2018, Number 1
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Arch Neurocien 2018; 23 (1)
Age related FLAIR-MRI brain Original article cortical changes in normal aging: central tendency versus dispersion values
Mendoza M, Eblen-Zajjur A
Language: Spanish
References: 57
Page: 23-38
PDF size: 757.80 Kb.
ABSTRACT
The normal aging leads to a decrease in brain volume, gray matter cortical thinning and altered
signal intensity on MRI. Efforts are needed to describe what normal brain aging is and which
parameter to use. The aim was to use central tendency and dispersion values of T2-FLAIR-MRI
signal intensity profiles from frontal, parietal, occipital and cingulate cortices and correlate them
with age and gender. Images from a total of 88 healthy subjects (22-80 years old, 58 females, 30
males) were evaluated with 1.5T MRI FLAIR sequences obtaining mean and coefficient of variation
(CV) signal intensities values from linear profiles of frontal, parietal, occipital and cingulate cortices.
Correlation and regression analysis were performed between age and gender. FLAIR signal mean
was negatively while CV was positively related to age for frontal, parietal and occipital cortex.
Cingulate cortex did not show significant changes. These changes were more intense for females
and for left hemisphere. Normal aging impacts inversely mean and CV values from cortical FLAIR
MRI image. The regression equations obtained could be used to detect abnormal age-related
cortical changes.
REFERENCES
1.Hurtz S, Kebets V, Green A, et al. Age Effects on Cortical Thickness in Cognitively Normal Elderly Individuals. Dement Geriatr Cong 2014; 4(2):221–7.
2.Steen RG, Gronemeyer SA, Taylor JS. Age-related changes in proton T1 values of normal human brain. J Magn Reson Imaging 1995; 5(1):43–8.
3.Salat DH, Lee SY, van der Kouwe AJ, et al. Age-associated alterations in cortical gray and white matter signal intensity and gray to white matter contrast. Neuroimage 2009; 48(1):21–8.
4.Magnaldi S, Ukmar M, Vasciaveo A, et al. Contrast between white and grey matter: MRI appearance with ageing. European Radiology 1993; 3(6):513–9.
5.Dotson VM, Szymkowicz SM, Sozda Ch, et al. Age differences in prefrontal surface area and thickness in middle aged to older adults. Front Aging Neurosci 2016; 7.
6.Long X, Liao W, Jiang C, et al. Healthy aging: an automatic analysis of global and regional morphological alterations of human brain. Acad Radiol 2012;19(7):785–93.
7.Thambisetty M, Wan J, Carass A, et al. Longitudinal changes in cortical thickness associated with normal aging. Neuroimage 2010; 52(4):1215–23.
8.Resnick SM, Pham DL, Kraut MA, et al. Longitudinal Magnetic Resonance Imaging Studies of Older Adults : a shrinking Brain. J Neurosci 2003; 23(8):3295–301.
9.Fjell AM, Westlye LT, Amlien I, et al. High consistency of regional cortical thinning in aging across multiple samples. Cereb Cortex 2009; 19(9):2001–12.
10.Raz N, Gunning-Dixon F, Head D, et al. Aging, sexual dimorphism, and hemispheric asymmetry of the cerebral cortex: replicability of regional differences in volume. Neurobiol Aging 2004; 25(3):377–96.
11.Grieve SM, Clark CR, Williams LM, et al. Preservation of limbic and paralimbic structures in aging. Hum Brain Mapp 2005; 25:391–401.
12.Allen JS, Bruss J, Brown CK, et al. Normal neuroanatomical variation due to age: the major lobes and a parcellation of the temporal region. Neurobiol Aging 2005; 9:1245–60.
13.Yang Z, Wen W, Jiang J, et al. Age-associated differences on structural brain MRI in nondemented individuals from 71 to 103 years. Neurobiol Aging 2016; 40:86–97.
14.Jernigan TL, Archibald SL, Fennema-Notestine C, et al. Effects of age on tissues and regions of the cerebrum and cerebellum. Neurobiol Aging 2001; 22(4):581–94. 15.Raz N, Lindenberger U, Rodrigue KM, et al. Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. Cereb Cortex 2005; 15(11):1676–89
16.Sullivan EV, Marsh L, Mathalon DH, et al. Age-related decline in MRI volumes of temporal lobe gray matter but not hippocampus. Neurobiol Aging 1995; 16(4):591–606.
17.Carne RP, Vogrin S, Litewka L, et al. Cerebral cortex: an MRI-based study of volume and variance with age and sex. J Clin Neurosci 2006;1:60–72.
18.Peng F, Wang L, Geng Z, et al. A cross-sectional voxel-based morphometric study of age- and sex-related changes in gray matter volume in the normal aging brain. J Comput Assist Tomogr 2016; 40(2):307–15.
19.Al-Hakim R, Nain D, Levitt J, et al. Semi-automatic parcellation of the corpus striatum. Proc SPIE - The Int Soc Opt Eng 2007; 6512:651236.
20.Heckemann R, Keihaninejad S, Aljabar P, et al. Automatic morphometry in alzheimer’s disease and mild cognitive impairment. Neuroimage 2011; 56(4):2024–37.
21.Dahnke R, Yotter RA, Gaser C. Cortical thickness and central surface estimation. Neuroimage 2013; 65:336–48.
22.Wenger E, Mårtensson J, Noack H, et al. Comparing manual and automatic segmentation of hippocampal volumes: reliability and validity issues in younger and older brains. Hum Brain Mapp 2014; 35(8):4236–48.
23.Peelle JE, Cusack R, Henson R. Adjusting for global effects in voxel-based morphometry: gray matter decline in normal aging. Neuroimage 2012; 60(2):1503–16.
24.Lyttelton OC, Karama S, Ad-Dab’bagh Y, et al. Positional and surface area asymmetry of the human cerebral cortex. Neuroimage 2009; 46(4):895–903.
25.Luders E, Narr KL, Thompson PM, et al. Hemispheric asymmetries in cortical thickness. Cereb Cortex 2005; 6(8):1232-1238.
26.Koelkebeck K, Miyata J, Kubota M, et al. The contribution of cortical thickness and surface area to gray matter asymmetries in the healthy human brain. Hum Brain Mapp 2014;35(12):6011–22.
27.Shaw ME, Sachdev PS, Anstey KJ, et al. Age-related cortical thinning in cognitively healthy individuals in their 60s: the PATH through life study. Neurobiol Aging 2016; 39:202–9.
28.Fjell AM, Westlye L, Grydeland H, et al. Accelerating cortical thinning: unique to dementia or universal in aging? Cereb Cortex 2014; 24(4):919–934.
29.Farahibozorg S, Hashemi-Golpayegani S, 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.
30.Sandu AL, Staff RT, McNeil CJ, et al. Structural brain complexity and cognitive decline in late life - a longitudinal study in the Aberdeen 1936 Birth Cohort. Neuroimage 2014; 100:558–63.
31.Hajnal JV, Bryant DJ, Kasuboski L, et al. Use of fluid-attenuated inversion recovery (FLAIR) pulse sequences in MRI of the brain. J Comput Assist Tomogr 1992; 16:841–4.
32.Ikeda Y, Matsumoto K, Hayashi T, et al. Use of fluid-attenuated inversion recovery (FLAIR) images in brain check-up. No To Shinkei 1999; 51(11):933–7.
33.Schneider CA, Rasband WS, Eliceiri KW. NIH Image to Image J: 25 years of image analysis. Nat Methods 2012; 9(7):671–5.
34.Aquino D, Bizzi A, Grisoli M, Garavaglia B, Bruzzone M, Nardocci N, Savoiardo M, Chiapparini L. Age-related Iron Deposition in the Basal Ganglia: Quantitative Analysis in Healthy Subjects. Radiology, 2009; 252: 165-72.
35.Hammer Ø, Harper DAT, Ryan PD. PAST-palaeontological statistics, ver. 3.19 (2018). Palaeontol electron, 2001; 4(1), 1-9.
36.Ziegler DA, Piguet O, Salat DH, et al. Cognition in healthy aging is related to regional white matter integrity, but not cortical thickness. Neurobiol Aging 2010; 31(11):1912–26.
37.Zhou D, Lebel C, Evans A, et al. Cortical thickness asymmetry from childhood to older adulthood. Neuroimage 2013; 83:66–74.
38.Good CD, Johnsrude I, Ashburner J, et al. Cerebral asymmetry and the effects of sex and handedness on brain structure: a voxel-based morphometric analysis of 465 normal adult human brains. Neuroimage 2001; 14(3):685-700
39.Chen C, Omiya Y. Brain asymmetry in cortical thickness is correlated with cognitive function. Front Hum Neurosci 2014; 8:877.
40.Pfefferbaum A, Rohlfing T, Rosenbloom MJ, et al. Variation in longitudinal trajectories of regional brain volumes of healthy men and women (ages 10 to 85years) measured with atlas-based parcellation of MRI. Neuroimage 2013; 65:176–93.
41.Hedman AM, van Haren NE, Schnack HG, et al. Human brain changes across the life span: a review of 56 longitudinal magnetic resonance imaging studies. Hum Brain Mapp 2012; 33(8):1987–2002.
42.Fotenos AF, Snyder AZ, Girton LE, et al. Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology 2005; 64(6):1032–9.
43.DeCarli C, Massaro J, Harvey D, et al. Measures of brain morphology and infarction in the framingham heart study: establishing what is normal. Neurobiol Aging 2005; 26:491–510.
44.Frisoni GB. Neuroimaging of Normal Brain Aging. In: Fillipi M, De Stefano N, Dousset V, McGowan J, editors. MR Imaging in white matter diseases of the brain and spinal cord. Heidelberg. Springer-Verlag; 2005, 355–61.
45.Westlye LT, Walhovd KB, Dale AM, et al. Differentiating maturational and aging-related changes of the cerebral cortex by use of thickness and signal intensity. Neuroimage 2010; 52(1):172–85.
46.de Groot JC, de Leeuw FE, Oudkerk M, et al. Cerebral white matter lesions and cognitive function. Ann Neurol 2000; 47(2):145–51.
47.Sowell ER, Peterson BS, Kan E, et al. Sex differences in cortical thickness mapped in 176 healthy individuals between 7 and 87 years of age. Cereb Cortex 2007; 17(7):1550–60.
48.Bocchetta M, Boccardi M, Ganzola R, Apostolova LG, Preboske G, Wolf D, et al. Harmonized benchmark labels of the hippocampus on magnetic resonance: the EADC-ADNI project. Alzheimer's and dementia, 2015; 11(2): 151-60.
Duchesne S, Valdivia F, Robitaille N, Mouiha A, Valdivia FA, Bocchetta M, et al. Manual segmentation qualification platform for the EADC-ADNI harmonized protocol for hippocampal segmentation project. Alzheimer's and dementia 2015;11(2): 161-74.
50.Frisoni GB, Jack CR, Bocchetta M, Bauer C, Frederiksen KS, Liu Y, et al. The EADC-ADNI harmonized protocol for manual hippocampal segmentation on magnetic resonance: evidence of validity. Alzheimer's and dementia 2015;11(2), 111-25.
51.Persson N, Wu J, Zhang Q, et al. Age and sex related differences in subcortical brain iron concentrations among healthy adults. Neuroimage 2015; 122:385–98.
52.Pfefferbaum A, Adalsteinsson E, Rohlfing T, et al. MRI estimates of brain iron concentration in normal aging: comparison of field-dependent (FDRI) and phase (SWI) methods. Neuroimage 2009; 47(2):493–500.
53.Ketonen LM. Neuroimaging of the aging brain. Neurol Clin 1998; 16(3):581–98.
54.Yi H-A, Möller C, Dieleman N, et al. Relation between subcortical grey matter atrophy and conversion from mild cognitive impairment to Alzheimer’s disease. J Neurol Neurosurg Psychiatry 2015: jnnp-2014-309105.
55.Caligiuri ME, Perrotta P, Augimeri A, et al. Automatic detection of white matter hyperintensities in healthy aging and pathology using magnetic resonance Imaging: A review. Neuroinformatics 2015;13:261–76.
56.Kwan JY, Jeong SY, van Gelderen P, et al. Iron accumulation in deep cortical layers accounts for MRI signal abnormalities in ALS: correlating 7 tesla MRI and pathology. PLoS One 2012; 7.
57.Xu X, Wang Q, Zhang M. Age, gender, and hemispheric differences in iron deposition in the human brain: an in vivo MRI study. Neuroimage 2008; 40:35–42.
58.Pujol J, López-Sala A, Deus J, et al. The lateral asymmetry of the human brain studied by volumetric magnetic resonance imaging. Neuroimage 2002; 17:670–9.