Research Article
BibTex RIS Cite

Fractal Dimension of Silhouette Magnetic Resonance Brain Images as a Measure of Age-Associated Changes in Cerebral Hemispheres

Year 2023, Volume: 25 Issue: 1, 27 - 37, 30.04.2023
https://doi.org/10.18678/dtfd.1180625

Abstract

Aim: The aim of the present study was to characterize age-associated changes in the spatial configuration of cerebral hemispheres (including changes in spatial complexity and space-filling capacity) using fractal analysis of silhouette magnetic resonance brain images.
Material and Methods: Magnetic resonance brain images of 100 (44 male, 56 female) participants aged between 18-86 years were studied. Five magnetic resonance images were selected from the magnetic resonance imaging dataset of each brain, including four tomographic sections in the coronal plane and one in the axial plane. Fractal dimension values of the cerebral hemispheres silhouettes were measured using the two-dimensional box-counting algorithm. Morphometric parameters based on Euclidean geometry (perimeter, area, and their derivative values) were determined as well.
Results: The average fractal dimension value of the five studied tomographic sections was 1.878±0.0009, the average value of four coronal sections was 1.868±0.0010. It was shown that fractal dimension values of cerebral silhouettes for all studied tomographic sections and four coronal sections significantly decrease with age (r=-0.512, p<0.001 and r=-0.491, p<0.001, respectively). The difference in the character of age-related changes in males and females was not statistically significant. Based on the age and the fractal dimension values of the studied sample, the confidence intervals of the fractal dimension values of cerebral hemispheres silhouettes were determined, which can be used as norm criteria in clinical neuroimaging.
Conclusion: The fractal analysis and obtained data can be used in neuroimaging for assessing the degree of age-related cerebral atrophy and for differentiating between normal aging and neurodegenerative diseases.

References

  • Mandelbrot BB. The fractal geometry of nature. San Francisco: W.H. Freeman and Company; 1982.
  • Di Ieva A, Esteban FJ, Grizzi F, Klonowski W, Martín-Landrove M. Fractals in the neurosciences, Part II: clinical applications and future perspectives. Neuroscientist. 2015;21(1):30-43.
  • Fjell AM, Walhovd KB. Structural brain changes in aging: courses, causes and cognitive consequences. Rev Neurosci. 2010;21(3):187-221.
  • MacDonald ME, Pike GB. MRI of healthy brain aging: A review. NMR Biomed. 2021;34(9):e4564.
  • Ota Y, Shah G. Imaging of normal brain aging. Neuroimaging Clin N Am. 2022;32(3):683-98.
  • Pini L, Pievani M, Bocchetta M, Altomare D, Bosco P, Cavedo E, et al. Brain atrophy in Alzheimer's disease and aging. Ageing Res Rev. 2016;30:25-48.
  • Ertekin A. Brain white matter hyperintensity changes associated with vascular cognitive impairment and dementia, Alzheimer's dementia and normal aging. Duzce Med J. 2021;23(3):305-12.
  • Ge Y, Grossman RI, Babb JS, Rabin ML, Mannon LJ, Kolson DL. Age-related total gray matter and white matter changes in normal adult brain. Part I: volumetric MR imaging analysis. AJNR Am J Neuroradiol. 2002;23(8):1327-33.
  • Walhovd KB, Fjell AM, Reinvang I, Lundervold A, Dale AM, Eilertsen DE, et al. Effects of age on volumes of cortex, white matter and subcortical structures. Neurobiol Aging. 2005;26(9):1261-70; discussion 1275-8.
  • Riello R, Sabattoli F, Beltramello A, Bonetti M, Bono G, Falini A, et al. Brain volumes in healthy adults aged 40 years and over: a voxel-based morphometry study. Aging Clin Exp Res. 2005;17(4):329-36.
  • Zheng F, Liu Y, Yuan Z, Gao X, He Y, Liu X, et al. Age-related changes in cortical and subcortical structures of healthy adult brains: A surface-based morphometry study. J Magn Reson Imaging. 2019;49(1):152-63.
  • Podgórski P, Bladowska J, Sasiadek M, Zimny A. Novel volumetric and surface-based magnetic resonance indices of the aging brain - does male and female brain age in the same way? Front Neurol. 2021;12:645729.
  • Li Z, Zhang J, Wang F, Yang Y, Hu J, Li Q, et al. Surface-based morphometry study of the brain in benign childhood epilepsy with centrotemporal spikes. Ann Transl Med. 2020;8(18):1150.
  • Hofman MA. The fractal geometry of convoluted brains. J Hirnforsch. 1991;32(1):103-11.
  • King RD, George AT, Jeon T, Hynan LS, Youn TS, Kennedy DN, et al. Characterization of atrophic changes in the cerebral cortex using fractal dimensional analysis. Brain Imaging Behav. 2009;3(2):154-66.
  • Kiselev VG, Hahn KR, Auer DP. Is the brain cortex a fractal? Neuroimage. 2003;20(3):1765-74.
  • Esteban FJ, Sepulcre J, de Miras JR, Navas J, de Mendizábal NV, Goñi J, et al. Fractal dimension analysis of grey matter in multiple sclerosis. J Neurol Sci. 2009;282(1-2):67-71.
  • Roura E, Maclair G, Andorrà M, Juanals F, Pulido-Valdeolivas I, Saiz A, et al. Cortical fractal dimension predicts disability worsening in Multiple Sclerosis patients. Neuroimage Clin. 2021;30:102653.
  • King RD, Brown B, Hwang M, Jeon T, George AT, Alzheimer's Disease Neuroimaging Initiative. Fractal dimension analysis of the cortical ribbon in mild Alzheimer's disease. Neuroimage. 2010;53(2):471-9.
  • Goñi J, Sporns O, Cheng H, Aznárez-Sanado M, Wang Y, Josa S, et al. Robust estimation of fractal measures for characterizing the structural complexity of the human brain: optimization and reproducibility. Neuroimage. 2013;83:646-57.
  • Madan CR, Kensinger EA. Cortical complexity as a measure of age-related brain atrophy. Neuroimage. 2016;134:617-29.
  • Kalmanti E, Maris TG. Fractal dimension as an index of brain cortical changes throughout life. In Vivo. 2007;21(4):641-6.
  • Ha TH, Yoon U, Lee KJ, Shin YW, Lee JM, Kim IY, et al. Fractal dimension of cerebral cortical surface in schizophrenia and obsessive-compulsive disorder. Neurosci Lett. 2005;384(1-2):172-6.
  • Zhuo C, Li G, Chen C, Ji F, Lin X, Jiang D, et al. Left cerebral cortex complexity differences in sporadic healthy individuals with auditory verbal hallucinations: A pilot study. Psychiatry Res. 2020;285:112834.
  • Im K, Lee JM, Yoon U, Shin YW, Hong SB, Kim IY, et al. Fractal dimension in human cortical surface: multiple regression analysis with cortical thickness, sulcal depth, and folding area. Hum Brain Mapp. 2006;27(12):994-1003.
  • Lee JM, Yoon U, Kim JJ, Kim IY, Lee DS, Kwon JS, Kim SI. Analysis of the hemispheric asymmetry using fractal dimension of a skeletonized cerebral surface. IEEE Trans Biomed Eng. 2004;51(8):1494-8.
  • Esteban FJ, Sepulcre J, de Mendizábal NV, Goñi J, Navas J, de Miras JR, et al. Fractal dimension and white matter changes in multiple sclerosis. Neuroimage. 2007;36(3):543-9.
  • Rajagopalan V, Das A, Zhang L, Hillary F, Wylie GR, Yue GH. Fractal dimension brain morphometry: a novel approach to quantify white matter in traumatic brain injury. Brain Imaging Behav. 2019;13(4):914-24.
  • Zhang L, Dean D, Liu JZ, Sahgal V, Wang X, Yue GH. Quantifying degeneration of white matter in normal aging using fractal dimension. Neurobiol Aging. 2007;28(10):1543-55.
  • Farahibozorg S, Hashemi-Golpayegani SM, Ashburner J. Age- and sex-related variations in the brain white matter fractal dimension throughout adulthood: an MRI study. Clin Neuroradiol. 2015;25(1):19-32.
  • Zhang L, Liu JZ, Dean D, Sahgal V, Yue GH. A three-dimensional fractal analysis method for quantifying white matter structure in human brain. J Neurosci Methods. 2006;150(2):242-53.
  • Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9(7):671-5.
  • Underwood EE. Quantitative stereology. Reading, Massachusetts: Addison-Wesley; 1970.

Serebral Hemisferdeki Yaşa Bağlı Değişikliklerin Bir Ölçüsü Olarak Silüet Manyetik Rezonans Beyin Görüntülerinin Fraktal Boyutu

Year 2023, Volume: 25 Issue: 1, 27 - 37, 30.04.2023
https://doi.org/10.18678/dtfd.1180625

Abstract

Amaç: Bu çalışmanın amacı, manyetik rezonans beyin görüntülerinden elde edilen silüet görüntülerin fraktal analizini kullanarak serebral hemisferlerin uzaysal konfigürasyonundaki yaşa bağlı değişiklikleri (uzaysal karmaşıklık ve boşluk doldurma kapasitesindeki değişiklikler dahil) tanımlamaktır.
Gereç ve Yöntemler: Yaşları 18-86 yıl arasında olan 100 (44 erkek, 56 kadın) katılımcının manyetik rezonans beyin görüntüleri incelenmiştir. Her bir beyin manyetik rezonans görüntülemeden, koronal düzlemde dört tomografik kesit ve aksiyal düzlemde bir kesit dahil olmak üzere beş manyetik rezonans görüntüsü seçildi. Serebral hemisfer silüetlerinin fraktal boyut değerleri, iki boyutlu kutu sayma algoritması kullanılarak ölçüldü. Öklid geometrisine dayalı morfometrik parametreler (çevre, alan ve bunlardan türetilen değerler) de belirlendi.
Bulgular: Çalışılan beş tomografik kesitin ortalama fraktal boyut değeri 1,878±0,0009 ve dört koronal kesitin ortalama değeri ise 1,868±0,0010 idi. İncelenen tüm tomografi kesitleri ve dört koronal kesit için serebral silüetlerin fraktal boyut değerlerinin yaş ile birlikte anlamlı olarak azaldığı gösterildi (sırasıyla r=-0,512, p<0,001 ve r=-0,491, p<0,001). Erkeklerde ve kadınlarda yaşa bağlı değişikliklerin karakterindeki fark istatistiksel olarak anlamlı değildi. Çalışılan örneğin yaş ve fraktal boyut değerleri esas alınarak, serebral hemisfer silüetlerinin fraktal boyut değerleri için klinik nörogörüntülemede norm kriter olarak kullanılabilecek güven aralıkları belirlendi.
Sonuç: Fraktal analiz ve elde edilen veriler, yaşa bağlı serebral atrofi derecesini değerlendirmek ve normal yaşlanma ile nörodejeneratif hastalıklar arasında ayrım yapmak için nörogörüntülemede kullanılabilir.

References

  • Mandelbrot BB. The fractal geometry of nature. San Francisco: W.H. Freeman and Company; 1982.
  • Di Ieva A, Esteban FJ, Grizzi F, Klonowski W, Martín-Landrove M. Fractals in the neurosciences, Part II: clinical applications and future perspectives. Neuroscientist. 2015;21(1):30-43.
  • Fjell AM, Walhovd KB. Structural brain changes in aging: courses, causes and cognitive consequences. Rev Neurosci. 2010;21(3):187-221.
  • MacDonald ME, Pike GB. MRI of healthy brain aging: A review. NMR Biomed. 2021;34(9):e4564.
  • Ota Y, Shah G. Imaging of normal brain aging. Neuroimaging Clin N Am. 2022;32(3):683-98.
  • Pini L, Pievani M, Bocchetta M, Altomare D, Bosco P, Cavedo E, et al. Brain atrophy in Alzheimer's disease and aging. Ageing Res Rev. 2016;30:25-48.
  • Ertekin A. Brain white matter hyperintensity changes associated with vascular cognitive impairment and dementia, Alzheimer's dementia and normal aging. Duzce Med J. 2021;23(3):305-12.
  • Ge Y, Grossman RI, Babb JS, Rabin ML, Mannon LJ, Kolson DL. Age-related total gray matter and white matter changes in normal adult brain. Part I: volumetric MR imaging analysis. AJNR Am J Neuroradiol. 2002;23(8):1327-33.
  • Walhovd KB, Fjell AM, Reinvang I, Lundervold A, Dale AM, Eilertsen DE, et al. Effects of age on volumes of cortex, white matter and subcortical structures. Neurobiol Aging. 2005;26(9):1261-70; discussion 1275-8.
  • Riello R, Sabattoli F, Beltramello A, Bonetti M, Bono G, Falini A, et al. Brain volumes in healthy adults aged 40 years and over: a voxel-based morphometry study. Aging Clin Exp Res. 2005;17(4):329-36.
  • Zheng F, Liu Y, Yuan Z, Gao X, He Y, Liu X, et al. Age-related changes in cortical and subcortical structures of healthy adult brains: A surface-based morphometry study. J Magn Reson Imaging. 2019;49(1):152-63.
  • Podgórski P, Bladowska J, Sasiadek M, Zimny A. Novel volumetric and surface-based magnetic resonance indices of the aging brain - does male and female brain age in the same way? Front Neurol. 2021;12:645729.
  • Li Z, Zhang J, Wang F, Yang Y, Hu J, Li Q, et al. Surface-based morphometry study of the brain in benign childhood epilepsy with centrotemporal spikes. Ann Transl Med. 2020;8(18):1150.
  • Hofman MA. The fractal geometry of convoluted brains. J Hirnforsch. 1991;32(1):103-11.
  • King RD, George AT, Jeon T, Hynan LS, Youn TS, Kennedy DN, et al. Characterization of atrophic changes in the cerebral cortex using fractal dimensional analysis. Brain Imaging Behav. 2009;3(2):154-66.
  • Kiselev VG, Hahn KR, Auer DP. Is the brain cortex a fractal? Neuroimage. 2003;20(3):1765-74.
  • Esteban FJ, Sepulcre J, de Miras JR, Navas J, de Mendizábal NV, Goñi J, et al. Fractal dimension analysis of grey matter in multiple sclerosis. J Neurol Sci. 2009;282(1-2):67-71.
  • Roura E, Maclair G, Andorrà M, Juanals F, Pulido-Valdeolivas I, Saiz A, et al. Cortical fractal dimension predicts disability worsening in Multiple Sclerosis patients. Neuroimage Clin. 2021;30:102653.
  • King RD, Brown B, Hwang M, Jeon T, George AT, Alzheimer's Disease Neuroimaging Initiative. Fractal dimension analysis of the cortical ribbon in mild Alzheimer's disease. Neuroimage. 2010;53(2):471-9.
  • Goñi J, Sporns O, Cheng H, Aznárez-Sanado M, Wang Y, Josa S, et al. Robust estimation of fractal measures for characterizing the structural complexity of the human brain: optimization and reproducibility. Neuroimage. 2013;83:646-57.
  • Madan CR, Kensinger EA. Cortical complexity as a measure of age-related brain atrophy. Neuroimage. 2016;134:617-29.
  • Kalmanti E, Maris TG. Fractal dimension as an index of brain cortical changes throughout life. In Vivo. 2007;21(4):641-6.
  • Ha TH, Yoon U, Lee KJ, Shin YW, Lee JM, Kim IY, et al. Fractal dimension of cerebral cortical surface in schizophrenia and obsessive-compulsive disorder. Neurosci Lett. 2005;384(1-2):172-6.
  • Zhuo C, Li G, Chen C, Ji F, Lin X, Jiang D, et al. Left cerebral cortex complexity differences in sporadic healthy individuals with auditory verbal hallucinations: A pilot study. Psychiatry Res. 2020;285:112834.
  • Im K, Lee JM, Yoon U, Shin YW, Hong SB, Kim IY, et al. Fractal dimension in human cortical surface: multiple regression analysis with cortical thickness, sulcal depth, and folding area. Hum Brain Mapp. 2006;27(12):994-1003.
  • Lee JM, Yoon U, Kim JJ, Kim IY, Lee DS, Kwon JS, Kim SI. Analysis of the hemispheric asymmetry using fractal dimension of a skeletonized cerebral surface. IEEE Trans Biomed Eng. 2004;51(8):1494-8.
  • Esteban FJ, Sepulcre J, de Mendizábal NV, Goñi J, Navas J, de Miras JR, et al. Fractal dimension and white matter changes in multiple sclerosis. Neuroimage. 2007;36(3):543-9.
  • Rajagopalan V, Das A, Zhang L, Hillary F, Wylie GR, Yue GH. Fractal dimension brain morphometry: a novel approach to quantify white matter in traumatic brain injury. Brain Imaging Behav. 2019;13(4):914-24.
  • Zhang L, Dean D, Liu JZ, Sahgal V, Wang X, Yue GH. Quantifying degeneration of white matter in normal aging using fractal dimension. Neurobiol Aging. 2007;28(10):1543-55.
  • Farahibozorg S, Hashemi-Golpayegani SM, Ashburner J. Age- and sex-related variations in the brain white matter fractal dimension throughout adulthood: an MRI study. Clin Neuroradiol. 2015;25(1):19-32.
  • Zhang L, Liu JZ, Dean D, Sahgal V, Yue GH. A three-dimensional fractal analysis method for quantifying white matter structure in human brain. J Neurosci Methods. 2006;150(2):242-53.
  • Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9(7):671-5.
  • Underwood EE. Quantitative stereology. Reading, Massachusetts: Addison-Wesley; 1970.
There are 33 citations in total.

Details

Primary Language English
Subjects Clinical Sciences
Journal Section Research Article
Authors

Nataliia Maryenko 0000-0002-7980-7039

Oleksandr Stepanenko 0000-0002-5686-0857

Publication Date April 30, 2023
Submission Date September 27, 2022
Published in Issue Year 2023 Volume: 25 Issue: 1

Cite

APA Maryenko, N., & Stepanenko, O. (2023). Fractal Dimension of Silhouette Magnetic Resonance Brain Images as a Measure of Age-Associated Changes in Cerebral Hemispheres. Duzce Medical Journal, 25(1), 27-37. https://doi.org/10.18678/dtfd.1180625
AMA Maryenko N, Stepanenko O. Fractal Dimension of Silhouette Magnetic Resonance Brain Images as a Measure of Age-Associated Changes in Cerebral Hemispheres. Duzce Med J. April 2023;25(1):27-37. doi:10.18678/dtfd.1180625
Chicago Maryenko, Nataliia, and Oleksandr Stepanenko. “Fractal Dimension of Silhouette Magnetic Resonance Brain Images As a Measure of Age-Associated Changes in Cerebral Hemispheres”. Duzce Medical Journal 25, no. 1 (April 2023): 27-37. https://doi.org/10.18678/dtfd.1180625.
EndNote Maryenko N, Stepanenko O (April 1, 2023) Fractal Dimension of Silhouette Magnetic Resonance Brain Images as a Measure of Age-Associated Changes in Cerebral Hemispheres. Duzce Medical Journal 25 1 27–37.
IEEE N. Maryenko and O. Stepanenko, “Fractal Dimension of Silhouette Magnetic Resonance Brain Images as a Measure of Age-Associated Changes in Cerebral Hemispheres”, Duzce Med J, vol. 25, no. 1, pp. 27–37, 2023, doi: 10.18678/dtfd.1180625.
ISNAD Maryenko, Nataliia - Stepanenko, Oleksandr. “Fractal Dimension of Silhouette Magnetic Resonance Brain Images As a Measure of Age-Associated Changes in Cerebral Hemispheres”. Duzce Medical Journal 25/1 (April 2023), 27-37. https://doi.org/10.18678/dtfd.1180625.
JAMA Maryenko N, Stepanenko O. Fractal Dimension of Silhouette Magnetic Resonance Brain Images as a Measure of Age-Associated Changes in Cerebral Hemispheres. Duzce Med J. 2023;25:27–37.
MLA Maryenko, Nataliia and Oleksandr Stepanenko. “Fractal Dimension of Silhouette Magnetic Resonance Brain Images As a Measure of Age-Associated Changes in Cerebral Hemispheres”. Duzce Medical Journal, vol. 25, no. 1, 2023, pp. 27-37, doi:10.18678/dtfd.1180625.
Vancouver Maryenko N, Stepanenko O. Fractal Dimension of Silhouette Magnetic Resonance Brain Images as a Measure of Age-Associated Changes in Cerebral Hemispheres. Duzce Med J. 2023;25(1):27-3.