/0000-0002-5250-7351

Ковтун

О. П.

Kovtun

Olga P.

Екатеринбург

Ekaterinburg

Дьяченко

В. В.

Dyachenko

Viktor V.

Москва

Moscow

Шилко

Н. С.

Shilko

Nikita S.

Москва

Moscow

Руденко

Е. Н.

Rudenko

Elena N.

Москва

Moscow

Мешков

А. В.

Meshkov

Alexey V.

Екатеринбург

Ekaterinburg

https://orcid.org/0000-0002-5036-8407

Сергиенко

Н. С.

Sergienko

Natalia S.

Москва

Moscow

https://orcid.org/0000-0002-0596-631X

Нестерова

Ю. В.

Nesterova

Yuliya V.

Москва

Moscow

https://orcid.org/0000-0003-0892-3726

Яцык

Л. М.

Yatsick

Leonid M.

Москва

Moscow

https://orcid.org/0000-0001-5790-6223

Рыкунова

А. И.

Rykunova

Anastasiya I.

Москва

Moscow

НИИ педиатрии и охраны здоровья детей НКЦ №2 ФГБНУ «РНЦХ им. акад. Б.В. Петровского»РоссияResearch Institute of Pediatrics and Children’s Health in Petrovsky National Research Centre of SurgeryRussian Federation

НИИ педиатрии и охраны здоровья детей НКЦ №2 ФГБНУ «РНЦХ им. акад. Б.В. Петровского»; РНИМУ им. Н.И. ПироговаРоссияResearch Institute of Pediatrics and Children’s Health in Petrovsky National Research Centre of Surgery; Pirogov Russian National Research Medical UniversityRussian Federation

Уральский государственный медицинский университетРоссияUral State Medical UniversityRussian Federation

Сеть многопрофильных клиник «Здоровье 365»РоссияMultidisciplinary clinic “Health-365”Russian Federation

2023

12012024

226521527

2023

Фирумянц А.И., Намазова-Баранова Л.С., Каркашадзе Г.А., Ковтун О.П., Дьяченко В.В., Шилко Н.С., Руденко Е.Н., Мешков А.В., Сергиенко Н.С., Нестерова Ю.В., Яцык Л.М., Рыкунова А.И.

Firumyants A.I., Namazova-Baranova L.S., Karkashadze G.A., Kovtun O.P., Dyachenko V.V., Shilko N.S., Rudenko E.N., Meshkov A.V., Sergienko N.S., Nesterova Y.V., Yatsick L.M., Rykunova A.I.

Данная работа распространяется под лицензией Creative Commons Attribution 4.0.

This work is licensed under a Creative Commons Attribution 4.0 License.

https://vsp.spr-journal.ru/jour/article/view/3360

Использование магнитно-резонансной томографии для морфометрии — количественной оценки параметров головного мозга (толщина, площадь, объем) — позволило обнаружить изменения при многих нервно-психических состояниях, ранее считавшихся интактными. В статье приведены сведения о нейровизуализационной морфометрии головного мозга и условиях эффективного применения этого метода в нейронауках.

The use of magnetic resonance imaging in morphometry, as quantitative assessment of brain parameters (thickness, surface area, volume), allows to detect changes in many neuropsichiatric conditions that were previously considered intact. This article provides data on neuroimaging brain morphometry and effective use of this method in neurosciences.

детимагнитно-резонансная томографиянейровизуализацияморфометрия мозга

childrenmagnetic resonance imagingneurovisualizationbrain morphometry

References1

van Erp TGM, Walton E, Hibar DP, et al. Cortical Brain Abnormalities in 4474 Individuals With Schizophrenia and 5098 Control Subjects via the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA) Consortium. Biol Psychiatry. 2018;84(9): 644–654. doi: https://doi.org/10.1016/j.biopsych.2018.04.023

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. 20201;88(9):678–686. doi: https://doi.org/10.1016/j.biopsych.2020.04.027

Vetter NC, Backhausen LL, Buse J, et al. Altered brain morphology in boys with attention deficit hyperactivity disorder with and without comorbid conduct disorder/oppositional defiant disorder. Hum Brain Mapp. 2020;41(4):973–983. doi: https://doi.org/10.1002/hbm.24853

Arribarat G, Peran P. Quantitative MRI markers in Parkinson’s disease and parkinsonian syndromes. Curr Opin Neurol. 2020;33(2):222–229. doi: https://doi.org/10.1097/WCO.0000000000000796

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

Martínez K, Colom R. Imaging the Intelligence of Humans. In: The Cambridge Handbook of Intelligence and Cognitive Neuroscience. Barbey AK, Karama S, Haier RJ, eds. Cambridge University Press; 2021. pp. 44–69. doi: https://doi.org/10.1017/9781108635462

Gaser C. Structural MRI: Morphometry. In: Neuroeconomics. Reuter M, Montag C, eds. Springer Berlin, Heidelberg; 2016. pp. 399–409. doi: https://doi.org/10.1007/978-3-642-35923-1

Marquand AF, Kia SM, Zabihi M, et al. Conceptualizing mental disorders as deviations from normative functioning. Mol Psychiatry. 2019;24(10):1415–1424. doi: https://doi.org/10.1038/s41380-019-0441-1

Shah PJ, Ebmeier KP, Glabus MF, Goodwin GM. Cortical grey matter reductions associated with treatment-resistant chronic unipolar depression. Controlled magnetic resonance imaging study. Br J Psychiatry. 1998;172:527–532. doi: https://doi.org/10.1192/bjp.172.6.527

Hayashi T, Hou Y, Glasser MF, et al. The nonhuman primate neuroimaging and neuroanatomy project. Neuroimage. 2021;229: 117726. doi: https://doi.org/10.1016/j.neuroimage.2021.117726

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

Kong XZ, Postema MC, Guadalupe T, et al. Mapping brain asymmetry in health and disease through the ENIGMA consortium. Hum Brain Mapp. 2022;43(1):167–181. doi: https://doi.org/10.1002/hbm.25033

Grasby KL, Jahanshad N, Painter JN, et al. The genetic architecture of the human cerebral cortex. Science. 2020 Mar 20;367(6484):eaay6690. doi: https://doi.org/10.1126/science.aay6690

Bookheimer SY, Salat DH, Terpstra M, et al. The Lifespan Human Connectome Project in Aging: An overview. Neuroimage. 2019;185:335–348. doi: https://doi.org/10.1016/j.neuroimage.2018.10.009

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: https://doi.org/10.1016/S2215-0366(17)30049-4

van Rooij D, Anagnostou E, Arango C, et al. Cortical and Subcortical Brain Morphometry Differences Between Patients With Autism Spectrum Disorder and Healthy Individuals Across the Lifespan: Results From the ENIGMA ASD Working Group. Am J Psychiatry. 2018;175(4):359–369. doi: https://doi.org/10.1176/appi.ajp.2017.17010100

Nam KW, Castellanos N, Simmons A, et al. Alterations in cortical thickness development in preterm-born individuals: Implications for high-order cognitive functions. Neuroimage. 2015;115:64–75. doi: https://doi.org/10.1016/j.neuroimage.2015.04.015

Vargha -Khadem F, Watkins KE, Price CJ, et al. Neural basis of an inherited speech and language disorder. Proc Natl Acad Sci U S A. 1998;95(21):12695–12700. doi: https://doi.org/10.1073/pnas.95.21.12695

Wright IC, Ellison ZR, Sharma T, et al. Mapping of grey matter changes in schizophrenia. Schizophr Res. 1999;35(1):1–14. doi: https://doi.org/10.1016/s0920-9964(98)00094-2

Wright IC, McGuire PK, Poline JB, et al. A voxel-based method for the statistical analysis of gray and white matter density applied to schizophrenia. Neuroimage. 1995;2(4):244–252. doi: https://doi.org/10.1006/nimg.1995.1032

Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage. 1999;9(2):179–194. doi: https://doi.org/10.1006/nimg.1998.0395

Ai L, Craddock RC, Tottenham N, et al. Is it time to switch your T1W sequence? Assessing the impact of prospective motion correction on the reliability and quality of structural imaging. Neuroimage. 2021;226:117585. doi: https://doi.org/10.1016/j.neuroimage.2020.117585

Ashburner J, Friston KJ. Voxel-based morphometry — the methods. Neuroimage. 2000;11(6 Pt 1):805–821. doi: https://doi.org/10.1006/nimg.2000.0582

De Bellis MD, Keshavan MS, Beers SR, et al. Sex differences in brain maturation during childhood and adolescence. Cereb Cortex. 2001;11(6):552–557. doi: https://doi.org/10.1093/cercor/11.6.552

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

Dong HM, Castellanos FX, Yang N, et al. Charting brain growth in tandem with brain templates at school age. Sci Bull (Beijing). 2020;65(22):1924–1934. doi: https://doi.org/10.1016/j.scib.020.07.027

Raznahan A, Shaw P, Lalonde F, et al. How does your cortex grow? J Neurosci. 2011;31(19):7174–7177. doi: https://doi.org/10.1523/JNEUROSCI.0054-11.2011

Greve DN. An Absolute Beginner’s Guide to Surface- and Voxel-based Morphometric Analysis. In: Proceedings of the International Society for Magnetic Resonance in Medicine. 2011. vol. 19. p. 33.

Noorde rmeer SDS, Luman M, Greven CU, et al. Structural Brain Abnormalities of Attention-Deficit/Hyperactivity Disorder With Oppositional Defiant Disorder. Biol Psychiatry. 2017;82(9): 642–650. doi: https://doi.org/10.1016/j.biopsych.2017.07.008

Whitwell JL. Voxel-based morphometry: an automated technique for assessing structural changes in the brain. J Neurosci. 2009; 29(31):9661–9664. doi: https://doi.org/10.1523/JNEUROSCI.2160-09.2009

Li Z, Zhang J, Wang F, et al. Surface-based morphometry study of the brain in benign childhood epilepsy with centrotemporal spikes. Ann Transl Med. 2020;8(18):1150. doi: https://doi.org/10.21037/atm-20-5845

Fischl B. FreeSurfer. Neuroimage. 2012;62(2):774–781. doi: https://doi.org/10.1016/j.neuroimage.2012.01.021

Winkler AM, Kochunov P, Blangero J, et al. Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. Neuroimage. 2010;53(3):1135–1146. doi: https://doi.org/10.1016/j.neuroimage.2009.12.028

Pua EPK, Barton S, Williams K, et al. Individualised MRI training for paediatric neuroimaging: A child-focused approach. Dev Cogn Neurosci. 2020;41:100750. doi: https://doi.org/10.1016/j.dcn.2019.100750

Raschle NM, Lee M, Buechler R, et al. Making MR imaging child’s play — pediatric neuroimaging protocol, guidelines and procedure. J Vis Exp. 2009;(29):1309. doi: https://doi.org/10.3791/1309

Reuter M, Tisdall MD, Qureshi A, et al. Head motion during MRI acquisition reduces gray matter volume and thickness estimates. Neuroimage. 2015;107:107–115. doi: https://doi.org/10.1016/j.neuroimage.2014.12.006

Tijsse n RH, Jansen JF, Backes WH. Assessing and minimizing the effects of noise and motion in clinical DTI at 3 T. Hum Brain Mapp. 2009;30(8):2641–2655. doi: https://doi.org/10.1002/hbm.20695

Barisano G, Sepehrband F, Ma S, et al. Clinical 7 T MRI: Are we there yet? A review about magnetic resonance imaging at ultra-high field. Br J Radiol. 2019;92(1094):20180492. doi: https://doi.org/10.1259/bjr.20180492

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

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. doi: https://doi.org/10.1016/j.neuroimage.2006.01.021

Destrieux C, Fischl B, Dale A, Halgren E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. Neuroimage. 2010;53(1):1–15. doi: https://doi.org/10.1016/j.neuroimage.2010.06.010

Ma J, Miller MI, Younes L. A bayesian generative model for surface template estimation. Int J Biomed Imaging. 2010:2010:974957. doi: https://doi.org/10.1155/2010/974957

Tsai CJ, Lin HY, Tseng IW, Gau SS. Brain voxel-based morphometry correlates of emotion dysregulation in attention-deficit hyperactivity disorder. Brain Imaging Behav. 2021;15(3):1388–1402. doi: https://doi.org/10.1007/s11682-020-00338-y

Paus T, Wong AP, Syme C, Pausova Z. Sex differences in the adolescent brain and body: Findings from the saguenay youth study. J Neurosci Res. 2017;95(1-2):362–370. doi: https://doi.org/10.1002/jnr.23825

Mills KL, Goddings AL, Herting MM, et al. Structural brain development between childhood and adulthood: Convergence across four longitudinal samples. Neuroimage. 2016;141:273–281. doi: https://doi.org/10.1016/j.neuroimage.2016.07.044

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

Ingre M. Why small low-powered studies are worse than large high-powered studies and how to protect against “trivial” findings in research: comment on Friston (2012). Neuroimage. 2013;81: 496–498. doi: https://doi.org/10.1016/j.neuroimage.2013.03.030

The authors declare that there are no conflicts of interest present.