Iranian Journal of Medical Physics

Iranian Journal of Medical Physics

A Review of Normal Brain Aging: Detailed Study of Physiological Changes, Risk Factors, and Neuroimaging Techniques

Document Type : Review Article

Authors
Erfan Hasanpour-khalesi 1
Farzane Nikparast 1
Zohreh Ganji 1
Nazanin Nahofteh 1
Hoda Zare 2
1 Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
2 Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
10.22038/ijmp.2025.83152.2465
Abstract
Introduction: The process of brain aging is a complex phenomenon that can manifest in a number of ways, including normal, pathological, and accelerated aging, each influenced by modifiable factors such as sex and lifestyle. Subtle, preclinical alterations, including iron accumulation, proteostasis disruption, and inflammation, often precede overt clinical manifestations of cognitive decline, highlighting the need for early detection and intervention.
Material and Methods: This review examines the neuropathological mechanisms of age-related cognitive decline, integrating current knowledge on the interplay of genetic, environmental, and lifestyle factors. Advanced neuroimaging techniques, particularly magnetic resonance imaging (MRI) and positron emission tomography (PET) offer powerful tools for investigating the complex biological and biochemical dynamics of the aging brain. Quantitative Susceptibility Mapping (QSM), a novel MRI technique, provides precise quantification of tissue magnetic susceptibility, enabling detailed assessment of iron deposition and myelin content, both crucial factors in age-related brain changes.
Results: We explore the diagnostic potential of QSM and other advanced neuroimaging techniques for identifying early biomarkers of brain aging and predicting cognitive trajectories. This research indicates that the accumulation of non-heme iron is a primary contributor to neuronal death in brain aging. This conclusion is supported by QSM studies, which have validated the role of iron in this process.
Conclusion: By integrating mechanistic understanding with practical prevention strategies, this research indicates that the accumulation of non-heme iron is a primary contributor to neuronal death in brain aging. Additionally, the literature suggests that dietary and physical activity interventions may beneficially mitigate neurodegeneration associated with aging.
Keywords
Magnetic Resonance Imaging
Aging
Neuroimaging
Iron
Brain
Subjects

  1. 1.   Isaev NK, Stelmashook EV, Stelmashook NN, Sharonova IN, Skrebitsky VG. Brain aging and mitochondria-targeted plastoquinone antioxidants of SkQ-type. Biochemistry (Mosc). 2013;78(3):295–300. doi: 10.1134/S0006297913030127.

    1. Isaev NK, Stelmashook EV, Genrikhs EE. Neurogenesis and brain aging. Rev Neurosci. 2019;30(6):573–580. doi: 10.1515/revneuro-2018-0084.
    2. Lee SY, Paolillo EW, Saloner R, Cobigo Y, Diaz VE, Gontrum EQ, et al. Moderating role of physical activity on hippocampal iron deposition and memory outcomes in typically aging older adults. Neurobiol Aging. 2023;131:124–131. doi: 10.1016/j.neurobiolaging.2023.07.026.
    3. Li G, Tong R, Zhang M, Gillen KM, Jiang W, Du Y, et al. Age-dependent changes in brain iron deposition and volume in deep gray matter nuclei using quantitative susceptibility mapping. Neuroimage. 2023;269:119923. doi: 10.1016/j.neuroimage.2023.119923.
    4. Li J, Zhang Q, Che Y, Zhang N, Guo L. Iron deposition characteristics of deep gray matter in elderly individuals in the community revealed by quantitative susceptibility mapping and multiple factor analysis. Front Aging Neurosci. 2021;13:611891. doi: 10.3389/fnagi.2021.611891.
    5. Stein KC, Morales-Polanco F, van der Lienden J, Rainbolt TK, Frydman J. Ageing exacerbates ribosome pausing to disrupt cotranslational proteostasis. Nature. 2022;601(7894):637–642. doi: 10.1038/s41586-021-04295-4.
    6. Li Y, Sethi SK, Zhang C, Miao Y, Yerramsetty KK, Palutla VK, et al. Iron content in deep gray matter as a function of age using quantitative susceptibility mapping: a multicenter study. Front Neurosci. 2020;14:607705. doi: 10.3389/fnins.2020.607705.
    7. Liu C, Wei H, Gong NJ, Cronin M, Dibb R, Decker K. Quantitative susceptibility mapping: contrast mechanisms and clinical applications. Tomography. 2015;1(1):3–17. doi: 10.18383/j.tom.2015.00136.
    8. Newman AB. The epidemiology and societal impact of aging-related functional limitations: a looming public health crisis. J Gerontol A Biol Sci Med Sci. 2023;78(Supplement_1):4–7. doi: 10.1093/gerona/glad021.
    9. Leveille SG, Penninx BW, Melzer D, Izmirlian G, Guralnik JM. Sex differences in the prevalence of mobility disability in old age: the dynamics of incidence, recovery, and mortality. J Gerontol B Psychol Sci Soc Sci. 2000;55(1):S41–50. doi: 10.1093/geronb/55.1.s41.
    10. Seidler RD, Bernard JA, Burutolu TB, Fling BW, Gordon MT, Gwin JT, et al. Motor control and aging: links to age-related brain structural, functional, and biochemical effects. Neurosci Biobehav Rev. 2010;34(5):721–733. doi: 10.1016/j.neubiorev.2009.10.005.
    11. Park DC, Reuter-Lorenz P. The adaptive brain: aging and neurocognitive scaffolding. Annu Rev Psychol. 2009;60:173–196. doi: 10.1146/annurev.psych.59.103006.093656.
    12. Höpker W. Das altern des nucleus dentatus. Z Altersforschung. 1951;5:256–277.
    13. Benkovic SA, Connor JR. Ferritin, transferrin, and iron in selected regions of the adult and aged rat brain. J Comp Neurol. 1993;338(1):97–113. doi: 10.1002/cne.903380108.
    14. Drayer B, Burger P, Darwin R, Riederer S, Herfkens R, Johnson GA. MRI of brain iron. AJR Am J Roentgenol. 1986;147(1):103–110. doi: 10.2214/ajr.147.1.103.
    15. Ramos P, Santos A, Pinto NR, Mendes R, Magalhaes T, Almeida A. Iron levels in the human brain: a post-mortem study of anatomical region differences and age-related changes. J Trace Elem Med Biol. 2014;28(1):13–17. doi: 10.1016/j.jtemb.2013.08.001.
    16. Ghassaban K, Liu S, Jiang C, Haacke EM. Quantifying iron content in magnetic resonance imaging. Neuroimage. 2019;187:77–92.
    17. Martin WR, Ye FQ, Allen PS. Increasing striatal iron content associated with normal aging. Mov Disord. 1998;13(2):281–286. doi: 10.1002/mds.870130214.
    18. Zecca L, Stroppolo A, Gatti A, Tampellini D, Toscani M, Gallorini M, et al. The role of iron and copper molecules in the neuronal vulnerability of locus coeruleus and substantia nigra during aging. Proc Natl Acad Sci U S A. 2004;101(26):9843–9848. doi: 10.1073/pnas.0403495101.
    19. Lauffer R. Iron, aging, and human disease: historical background and new hypotheses. Iron and human disease. 1992:1–20.
    20. Kim HG, Park S, Rhee HY, Lee KM, Ryu CW, Rhee SJ, et al. Quantitative susceptibility mapping to evaluate the early stage of Alzheimer’s disease. Neuroimage Clin. 2017;16:429–438. doi: 10.1016/j.nicl.2017.08.019.
    21. Acosta-Cabronero J, Machts J, Schreiber S, Abdulla S, Kollewe K, Petri S, et al. Quantitative susceptibility MRI to detect brain iron in amyotrophic lateral sclerosis. Radiology. 2018;289(1):195–203. doi: 10.1148/radiol.2018180112.
    22. van Bergen JMG, Li X, Quevenco FC, Gietl AF, Treyer V, Meyer R, et al. Simultaneous quantitative susceptibility mapping and Flutemetamol-PET suggests local correlation of iron and beta-amyloid as an indicator of cognitive performance at high age. Neuroimage. 2018;174:308–316. doi: 10.1016/j.neuroimage.2018.03.021.
    23. Zachariou V, Bauer CE, Seago ER, Raslau FD, Powell DK, Gold BT. Cortical iron disrupts functional connectivity networks supporting working memory performance in older adults. Neuroimage. 2020;223:117309. doi: 10.1016/j.neuroimage.2020.117309.
    24. Salami A, Avelar-Pereira B, Garzon B, Sitnikov R, Kalpouzos G. Functional coherence of striatal resting-state networks is modulated by striatal iron content. Neuroimage. 2018;183:495–503. doi: 10.1016/j.neuroimage.2018.08.036.
    25. Rodrigue KM, Daugherty AM, Foster CM, Kennedy KM. Striatal iron content is linked to reduced fronto-striatal brain function under working memory load. Neuroimage. 2020;210:116544. doi: 10.1016/j.neuroimage.2020.116544.
    26. Stys PK. General mechanisms of axonal damage and its prevention. J Neurol Sci. 2005;233(1–2):3–13. doi: 10.1016/j.jns.2005.03.031.
    27. Lassmann H, van Horssen J. Oxidative stress and its impact on neurons and glia in multiple sclerosis lesions. Biochim Biophys Acta. 2016;1862(3):506–510. doi: 10.1016/j.bbadis.2015.09.018.
    28. Raz N, Daugherty AM. Pathways to brain aging and their modifiers: free-radical-induced energetic and neural decline in senescence (FRIENDS) model - a mini-review. Gerontology. 2018;64(1):49–57. doi: 10.1159/000479508.
    29. Todorich B, Pasquini JM, Garcia CI, Paez PM, Connor JR. Oligodendrocytes and myelination: the role of iron. Glia. 2009;57(5):467–478. doi: 10.1002/glia.20784.
    30. Bartzokis G, Lu PH, Tingus K, Peters DG, Amar CP, Tishler TA, et al. Gender and iron genes may modify associations between brain iron and memory in healthy aging. Neuropsychopharmacology. 2011;36(7):1375–84. doi: 10.1038/npp.2011.22.
    31. Hare D, Ayton S, Bush A, Lei P. A delicate balance: iron metabolism and diseases of the brain. Front Aging Neurosci. 2013;5:34. doi: 10.3389/fnagi.2013.00034.
    32. Muhoberac BB, Vidal R. Iron, ferritin, hereditary ferritinopathy, and neurodegeneration. Front Neurosci. 2019;13:1195. doi: 10.3389/fnins.2019.01195.
    33. Ward RJ, Zucca FA, Duyn JH, Crichton RR, Zecca L. The role of iron in brain ageing and neurodegenerative disorders. Lancet Neurol. 2014;13(10):1045–60. doi: 10.1016/S1474-4422(14)70117-6.
    34. Hosking DE, Ayton S, Beckett N, Booth A, Peters R. More evidence is needed: iron, incident cognitive decline and dementia—a systematic review. Ther Adv Chronic Dis. 2018;9(12):241–56. doi: 10.1177/2040622318788485.
    35. Daugherty AM, Haacke EM, Raz N. Striatal iron content predicts its shrinkage and changes in verbal working memory after two years in healthy adults. J Neurosci. 2015;35(17):6731–43. doi: 10.1523/JNEUROSCI.4717-14.2015.
    36. Chen L, Soldan A, Oishi K, Faria A, Zhu Y, Albert M, et al. Quantitative susceptibility mapping of brain iron and beta-amyloid in MRI and PET relating to cognitive performance in cognitively normal older adults. Radiology. 2021;298(2):353–62. doi: 10.1148/radiol.2020201603.
    37. Rodrigue KM, Daugherty AM, Haacke EM, Raz N. The role of hippocampal iron concentration and hippocampal volume in age-related differences in memory. Cereb Cortex. 2013;23(7):1533–41. doi: 10.1093/cercor/bhs139.
    38. Daugherty AM, Raz N. A virtual water maze revisited: two-year changes in navigation performance and their neural correlates in healthy adults. Neuroimage. 2017;146:492–506. doi: 10.1016/j.neuroimage.2016.09.044.
    39. Ding B, Chen KM, Ling HW, Sun F, Li X, Wan T, et al. Correlation of iron in the hippocampus with MMSE in patients with Alzheimer’s disease. J Magn Reson Imaging. 2009;29(4):793–8. doi: 10.1002/jmri.21730.
    40. Raven EP, Lu PH, Tishler TA, Heydari P, Bartzokis G. Increased iron levels and decreased tissue integrity in hippocampus of Alzheimer’s disease detected in vivo with magnetic resonance imaging. J Alzheimers Dis. 2013;37(1):127–36. doi: 10.3233/JAD-130209.
    41. Honda K, Casadesus G, Petersen RB, Perry G, Smith MA. Oxidative stress and redox-active iron in Alzheimer’s disease. Ann N Y Acad Sci. 2004;1012:179–82. doi: 10.1196/annals.1306.015.
    42. Reuter-Lorenz PA, Park DC. Human neuroscience and the aging mind: a new look at old problems. J Gerontol B Psychol Sci Soc Sci. 2010;65(4):405–15. doi: 10.1093/geronb/gbq035.
    43. Harada CN, Natelson Love MC, Triebel KL. Normal cognitive aging. Clin Geriatr Med. 2013;29(4):737–52. doi: 10.1016/j.cger.2013.07.002.
    44. Kaup AR, Mirzakhanian H, Jeste DV, Eyler LT. A review of the brain structure correlates of successful cognitive aging. J Neuropsychiatry Clin Neurosci. 2011;23(1):6–15. doi: 10.1176/jnp.23.1.jnp6.
    45. Persson J, Pudas S, Lind J, Kauppi K, Nilsson LG, Nyberg L. Longitudinal structure-function correlates in elderly reveal MTL dysfunction with cognitive decline. Cereb Cortex. 2012;22(10):2297–304. doi: 10.1093/cercor/bhr306.
    46. Leong RLF, Lo JC, Sim SKY, Zheng H, Tandi J, Zhou J, et al. Longitudinal brain structure and cognitive changes over 8 years in an East Asian cohort. Neuroimage. 2017;147:852–60. doi: 10.1016/j.neuroimage.2016.10.016.
    47. Fjell AM, Walhovd KB. Structural brain changes in aging: courses, causes and cognitive consequences. Rev Neurosci. 2010;21(3):187–221. doi: 10.1515/revneuro.2010.21.3.187.
    48. Zhao L, Matloff W, Ning K, Kim H, Dinov ID, Toga AW. Age-related differences in brain morphology and the modifiers in middle-aged and older adults. Cereb Cortex. 2019;29(10):4169–93. doi: 10.1093/cercor/bhy300.
    49. Tullo S, Patel R, Devenyi GA, Salaciak A, Bedford SA, Farzin S, et al. MR-based age-related effects on the striatum, globus pallidus, and thalamus in healthy individuals across the adult lifespan. Hum Brain Mapp. 2019;40(18):5269–88. doi: 10.1002/hbm.24771.
    50. Wang Y, Xu Q, Luo J, Hu M, Zuo C. Effects of age and sex on subcortical volumes. Front Aging Neurosci. 2019;11:259. doi: 10.3389/fnagi.2019.00259.
    51. Zachariou V, Bauer CE, Pappas C, Gold BT. High cortical iron is associated with the disruption of white matter tracts supporting cognitive function in healthy older adults. Cereb Cortex. 2023;33(8):4815–4828. doi: 10.1093/cercor/bhac382.
    52. Han S, An Y, Carass A, Prince JL, Resnick SM. Longitudinal analysis of regional cerebellum volumes during normal aging. Neuroimage. 2020;220:117062. doi: 10.1016/j.neuroimage.2020.117062.
    53. Hoogendam YY, van der Geest JN, van der Lijn F, van der Lugt A, Niessen WJ, Krestin GP, et al. Determinants of cerebellar and cerebral volume in the general elderly population. Neurobiol Aging. 2012;33(12):2774–81. doi: 10.1016/j.neurobiolaging.2012.02.012.
    54. Koppelmans V, Hirsiger S, Merillat S, Jancke L, Seidler RD. Cerebellar gray and white matter volume and their relation with age and manual motor performance in healthy older adults. Hum Brain Mapp. 2015;36(6):2352–63. doi: 10.1002/hbm.22775.
    55. Hulst T, van der Geest JN, Thurling M, Goericke S, Frens MA, Timmann D, et al. Ageing shows a pattern of cerebellar degeneration analogous, but not equal, to that in patients suffering from cerebellar degenerative disease. Neuroimage. 2015;116:196–206. doi: 10.1016/j.neuroimage.2015.03.084.
    56. Bernard JA, Seidler RD. Relationships between regional cerebellar volume and sensorimotor and cognitive function in young and older adults. Cerebellum. 2013;12(5):721–37. doi: 10.1007/s12311-013-0481-z.
    57. Andersen BB, Gundersen HJ, Pakkenberg B. Aging of the human cerebellum: a stereological study. J Comp Neurol. 2003;466(3):356–65. doi: 10.1002/cne.10884.
    58. Dimitrova A, Gerwig M, Brol B, Gizewski ER, Forsting M, Beck A, et al. Correlation of cerebellar volume with eyeblink conditioning in healthy subjects and in patients with cerebellar cortical degeneration. Brain Res. 2008;1198:73–84. doi: 10.1016/j.brainres.2008.01.034.
    59. Ramanoel S, Hoyau E, Kauffmann L, Renard F, Pichat C, Boudiaf N, et al. Gray matter volume and cognitive performance during normal aging: a voxel-based morphometry study. Front Aging Neurosci. 2018;10:235. doi: 10.3389/fnagi.2018.00235.
    60. Pagnozzi AM, Fripp J, Rose SE. Quantifying deep grey matter atrophy using automated segmentation approaches: a systematic review of structural MRI studies. Neuroimage. 2019;201:116018. doi: 10.1016/j.neuroimage.2019.116018.
    61. Boecker H, Jankowski J, Ditter P, Scheef L. A role of the basal ganglia and midbrain nuclei for initiation of motor sequences. Neuroimage. 2008;39(3):1356–69. doi: 10.1016/j.neuroimage.2007.09.069.
    62. Amin M, Ontaneda D. Thalamic injury and cognition in multiple sclerosis. Front Neurol. 2020;11:623914. doi: 10.3389/fneur.2020.623914.
    63. Cogswell PM, Wiste HJ, Senjem ML, Gunter JL, Weigand SD, Schwarz CG, et al. Associations of quantitative susceptibility mapping with Alzheimer’s disease clinical and imaging markers. Neuroimage. 2021;224:117433. doi: 10.1016/j.neuroimage.2020.117433.
    64. Haider L, Simeonidou C, Steinberger G, Hametner S, Grigoriadis N, Deretzi G, et al. Multiple sclerosis deep grey matter: the relation between demyelination, neurodegeneration, inflammation and iron. J Neurol Neurosurg Psychiatry. 2014;85(12):1386–95. doi: 10.1136/jnnp-2014-307712.
    65. Li G, Zhai G, Zhao X, An H, Spincemaille P, Gillen KM, et al. 3D texture analyses within the substantia nigra of Parkinson’s disease patients on quantitative susceptibility maps and R2* maps. Neuroimage. 2019;188:465–72. doi: 10.1016/j.neuroimage.2018.12.041.
    66. Zucca FA, Segura-Aguilar J, Ferrari E, Munoz P, Paris I, Sulzer D, et al. Interactions of iron, dopamine and neuromelanin pathways in brain aging and Parkinson’s disease. Prog Neurobiol. 2017;155:96–119. doi: 10.1016/j.pneurobio.2015.09.012.
    67. Schippling S, Ostwaldt AC, Suppa P, Spies L, Manogaran P, Gocke C, et al. Global and regional annual brain volume loss rates in physiological aging. J Neurol. 2017;264(3):520–28. doi: 10.1007/s00415-016-8374-y.
    68. Sanchis-Segura C, Ibanez-Gual MV, Adrian-Ventura J, Aguirre N, Gomez-Cruz AJ, Avila C, et al. Sex differences in gray matter volume: how many and how large are they really? Biol Sex Differ. 2019;10(1):32. doi: 10.1186/s13293-019-0245-7.
    69. 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. doi: 10.1002/jmri.26037.
    70. Vinke EJ, de Groot M, Venkatraghavan V, Klein S, Niessen WJ, Ikram MA, et al. Trajectories of imaging markers in brain aging: the Rotterdam Study. Neurobiol Aging. 2018;71:32–40. doi: 10.1016/j.neurobiolaging.2018.07.001.
    71. Helms G, Draganski B, Frackowiak R, Ashburner J, Weiskopf N. Improved segmentation of deep brain grey matter structures using magnetization transfer (MT) parameter maps. Neuroimage. 2009;47(1):194–8. doi: 10.1016/j.neuroimage.2009.03.053.
    72. Derakhshan M, Caramanos Z, Giacomini PS, Narayanan S, Maranzano J, Francis SJ, et al. Evaluation of automated techniques for the quantification of grey matter atrophy in patients with multiple sclerosis. Neuroimage. 2010;52(4):1261–7. doi: 10.1016/j.neuroimage.2010.05.029.
    73. Li X, Chen L, Kutten K, Ceritoglu C, Li Y, Kang N, et al. Multi-atlas tool for automated segmentation of brain gray matter nuclei and quantification of their magnetic susceptibility. Neuroimage. 2019;191:337–49. doi: 10.1016/j.neuroimage.2019.02.016.
    74. Feng X, Deistung A, Dwyer MG, Hagemeier J, Polak P, Lebenberg J, et al. An improved FSL-FIRST pipeline for subcortical gray matter segmentation to study abnormal brain anatomy using quantitative susceptibility mapping (QSM). Magn Reson Imaging. 2017;39:110–22. doi: 10.1016/j.mri.2017.02.002.
    75. Acosta-Cabronero J, Betts MJ, Cardenas-Blanco A, Yang S, Nestor PJ. In vivo MRI mapping of brain iron deposition across the adult lifespan. J Neurosci. 2016;36(2):364–74. doi: 10.1523/JNEUROSCI.1907-15.2016.
    76. Schafer A, Forstmann BU, Neumann J, Wharton S, Mietke A, Bowtell R, et al. Direct visualization of the subthalamic nucleus and its iron distribution using high-resolution susceptibility mapping. Hum Brain Mapp. 2012;33(12):2831–42. doi: 10.1002/hbm.21404.
    77. Wyss-Coray T. Ageing, neurodegeneration and brain rejuvenation. Nature. 2016;539(7628):180–6. doi: 10.1038/nature20411.
    78. Zecca L, Youdim MB, Riederer P, Connor JR, Crichton RR. Iron, brain ageing and neurodegenerative disorders. Nat Rev Neurosci. 2004;5(11):863–73. doi: 10.1038/nrn1537.
    79. Proneth B, Conrad M. Ferroptosis and necroinflammation, a yet poorly explored link. Cell Death Differ. 2019;26(1):14–24. doi: 10.1038/s41418-018-0173-9.
    80. Smith MA, Zhu X, Tabaton M, Liu G, McKeel DW Jr, Cohen ML, et al. Increased iron and free radical generation in preclinical Alzheimer disease and mild cognitive impairment. J Alzheimers Dis. 2010;19(1):363–72. doi: 10.3233/JAD-2010-1239.
    81. Chakravarti B, Chakravarti DN. Oxidative modification of proteins: age-related changes. Gerontology. 2007;53(3):128–39. doi: 10.1159/000097865.
    82. Nikparast F, Shoeibi A, Niroumand S, Akbari-Lalimi H, Zare H. Application of Nobel QSM technique in MRI for diagnosis of Alzheimer’s disease: what is the relationship between iron deposits in brain nuclei with age and severity of disorders? Med J Tabriz Univ Med Sci. 2023;45(2):130–40. doi: 10.34172/mj.2023.021.
    83. Butterfield DA, Halliwell B. Oxidative stress, dysfunctional glucose metabolism and Alzheimer disease. Nat Rev Neurosci. 2019;20(3):148–60. doi: 10.1038/s41583-019-0132-6.
    84. Darki F, Nemmi F, Moller A, Sitnikov R, Klingberg T. Quantitative susceptibility mapping of striatum in children and adults, and its association with working memory performance. Neuroimage. 2016;136:208–14. doi: 10.1016/j.neuroimage.2016.04.065.
    85. Penke L, Valdes Hernandez MC, Maniega SM, Gow AJ, Murray C, Starr JM, et al. Brain iron deposits are associated with general cognitive ability and cognitive aging. Neurobiol Aging. 2012;33(3):510–7.e2. doi: 10.1016/j.neurobiolaging.2010.04.032.
    86. Prenderville JA, Kennedy PJ, Dinan TG, Cryan JF. Adding fuel to the fire: the impact of stress on the ageing brain. Trends Neurosci. 2015;38(1):13–25. doi: 10.1016/j.tins.2014.11.001.
    87. Corder EH, Saunders AM, Strittmatter WJ, Schmechel DE, Gaskell PC, Small GW, et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science. 1993;261(5123):921–3. doi: 10.1126/science.8346443.
    88. Farrer LA. Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. JAMA. 1997;278(16). doi: 10.1001/jama.1997.03550160069041.
    89. Langley J, Dara S, Bennett IJ, Hu XP. [Preprint]. 2022. doi: 10.1101/2022.08.16.22278811.
    90. Roses AD, Saunders AM. APOE is a major susceptibility gene for Alzheimer’s disease. Curr Opin Biotechnol. 1994;5(6):663–7. doi: 10.1016/0958-1669(94)90091-4.
    91. Powers ET, Morimoto RI, Dillin A, Kelly JW, Balch WE. Biological and chemical approaches to diseases of proteostasis deficiency. Annu Rev Biochem. 2009;78:959–91. doi: 10.1146/annurev.biochem.052308.114844.
    92. Hipp MS, Kasturi P, Hartl FU. The proteostasis network and its decline in ageing. Nat Rev Mol Cell Biol. 2019;20(7):421–35. doi: 10.1038/s41580-019-0101-y.
    93. Rodriguez-Oroz MC, Jahanshahi M, Krack P, Litvan I, Macias R, Bezard E, et al. Initial clinical manifestations of Parkinson’s disease: features and pathophysiological mechanisms. Lancet Neurol. 2009;8(12):1128–39. doi: 10.1016/S1474-4422(09)70293-5.
    94. Goyal MS, Vlassenko AG, Blazey TM, Su Y, Couture LE, Durbin TJ, et al. Loss of brain aerobic glycolysis in normal human aging. Cell Metab. 2017;26(2):353–60.e3. doi: 10.1016/j.cmet.2017.07.010.
    95. Kato T, Inui Y, Nakamura A, Ito K. Brain fluorodeoxyglucose (FDG) PET in dementia. Ageing Res Rev. 2016;30:73–84. doi: 10.1016/j.arr.2016.02.003.
    96. Masternak MM, Panici JA, Bonkowski MS, Hughes LF, Bartke A. Insulin sensitivity as a key mediator of growth hormone actions on longevity. J Gerontol A Biol Sci Med Sci. 2009;64(5):516–21. doi: 10.1093/gerona/glp024.
    97. Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153(6):1194–217. doi: 10.1016/j.cell.2013.05.039.
    98. Hagemeier J, Dwyer MG, Bergsland N, Schweser F, Magnano CR, Heininen-Brown M, et al. Effect of age on MRI phase behavior in the subcortical deep gray matter of healthy individuals. AJNR Am J Neuroradiol. 2013;34(11):2144–51. doi: 10.3174/ajnr.A3569.
    99. Persson N, Wu J, Zhang Q, Liu T, Shen J, Bao R, et al. Age and sex related differences in subcortical brain iron concentrations among healthy adults. Neuroimage. 2015;122:385–98. doi: 10.1016/j.neuroimage.2015.07.050.
    100. Fish AM, Nadig A, Seidlitz J, Reardon PK, Mankiw C, McDermott CL, et al. Sex-biased trajectories of amygdalo-hippocampal morphology change over human development. Neuroimage. 2020;204:116122. doi: 10.1016/j.neuroimage.2019.116122.
    101. Ogluszka M, Lipinski P, Starzynski RR. Interaction between iron and omega-3 fatty acids metabolisms: where is the cross-link? Crit Rev Food Sci Nutr. 2022;62(11):3002–22. doi: 10.1080/10408398.2020.1862047.
    102. Molinari C, Morsanuto V, Ghirlanda S, Ruga S, Notte F, Gaetano L, et al. Role of combined lipoic acid and vitamin D3 on astrocytes as a way to prevent brain ageing by induced oxidative stress and iron accumulation. Oxid Med Cell Longev. 2019;2019:2843121. doi: 10.1155/2019/2843121.
    103. Fernandez-Moriano C, Gonzalez-Burgos E, Gomez-Serranillos MP. Mitochondria-targeted protective compounds in Parkinson’s and Alzheimer’s diseases. Oxid Med Cell Longev. 2015;2015:408927. doi: 10.1155/2015/408927.
    104. Zaidi SM, Banu N. Antioxidant potential of vitamins A, E and C in modulating oxidative stress in rat brain. Clin Chim Acta. 2004;340(1–2):229–33. doi: 10.1016/j.cccn.2003.11.003.
    105. Dixon SJ, Lemberg KM, Lamprecht MR, Skouta R, Zaitsev EM, Gleason CE, et al. Ferroptosis: an iron-dependent form of nonapoptotic cell death. Cell. 2012;149(5):1060–72. doi: 10.1016/j.cell.2012.03.042.
    106. Stavrinou PS, Andreou E, Aphamis G, Pantzaris M, Ioannou M, Patrikios IS, et al. The effects of a 6-month high dose omega-3 and omega-6 polyunsaturated fatty acids and antioxidant vitamins supplementation on cognitive function and functional capacity in older adults with mild cognitive impairment. Nutrients. 2020;12(2):404. doi: 10.3390/nu12020325.
    107. Carmichael OT, Pillai S, Shankapal P, McLellan A, Kay DG, Gold BT, et al. A combination of essential fatty acids, Panax ginseng extract, and green tea catechins modifies brain fMRI signals in healthy older adults. J Nutr Health Aging. 2018;22(7):837–46. doi: 10.1007/s12603-018-1028-2.
    108. Butterfield D, Castegna A, Pocernich C, Drake J, Scapagnini G, Calabrese V. Nutritional approaches to combat oxidative stress in Alzheimer’s disease. J Nutr Biochem. 2002;13(8):444. doi: 10.1016/s0955-2863(02)00205-x.
    109. Hager K, Marahrens A, Kenklies M, Riederer P, Munch G. Alpha-lipoic acid as a new treatment option for Alzheimer [corrected] type dementia. Arch Gerontol Geriatr. 2001;32(3):275–82. doi: 10.1016/s0167-4943(01)00104-2.
    110. Moirand R, Lescoat G, Delamaire D, Lauvin L, Campion JP, Deugnier Y, et al. Increase in glycosylated and nonglycosylated serum ferritin in chronic alcoholism and their evolution during alcohol withdrawal. Alcohol Clin Exp Res. 1991;15(6):963–9. doi: 10.1111/j.1530-0277.1991.tb05196.x.
    111. Barnes DE, Yaffe K, Satariano WA, Tager IB. A longitudinal study of cardiorespiratory fitness and cognitive function in healthy older adults. J Am Geriatr Soc. 2003;51(4):459–65. doi: 10.1046/j.1532-5415.2003.51153.x.
    112. Buchman AS, Boyle PA, Yu L, Shah RC, Wilson RS, Bennett DA. Total daily physical activity and the risk of AD and cognitive decline in older adults. Neurology. 2012;78(17):1323–9. doi: 10.1212/WNL.0b013e3182535d35.
    113. Livingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020;396(10248):413–46. doi: 10.1016/S0140-6736(20)30367-6.
    114. Moller HE, Bossoni L, Connor JR, Crichton RR, Does MD, Ward RJ, et al. Iron, myelin, and the brain: neuroimaging meets neurobiology. Trends Neurosci. 2019;42(6):384–401. doi: 10.1016/j.tins.2019.03.009.
    115. Garcia-Mesa Y, Colie S, Corpas R, Cristofol R, Comellas F, Nebreda AR, et al. Oxidative stress is a central target for physical exercise neuroprotection against pathological brain aging. J Gerontol A Biol Sci Med Sci. 2016;71(1):40–9. doi: 10.1093/gerona/glv005.
    116. El Assar M, Alvarez-Bustos A, Sosa P, Angulo J, Rodriguez-Manas L. Effect of physical activity/exercise on oxidative stress and inflammation in muscle and vascular aging. Int J Mol Sci. 2022;23(15):8713. doi: 10.3390/ijms23158713.
    117. Radak Z, Hart N, Sarga L, Koltai E, Atalay M, Ohno H, et al. Exercise plays a preventive role against Alzheimer’s disease. J Alzheimers Dis. 2010;20(3):777–83. doi: 10.3233/JAD-2010-091531.
    118. Kim GH, Kim JE, Rhie SJ, Yoon S. The role of oxidative stress in neurodegenerative diseases. Exp Neurobiol. 2015;24(4):325–40. doi: 10.5607/en.2015.24.4.325.
    119. Leonardsen EH, Vidal-Pineiro D, Roe JM, Frei O, Shadrin AA, Iakunchykova O, et al. Genetic architecture of brain age and its causal relations with brain and mental disorders. Mol Psychiatry. 2023;28(7):3111–20. doi: 10.1038/s41380-023-02087-y.
    120. Andronie-Cioara FL, Ardelean AI, Nistor-Cseppento CD, Jurcau A, Jurcau MC, Pascalau N, et al. Molecular mechanisms of neuroinflammation in aging and Alzheimer’s disease progression. Int J Mol Sci. 2023;24(3):1869. doi: 10.3390/ijms24031869.
    121. Zarate S, Stevnsner T, Gredilla R. Role of estrogen and other sex hormones in brain aging: neuroprotection and DNA repair. Front Aging Neurosci. 2017;9:430. doi: 10.3389/fnagi.2017.00430.
    122. Tsapanou A, Scarmeas N, Stern Y. Sleep and the aging brain: a multifaceted approach. Sleep Sci. 2020;13(2):152–6. doi: 10.5935/1984-0063.20190128.
    123. Cherbuin N, Walsh EI, Shaw M, Luders E, Anstey KJ, Sachdev PS, et al. Optimal blood pressure keeps our brains younger. Front Aging Neurosci. 2021;13:694982. doi: 10.3389/fnagi.2021.694982.
    124. Wahidi N, Lerner AJ. Blood pressure control and protection of the aging brain. Neurotherapeutics. 2019;16(3):569–79. doi: 10.1007/s13311-019-00747-y.
    125. Fox NC, Schott JM. Imaging cerebral atrophy: normal ageing to Alzheimer’s disease. Lancet. 2004;363(9406):392–4. doi: 10.1016/S0140-6736(04)15441-X.
    126. Nagano-Saito A, Washimi Y, Arahata Y, Kachi T, Lerch JP, Evans AC, et al. Cerebral atrophy and its relation to cognitive impairment in Parkinson disease. Neurology. 2005;64(2):224–9. doi: 10.1212/01.WNL.0000149510.41793.50.
    127. Franke K, Ziegler G, Kloppel S, Gaser C, Alzheimer’s Disease Neuroimaging Initiative. Estimating the age of healthy subjects from T1-weighted MRI scans using kernel methods: exploring the influence of various parameters. Neuroimage. 2010;50(3):883–92. doi: 10.1016/j.neuroimage.2010.01.005.
    128. Kolbeinsson A, Filippi S, Panagakis Y, Matthews PM, Elliott P, Dehghan A, et al. Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders. Sci Rep. 2020;10(1):19940. doi: 10.1038/s41598-020-76518-z.
    129. Jonsson BA, Bjornsdottir G, Thorgeirsson TE, Ellingsen LM, Walters GB, Gudbjartsson DF, et al. Brain age prediction using deep learning uncovers associated sequence variants. Nat Commun. 2019;10(1):5409. doi: 10.1038/s41467-019-13163-9.
    130. Ning K, Duffy BA, Franklin M, Matloff W, Zhao L, Arzouni N, et al. Improving brain age estimates with deep learning leads to identification of novel genetic factors associated with brain aging. Neurobiol Aging. 2021;105:199–204. doi: 10.1016/j.neurobiolaging.2021.03.014.
    131. Goallec AL, Diai S, Collin S, Vincent T, Patel CJ. Using deep learning to predict brain age from brain magnetic resonance images and cognitive tests reveals that anatomical and functional brain aging are phenotypically and genetically distinct. medRxiv. 2021. doi: 10.1101/2021.06.22.21259280.
    132. Litwinczuk MC, Trujillo-Barreto N, Muhlert N, Cloutman L, Woollams A. Relating cognition to both brain structure and function: a systematic review of methods. Brain Connect. 2023;13(3):120–32. doi: 10.1089/brain.2022.0036.
    133. Morita T, Asada M, Naito E. Contribution of neuroimaging studies to understanding development of human cognitive brain functions. Front Hum Neurosci. 2016;10:464. doi: 10.3389/fnhum.2016.00464.
    134. Elam JS, Glasser MF, Harms MP, Sotiropoulos SN, Andersson JLR, Burgess GC, et al. The Human Connectome Project: a retrospective. Neuroimage. 2021;244:118543. doi: 10.1016/j.neuroimage.2021.118543.
    135. Ebrahimzadeh E, Saharkhiz S, Rajabion L, Oskouei HB, Seraji M, Fayaz F, et al. Simultaneous electroencephalography-functional magnetic resonance imaging for assessment of human brain function. Front Syst Neurosci. 2022;16:934266. doi: 10.3389/fnsys.2022.934266.
    136. Bringas-Vega ML, Michel CM, Saxena S, White T, Valdes-Sosa PA. Neuroimaging and global health. Neuroimage. 2022;260:119458. doi: 10.1016/j.neuroimage.2022.119458.
    137. Faraji R, Ganji Z, Zamanpour SA, Nikparast F, Akbari-Lalimi H, Zare H. Impaired white matter integrity in infants and young children with autism spectrum disorder: what evidence does diffusion tensor imaging provide? Psychiatry Res Neuroimaging. 2023;335:111711. doi: 10.1016/j.pscychresns.2023.111711.
    138. Cogswell PM, Fan AP. Multimodal comparisons of QSM and PET in neurodegeneration and aging. Neuroimage. 2023;273:120068. doi: 10.1016/j.neuroimage.2023.120068.
    139. Mosconi L, Tsui WH, Herholz K, Pupi A, Drzezga A, Lucignani G, et al. Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer’s disease, and other dementias. J Nucl Med. 2008;49(3):390–8. doi: 10.2967/jnumed.107.045385.
    140. Gouilly D, Saint-Aubert L, Ribeiro MJ, Salabert AS, Tauber C, Peran P, et al. Neuroinflammation PET imaging of the translocator protein (TSPO) in Alzheimer’s disease: an update. Eur J Neurosci. 2022;55(5):1322–43. doi: 10.1111/ejn.15613.
    141. Swanson CJ, Zhang Y, Dhadda S, Wang J, Kaplow J, Lai RYK, et al. A randomized, double-blind, phase 2b proof-of-concept clinical trial in early Alzheimer’s disease with lecanemab, an anti-Abeta protofibril antibody. Alzheimers Res Ther. 2021;13(1):80. doi: 10.1186/s13195-021-00813-8.
    142. Cummings J, Rabinovici GD, Atri A, Aisen PA, Apostolova LG, Hendrix S, et al. Aducanumab: appropriate use recommendations update. J Prev Alzheimers Dis. 2022;9(2):221–30. doi: 10.14283/jpad.2022.34.
    143. Ossenkoppele R, Schonhaut DR, Scholl M, Lockhart SN, Ayakta N, Baker SL, et al. Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer’s disease. Brain. 2016;139(Pt 5):1551–67. doi: 10.1093/brain/aww027.
    144. Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991;82(4):239–59. doi: 10.1007/BF00308809.
    145. Insel PS, Mormino EC, Aisen PS, Thompson WK, Donohue MC. Neuroanatomical spread of amyloid beta and tau in Alzheimer’s disease: implications for primary prevention. Brain Commun. 2020;2(1):fcaa007. doi: 10.1093/braincomms/fcaa007.
    146. Haacke EM, Cheng NY, House MJ, Liu Q, Neelavalli J, Ogg RJ, et al. Imaging iron stores in the brain using magnetic resonance imaging. Magn Reson Imaging. 2005;23(1):1–25. doi: 10.1016/j.mri.2004.10.001.
    147. Larsen B, Bourque J, Moore TM, Adebimpe A, Calkins ME, Elliott MA, et al. Longitudinal development of brain iron is linked to cognition in youth. J Neurosci. 2020;40(9):1810–8. doi: 10.1523/JNEUROSCI.2434-19.2020.
    148. Nikparast F, Ganji Z, Danesh Doust M, Faraji R, Zare H. Brain pathological changes during neurodegenerative diseases and their identification methods: how does QSM perform in detecting this process? Insights Imaging. 2022;13(1):13244. doi: 10.1186/s13244-022-01207-6.
    149. Nikparast F, Ganji Z, Zare H. Early differentiation of neurodegenerative diseases using the novel QSM technique: what is the biomarker of each disorder? BMC Neurosci. 2022;23(1):7259. doi: 10.1186/s12868-022-00725-9.
    150. Liu S, Buch S, Chen Y, Choi HS, Dai Y, Habib C, et al. Susceptibility-weighted imaging: current status and future directions. NMR Biomed. 2017;30(4). doi: 10.1002/nbm.3552.
    151. Santin MD, Didier M, Valabregue R, Yahia Cherif LY, Garcia-Lorenzo D, Loureiro de Sousa P, et al. Reproducibility of R2* and quantitative susceptibility mapping (QSM) reconstruction methods in the basal ganglia of healthy subjects. NMR Biomed. 2017;30(4). doi: 10.1002/nbm.3491.
    152. Langkammer C, Schweser F, Krebs N, Deistung A, Goessler W, Scheurer E, et al. Quantitative susceptibility mapping (QSM) as a means to measure brain iron? A post mortem validation study. Neuroimage. 2012;62(3):1593–9. doi: 10.1016/j.neuroimage.2012.05.049.
    153. Zheng W, Nichol H, Liu S, Cheng YC, Haacke EM. Measuring iron in the brain using quantitative susceptibility mapping and X-ray fluorescence imaging. Neuroimage. 2013;78:68–74. doi: 10.1016/j.neuroimage.2013.04.022.
    154. Li W, Wu B, Liu C. Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition. Neuroimage. 2011;55(4):1645–56. doi: 10.1016/j.neuroimage.2010.11.088.
    155. Schweser F, Deistung A, Lehr BW, Reichenbach JR. Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: an approach to in vivo brain iron metabolism? Neuroimage. 2011;54(4):2789–807. doi: 10.1016/j.neuroimage.2010.10.070.
    156. Vinayagamani S, Sheelakumari R, Sabarish S, Senthilvelan S, Ros R, Thomas B, et al. Quantitative susceptibility mapping: technical considerations and clinical applications in neuroimaging. J Magn Reson Imaging. 2021;53(1):23–37. doi: 10.1002/jmri.27058.
    157. Bartzokis G, Lu PH, Tishler TA, Fong SM, Oluwadara B, Finn JP, et al. Myelin breakdown and iron changes in Huntington’s disease: pathogenesis and treatment implications. Neurochem Res. 2007;32(10):1655–64. doi: 10.1007/s11064-007-9352-7.
    158. Betts MJ, Acosta-Cabronero J, Cardenas-Blanco A, Nestor PJ, Duzel E. High-resolution characterisation of the aging brain using simultaneous quantitative susceptibility mapping (QSM) and R2* measurements at 7T. Neuroimage. 2016;138:43–63. doi: 10.1016/j.neuroimage.2016.05.024.
    159. He C, Guan X, Zhang W, Li J, Liu C, Wei H, et al. Quantitative susceptibility atlas construction in Montreal Neurological Institute space: towards histological-consistent iron-rich deep brain nucleus subregion identification. Brain Struct Funct. 2023;228(5):1045–67. doi: 10.1007/s00429-022-02547-1.
    160. de Rochefort L, Liu T, Kressler B, Liu J, Spincemaille P, Lebon V, et al. Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: validation and application to brain imaging. Magn Reson Med. 2010;63(1):194–206. doi: 10.1002/mrm.22187.
    161. Burgetova R, Dusek P, Burgetova A, Pudlac A, Vaneckova M, Horakova D, et al. Age-related magnetic susceptibility changes in deep grey matter and cerebral cortex of normal young and middle-aged adults depicted by whole brain analysis. Quant Imaging Med Surg. 2021;11(9):3906–19. doi: 10.21037/qims-21-87.
    162. Zhang Y, Wei H, Cronin MJ, He N, Yan F, Liu C. Longitudinal atlas for normative human brain development and aging over the lifespan using quantitative susceptibility mapping. Neuroimage. 2018;171:176–89. doi: 10.1016/j.neuroimage.2018.01.008.
    163. Haacke EM, Miao Y, Liu M, Habib CA, Katkuri Y, Liu T, et al. Correlation of putative iron content as represented by changes in R2* and phase with age in deep gray matter of healthy adults. J Magn Reson Imaging. 2010;32(3):561–76. doi: 10.1002/jmri.22293.
    164. Liu M, Liu S, Ghassaban K, Zheng W, Dicicco D, Miao Y, et al. Assessing global and regional iron content in deep gray matter as a function of age using susceptibility mapping. J Magn Reson Imaging. 2016;44(1):59–71. doi: 10.1002/jmri.25130.
    165. Treit S, Naji N, Seres P, Rickard J, Stolz E, Wilman AH, et al. R2* and quantitative susceptibility mapping in deep gray matter of 498 healthy controls from 5 to 90 years. Hum Brain Mapp. 2021;42(14):4597–610. doi: 10.1002/hbm.25569.
    166. Guevara M, Roche S, Brochard V, Cam D, Badagbon J, Leprince Y, et al. Iron load in the normal aging brain measured with QSM and R2* at 7T: findings of the SENIOR cohort. Front Neuroimaging. 2024;3:1359630. doi: 10.3389/fnimg.2024.1359630.

     

Iranian Journal of Medical Physics
Volume 22, Issue 3 - Serial Number 3
May and June 2025
Pages 130-148

  • Receive Date 08 October 2024
  • Revise Date 01 July 2025
  • Accept Date 01 July 2025