Morphometry of the encephalic ventricular system in adults with normal cognitive functions
Keywords:
biomarkers, volumetry, cerebral ventricles.Abstract
Introduction: With the introduction of modern machine learning techniques in neuroimaging, it has been possible to develop automatic classification systems and discover aging biomarkers.Objective: To determine the volumetry of the encephalic ventricular system according to age and sex.
Method: An analytical observational study was developed in 320 subjects with normal neurocognitive functions and neuropsychiatric examination, aged between 30 and 79 years, who underwent single-slice computed tomography of the skull. An image segmentation method based on the analysis of homogeneous textures and interpolation was used.
Results:The volumes of the brain ventricles increased with increasing age. While sex had a significant effect, obtaining higher magnitudes in the male sex.
Conclusions: The neuroimaging acquisition protocol implemented allowed obtaining brain volumetric parameters, according to sex and age, in a population with normal global cognitive functions, from computed tomography images.
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References
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2. Mesa Pujals AA, Hernández Cortés KS, Montoya Pedrón A, Bolaños Vaillant S, Álvarez Guerra ED. Análisis de texturas homogéneas para la estimación volumétrica de la materia cerebral por tomografía computarizada. RCIM. 2022 [acceso: 27/01/2023]; 14(1): [aprox. 14 p.]. Disponible en: http://scielo.sld.cu/scielo.php?pid=S1684-18592022000100003&script=sci_abstract&tlng=en
3. Valdés Sosa PA, Galán García L, Bosch Bayard J, Bringas Vega ML, Aubert Vazquez E, Rodríguez Gil I, et al. The Cuban Human Brain Mapping Project, a young and middle age population-based EEG, MRI, and cognition dataset. Sci data. 2021; 8(1): 45. DOI: 10.1038/s41597-021-00829-7
4. Spalletta G, Piras F, Gili T. Brain Morphometry, Neuromethods. En: Hiroshi M. Morphometry in Normal Aging. New Jersey: Human Press; 2018. p. 165-70. DOI: 10.1007/978-1-4939-7647-8
5. Hernández- Cortés Katherine S, Mesa- Pujals Adrián A, García- Gómez Odalis, Montoya Pedrón A. Brain morphometry in adult: volumetric visualization as a tool in image processing. Rev mex neurocienc. 2021; 22(3): 101-11. DOI: 10.24875/rmn.20000074
6. Kang DW, Wang SM, Na HR, Park SY, Kim NY, Lee CU, et al. Differences in cortical Structure between cognitively normal East Asian and Caucasian older adults: a surface-based morphometry study. Sci. Rep. 2020; 10(1): 1-9. DOI: 10.1038/s41598-020-77848-8
7. Rueda A, Enriquez LF. Una revisión de técnicas básicas de neuroimagen para el diagnóstico de enfermedades neurodegenerativas. Biosalud. 2018; 17(2): 59-90. DOI: 10.17151/biosa.2018.17.2.5
8. Rouviere H, Delmas A. Anatomía Humana Descriptiva, Topográfica y Funcional. 11.ª ed. Francia; 2005.
9. Mohanty A, Mahapatra S, Bhanja U. Traffic congestion detection in a city using clustering techniques in VANETs. Indones. J Electr Eng Comput Sci. 2019; 13(2): 884-91. DOI: 10.11591/ijeecs.v13.i3.pp884-891
10. Heurtier A. Texture feature extraction methods: A survey. IEEE Access. 2019; 7: 8975-9000. DOI: 10.1109/ACCESS.2018.2890743
11. Baecker L, Dafflon J, Da Costa PF, García-Días R, Vieira S, Scarpazza C, et al. Brain age prediction: A comparison between machine learning models using region-and voxel-based morphometric data. Hum Brain Mapp. 2021; 42(8):2332-2346. DOI: 10.1002/hbm.25368
12. Soltanian Zadeh H, Windham JP. A multiresolution approach for contour extraction from brain images. J Med Phys.1997; 24(12):1844-53. DOI: 10.1118/1.598099
13. Kollem S, Reddy KR, Rao DS. A review of image denoising and segmentation methods based on medical images. Int J Mach Learn Comput. 2019; 9(3):288-95. DOI: 10.18178/ijmlc.2019.9.3.800
14. Rencher AC. Methods of Multivariate Analysis. Second Edition. Brigham Young University; 2002.
15. Shaikh Shamama F, Sukre SB. Morphometric study of frontal horn of lateral ventricle by Computerised Tomography. Int J Anat Res. 2017; 5(3.1):4063-66. DOI: 10.16965/ijar.2017.250
16. Namrata K, Radhika PM , Shailaja S, Ashok K. Morphometric study of ventricular indices in human brain using computed tomography scans in indian population. Int J Anat Res. 2018; 6(3.2):5574-80. DOI: 10.16965/ijar.2018.286
17. Polat S, Öksüzler FY, Öksüzler M, Kabakci AG, Yücel AH. Morphometric MRI study of the brain ventricles in healthy Turkish subjects. Int J Morpho. 2019; 37(2):554-60. DOI: 10.4067/S0717-95022019000200554
18. Dzefi-Tettey K, Edzie E, Gorleku PN, Brakohiapa EK, Osei B, Asemah AR, et al . Evans index among adult Ghanaians on normal head computerized tomography scan. 2021; 7(5): e06982. DOI: 10.1016/j.heliyon.2021.e06982
19. Hirnsteina M, Hausmann M. Sex/ gender differences in the brain are not trivial. Neuroscience and Biobehavioral Reviews. 2021; 130:408-9. DOI: 10.1016/j.neubiorev.2021.09.012
20. Pintzka CW, Hansen TI, Evensmoen HR, Håberg AK. Marked effects of intracranial volume correction methods on sex differences in neuroanatomical structures: a HUNT MRI study. Frontiers in neuroscience. 2015; 9: 238. DOI: 10.3389/fnins.2015.00238
21. Sanchis Segura C, Ibañez Gual MV, Aguirre N, Cruz Gómez ÁJ, Forn C. Effects of different intracranial volume correction methods on univariate sex differences in grey matter volume and multivariate sex prediction. Scientific Reports. 2020; 10(1):1-15. DOI: 10.1038/s41598.020.69361.9
Published
2023-08-21
How to Cite
1.
Hernández Cortés KS, Sagaró del Campo NM, Montoya Pedrón A. Morphometry of the encephalic ventricular system in adults with normal cognitive functions. Rev Cubana Med Milit [Internet]. 2023 Aug. 21 [cited 2025 Jan. 8];52(3):e02303014. Available from: https://revmedmilitar.sld.cu/index.php/mil/article/view/3014
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