Korean Journal of Biological Psychiatry (생물정신의학)

The Korean Society of Biological Psychiatry (대한생물정신의학회)
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A Review of Brain Magnetic Resonance Imaging Correlates of Successful Cognitive Aging

뇌자기공명영상의 노화에 따른 변화

  • Ji, Eun-Kyung (Dongnam Institute of Radiological & Medical Sciences) ;
  • Chung, In-Won (Department of Neuropsychiatry, Dongguk University International Hospital) ;
  • Youn, Tak (Department of Neuropsychiatry, Dongguk University International Hospital)
  • 지은경 (동남권원자력의학원 영상의학과) ;
  • 정인원 (동국대학교 일산병원 정신건강의학과) ;
  • 윤탁 (동국대학교 일산병원 정신건강의학과)
  • Received : 2013.11.11
  • Accepted : 2013.11.25
  • Published : 2014.02.28
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Abstract

Normal aging causes changes in the brain volume, connection, function and cognition. The brain changes with increases in age and difference of gender varies at all levels. Studies about normal brain aging using various brain magnetic resonance imaging (MRI) variables such as gray and white matter structural imaging, proton spectroscopy, apparent diffusion coefficient, diffusion tensor imaging and functional MRI are reviewed. Total volume of brain increases after birth but decreases after 9 years old. During adulthood, total volume of brain is relatively stable. After 35 years old, brain shrinks gradually. The changes of gray and white matters by aging show different features. N-acetylaspartate decreases or remains unchanged but choline, creatine and myo-inositol increase with aging. Apparent diffusion coefficient decreases till 20 years old and then becomes stable during adulthood and increase after 60 years old. Diffusion tensor properties in white matter tissue are variable during aging. Resting-state functional connectivity decreases after middle age. Structural and functional brain changes with normal aging are important for studying various psychiatric diseases such as dementia, schizophrenia and bipolar disorder. Our review may be helpful for studying longitudinal changes of these diseases and successful aging.

Keywords

References

  1. Statistics Korea. 2010 Population and housing census report: based on complete enumeration. Daejeon: Statistics Korea;2011.
  2. Mora F. Successful brain aging: plasticity, environmental enrichment, and lifestyle. Dialogues Clin Neurosci 2013;15:45-52.
  3. Nasrallah HA, Weinberger DR. The Neurology of Schizophrenia. Amsterdam: Elsevier;1986.
  4. Walker J, Curtis V, Murray RM. Schizophrenia and bipolar disorder: similarities in pathogenic mechanisms but differences in neurodevelopment. Int Clin Psychopharmacol 2002;17 Suppl 3: S11-S19.
  5. Dekaban AS. Changes in brain weights during the span of human life: relation of brain weights to body heights and body weights. Ann Neurol 1978;4:345-356. https://doi.org/10.1002/ana.410040410
  6. Hedman AM, van Haren NE, Schnack HG, Kahn RS, Hulshoff Pol HE. Human brain changes across the life span: a review of 56 longitudinal magnetic resonance imaging studies. Hum Brain Mapp 2012;33:1987-2002. https://doi.org/10.1002/hbm.21334
  7. Enzinger C, Fazekas F, Matthews PM, Ropele S, Schmidt H, Smith S, et al. Risk factors for progression of brain atrophy in aging: six-year follow-up of normal subjects. Neurology 2005;64:1704-1711. https://doi.org/10.1212/01.WNL.0000161871.83614.BB
  8. Liu RS, Lemieux L, Bell GS, Sisodiya SM, Shorvon SD, Sander JW, et al. A longitudinal study of brain morphometrics using quantitative magnetic resonance imaging and difference image analysis. Neuroimage 2003;20:22-33. https://doi.org/10.1016/S1053-8119(03)00219-2
  9. Fotenos AF, Snyder AZ, Girton LE, Morris JC, Buckner RL. Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology 2005;64:1032-1039. https://doi.org/10.1212/01.WNL.0000154530.72969.11
  10. Sluimer JD, van der Flier WM, Karas GB, Fox NC, Scheltens P, Barkhof F, et al. Whole-brain atrophy rate and cognitive decline: longitudinal MR study of memory clinic patients. Radiology 2008; 248:590-598. https://doi.org/10.1148/radiol.2482070938
  11. Ikram MA, Vrooman HA, Vernooij MW, van der Lijn F, Hofman A, van der Lugt A, et al. Brain tissue volumes in the general elderly population. The Rotterdam Scan Study. Neurobiol Aging 2008; 29:882-890. https://doi.org/10.1016/j.neurobiolaging.2006.12.012
  12. Schmidt R, Ropele S, Enzinger C, Petrovic K, Smith S, Schmidt H, et al. White matter lesion progression, brain atrophy, and cognitive decline: the Austrian stroke prevention study. Ann Neurol 2005;58:610-616. https://doi.org/10.1002/ana.20630
  13. Driscoll I, Davatzikos C, An Y, Wu X, Shen D, Kraut M, et al. Longitudinal pattern of regional brain volume change differentiates normal aging from MCI. Neurology 2009;72:1906-1913. https://doi.org/10.1212/WNL.0b013e3181a82634
  14. Autti TH, Hamalainen J, Mannerkoski M, Van Leemput KV, Aberg LE. JNCL patients show marked brain volume alterations on longitudinal MRI in adolescence. J Neurol 2008;255:1226-1230. https://doi.org/10.1007/s00415-008-0891-x
  15. Chan D, Fox NC, Jenkins R, Scahill RI, Crum WR, Rossor MN. Rates of global and regional cerebral atrophy in AD and frontotemporal dementia. Neurology 2001;57:1756-1763. https://doi.org/10.1212/WNL.57.10.1756
  16. DeLisi LE, Sakuma M, Maurizio AM, Relja M, Hoff AL. Cerebral ventricular change over the first 10 years after the onset of schizophrenia. Psychiatry Res 2004;130:57-70. https://doi.org/10.1016/j.pscychresns.2003.08.004
  17. Fazekas F, Kapeller P, Schmidt R, Offenbacher H, Payer F, Fazekas G. The relation of cerebral magnetic resonance signal hyperintensities to Alzheimer's disease. J Neurol Sci 1996;142:121-125. https://doi.org/10.1016/0022-510X(96)00169-4
  18. Fotenos AF, Mintun MA, Snyder AZ, Morris JC, Buckner RL. Brain volume decline in aging: evidence for a relation between socioeconomic status, preclinical Alzheimer disease, and reserve. Arch Neurol 2008;65:113-120.
  19. Resnick SM, Pham DL, Kraut MA, Zonderman AB, Davatzikos C. Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain. J Neurosci 2003;23:3295-3301.
  20. Ridha BH, Barnes J, Bartlett JW, Godbolt A, Pepple T, Rossor MN, et al. Tracking atrophy progression in familial Alzheimer's disease: a serial MRI study. Lancet Neurol 2006;5:828-834. https://doi.org/10.1016/S1474-4422(06)70550-6
  21. Scahill RI, Frost C, Jenkins R, Whitwell JL, Rossor MN, Fox NC. A longitudinal study of brain volume changes in normal aging using serial registered magnetic resonance imaging. Arch Neurol 2003;60:989-994. https://doi.org/10.1001/archneur.60.7.989
  22. Tang Y, Whitman GT, Lopez I, Baloh RW. Brain volume changes on longitudinal magnetic resonance imaging in normal older people. J Neuroimaging 2001;11:393-400. https://doi.org/10.1111/j.1552-6569.2001.tb00068.x
  23. Taki Y, Kinomura S, Sato K, Goto R, Kawashima R, Fukuda H. A longitudinal study of gray matter volume decline with age and modifying factors. Neurobiol Aging 2011;32:907-915. https://doi.org/10.1016/j.neurobiolaging.2009.05.003
  24. Terry RD, DeTeresa R, Hansen LA. Neocortical cell counts in normal human adult aging. Ann Neurol 1987;21:530-539. https://doi.org/10.1002/ana.410210603
  25. Jacobs B, Driscoll L, Schall M. Life-span dendritic and spine changes in areas 10 and 18 of human cortex: a quantitative Golgi study. J Comp Neurol 1997;386:661-680. https://doi.org/10.1002/(SICI)1096-9861(19971006)386:4<661::AID-CNE11>3.0.CO;2-N
  26. Sowell ER, Peterson BS, Thompson PM, Welcome SE, Henkenius AL, Toga AW. Mapping cortical change across the human life span. Nat Neurosci 2003;6:309-315. https://doi.org/10.1038/nn1008
  27. Thambisetty M, Wan J, Carass A, An Y, Prince JL, Resnick SM. Longitudinal changes in cortical thickness associated with normal aging. Neuroimage 2010;52:1215-1223. https://doi.org/10.1016/j.neuroimage.2010.04.258
  28. Salat DH, Lee SY, van der Kouwe AJ, Greve DN, Fischl B, Rosas HD. Age-associated alterations in cortical gray and white matter signal intensity and gray to white matter contrast. Neuroimage 2009;48:21-28. https://doi.org/10.1016/j.neuroimage.2009.06.074
  29. Burggren AC, Zeineh MM, Ekstrom AD, Braskie MN, Thompson PM, Small GW, et al. Reduced cortical thickness in hippocampal subregions among cognitively normal apolipoprotein E e4 carriers. Neuroimage 2008;41:1177-1183. https://doi.org/10.1016/j.neuroimage.2008.03.039
  30. Dickerson BC, Bakkour A, Salat DH, Feczko E, Pacheco J, Greve DN, et al. The cortical signature of Alzheimer's disease: regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid-positive individuals. Cereb Cortex 2009;19:497-510. https://doi.org/10.1093/cercor/bhn113
  31. Singh V, Chertkow H, Lerch JP, Evans AC, Dorr AE, Kabani NJ. Spatial patterns of cortical thinning in mild cognitive impairment and Alzheimer's disease. Brain 2006;129(Pt 11):2885-2893. https://doi.org/10.1093/brain/awl256
  32. Giorgio A, Santelli L, Tomassini V, Bosnell R, Smith S, De Stefano N, et al. Age-related changes in grey and white matter structure throughout adulthood. Neuroimage 2010;51:943-951. https://doi.org/10.1016/j.neuroimage.2010.03.004
  33. Taki Y, Thyreau B, Kinomura S, Sato K, Goto R, Wu K, et al. A longitudinal study of age- and gender-related annual rate of volume changes in regional gray matter in healthy adults. Hum Brain Mapp 2013;34:2292-2301. https://doi.org/10.1002/hbm.22067
  34. Greenwood PM. The frontal aging hypothesis evaluated. J Int Neuropsychol Soc 2000;6:705-726. https://doi.org/10.1017/S1355617700666092
  35. Grieve SM, Clark CR, Williams LM, Peduto AJ, Gordon E. Preservation of limbic and paralimbic structures in aging. Hum Brain Mapp 2005;25:391-401. https://doi.org/10.1002/hbm.20115
  36. Kochunov P, Glahn DC, Lancaster J, Thompson PM, Kochunov V, Rogers B, et al. Fractional anisotropy of cerebral white matter and thickness of cortical gray matter across the lifespan. Neuroimage 2011;58:41-49. https://doi.org/10.1016/j.neuroimage.2011.05.050
  37. Leung KK, Barnes J, Ridgway GR, Bartlett JW, Clarkson MJ, Macdonald K, et al. Automated cross-sectional and longitudinal hippocampal volume measurement in mild cognitive impairment and Alzheimer's disease. Neuroimage 2010;51:1345-1359. https://doi.org/10.1016/j.neuroimage.2010.03.018
  38. Barnes J, Bartlett JW, van de Pol LA, Loy CT, Scahill RI, Frost C, et al. A meta-analysis of hippocampal atrophy rates in Alzheimer's disease. Neurobiol Aging 2009;30:1711-1723. https://doi.org/10.1016/j.neurobiolaging.2008.01.010
  39. den Heijer T, van der Lijn F, Koudstaal PJ, Hofman A, van der Lugt A, Krestin GP, et al. A 10-year follow-up of hippocampal volume on magnetic resonance imaging in early dementia and cognitive decline. Brain 2010;133(Pt 4):1163-1172. https://doi.org/10.1093/brain/awq048
  40. Barha CK, Galea LA. Influence of different estrogens on neuroplasticity and cognition in the hippocampus. Biochim Biophys Acta 2010;1800:1056-1067. https://doi.org/10.1016/j.bbagen.2010.01.006
  41. Schmidt R, Schmidt H, Haybaeck J, Loitfelder M, Weis S, Cavalieri M, et al. Heterogeneity in age-related white matter changes. Acta Neuropathol 2011;122:171-185. https://doi.org/10.1007/s00401-011-0851-x
  42. Vernooij MW, Smits M. Structural neuroimaging in aging and Alzheimer's disease. Neuroimaging Clin N Am 2012;22:33-55, viiviii. https://doi.org/10.1016/j.nic.2011.11.007
  43. Fazekas F, Kleinert R, Offenbacher H, Schmidt R, Kleinert G, Payer F, et al. Pathologic correlates of incidental MRI white matter signal hyperintensities. Neurology 1993;43:1683-1689. https://doi.org/10.1212/WNL.43.9.1683
  44. Sze G, De Armond SJ, Brant-Zawadzki M, Davis RL, Norman D, Newton TH. Foci of MRI signal (pseudo lesions) anterior to the frontal horns: histologic correlations of a normal finding. AJR Am J Roentgenol 1986;147:331-337. https://doi.org/10.2214/ajr.147.2.331
  45. Scheltens P, Barkhof F, Leys D, Wolters EC, Ravid R, Kamphorst W. Histopathologic correlates of white matter changes on MRI in Alzheimer's disease and normal aging. Neurology 1995;45:883-888. https://doi.org/10.1212/WNL.45.5.883
  46. Schmidt R, Enzinger C, Ropele S, Schmidt H, Fazekas F; Austrian Stroke Prevention Study. Progression of cerebral white matter lesions: 6-year results of the Austrian Stroke Prevention Study. Lancet 2003;361:2046-2048. https://doi.org/10.1016/S0140-6736(03)13616-1
  47. Schmidt R, Petrovic K, Ropele S, Enzinger C, Fazekas F. Progression of leukoaraiosis and cognition. Stroke 2007;38:2619-2625. https://doi.org/10.1161/STROKEAHA.107.489112
  48. van den Heuvel MP, Hulshoff Pol HE. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol 2010;20:519-534. https://doi.org/10.1016/j.euroneuro.2010.03.008
  49. van der Flier WM, van Straaten EC, Barkhof F, Verdelho A, Madureira S, Pantoni L, et al. Small vessel disease and general cognitive function in nondisabled elderly: the LADIS study. Stroke 2005; 36:2116-2120. https://doi.org/10.1161/01.STR.0000179092.59909.42
  50. Teodorczuk A, O'Brien JT, Firbank MJ, Pantoni L, Poggesi A, Erkinjuntti T, et al. White matter changes and late-life depressive symptoms: longitudinal study. Br J Psychiatry 2007;191:212-217. https://doi.org/10.1192/bjp.bp.107.036756
  51. Baezner H, Blahak C, Poggesi A, Pantoni L, Inzitari D, Chabriat H, et al. Association of gait and balance disorders with age-related white matter changes: the LADIS study. Neurology 2008;70:935-942. https://doi.org/10.1212/01.wnl.0000305959.46197.e6
  52. Poggesi A, Pracucci G, Chabriat H, Erkinjuntti T, Fazekas F, Verdelho A, et al. Urinary complaints in nondisabled elderly people with age-related white matter changes: the Leukoaraiosis And DISability (LADIS) Study. J Am Geriatr Soc 2008;56:1638-1643. https://doi.org/10.1111/j.1532-5415.2008.01832.x
  53. Inzitari D, Pracucci G, Poggesi A, Carlucci G, Barkhof F, Chabriat H, et al. Changes in white matter as determinant of global functional decline in older independent outpatients: three year followup of LADIS (leukoaraiosis and disability) study cohort. BMJ 2009; 339:b2477. https://doi.org/10.1136/bmj.b2477
  54. Vernooij MW, van der Lugt A, Ikram MA, Wielopolski PA, Niessen WJ, Hofman A, et al. Prevalence and risk factors of cerebral microbleeds: the Rotterdam Scan Study. Neurology 2008;70:1208-1214. https://doi.org/10.1212/01.wnl.0000307750.41970.d9
  55. Poels MM, Ikram MA, van der Lugt A, Hofman A, Niessen WJ, Krestin GP, et al. Cerebral microbleeds are associated with worse cognitive function: the Rotterdam Scan Study. Neurology 2012; 78:326-333. https://doi.org/10.1212/WNL.0b013e3182452928
  56. Goos JD, Henneman WJ, Sluimer JD, Vrenken H, Sluimer IC, Barkhof F, et al. Incidence of cerebral microbleeds: a longitudinal study in a memory clinic population. Neurology 2010;74:1954-1960. https://doi.org/10.1212/WNL.0b013e3181e396ea
  57. Zhu YC, Dufouil C, Tzourio C, Chabriat H. Silent brain infarcts: a review of MRI diagnostic criteria. Stroke 2011;42:1140-1145. https://doi.org/10.1161/STROKEAHA.110.600114
  58. Pollock H, Hutchings M, Weller RO, Zhang ET. Perivascular spaces in the basal ganglia of the human brain: their relationship to lacunes. J Anat 1997;191(Pt 3):337-346. https://doi.org/10.1046/j.1469-7580.1997.19130337.x
  59. Roher AE, Kuo YM, Esh C, Knebel C, Weiss N, Kalback W, et al. Cortical and leptomeningeal cerebrovascular amyloid and white matter pathology in Alzheimer's disease. Mol Med 2003;9:112-122.
  60. Kwee RM, Kwee TC. Virchow-Robin spaces at MR imaging. Radiographics 2007;27:1071-1086. https://doi.org/10.1148/rg.274065722
  61. van Swieten JC, van den Hout JH, van Ketel BA, Hijdra A, Wokke JH, van Gijn J. Periventricular lesions in the white matter on magnetic resonance imaging in the elderly. A morphometric correlation with arteriolosclerosis and dilated perivascular spaces. Brain. 1991;114(Pt 2):761-774. https://doi.org/10.1093/brain/114.2.761
  62. Zhu YC, Dufouil C, Mazoyer B, Soumaré A, Ricolfi F, Tzourio C, et al. Frequency and location of dilated Virchow-Robin spaces in elderly people: a population-based 3D MR imaging study. AJNR Am J Neuroradiol 2011;32:709-713. https://doi.org/10.3174/ajnr.A2366
  63. Groeschel S, Chong WK, Surtees R, Hanefeld F. Virchow-Robin spaces on magnetic resonance images: normative data, their dilatation, and a review of the literature. Neuroradiology 2006;48:745-754. https://doi.org/10.1007/s00234-006-0112-1
  64. Patankar TF, Baldwin R, Mitra D, Jeffries S, Sutcliffe C, Burns A, et al. Virchow-Robin space dilatation may predict resistance to antidepressant monotherapy in elderly patients with depression. J Affect Disord 2007;97:265-270. https://doi.org/10.1016/j.jad.2006.06.024
  65. Patankar TF, Mitra D, Varma A, Snowden J, Neary D, Jackson A. Dilatation of the Virchow-Robin space is a sensitive indicator of cerebral microvascular disease: study in elderly patients with dementia. AJNR Am J Neuroradiol 2005;26:1512-1520.
  66. Zhu YC, Dufouil C, Soumaré A, Mazoyer B, Chabriat H, Tzourio C. High degree of dilated Virchow-Robin spaces on MRI is associated with increased risk of dementia. J Alzheimers Dis 2010;22: 663-672. https://doi.org/10.3233/JAD-2010-100378
  67. Maclullich AM, Wardlaw JM, Ferguson KJ, Starr JM, Seckl JR, Deary IJ. Enlarged perivascular spaces are associated with cognitive function in healthy elderly men. J Neurol Neurosurg Psychiatry 2004;75:1519-1523. https://doi.org/10.1136/jnnp.2003.030858
  68. Laitinen LV, Chudy D, Tengvar M, Hariz MI, Bergenheim AT. Dilated perivascular spaces in the putamen and pallidum in patients with Parkinson's disease scheduled for pallidotomy: a comparison between MRI findings and clinical symptoms and signs. Mov Disord 2000;15:1139-1144. https://doi.org/10.1002/1531-8257(200011)15:6<1139::AID-MDS1012>3.0.CO;2-E
  69. Zhu YC, Tzourio C, Soumaré A, Mazoyer B, Dufouil C, Chabriat H. Severity of dilated Virchow-Robin spaces is associated with age, blood pressure, and MRI markers of small vessel disease: a population-based study. Stroke 2010;41:2483-2490. https://doi.org/10.1161/STROKEAHA.110.591586
  70. Potter GM, Doubal FN, Jackson CA, Chappell FM, Sudlow CL, Dennis MS, et al. Enlarged perivascular spaces and cerebral small vessel disease. Int J Stroke. Forthcoming 2013.
  71. Barboriak DP, Doraiswamy PM, Krishnan KR, Vidyarthi S, Sylvester J, Charles HC. Hippocampal sulcal cavities on MRI: relationship to age and apolipoprotein E genotype. Neurology 2000; 54:2150-2153. https://doi.org/10.1212/WNL.54.11.2150
  72. Ross B, Bluml S. Magnetic resonance spectroscopy of the human brain. Anat Rec 2001;265:54-84. https://doi.org/10.1002/ar.1058
  73. Gruber S, Pinker K, Riederer F, Chmelík M, Stadlbauer A, Bittsanský M, et al. Metabolic changes in the normal ageing brain: consistent findings from short and long echo time proton spectroscopy. Eur J Radiol 2008;68:320-327. https://doi.org/10.1016/j.ejrad.2007.08.038
  74. Miller BL, Chang L, Booth R, Ernst T, Cornford M, Nikas D, et al. In vivo 1H MRS choline: correlation with in vitro chemistry/histology. Life Sci 1996;58:1929-1935. https://doi.org/10.1016/0024-3205(96)00182-8
  75. Brooks JC, Roberts N, Kemp GJ, Gosney MA, Lye M, Whitehouse GH. A proton magnetic resonance spectroscopy study of age-related changes in frontal lobe metabolite concentrations. Cereb Cortex 2001;11:598-605. https://doi.org/10.1093/cercor/11.7.598
  76. Maudsley AA, Govind V, Arheart KL. Associations of age, gender and body mass with 1H MR-observed brain metabolites and tissue distributions. NMR Biomed 2012;25:580-593. https://doi.org/10.1002/nbm.1775
  77. Saunders DE, Howe FA, van den Boogaart A, Griffiths JR, Brown MM. Aging of the adult human brain: in vivo quantitation of metabolite content with proton magnetic resonance spectroscopy. J Magn Reson Imaging 1999;9:711-716. https://doi.org/10.1002/(SICI)1522-2586(199905)9:5<711::AID-JMRI14>3.0.CO;2-3
  78. Kadota T, Horinouchi T, Kuroda C. Development and aging of the cerebrum: assessment with proton MR spectroscopy. AJNR Am J Neuroradiol 2001;22:128-135.
  79. Chang L, Ernst T, Poland RE, Jenden DJ. In vivo proton magnetic resonance spectroscopy of the normal aging human brain. Life Sci 1996;58:2049-2056. https://doi.org/10.1016/0024-3205(96)00197-X
  80. Angelie E, Bonmartin A, Boudraa A, Gonnaud PM, Mallet JJ, Sappey-Marinier D. Regional differences and metabolic changes in normal aging of the human brain: proton MR spectroscopic imaging study. AJNR Am J Neuroradiol 2001;22:119-127.
  81. Neeb H, Zilles K, Shah NJ. Fully-automated detection of cerebral water content changes: study of age- and gender-related H2O patterns with quantitative MRI. Neuroimage 2006;29:910-922. https://doi.org/10.1016/j.neuroimage.2005.08.062
  82. 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. Magn Reson Med 2007;57:308-318. https://doi.org/10.1002/mrm.21122
  83. Zhang L, Thomas KM, Davidson MC, Casey BJ, Heier LA, Ulug AM. MR quantitation of volume and diffusion changes in the developing brain. AJNR Am J Neuroradiol 2005;26:45-49.
  84. Barkovich AJ, Kjos BO, Jackson DE Jr, Norman D. Normal maturation of the neonatal and infant brain: MR imaging at 1.5 T. Radiology 1988;166(1 Pt 1):173-180. https://doi.org/10.1148/radiology.166.1.3336675
  85. Dobbing J, Sands J. Quantitative growth and development of human brain. Arch Dis Child 1973;48:757-767. https://doi.org/10.1136/adc.48.10.757
  86. Hüppi PS, Maier SE, Peled S, Zientara GP, Barnes PD, Jolesz FA, et al. Microstructural development of human newborn cerebral white matter assessed in vivo by diffusion tensor magnetic resonance imaging. Pediatr Res 1998;44:584-590. https://doi.org/10.1203/00006450-199810000-00019
  87. Nomura Y, Sakuma H, Takeda K, Tagami T, Okuda Y, Nakagawa T. Diffusional anisotropy of the human brain assessed with diffusion- weighted MR: relation with normal brain development and aging. AJNR Am J Neuroradiol 1994;15:231-238.
  88. Càmara E, Bodammer N, Rodríguez-Fornells A, Tempelmann C. Age-related water diffusion changes in human brain: a voxel-based approach. Neuroimage 2007;34:1588-1599. https://doi.org/10.1016/j.neuroimage.2006.09.045
  89. Chen ZG, Li TQ, Hindmarsh T. Diffusion tensor trace mapping in normal adult brain using single-shot EPI technique. A methodological study of the aging brain. Acta Radiol 2001;42:447-458.
  90. Chun T, Filippi CG, Zimmerman RD, Ulug AM. Diffusion changes in the aging human brain. AJNR Am J Neuroradiol 2000;21: 1078-1083.
  91. Gideon P, Thomsen C, Henriksen O. Increased self-diffusion of brain water in normal aging. J Magn Reson Imaging 1994;4:185-188. https://doi.org/10.1002/jmri.1880040216
  92. Naganawa S, Sato K, Katagiri T, Mimura T, Ishigaki T. Regional ADC values of the normal brain: differences due to age, gender, and laterality. Eur Radiol 2003;13:6-11. https://doi.org/10.1007/BF03323607
  93. Pfefferbaum A, Sullivan EV. Increased brain white matter diffusivity in normal adult aging: relationship to anisotropy and partial voluming. Magn Reson Med 2003;49:953-961. https://doi.org/10.1002/mrm.10452
  94. Hsu JL, Leemans A, Bai CH, Lee CH, Tsai YF, Chiu HC, et al. Gender differences and age-related white matter changes of the human brain: a diffusion tensor imaging study. Neuroimage 2008;39:566-577. https://doi.org/10.1016/j.neuroimage.2007.09.017
  95. Basser PJ, Jones DK. Diffusion-tensor MRI: theory, experimental design and data analysis - a technical review. NMR Biomed 2002; 15:456-467. https://doi.org/10.1002/nbm.783
  96. Salat DH, Tuch DS, Greve DN, van der Kouwe AJ, Hevelone ND, Zaleta AK, et al. Age-related alterations in white matter microstructure measured by diffusion tensor imaging. Neurobiol Aging 2005;26:1215-1227. https://doi.org/10.1016/j.neurobiolaging.2004.09.017
  97. Bennett IJ, Madden DJ, Vaidya CJ, Howard DV, Howard JH Jr. Age-related differences in multiple measures of white matter integrity: A diffusion tensor imaging study of healthy aging. Hum Brain Mapp 2010;31:378-390.
  98. Burzynska AZ, Preuschhof C, Bäckman L, Nyberg L, Li SC, Lindenberger U, et al. Age-related differences in white matter microstructure: region-specific patterns of diffusivity. Neuroimage 2010; 49:2104-2112. https://doi.org/10.1016/j.neuroimage.2009.09.041
  99. Davis SW, Dennis NA, Buchler NG, White LE, Madden DJ, Cabeza R. Assessing the effects of age on long white matter tracts using diffusion tensor tractography. Neuroimage 2009;46:530-541. https://doi.org/10.1016/j.neuroimage.2009.01.068
  100. Sullivan EV, Pfefferbaum A. Diffusion tensor imaging in normal aging and neuropsychiatric disorders. Eur J Radiol 2003;45:244-255. https://doi.org/10.1016/S0720-048X(02)00313-3
  101. Sullivan EV, Zahr NM, Rohlfing T, Pfefferbaum A. Fiber tracking functionally distinct components of the internal capsule. Neuropsychologia 2010;48:4155-4163. https://doi.org/10.1016/j.neuropsychologia.2010.10.023
  102. Madden DJ, Bennett IJ, Burzynska A, Potter GG, Chen NK, Song AW. Diffusion tensor imaging of cerebral white matter integrity in cognitive aging. Biochim Biophys Acta 2012;1822:386-400. https://doi.org/10.1016/j.bbadis.2011.08.003
  103. Klawiter EC, Schmidt RE, Trinkaus K, Liang HF, Budde MD, Naismith RT, et al. Radial diffusivity predicts demyelination in ex vivo multiple sclerosis spinal cords. Neuroimage 2011;55:1454-1460. https://doi.org/10.1016/j.neuroimage.2011.01.007
  104. Sullivan EV, Adalsteinsson E, Hedehus M, Ju C, Moseley M, Lim KO, et al. Equivalent disruption of regional white matter microstructure in ageing healthy men and women. Neuroreport 2001;12:99-104. https://doi.org/10.1097/00001756-200101220-00027
  105. O'Sullivan M, Jones DK, Summers PE, Morris RG, Williams SC, Markus HS. Evidence for cortical "disconnection" as a mechanism of age-related cognitive decline. Neurology 2001;57:632-638.
  106. Huettel SA, Song AW, McCarthy G. Functional Magnetic Resonance Imaging. 2nd ed. Sunderland, MA: Sinauer Associates;2009.
  107. Andrews-Hanna JR, Snyder AZ, Vincent JL, Lustig C, Head D, Raichle ME, et al. Disruption of large-scale brain systems in advanced aging. Neuron 2007;56:924-935. https://doi.org/10.1016/j.neuron.2007.10.038
  108. Bollinger J, Rubens MT, Masangkay E, Kalkstein J, Gazzaley A. An expectation-based memory deficit in aging. Neuropsychologia 2011;49:1466-1475. https://doi.org/10.1016/j.neuropsychologia.2010.12.021
  109. Campbell KL, Grady CL, Ng C, Hasher L. Age differences in the frontoparietal cognitive control network: implications for distractibility. Neuropsychologia 2012;50:2212-2223. https://doi.org/10.1016/j.neuropsychologia.2012.05.025
  110. Grady CL, Grigg O, Ng C. Age differences in default and reward networks during processing of personally relevant information. Neuropsychologia 2012;50:1682-1697. https://doi.org/10.1016/j.neuropsychologia.2012.03.024
  111. Kalkstein J, Checksfield K, Bollinger J, Gazzaley A. Diminished top-down control underlies a visual imagery deficit in normal aging. J Neurosci 2011;31:15768-15774. https://doi.org/10.1523/JNEUROSCI.3209-11.2011
  112. Sambataro F, Murty VP, Callicott JH, Tan HY, Das S, Weinberger DR, et al. Age-related alterations in default mode network: impact on working memory performance. Neurobiol Aging 2010;31:839-852. https://doi.org/10.1016/j.neurobiolaging.2008.05.022
  113. Voss MW, Erickson KI, Prakash RS, Chaddock L, Malkowski E, Alves H, et al. Functional connectivity: a source of variance in the association between cardiorespiratory fitness and cognition? Neuropsychologia 2010;48:1394-1406. https://doi.org/10.1016/j.neuropsychologia.2010.01.005
  114. Van Dijk KR, Hedden T, Venkataraman A, Evans KC, Lazar SW, Buckner RL. Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J Neurophysiol 2010;103:297-321. https://doi.org/10.1152/jn.00783.2009
  115. Friston KJ, Frith CD, Liddle PF, Frackowiak RS. Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab 1993;13:5-14. https://doi.org/10.1038/jcbfm.1993.4
  116. De Luca M, Beckmann CF, De Stefano N, Matthews PM, Smith SM. fMRI resting state networks define distinct modes of longdistance interactions in the human brain. Neuroimage 2006;29:1359-1367. https://doi.org/10.1016/j.neuroimage.2005.08.035
  117. Ferreira LK, Busatto GF. Resting-state functional connectivity in normal brain aging. Neurosci Biobehav Rev 2013;37:384-400. https://doi.org/10.1016/j.neubiorev.2013.01.017
  118. Hafkemeijer A, van der Grond J, Rombouts SA. Imaging the default mode network in aging and dementia. Biochim Biophys Acta 2012; 1822:431-441. https://doi.org/10.1016/j.bbadis.2011.07.008
  119. Mevel K, Chételat G, Eustache F, Desgranges B. The default mode network in healthy aging and Alzheimer's disease. Int J Alzheimers Dis 2011;2011:535816.
  120. Prvulovic D, Bokde AL, Faltraco F, Hampel H. Functional magnetic resonance imaging as a dynamic candidate biomarker for Alzheimer's disease. Prog Neurobiol 2011;95:557-569. https://doi.org/10.1016/j.pneurobio.2011.05.008
  121. Terribilli D, Schaufelberger MS, Duran FL, Zanetti MV, Curiati PK, Menezes PR, et al. Age-related gray matter volume changes in the brain during non-elderly adulthood. Neurobiol Aging 2011; 32:354-368. https://doi.org/10.1016/j.neurobiolaging.2009.02.008
  122. Evers EA, Klaassen EB, Rombouts SA, Backes WH, Jolles J. The effects of sustained cognitive task performance on subsequent resting state functional connectivity in healthy young and middle- aged male schoolteachers. Brain Connect 2012;2:102-112. https://doi.org/10.1089/brain.2011.0060
  123. Hedden T, Gabrieli JD. Insights into the ageing mind: a view from cognitive neuroscience. Nat Rev Neurosci 2004;5:87-96.
  124. Tomasi D, Volkow ND. Aging and functional brain networks. Mol Psychiatry 2012;17:471, 549-558.
  125. Zuo XN, Kelly C, Di Martino A, Mennes M, Margulies DS, Bangaru S, et al. Growing together and growing apart: regional and sex differences in the lifespan developmental trajectories of functional homotopy. J Neurosci 2010;30:15034-15043. https://doi.org/10.1523/JNEUROSCI.2612-10.2010
  126. Yan L, Zhuo Y, Wang B, Wang DJ. Loss of Coherence of Low Frequency Fluctuations of BOLD FMRI in Visual Cortex of Healthy Aged Subjects. Open Neuroimag J 2011;5:105-111. https://doi.org/10.2174/1874440001105010105
  127. Toussaint PJ, Maiz S, Coynel D, Messe A, Perlbarg V, Habert MO, et al. Characterization of the default mode functional connectivity in normal aging and Alzheimer's disease: An approach combining entropy-based and graph theoretical measurements. Conf Proc IEEE ISBI 2011:853-856.
  128. Meier TB, Desphande AS, Vergun S, Nair VA, Song J, Biswal BB, et al. Support vector machine classification and characterization of age-related reorganization of functional brain networks. Neuroimage 2012;60:601-613. https://doi.org/10.1016/j.neuroimage.2011.12.052