Yıl: 2023 Cilt: 53 Sayı: 3 Sayfa Aralığı: 675 - 684 Metin Dili: İngilizce DOI: 10.55730/1300-0144.5630 İndeks Tarihi: 01-08-2023

T1 relaxation time is prolonged in healthy aging: a whole brain study

Öz:
Background/aim: Measurement of tissue characteristics such as the longitudinal relaxation time (T1) provides complementary information to the volumetric and surface based structural analyses. We aimed to investigate T1 relaxation time characteristics in healthy aging via an exploratory design in the whole brain. The data processing pipeline was designed to minimize errors related to aging effects such as atrophy. Materials and methods: Sixty healthy participants underwent MRI scanning (28 F, 32 M, age range: 18–78, 30 young and 30 old) in November 2017–March 2018 at the Bilkent University UMRAM Center. Four images with varying flip angles with FLASH (fast low angle shot magnetic resonance imaging) sequence and a high-resolution structural image with MP-RAGE (Magnetization Prepared - RApid Gradient Echo) were acquired. $T_1$relaxation times of the entire brain were mapped by using the region of interest (ROI) based method on 134 brain areas in young and old populations. Results: $T_1$ prolongation was observed in various subcortical (bilateral hippocampus, caudate and thalamus) and cortical brain structures (bilateral precentral gyrus, bilateral middle frontal gyrus, bilateral supplementary motor area (SMA), left middle occipital gyrus, bilateral postcentral gyrus and bilateral Heschl’s gyrus) as well as cerebellar regions (GM regions of cerebellum: bilateral cerebellum III, cerebellum IV V, cerebellum X, cerebellar vermis u 4 5, cerebellar vermis u 9 and WM cerebellar regions: left cerebellum IX, bilateral cerebellum X and cerebellar vermis u 4 5). Conclusion: $T_1$ mapping provides a practical quantitative MRI (qMRI) methodology for studying the tissue characteristics in healthy aging. $T_1$ values are significantly increased in the aging group among half of the studied ROIs (57 ROIs out of 134).
Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • 1. United Nations. Department of Economic and Social Affairs, Population Division. World Population Prospects 2019: Highlights (ST/ESA/SER.A/423). New York, USA; 2019.
  • 2. Pergher V, Demaerel P, Soenen O, Saarela C, Tournoy J et al. Identifying brain changes related to cognitive aging using VBM and visual rating scales. NeuroImage: Clinical 2019 (22): 101697. https://doi.org/10.1016/j. nicl.2019.101697
  • 3. Erickson KI, Miller DL, and Roecklein KA. The aging hippocampus: Interactions between exercise, depression, and BDNF. The Neuroscientist 2012; 18 (1): 82–97. https://doi. org/10.1177/1073858410397054
  • 4. Pichla M, Bartosz G, and Sadowska-Bartosz I. The Antiaggregative and Antiamyloidogenic Properties of Nanoparticles: A Promising Tool for the Treatment and Diagnostics of Neurodegenerative Diseases. Oxidative Medicine and Cellular Longevity 2020. https://doi. org/10.1155/2020/3534570
  • 5. Salat DH, Lee SY, van der Kouwe AJ, Greve DN, FischlB et al. Age-associated alterations in cortical gray and white matter signal intensity and gray to white matter contrast. Neuroimage 2009; 48 (1): 21–28. https://doi.org/10.1016/j. neuroimage.2009.06.074
  • 6. Varikuti DP, Genon S, Sotiras A, Schwender H, Hoffstaedter F et al. Evaluation of non-negative matrix factorization of grey matter in age prediction. Neuroimage 2018; 173: 394–410. https://doi.org/10.1016/j.neuroimage.2018.03.007
  • 7. Lewis JD, Evans AC, and Tohka J. T1 white/gray contrast as a predictor of chronological age, and an index of cognitive performance. Neuroimage 2018; 173: 341–350. https://doi. org/10.1016/j.neuroimage.2018.02.050
  • 8. Lorio S, Lutti A, Kherif F, Ruef A, Dukart JS et al. Disentangling in vivo the effects of iron content and atrophy on the ageing human brain. Neuroimage 2014; 103: 280–289. https://doi. org/10.1016/j.neuroimage.2014.09.044
  • 9. Tofts PS, Steens SCA, Cercignani M, Admiraal-Behloul F, Hofman PAM et al. Sources of variation in multi-centre brain MTR histogram studies: Body-coil transmission eliminates inter-centre differences. Magnetic Resonance Materials in Physics, Biology and Medicine 2006; 19: 209–222. https://doi. org/10.1007/s10334-006-0049-8
  • 10. Deoni SCL. Quantitative relaxometry of the brain. Topics in Magnetic Resonance Imaging 2010; 21 (2): 101-113.https://doi. org/10.1097/RMR.0b013e31821e56d8
  • 11. Wahlund LO, Agartz I, Almqvist O, Basun H, Forssell L et al. The brain in healthy aged individuals: MR imaging. Radiology 1990; 174 (3): 675–679. https://doi.org/10.1148/ radiology.174.3.2305048
  • 12. Steen RG, Gronemeyer SA, and Taylor JS. Age related changes in proton T1 values of normal human brain. Journal of Magnetic Resonance Imaging 1995; 5 (1): 43–48. https://doi.org/10.1002/ jmri.1880050111
  • 13. Cho S, Jones D, Reddick WE, Ogg RJ, and Steen GR. Establishing norms for age-related changes in proton T1 of human brain tissue in vivo. Magnetic Resonance Imaging 1997; 15 (1): 123–126. https://doi.org/10.1016/S0730- 725X(97)00202-6
  • 14. Badve C, Yu A, Rogers M, Ma D, Liu Y et al. Simultaneous T1 and T2 Brain Relaxometry in Asymptomatic Volunteers Using Magnetic Resonance Fingerprinting. Tomography 2015; 1(2): 136–144. https://doi.org/10.18383/j.tom.2015.00166
  • 15. Badve C, Yu A, Rogers M, Ma D, Sunshine J et al. Regional brain T1 and T2 relaxometry in healthy volunteers using magnetic resonance fingerprinting. In: Proceedings of the 23rd International Society for Magnetic Resonance in Medicine; Toronto, Ontorio, Canada. pp. 01-21.
  • 16. Okubo G, Okada T, Yamamoto A, Fushimi Y, Okada T et al. Relationship between aging and T1 relaxation time in deep gray matter: A voxel-based analysis. Journal of Magnetic Resonance Imaging 2017; 46 (3): 724–731. https://doi.org/10.1002/ jmri.25590
  • 17. Kupeli A, Kocak M, Goktepeli M, Karavas E and Danisan G. Role of T1 mapping to evaluate brain aging in a healthy population. Clinical Imaging 2020; 59 (1): 56–60. https://doi. org/10.1016/j.clinimag.2019.09.005
  • 18. Anblagan D, Valdés Hernández MC, Ritchie SJ, Aribisala BS, Royle NA et al. Coupled changes in hippocampal structure and cognitive ability in later life. Brain and Behavior 2018; 8, (2): e00838. https://doi.org/10.1002/brb3.838
  • 19. Gracien RM, Nürnberger L, Hok P, Hof SM, Reitz SC et al. Evaluation of brain ageing: a quantitative longitudinal MRI study over 7 years. European Radiology 2016; 27 (4): 1568– 1576. https://doi.org/10.1007/s00330-016-4485-1
  • 20. Courchesne E, Chisum HJ, Townsend J, Cowles A, Covington J et al. Normal Brain Development and Aging: Quantitative Analysis at in Vivo MR Imaging in Healthy Volunteers. Radiology 2000; 216 (3): 672–682. https://doi.org/10.1148/ radiology.216.3.r00au37672
  • 21. United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects: The 2017 Revision, Key Findings and Advance Tables. Working Paper No. ESA/P/WP/248. 2017.
  • 22. Güngen C, Ertan T, and Eker E. Standardize Mini Mental Test’in Geçerlik ve Güvenilirliği. Türk Psikiyatri Dergisi 2002; 13 (4): 273–281 (in Turkish).
  • 23. Ertan T and Eker E. Reliability, validity, and factor structure of the geriatric depression scale in Turkish elderly: Are there different factor structures for different cultures? International Psychogeriatrics 2000; 12, (2): 163-172.https://doi.org/10.1017/ S1041610200006293
  • 24. Cox RW. AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research 1996; 29 (3): 162–173. https://doi. org/10.1006/cbmr.1996.0014
  • 25. Zhang Y, Brady M, and Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging 2001; 20 (1): 45–57. https://doi. org/10.1109/42.906424
  • 26. Deoni SCL, Peters TM, and Rutt BK. High-resolution T1 and T2 mapping of the brain in a clinically acceptable time with DESPOT1 and DESPOT2. Magnetic Resonance in Medicine 2005; 53 (1): 237–241. https://doi.org/10.1002/mrm.20314
  • 27. Fischl B, Salat DH, Van Der Kouwe AJ, Makris N, Ségonne F et al. Sequence-independent segmentation of magnetic resonance images. NeuroImage, 2004; 23 (SUPPL. 1): S69–S84. https:// doi.org/10.1016/j.neuroimage.2004.07.016
  • 28. Buxton RB. Introduction to Functional Magnetic Resonance Imaging. 2nd ed. San Diego, USA: Cambridge University Press; 2009.
  • 29. MATLAB version 7.10.0 (R2010a). Natick, Massachusetts: The MathWorks Inc. 2010.
  • 30. Geyer S, Weiss M, Reimann K, Lohmann G and Turner R. Microstructural parcellation of the human cerebral cortex– from Brodmann’s post-mortem map to in vivo mapping with high-field magnetic resonance imaging. Frontiers in Human Neuroscience 2011; 5(19). https://doi.org/10.3389/ fnhum.2011.00019
  • 31. Stüber C, Morawski M, Schäfer A, Labadie C, Wähnert MC et al. Myelin and iron concentration in the human brain: A quantitative study of MRI contrast. Neuroimage 2014; 93: 95– 106. https://doi.org/10.1016/j.neuroimage.2014.02.026
  • 32. Lutti A, Dick F, Sereno MI and Weiskopf N. Using high- resolution quantitative mapping of R1 as an index of cortical myelination. Neuroimage 2014; 93: 176–188. https://doi. org/10.1016/j.neuroimage.2013.06.005
  • 33. Rooney WD, Johnson G, Li X, Cohen ER, Kim SG, Ugurbil K et al. Magnetic field and tissue dependencies of human brain longitudinal 1H2O relaxation in vivo. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 2007; 57 (2): 308–318. https://doi.org/10.1002/mrm.21122
  • 34. Gelman N, Ewing JR, Gorell J M, Spickler EM and Solomon EG. Interregional variation of longitudinal relaxation rates in human brain at 3.0 T: Relation to estimated iron and water contents. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 2001; 45 (1): 71–79. https://doi.org/10.1002/1522- 2594(200101)45:1<71::AID-MRM1011>3.0.CO;2-2
  • 35. Ogg RJ and Steen RG. Age-related changes in brain T1 are correlated with iron concentration. Magnetic Resonance Imaging in Medicine 1998; 40 (5): 749–753. https://doi. org/10.1002/mrm.1910400516
  • 36. Neeb H, Zilles K, and Shah NJ. Fully-automated detection of cerebral water content changes: Study of age- and gender-related H2O patterns with quantitative MRI. Neuroimage 2006; 29 (3): 910–922. https://doi.org/10.1016/j.neuroimage.2005.08.062
  • 37. Nürnberger L, Gracien RM, Hok P, Hof SM, Rüb U et al. Longitudinal changes of cortical microstructure in Parkinson’s disease assessed with T1 relaxometry. NeuroImage: Clinical 2017; 13: 405–414. https://doi. org/10.1016/j.nicl.2016.12.025
  • 38. Su L, Blamire AM, Watson R, He J, Aribisala B et al. Cortical and Subcortical Changes in Alzheimer’s Disease: A Longitudinal and Quantitative MRI Study. Current Alzheimer Research 2016; 13 (5): 534–544. https://doi.org/10.2174/156720501366 6151116141416
  • 39. Sereno MI, Lutti A, Weiskopf N, and Dick F. Mapping the human cortical surface by combining quantitative T1 with retinotopy. Cerebral Cortex 2013; 23 (9): 2261–2268. https:// doi.org/10.1093/cercor/bhs213
  • 40. Keuken MC, Bazin PL, Backhouse K, Beekhuizen S, Himmer L et al. Effects of aging on T1, T2*, and QSM MRI values in the subcortex. Brain Structure and Function 2017; 222 (6): 2487– 2505. https://doi.org/10.1007/s00429-016-1352-4
  • 41. Callaghan MF, Freund P, Draganski B, Anderson E, Cappelletti M et al. Widespread age-related differences in the human brain microstructure revealed by quantitative magnetic resonance imaging. Neurobiology of Aging 2014; 35(8): 1862-1872. https://doi.org/10.1016/j.neurobiolaging.2014.02.008
  • 42. Steiger TK, Weiskopf N, and Bunzeck N. Iron Level and Myelin Content in the Ventral Striatum Predict Memory Performance in the Aging Brain. Journal of Neuroscience 2016; 36 (12): 3552– 3558. https://doi.org/10.1523/JNEUROSCI.3617-15.2016
  • 43. Peters A. The effects of normal aging on myelin and nerve fibers: a review. Journal of Neurocytology 2002; 31: 581-593. https://doi.org/10.1023/A:1025731309829
  • 44. Gracien RM, Nürnberger L, Hok P, Hof SM, Reitz SC et al. Evaluation of brain ageing: a quantitative longitudinal MRI study over 7 years. European radiology 2017; 27: 1568–1576. https://doi.org/10.1007/s00330-016-4485-1
  • 45. Allen JS, Bruss J, Brown CK, and Damasio H. Normal neuroanatomical variation due to age: The major lobes and a parcellation of the temporal region. Neurobiology of Aging 2005; 26 (9): 1245–1260. https://doi.org/10.1016/j. neurobiolaging.2005.05.023
  • 46. Salat DH, Fischl B, van der Kouwe AJ, Clarke RJ, Segonne F et al. Age-related changes in T1 relaxation times across the surface of the cortex. In: 8th International Conference on Functional Mapping of the Human Brain; Sendai, Japan. 2002.
  • 47. Erramuzpe A, Schurr R, Yeatman JD, Gotlib IH, Sacchet MD et al. A Comparison of Quantitative R1 and Cortical Thickness in Identifying Age, Lifespan Dynamics, and Disease States of the Human Cortex. Cerebral Cortex 2021; 31 (2): 1211–1226. https://doi.org/10.1093/cercor/bhaa288
  • 48. Suzuki S, Sakai O, and Jara H. Combined volumetric T1, T2 and secular-T2 quantitative MRI of the brain: age-related global changes (preliminary results). Magnetic Resonance Imaging 2006; 24 (7): 877–887. https://doi.org/10.1016/j. mri.2006.04.011
  • 49. Saito N, Sakai O, Ozonoff A, and Jara H. Relaxo-volumetric multispectral quantitative magnetic resonance imaging of the brain over the human lifespan: global and regional aging patterns. Magnetic Resonance Imaging 2009; 27 (7): 895–906. https://doi.org/10.1016/j.mri.2009.05.006
  • 50. Papadopoulos K, Tozer DJ, Fisniku L, Altmann DR, Davies G et al. T1-relaxation time changes over five years in relapsing- remitting multiple sclerosis. Multiple Sclerosis Journal 2010; 16(4): 427-433. https://doi.org/10.1177/1352458509359924
  • 51. Van Essen D C, Donahue C J, and Glasser M F. Development and evolution of cerebral and cerebellar cortex. Brain, Behavior and Evolution 2018; 91 (3): 158–169. https://doi. org/10.1159/000489943
  • 52. Wyatt KD, Tanapat P, and Wang SSH. Speed limits in the cerebellum: Constraints from myelinated and unmyelinated parallel fibers. European Journal of Neuroscience 2005; 21 (8): 2285–2290. https://doi.org/10.1111/j.1460-9568.2005.04053.x
  • 53. Müller U and Heinsen H. Regional differences in the ultrastructure of Purkinje cells of the rat. Cell and Tissue Research 1984; 235 (1): 91–98. https://doi.org/10.1007/ BF00213728
  • 54. Stikov N, Campbell J S, Stroh T, Lavelée M, Frey S et al. In vivo histology of the myelin g-ratio with magnetic resonance imaging. Neuroimage 2015; 118: 397–405. https://doi. org/10.1016/j.neuroimage.2015.05.023
  • 55. Helms G, Dathe H, and Dechent P. Quantitative FLASH MRI at 3T using a rational approximation of the Ernst equation. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine 2008; 59 (3): 667–672. https://doi.org/10.1002/mrm.21542
APA AKTAŞ DİNÇER H, Ağıldere A, Gokcay D (2023). T1 relaxation time is prolonged in healthy aging: a whole brain study. , 675 - 684. 10.55730/1300-0144.5630
Chicago AKTAŞ DİNÇER HAYRIYE,Ağıldere Ahmet Muhteşem,Gokcay Didem T1 relaxation time is prolonged in healthy aging: a whole brain study. (2023): 675 - 684. 10.55730/1300-0144.5630
MLA AKTAŞ DİNÇER HAYRIYE,Ağıldere Ahmet Muhteşem,Gokcay Didem T1 relaxation time is prolonged in healthy aging: a whole brain study. , 2023, ss.675 - 684. 10.55730/1300-0144.5630
AMA AKTAŞ DİNÇER H,Ağıldere A,Gokcay D T1 relaxation time is prolonged in healthy aging: a whole brain study. . 2023; 675 - 684. 10.55730/1300-0144.5630
Vancouver AKTAŞ DİNÇER H,Ağıldere A,Gokcay D T1 relaxation time is prolonged in healthy aging: a whole brain study. . 2023; 675 - 684. 10.55730/1300-0144.5630
IEEE AKTAŞ DİNÇER H,Ağıldere A,Gokcay D "T1 relaxation time is prolonged in healthy aging: a whole brain study." , ss.675 - 684, 2023. 10.55730/1300-0144.5630
ISNAD AKTAŞ DİNÇER, HAYRIYE vd. "T1 relaxation time is prolonged in healthy aging: a whole brain study". (2023), 675-684. https://doi.org/10.55730/1300-0144.5630
APA AKTAŞ DİNÇER H, Ağıldere A, Gokcay D (2023). T1 relaxation time is prolonged in healthy aging: a whole brain study. Turkish Journal of Medical Sciences, 53(3), 675 - 684. 10.55730/1300-0144.5630
Chicago AKTAŞ DİNÇER HAYRIYE,Ağıldere Ahmet Muhteşem,Gokcay Didem T1 relaxation time is prolonged in healthy aging: a whole brain study. Turkish Journal of Medical Sciences 53, no.3 (2023): 675 - 684. 10.55730/1300-0144.5630
MLA AKTAŞ DİNÇER HAYRIYE,Ağıldere Ahmet Muhteşem,Gokcay Didem T1 relaxation time is prolonged in healthy aging: a whole brain study. Turkish Journal of Medical Sciences, vol.53, no.3, 2023, ss.675 - 684. 10.55730/1300-0144.5630
AMA AKTAŞ DİNÇER H,Ağıldere A,Gokcay D T1 relaxation time is prolonged in healthy aging: a whole brain study. Turkish Journal of Medical Sciences. 2023; 53(3): 675 - 684. 10.55730/1300-0144.5630
Vancouver AKTAŞ DİNÇER H,Ağıldere A,Gokcay D T1 relaxation time is prolonged in healthy aging: a whole brain study. Turkish Journal of Medical Sciences. 2023; 53(3): 675 - 684. 10.55730/1300-0144.5630
IEEE AKTAŞ DİNÇER H,Ağıldere A,Gokcay D "T1 relaxation time is prolonged in healthy aging: a whole brain study." Turkish Journal of Medical Sciences, 53, ss.675 - 684, 2023. 10.55730/1300-0144.5630
ISNAD AKTAŞ DİNÇER, HAYRIYE vd. "T1 relaxation time is prolonged in healthy aging: a whole brain study". Turkish Journal of Medical Sciences 53/3 (2023), 675-684. https://doi.org/10.55730/1300-0144.5630